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@@ -1735,7 +1780,7 @@
     "\n",
     "## Municipal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
     "\n",
     "::::::::::{tab-set}\n",
     "\n",
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index 9d9cddf..5bf0954 100644
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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "prior-likelihood"}}, "output_type": "display_data"}, "scatter-prior-likelihood": {"data": {"image/png": 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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "scatter-prior-likelihood"}}, "output_type": "display_data"}, "cumumlative-dist-forecast-prior": {"data": {"image/png": 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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "cumumlative-dist-forecast-prior"}}, "output_type": "display_data"}, "caption-histo": {"data": {"text/markdown": "Survey total pcs/m 2020 - 2021 versus 2015 - 2019. All locations considered.", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "caption-histo"}}, "output_type": "display_data"}, "material-report": {"data": {"text/html": "<style type=\"text/css\">\n#T_d466c tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_d466c tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_d466c tr {\n  font-size: 10px;\n}\n#T_d466c th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_d466c td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_d466c caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_d466c\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_d466c_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_d466c_level0_row0\" class=\"row_heading level0 row0\" >glass</th>\n      <td id=\"T_d466c_row0_col0\" class=\"data row0 col0\" >6%</td>\n    </tr>\n    <tr>\n      <th id=\"T_d466c_level0_row1\" class=\"row_heading level0 row1\" >metal</th>\n      <td id=\"T_d466c_row1_col0\" class=\"data row1 col0\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_d466c_level0_row2\" class=\"row_heading level0 row2\" >paper</th>\n      <td id=\"T_d466c_row2_col0\" class=\"data row2 col0\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_d466c_level0_row3\" class=\"row_heading level0 row3\" >plastic</th>\n      <td id=\"T_d466c_row3_col0\" class=\"data row3 col0\" >81%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb2c9a0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "material-report"}}, "output_type": "display_data"}, "forecast-weighted-prior": {"data": {"text/markdown": "__Given the weighted prior__\n* Average: 3.33\n* HDI 95%: 0.1 - 8.1\n* 90% Range: 0.4 - 7.05", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-weighted-prior"}}, "output_type": "display_data"}, "forecast-max-val": {"data": {"text/markdown": "__Given the observed max__\n* Average: 6.38\n* HDI 95%: 0.02 - 31.4\n* 90% Range: 0.14 - 38.0", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-max-val"}}, "output_type": "display_data"}, "forecast-99-max": {"data": {"text/markdown": "__Given the 99th percentile__\n* Average: 3.63\n* HDI 95%: 0.1 - 10.4\n* 90% Range: 0.2 - 9.83", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-99-max"}}, "output_type": "display_data"}, "ratio-most-common": {"data": {"text/markdown": "__The most common objects account for 72% of all objects__", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "ratio-most-common"}}, "output_type": "display_data"}, "most_common_objects": {"data": {"text/html": "<style type=\"text/css\">\n#T_b04cc tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_b04cc tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_b04cc tr {\n  font-size: 10px;\n}\n#T_b04cc th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_b04cc td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_b04cc caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_b04cc caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_b04cc td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_b04cc\">\n  <thead>\n    <tr>\n      <th id=\"T_b04cc_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_b04cc_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_b04cc_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_b04cc_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_b04cc_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_b04cc_row0_col0\" class=\"data row0 col0\" >Fragmented plastics</td>\n      <td id=\"T_b04cc_row0_col1\" class=\"data row0 col1\" >6'796</td>\n      <td id=\"T_b04cc_row0_col2\" class=\"data row0 col2\" >0,83</td>\n      <td id=\"T_b04cc_row0_col3\" class=\"data row0 col3\" >0,15</td>\n      <td id=\"T_b04cc_row0_col4\" class=\"data row0 col4\" >0,88</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_b04cc_row1_col1\" class=\"data row1 col1\" >6'306</td>\n      <td id=\"T_b04cc_row1_col2\" class=\"data row1 col2\" >0,60</td>\n      <td id=\"T_b04cc_row1_col3\" class=\"data row1 col3\" >0,14</td>\n      <td id=\"T_b04cc_row1_col4\" class=\"data row1 col4\" >0,87</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row2_col0\" class=\"data row2 col0\" >Expanded polystyrene</td>\n      <td id=\"T_b04cc_row2_col1\" class=\"data row2 col1\" >4'319</td>\n      <td id=\"T_b04cc_row2_col2\" class=\"data row2 col2\" >0,55</td>\n      <td id=\"T_b04cc_row2_col3\" class=\"data row2 col3\" >0,10</td>\n      <td id=\"T_b04cc_row2_col4\" class=\"data row2 col4\" >0,67</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row3_col0\" class=\"data row3 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_b04cc_row3_col1\" class=\"data row3 col1\" >2'890</td>\n      <td id=\"T_b04cc_row3_col2\" class=\"data row3 col2\" >0,31</td>\n      <td id=\"T_b04cc_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_b04cc_row3_col4\" class=\"data row3 col4\" >0,88</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row4_col0\" class=\"data row4 col0\" >Industrial sheeting</td>\n      <td id=\"T_b04cc_row4_col1\" class=\"data row4 col1\" >2'031</td>\n      <td id=\"T_b04cc_row4_col2\" class=\"data row4 col2\" >0,24</td>\n      <td id=\"T_b04cc_row4_col3\" class=\"data row4 col3\" >0,05</td>\n      <td id=\"T_b04cc_row4_col4\" class=\"data row4 col4\" >0,69</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row5_col0\" class=\"data row5 col0\" >Industrial pellets (nurdles)</td>\n      <td id=\"T_b04cc_row5_col1\" class=\"data row5 col1\" >1'715</td>\n      <td id=\"T_b04cc_row5_col2\" class=\"data row5 col2\" >0,18</td>\n      <td id=\"T_b04cc_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_b04cc_row5_col4\" class=\"data row5 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row6_col0\" class=\"data row6 col0\" >plastic caps, lid rings: G21, G22, G23, G24</td>\n      <td id=\"T_b04cc_row6_col1\" class=\"data row6 col1\" >1'704</td>\n      <td id=\"T_b04cc_row6_col2\" class=\"data row6 col2\" >0,19</td>\n      <td id=\"T_b04cc_row6_col3\" class=\"data row6 col3\" >0,04</td>\n      <td id=\"T_b04cc_row6_col4\" class=\"data row6 col4\" >0,71</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row7_col0\" class=\"data row7 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_b04cc_row7_col1\" class=\"data row7 col1\" >1'502</td>\n      <td id=\"T_b04cc_row7_col2\" class=\"data row7 col2\" >0,19</td>\n      <td id=\"T_b04cc_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_b04cc_row7_col4\" class=\"data row7 col4\" >0,65</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row8_col0\" class=\"data row8 col0\" >Foam packaging/insulation/polyurethane</td>\n      <td id=\"T_b04cc_row8_col1\" class=\"data row8 col1\" >1'435</td>\n      <td id=\"T_b04cc_row8_col2\" class=\"data row8 col2\" >0,09</td>\n      <td id=\"T_b04cc_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_b04cc_row8_col4\" class=\"data row8 col4\" >0,87</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row9_col0\" class=\"data row9 col0\" >Cotton bud/swab sticks</td>\n      <td id=\"T_b04cc_row9_col1\" class=\"data row9 col1\" >1'385</td>\n      <td id=\"T_b04cc_row9_col2\" class=\"data row9 col2\" >0,16</td>\n      <td id=\"T_b04cc_row9_col3\" class=\"data row9 col3\" >0,03</td>\n      <td id=\"T_b04cc_row9_col4\" class=\"data row9 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row10_col0\" class=\"data row10 col0\" >Plastic construction waste</td>\n      <td id=\"T_b04cc_row10_col1\" class=\"data row10 col1\" >836</td>\n      <td id=\"T_b04cc_row10_col2\" class=\"data row10 col2\" >0,09</td>\n      <td id=\"T_b04cc_row10_col3\" class=\"data row10 col3\" >0,02</td>\n      <td id=\"T_b04cc_row10_col4\" class=\"data row10 col4\" >0,53</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row11_col0\" class=\"data row11 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_b04cc_row11_col1\" class=\"data row11 col1\" >551</td>\n      <td id=\"T_b04cc_row11_col2\" class=\"data row11 col2\" >0,05</td>\n      <td id=\"T_b04cc_row11_col3\" class=\"data row11 col3\" >0,01</td>\n      <td id=\"T_b04cc_row11_col4\" class=\"data row11 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row12_col0\" class=\"data row12 col0\" >Tobacco; plastic packaging, containers</td>\n      <td id=\"T_b04cc_row12_col1\" class=\"data row12 col1\" >529</td>\n      <td id=\"T_b04cc_row12_col2\" class=\"data row12 col2\" >0,06</td>\n      <td id=\"T_b04cc_row12_col3\" class=\"data row12 col3\" >0,01</td>\n      <td id=\"T_b04cc_row12_col4\" class=\"data row12 col4\" >0,50</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row13_col0\" class=\"data row13 col0\" >Foil wrappers, aluminum foil</td>\n      <td id=\"T_b04cc_row13_col1\" class=\"data row13 col1\" >386</td>\n      <td id=\"T_b04cc_row13_col2\" class=\"data row13 col2\" >0,04</td>\n      <td id=\"T_b04cc_row13_col3\" class=\"data row13 col3\" >0,01</td>\n      <td id=\"T_b04cc_row13_col4\" class=\"data row13 col4\" >0,50</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row14_col0\" class=\"data row14 col0\" >Straws and stirrers</td>\n      <td id=\"T_b04cc_row14_col1\" class=\"data row14 col1\" >382</td>\n      <td id=\"T_b04cc_row14_col2\" class=\"data row14 col2\" >0,04</td>\n      <td id=\"T_b04cc_row14_col3\" class=\"data row14 col3\" >0,01</td>\n      <td id=\"T_b04cc_row14_col4\" class=\"data row14 col4\" >0,50</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb18f40>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "most_common_objects"}}, "output_type": "display_data"}, "l-sampling-summary": {"data": {"text/markdown": "<font color=#1e90ff>2020 - 2021 </font>\n* Number of samples: 263\n* Total objects: 45003\n* Average pcs/m: 5.01\n* Standard deviation: 8.24\n* Maximum pcs/m: 66.17\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "l-sampling-summary"}}, "output_type": "display_data"}, "prior-sampling-summary": {"data": {"text/markdown": "<font color=#daa520>2015 - 2019</font>\n* Number of samples: 568\n* Total objects: 85733\n* Average pcs/m: 4.01\n* Standard deviation: 6.73\n* Maximum pcs/m: 77.1\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "prior-sampling-summary"}}, "output_type": "display_data"}, "sampling-summary": {"data": {"text/markdown": "2015 - 2021\n* Number of samples: 831\n* Total objects: 130736\n* Average pcs/m: 4.32\n* Standard deviation: 7.25\n* Maximum pcs/m: 77.1\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "sampling-summary"}}, "output_type": "display_data"}, "feature-inventory": {"data": {"text/markdown": "__Features surveyed__\n* Rivers: 19\n* Lakes: 8\n\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "feature-inventory"}}, "output_type": "display_data"}, "administrative-boundaries": {"data": {"text/markdown": "__Administrative boundaries__\n* Survey locations: 131\n* Cities: 70\n\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "administrative-boundaries"}}, "output_type": "display_data"}, "lake-prior-likelihood": {"data": {"image/png": 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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-prior-likelihood"}}, "output_type": "display_data"}, "lake-cumumlative-dist-forecast-prior": {"data": {"image/png": 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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-cumumlative-dist-forecast-prior"}}, "output_type": "display_data"}, "caption-histo-l": {"data": {"text/markdown": "Survey total pcs/m 2020 - 2021 v/s 2015 - 2019. All locations considered.", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "caption-histo-l"}}, "output_type": "display_data"}, "material-report-l": {"data": {"text/html": "<style type=\"text/css\">\n#T_0b524 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_0b524 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_0b524 tr {\n  font-size: 10px;\n}\n#T_0b524 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_0b524 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_0b524 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_0b524\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_0b524_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_0b524_level0_row0\" class=\"row_heading level0 row0\" >glass</th>\n      <td id=\"T_0b524_row0_col0\" class=\"data row0 col0\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_0b524_level0_row1\" class=\"row_heading level0 row1\" >metal</th>\n      <td id=\"T_0b524_row1_col0\" class=\"data row1 col0\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_0b524_level0_row2\" class=\"row_heading level0 row2\" >paper</th>\n      <td id=\"T_0b524_row2_col0\" class=\"data row2 col0\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_0b524_level0_row3\" class=\"row_heading level0 row3\" >plastic</th>\n      <td id=\"T_0b524_row3_col0\" class=\"data row3 col0\" >83%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb21c40>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "material-report-l"}}, "output_type": "display_data"}, "forecast-weighted-prior-l": {"data": {"text/markdown": "__Given the weighted prior__\n* Average: 4.24\n* HDI 95%: 0.3 - 13.0\n* 90% Range: 0.5 - 13.0", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-weighted-prior-l"}}, "output_type": "display_data"}, "forecast-max-val-l": {"data": {"text/markdown": "__Given the observed max__\n* Average: 10.81\n* HDI 95%: 0.14 - 48.54\n* 90% Range: 0.55 - 51.72", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-max-val-l"}}, "output_type": "display_data"}, "forecast-99-max-l": {"data": {"text/markdown": "__Given the 99th percentile__\n* Average: 5.53\n* HDI 95%: 0.4 - 18.7\n* 90% Range: 0.7 - 17.94", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-99-max-l"}}, "output_type": "display_data"}, "ratio-most-common-l": {"data": {"text/markdown": "__The most common objects account for 74% of all objects__", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "ratio-most-common-l"}}, "output_type": "display_data"}, "most_common_objects-l": {"data": {"text/html": "<style type=\"text/css\">\n#T_0ece9 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_0ece9 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_0ece9 tr {\n  font-size: 10px;\n}\n#T_0ece9 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_0ece9 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_0ece9 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_0ece9 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_0ece9 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_0ece9\">\n  <thead>\n    <tr>\n      <th id=\"T_0ece9_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_0ece9_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_0ece9_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_0ece9_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_0ece9_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_0ece9_row0_col0\" class=\"data row0 col0\" >Fragmented plastics</td>\n      <td id=\"T_0ece9_row0_col1\" class=\"data row0 col1\" >6'735</td>\n      <td id=\"T_0ece9_row0_col2\" class=\"data row0 col2\" >0,94</td>\n      <td id=\"T_0ece9_row0_col3\" class=\"data row0 col3\" >0,16</td>\n      <td id=\"T_0ece9_row0_col4\" class=\"data row0 col4\" >0,93</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_0ece9_row1_col1\" class=\"data row1 col1\" >6'081</td>\n      <td id=\"T_0ece9_row1_col2\" class=\"data row1 col2\" >0,66</td>\n      <td id=\"T_0ece9_row1_col3\" class=\"data row1 col3\" >0,14</td>\n      <td id=\"T_0ece9_row1_col4\" class=\"data row1 col4\" >0,91</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row2_col0\" class=\"data row2 col0\" >Expanded polystyrene</td>\n      <td id=\"T_0ece9_row2_col1\" class=\"data row2 col1\" >4'302</td>\n      <td id=\"T_0ece9_row2_col2\" class=\"data row2 col2\" >0,63</td>\n      <td id=\"T_0ece9_row2_col3\" class=\"data row2 col3\" >0,10</td>\n      <td id=\"T_0ece9_row2_col4\" class=\"data row2 col4\" >0,75</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row3_col0\" class=\"data row3 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_0ece9_row3_col1\" class=\"data row3 col1\" >2'789</td>\n      <td id=\"T_0ece9_row3_col2\" class=\"data row3 col2\" >0,34</td>\n      <td id=\"T_0ece9_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_0ece9_row3_col4\" class=\"data row3 col4\" >0,91</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row4_col0\" class=\"data row4 col0\" >Industrial sheeting</td>\n      <td id=\"T_0ece9_row4_col1\" class=\"data row4 col1\" >1'882</td>\n      <td id=\"T_0ece9_row4_col2\" class=\"data row4 col2\" >0,26</td>\n      <td id=\"T_0ece9_row4_col3\" class=\"data row4 col3\" >0,04</td>\n      <td id=\"T_0ece9_row4_col4\" class=\"data row4 col4\" >0,74</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row5_col0\" class=\"data row5 col0\" >Industrial pellets (nurdles)</td>\n      <td id=\"T_0ece9_row5_col1\" class=\"data row5 col1\" >1'705</td>\n      <td id=\"T_0ece9_row5_col2\" class=\"data row5 col2\" >0,21</td>\n      <td id=\"T_0ece9_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_0ece9_row5_col4\" class=\"data row5 col4\" >0,40</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row6_col0\" class=\"data row6 col0\" >plastic caps, lid rings: G21, G22, G23, G24</td>\n      <td id=\"T_0ece9_row6_col1\" class=\"data row6 col1\" >1'677</td>\n      <td id=\"T_0ece9_row6_col2\" class=\"data row6 col2\" >0,21</td>\n      <td id=\"T_0ece9_row6_col3\" class=\"data row6 col3\" >0,04</td>\n      <td id=\"T_0ece9_row6_col4\" class=\"data row6 col4\" >0,78</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row7_col0\" class=\"data row7 col0\" >Foam packaging/insulation/polyurethane</td>\n      <td id=\"T_0ece9_row7_col1\" class=\"data row7 col1\" >1'422</td>\n      <td id=\"T_0ece9_row7_col2\" class=\"data row7 col2\" >0,10</td>\n      <td id=\"T_0ece9_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_0ece9_row7_col4\" class=\"data row7 col4\" >0,98</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row8_col0\" class=\"data row8 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_0ece9_row8_col1\" class=\"data row8 col1\" >1'400</td>\n      <td id=\"T_0ece9_row8_col2\" class=\"data row8 col2\" >0,20</td>\n      <td id=\"T_0ece9_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_0ece9_row8_col4\" class=\"data row8 col4\" >0,69</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row9_col0\" class=\"data row9 col0\" >Cotton bud/swab sticks</td>\n      <td id=\"T_0ece9_row9_col1\" class=\"data row9 col1\" >1'379</td>\n      <td id=\"T_0ece9_row9_col2\" class=\"data row9 col2\" >0,18</td>\n      <td id=\"T_0ece9_row9_col3\" class=\"data row9 col3\" >0,03</td>\n      <td id=\"T_0ece9_row9_col4\" class=\"data row9 col4\" >0,64</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row10_col0\" class=\"data row10 col0\" >Plastic construction waste</td>\n      <td id=\"T_0ece9_row10_col1\" class=\"data row10 col1\" >804</td>\n      <td id=\"T_0ece9_row10_col2\" class=\"data row10 col2\" >0,10</td>\n      <td id=\"T_0ece9_row10_col3\" class=\"data row10 col3\" >0,02</td>\n      <td id=\"T_0ece9_row10_col4\" class=\"data row10 col4\" >0,59</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row11_col0\" class=\"data row11 col0\" >Tobacco; plastic packaging, containers</td>\n      <td id=\"T_0ece9_row11_col1\" class=\"data row11 col1\" >522</td>\n      <td id=\"T_0ece9_row11_col2\" class=\"data row11 col2\" >0,07</td>\n      <td id=\"T_0ece9_row11_col3\" class=\"data row11 col3\" >0,01</td>\n      <td id=\"T_0ece9_row11_col4\" class=\"data row11 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row12_col0\" class=\"data row12 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_0ece9_row12_col1\" class=\"data row12 col1\" >488</td>\n      <td id=\"T_0ece9_row12_col2\" class=\"data row12 col2\" >0,05</td>\n      <td id=\"T_0ece9_row12_col3\" class=\"data row12 col3\" >0,01</td>\n      <td id=\"T_0ece9_row12_col4\" class=\"data row12 col4\" >0,60</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row13_col0\" class=\"data row13 col0\" >Straws and stirrers</td>\n      <td id=\"T_0ece9_row13_col1\" class=\"data row13 col1\" >374</td>\n      <td id=\"T_0ece9_row13_col2\" class=\"data row13 col2\" >0,05</td>\n      <td id=\"T_0ece9_row13_col3\" class=\"data row13 col3\" >0,01</td>\n      <td id=\"T_0ece9_row13_col4\" class=\"data row13 col4\" >0,54</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row14_col0\" class=\"data row14 col0\" >Foil wrappers, aluminum foil</td>\n      <td id=\"T_0ece9_row14_col1\" class=\"data row14 col1\" >371</td>\n      <td id=\"T_0ece9_row14_col2\" class=\"data row14 col2\" >0,04</td>\n      <td id=\"T_0ece9_row14_col3\" class=\"data row14 col3\" >0,01</td>\n      <td id=\"T_0ece9_row14_col4\" class=\"data row14 col4\" >0,53</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row15_col0\" class=\"data row15 col0\" >Lollypop sticks</td>\n      <td id=\"T_0ece9_row15_col1\" class=\"data row15 col1\" >354</td>\n      <td id=\"T_0ece9_row15_col2\" class=\"data row15 col2\" >0,04</td>\n      <td id=\"T_0ece9_row15_col3\" class=\"data row15 col3\" >0,01</td>\n      <td id=\"T_0ece9_row15_col4\" class=\"data row15 col4\" >0,52</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a46a534ea60>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "most_common_objects-l"}}, "output_type": "display_data"}, "l-sampling-summary-l": {"data": {"text/markdown": "<font color=#1e90ff>2020 - 2021 </font>\n* Number of samples: 230\n* Total objects: 43151\n* Average pcs/m: 5.5\n* Standard deviation: 8.63\n* Maximum pcs/m: 66.17\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "l-sampling-summary-l"}}, "output_type": "display_data"}, "prior-sampling-summary-l": {"data": {"text/markdown": "<font color=#daa520>2015 - 2019</font>\n* Number of samples: 311\n* Total objects: 74895\n* Average pcs/m: 6.28\n* Standard deviation: 8.34\n* Maximum pcs/m: 77.1\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "prior-sampling-summary-l"}}, "output_type": "display_data"}, "sampling-summary-l": {"data": {"text/markdown": "2015 - 2021\n* Number of samples: 541\n* Total objects: 118046\n* Average pcs/m: 5.95\n* Standard deviation: 8.47\n* Maximum pcs/m: 77.1\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "sampling-summary-l"}}, "output_type": "display_data"}, "feature-inventory-l": {"data": {"text/markdown": "__Features surveyed__\n* Lakes: 8\n\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "feature-inventory-l"}}, "output_type": "display_data"}, "administrative-boundaries-l": {"data": {"text/markdown": "__Administrative boundaries__\n* Survey locations: 78\n* Cities: 40\n\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "administrative-boundaries-l"}}, "output_type": "display_data"}, "river-prior-likelihood": {"data": {"image/png": 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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-prior-likelihood"}}, "output_type": "display_data"}, "river-cumumlative-dist-forecast-prior": {"data": {"image/png": 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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-cumumlative-dist-forecast-prior"}}, "output_type": "display_data"}, "caption-histo-r": {"data": {"text/markdown": "Survey total pcs/m 2020 - 2021 v/s 2017 - 2018. All locations considered.", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "caption-histo-r"}}, "output_type": "display_data"}, "material-report-r": {"data": {"text/html": "<style type=\"text/css\">\n#T_a28fd tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_a28fd tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_a28fd tr {\n  font-size: 10px;\n}\n#T_a28fd th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_a28fd td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_a28fd caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_a28fd\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a28fd_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a28fd_level0_row0\" class=\"row_heading level0 row0\" >cloth</th>\n      <td id=\"T_a28fd_row0_col0\" class=\"data row0 col0\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row1\" class=\"row_heading level0 row1\" >glass</th>\n      <td id=\"T_a28fd_row1_col0\" class=\"data row1 col0\" >7%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row2\" class=\"row_heading level0 row2\" >metal</th>\n      <td id=\"T_a28fd_row2_col0\" class=\"data row2 col0\" >9%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row3\" class=\"row_heading level0 row3\" >paper</th>\n      <td id=\"T_a28fd_row3_col0\" class=\"data row3 col0\" >7%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row4\" class=\"row_heading level0 row4\" >plastic</th>\n      <td id=\"T_a28fd_row4_col0\" class=\"data row4 col0\" >65%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row5\" class=\"row_heading level0 row5\" >wood</th>\n      <td id=\"T_a28fd_row5_col0\" class=\"data row5 col0\" >2%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a46a53f4bb0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "material-report-r"}}, "output_type": "display_data"}, "forecast-weighted-prior-r": {"data": {"text/markdown": "__Given the weighted prior__\n* Average: 1.24\n* HDI 95%: 0.1 - 5.0\n* 90% Range: 0.1 - 4.62", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-weighted-prior-r"}}, "output_type": "display_data"}, "forecast-max-val-r": {"data": {"text/markdown": "__Given the observed max__\n* Average: 1.76\n* HDI 95%: 0.02 - 10.96\n* 90% Range: 0.1 - 4.93", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-max-val-r"}}, "output_type": "display_data"}, "forecast-99-max-r": {"data": {"text/markdown": "__Given the 99th percentile__\n* Average: 1.16\n* HDI 95%: 0.1 - 3.6\n* 90% Range: 0.1 - 3.41", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-99-max-r"}}, "output_type": "display_data"}, "ratio-most-common-r": {"data": {"text/markdown": "__The most common objects account for 62% of all objects__", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "ratio-most-common-r"}}, "output_type": "display_data"}, "most_common_objects-r": {"data": {"text/html": "<style type=\"text/css\">\n#T_0a3c6 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_0a3c6 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_0a3c6 tr {\n  font-size: 10px;\n}\n#T_0a3c6 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_0a3c6 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_0a3c6 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_0a3c6 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_0a3c6 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_0a3c6\">\n  <thead>\n    <tr>\n      <th id=\"T_0a3c6_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_0a3c6_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_0a3c6_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_0a3c6_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_0a3c6_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_0a3c6_row0_col0\" class=\"data row0 col0\" >Diapers - wipes</td>\n      <td id=\"T_0a3c6_row0_col1\" class=\"data row0 col1\" >299</td>\n      <td id=\"T_0a3c6_row0_col2\" class=\"data row0 col2\" >0,21</td>\n      <td id=\"T_0a3c6_row0_col3\" class=\"data row0 col3\" >0,16</td>\n      <td id=\"T_0a3c6_row0_col4\" class=\"data row0 col4\" >0,30</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_0a3c6_row1_col1\" class=\"data row1 col1\" >225</td>\n      <td id=\"T_0a3c6_row1_col2\" class=\"data row1 col2\" >0,21</td>\n      <td id=\"T_0a3c6_row1_col3\" class=\"data row1 col3\" >0,12</td>\n      <td id=\"T_0a3c6_row1_col4\" class=\"data row1 col4\" >0,64</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row2_col0\" class=\"data row2 col0\" >Industrial sheeting</td>\n      <td id=\"T_0a3c6_row2_col1\" class=\"data row2 col1\" >149</td>\n      <td id=\"T_0a3c6_row2_col2\" class=\"data row2 col2\" >0,09</td>\n      <td id=\"T_0a3c6_row2_col3\" class=\"data row2 col3\" >0,08</td>\n      <td id=\"T_0a3c6_row2_col4\" class=\"data row2 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row3_col0\" class=\"data row3 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_0a3c6_row3_col1\" class=\"data row3 col1\" >102</td>\n      <td id=\"T_0a3c6_row3_col2\" class=\"data row3 col2\" >0,11</td>\n      <td id=\"T_0a3c6_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_0a3c6_row3_col4\" class=\"data row3 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row4_col0\" class=\"data row4 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_0a3c6_row4_col1\" class=\"data row4 col1\" >101</td>\n      <td id=\"T_0a3c6_row4_col2\" class=\"data row4 col2\" >0,11</td>\n      <td id=\"T_0a3c6_row4_col3\" class=\"data row4 col3\" >0,05</td>\n      <td id=\"T_0a3c6_row4_col4\" class=\"data row4 col4\" >0,67</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row5_col0\" class=\"data row5 col0\" >Packaging films nonfood or unknown</td>\n      <td id=\"T_0a3c6_row5_col1\" class=\"data row5 col1\" >68</td>\n      <td id=\"T_0a3c6_row5_col2\" class=\"data row5 col2\" >0,09</td>\n      <td id=\"T_0a3c6_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_0a3c6_row5_col4\" class=\"data row5 col4\" >0,27</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row6_col0\" class=\"data row6 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_0a3c6_row6_col1\" class=\"data row6 col1\" >63</td>\n      <td id=\"T_0a3c6_row6_col2\" class=\"data row6 col2\" >0,08</td>\n      <td id=\"T_0a3c6_row6_col3\" class=\"data row6 col3\" >0,03</td>\n      <td id=\"T_0a3c6_row6_col4\" class=\"data row6 col4\" >0,30</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row7_col0\" class=\"data row7 col0\" >Fragmented plastics</td>\n      <td id=\"T_0a3c6_row7_col1\" class=\"data row7 col1\" >61</td>\n      <td id=\"T_0a3c6_row7_col2\" class=\"data row7 col2\" >0,05</td>\n      <td id=\"T_0a3c6_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_0a3c6_row7_col4\" class=\"data row7 col4\" >0,52</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row8_col0\" class=\"data row8 col0\" >Bags; plastic shopping/carrier/grocery and pieces</td>\n      <td id=\"T_0a3c6_row8_col1\" class=\"data row8 col1\" >55</td>\n      <td id=\"T_0a3c6_row8_col2\" class=\"data row8 col2\" >0,05</td>\n      <td id=\"T_0a3c6_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_0a3c6_row8_col4\" class=\"data row8 col4\" >0,24</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row9_col0\" class=\"data row9 col0\" >Construction material; bricks, pipes, cement</td>\n      <td id=\"T_0a3c6_row9_col1\" class=\"data row9 col1\" >42</td>\n      <td id=\"T_0a3c6_row9_col2\" class=\"data row9 col2\" >0,02</td>\n      <td id=\"T_0a3c6_row9_col3\" class=\"data row9 col3\" >0,02</td>\n      <td id=\"T_0a3c6_row9_col4\" class=\"data row9 col4\" >0,12</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a46a3fd0c10>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "most_common_objects-r"}}, "output_type": "display_data"}, "l-sampling-summary-r": {"data": {"text/markdown": "<font color=#1e90ff>2020 - 2021 </font>\n* Number of samples: 33\n* Total objects: 1852\n* Average pcs/m: 1.58\n* Standard deviation: 2.82\n* Maximum pcs/m: 16.0\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "l-sampling-summary-r"}}, "output_type": "display_data"}, "prior-sampling-summary-r": {"data": {"text/markdown": "<font color=#daa520>2015 - 2019</font>\n* Number of samples: 257\n* Total objects: 10838\n* Average pcs/m: 1.25\n* Standard deviation: 1.39\n* Maximum pcs/m: 11.1\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "prior-sampling-summary-r"}}, "output_type": "display_data"}, "sampling-summary-r": {"data": {"text/markdown": "2017 - 2021\n* Number of samples: 290\n* Total objects: 12690\n* Average pcs/m: 1.29\n* Standard deviation: 1.62\n* Maximum pcs/m: 16.0\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "sampling-summary-r"}}, "output_type": "display_data"}, "feature-inventory-r": {"data": {"text/markdown": "__Features surveyed__\n* Rivers: 19\n\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "feature-inventory-r"}}, "output_type": "display_data"}, "administrative-boundaries-r": {"data": {"text/markdown": "__Administrative boundaries__\n* Survey locations: 53\n* Cities: 36\n\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "administrative-boundaries-r"}}, "output_type": "display_data"}, "map-of-survey-locations": {"data": {"image/png": 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", "text/plain": "<Figure size 1200x1000 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "map-of-survey-locations"}}, "output_type": "display_data"}, "rate-per-feature": {"data": {"text/html": "<style type=\"text/css\">\n#T_1c1b9 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_1c1b9 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_1c1b9 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_1c1b9_row0_col0, #T_1c1b9_row1_col0, #T_1c1b9_row2_col1, #T_1c1b9_row3_col2, #T_1c1b9_row4_col1, #T_1c1b9_row5_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_1c1b9\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_1c1b9_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_1c1b9_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_1c1b9_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_1c1b9_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_1c1b9_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_1c1b9_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_1c1b9_row0_col0\" class=\"data row0 col0\" >5.03</td>\n      <td id=\"T_1c1b9_row0_col1\" class=\"data row0 col1\" >0.09</td>\n      <td id=\"T_1c1b9_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_1c1b9_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_1c1b9_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_1c1b9_row1_col0\" class=\"data row1 col0\" >5.04</td>\n      <td id=\"T_1c1b9_row1_col1\" class=\"data row1 col1\" >0.56</td>\n      <td id=\"T_1c1b9_row1_col2\" class=\"data row1 col2\" >5.01</td>\n      <td id=\"T_1c1b9_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_1c1b9_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_1c1b9_row2_col0\" class=\"data row2 col0\" >2.62</td>\n      <td id=\"T_1c1b9_row2_col1\" class=\"data row2 col1\" >7.46</td>\n      <td id=\"T_1c1b9_row2_col2\" class=\"data row2 col2\" >3.80</td>\n      <td id=\"T_1c1b9_row2_col3\" class=\"data row2 col3\" >2.92</td>\n      <td id=\"T_1c1b9_row2_col4\" class=\"data row2 col4\" >6.27</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_1c1b9_row3_col0\" class=\"data row3 col0\" >4.86</td>\n      <td id=\"T_1c1b9_row3_col1\" class=\"data row3 col1\" >2.73</td>\n      <td id=\"T_1c1b9_row3_col2\" class=\"data row3 col2\" >13.13</td>\n      <td id=\"T_1c1b9_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_1c1b9_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_1c1b9_row4_col0\" class=\"data row4 col0\" >5.45</td>\n      <td id=\"T_1c1b9_row4_col1\" class=\"data row4 col1\" >6.22</td>\n      <td id=\"T_1c1b9_row4_col2\" class=\"data row4 col2\" >2.95</td>\n      <td id=\"T_1c1b9_row4_col3\" class=\"data row4 col3\" >1.84</td>\n      <td id=\"T_1c1b9_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_1c1b9_row5_col0\" class=\"data row5 col0\" >4.26</td>\n      <td id=\"T_1c1b9_row5_col1\" class=\"data row5 col1\" >14.60</td>\n      <td id=\"T_1c1b9_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_1c1b9_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_1c1b9_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a46a53f4340>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "rate-per-feature"}}, "output_type": "display_data"}, "sampling-profile": {"data": {"text/html": "<style type=\"text/css\">\n#T_fc5d9 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_fc5d9 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_fc5d9 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_fc5d9_row0_col0, #T_fc5d9_row1_col0, #T_fc5d9_row2_col4, #T_fc5d9_row3_col0, #T_fc5d9_row4_col0, #T_fc5d9_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_fc5d9\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_fc5d9_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_fc5d9_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_fc5d9_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_fc5d9_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_fc5d9_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_fc5d9_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_fc5d9_row0_col0\" class=\"data row0 col0\" >100%</td>\n      <td id=\"T_fc5d9_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_fc5d9_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_fc5d9_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_fc5d9_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_fc5d9_row1_col0\" class=\"data row1 col0\" >98%</td>\n      <td id=\"T_fc5d9_row1_col1\" class=\"data row1 col1\" >1%</td>\n      <td id=\"T_fc5d9_row1_col2\" class=\"data row1 col2\" >1%</td>\n      <td id=\"T_fc5d9_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_fc5d9_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_fc5d9_row2_col0\" class=\"data row2 col0\" >14%</td>\n      <td id=\"T_fc5d9_row2_col1\" class=\"data row2 col1\" >18%</td>\n      <td id=\"T_fc5d9_row2_col2\" class=\"data row2 col2\" >10%</td>\n      <td id=\"T_fc5d9_row2_col3\" class=\"data row2 col3\" >22%</td>\n      <td id=\"T_fc5d9_row2_col4\" class=\"data row2 col4\" >36%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_fc5d9_row3_col0\" class=\"data row3 col0\" >68%</td>\n      <td id=\"T_fc5d9_row3_col1\" class=\"data row3 col1\" >24%</td>\n      <td id=\"T_fc5d9_row3_col2\" class=\"data row3 col2\" >8%</td>\n      <td id=\"T_fc5d9_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_fc5d9_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_fc5d9_row4_col0\" class=\"data row4 col0\" >57%</td>\n      <td id=\"T_fc5d9_row4_col1\" class=\"data row4 col1\" >22%</td>\n      <td id=\"T_fc5d9_row4_col2\" class=\"data row4 col2\" >16%</td>\n      <td id=\"T_fc5d9_row4_col3\" class=\"data row4 col3\" >6%</td>\n      <td id=\"T_fc5d9_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_fc5d9_row5_col0\" class=\"data row5 col0\" >93%</td>\n      <td id=\"T_fc5d9_row5_col1\" class=\"data row5 col1\" >7%</td>\n      <td id=\"T_fc5d9_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_fc5d9_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_fc5d9_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb76b80>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "sampling-profile"}}, "output_type": "display_data"}, "street-profile": {"data": {"text/html": "<style type=\"text/css\">\n#T_34a15 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_34a15 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_34a15 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_34a15_row0_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_34a15\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_34a15_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_34a15_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_34a15_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_34a15_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_34a15_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_34a15_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_34a15_row0_col0\" class=\"data row0 col0\" >31%</td>\n      <td id=\"T_34a15_row0_col1\" class=\"data row0 col1\" >46%</td>\n      <td id=\"T_34a15_row0_col2\" class=\"data row0 col2\" >21%</td>\n      <td id=\"T_34a15_row0_col3\" class=\"data row0 col3\" >2%</td>\n      <td id=\"T_34a15_row0_col4\" class=\"data row0 col4\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a473ebe8520>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "street-profile"}}, "output_type": "display_data"}, "street-rates-feature": {"data": {"text/html": "<style type=\"text/css\">\n#T_a1596 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_a1596 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_a1596 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_a1596_row0_col4 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_a1596\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a1596_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_a1596_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_a1596_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_a1596_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_a1596_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a1596_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_a1596_row0_col0\" class=\"data row0 col0\" >5.26</td>\n      <td id=\"T_a1596_row0_col1\" class=\"data row0 col1\" >4.33</td>\n      <td id=\"T_a1596_row0_col2\" class=\"data row0 col2\" >5.66</td>\n      <td id=\"T_a1596_row0_col3\" class=\"data row0 col3\" >4.12</td>\n      <td id=\"T_a1596_row0_col4\" class=\"data row0 col4\" >14.56</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a46a5360130>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "street-rates-feature"}}, "output_type": "display_data"}, "lake-rate-per-feature": {"data": {"text/html": "<style type=\"text/css\">\n#T_13be6 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_13be6 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_13be6 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_13be6_row0_col0, #T_13be6_row1_col0, #T_13be6_row2_col1, #T_13be6_row3_col2, #T_13be6_row4_col1, #T_13be6_row5_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_13be6\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_13be6_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_13be6_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_13be6_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_13be6_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_13be6_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_13be6_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_13be6_row0_col0\" class=\"data row0 col0\" >5.50</td>\n      <td id=\"T_13be6_row0_col1\" class=\"data row0 col1\" >0.00</td>\n      <td id=\"T_13be6_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_13be6_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_13be6_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_13be6_row1_col0\" class=\"data row1 col0\" >5.51</td>\n      <td id=\"T_13be6_row1_col1\" class=\"data row1 col1\" >0.00</td>\n      <td id=\"T_13be6_row1_col2\" class=\"data row1 col2\" >5.01</td>\n      <td id=\"T_13be6_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_13be6_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_13be6_row2_col0\" class=\"data row2 col0\" >2.94</td>\n      <td id=\"T_13be6_row2_col1\" class=\"data row2 col1\" >7.83</td>\n      <td id=\"T_13be6_row2_col2\" class=\"data row2 col2\" >5.12</td>\n      <td id=\"T_13be6_row2_col3\" class=\"data row2 col3\" >3.33</td>\n      <td id=\"T_13be6_row2_col4\" class=\"data row2 col4\" >6.39</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_13be6_row3_col0\" class=\"data row3 col0\" >5.20</td>\n      <td id=\"T_13be6_row3_col1\" class=\"data row3 col1\" >3.13</td>\n      <td id=\"T_13be6_row3_col2\" class=\"data row3 col2\" >14.92</td>\n      <td id=\"T_13be6_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_13be6_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_13be6_row4_col0\" class=\"data row4 col0\" >5.88</td>\n      <td id=\"T_13be6_row4_col1\" class=\"data row4 col1\" >7.42</td>\n      <td id=\"T_13be6_row4_col2\" class=\"data row4 col2\" >3.00</td>\n      <td id=\"T_13be6_row4_col3\" class=\"data row4 col3\" >1.91</td>\n      <td id=\"T_13be6_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_13be6_row5_col0\" class=\"data row5 col0\" >4.64</td>\n      <td id=\"T_13be6_row5_col1\" class=\"data row5 col1\" >16.31</td>\n      <td id=\"T_13be6_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_13be6_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_13be6_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a46a536dd60>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-rate-per-feature"}}, "output_type": "display_data"}, "lake-sampling-profile": {"data": {"text/html": "<style type=\"text/css\">\n#T_81787 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_81787 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_81787 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_81787_row0_col0, #T_81787_row1_col0, #T_81787_row2_col4, #T_81787_row3_col0, #T_81787_row4_col0, #T_81787_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_81787\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_81787_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_81787_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_81787_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_81787_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_81787_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_81787_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_81787_row0_col0\" class=\"data row0 col0\" >100%</td>\n      <td id=\"T_81787_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_81787_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_81787_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_81787_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_81787_row1_col0\" class=\"data row1 col0\" >99%</td>\n      <td id=\"T_81787_row1_col1\" class=\"data row1 col1\" >0%</td>\n      <td id=\"T_81787_row1_col2\" class=\"data row1 col2\" >1%</td>\n      <td id=\"T_81787_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_81787_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_81787_row2_col0\" class=\"data row2 col0\" >12%</td>\n      <td id=\"T_81787_row2_col1\" class=\"data row2 col1\" >20%</td>\n      <td id=\"T_81787_row2_col2\" class=\"data row2 col2\" >8%</td>\n      <td id=\"T_81787_row2_col3\" class=\"data row2 col3\" >21%</td>\n      <td id=\"T_81787_row2_col4\" class=\"data row2 col4\" >39%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_81787_row3_col0\" class=\"data row3 col0\" >70%</td>\n      <td id=\"T_81787_row3_col1\" class=\"data row3 col1\" >22%</td>\n      <td id=\"T_81787_row3_col2\" class=\"data row3 col2\" >8%</td>\n      <td id=\"T_81787_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_81787_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_81787_row4_col0\" class=\"data row4 col0\" >58%</td>\n      <td id=\"T_81787_row4_col1\" class=\"data row4 col1\" >20%</td>\n      <td id=\"T_81787_row4_col2\" class=\"data row4 col2\" >16%</td>\n      <td id=\"T_81787_row4_col3\" class=\"data row4 col3\" >6%</td>\n      <td id=\"T_81787_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_81787_row5_col0\" class=\"data row5 col0\" >93%</td>\n      <td id=\"T_81787_row5_col1\" class=\"data row5 col1\" >7%</td>\n      <td id=\"T_81787_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_81787_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_81787_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a46a7f1a220>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-sampling-profile"}}, "output_type": "display_data"}, "lake-street-profile": {"data": {"text/html": "<style type=\"text/css\">\n#T_edd53 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_edd53 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_edd53 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_edd53_row0_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_edd53\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_edd53_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_edd53_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_edd53_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_edd53_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_edd53_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_edd53_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_edd53_row0_col0\" class=\"data row0 col0\" >35%</td>\n      <td id=\"T_edd53_row0_col1\" class=\"data row0 col1\" >48%</td>\n      <td id=\"T_edd53_row0_col2\" class=\"data row0 col2\" >16%</td>\n      <td id=\"T_edd53_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_edd53_row0_col4\" class=\"data row0 col4\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a46a52ce190>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-street-profile"}}, "output_type": "display_data"}, "lake-street-rates-feature": {"data": {"text/html": "<style type=\"text/css\">\n#T_43d42 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_43d42 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_43d42 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_43d42_row0_col4 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_43d42\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_43d42_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_43d42_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_43d42_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_43d42_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_43d42_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_43d42_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_43d42_row0_col0\" class=\"data row0 col0\" >5.33</td>\n      <td id=\"T_43d42_row0_col1\" class=\"data row0 col1\" >4.60</td>\n      <td id=\"T_43d42_row0_col2\" class=\"data row0 col2\" >7.68</td>\n      <td id=\"T_43d42_row0_col3\" class=\"data row0 col3\" >0</td>\n      <td id=\"T_43d42_row0_col4\" class=\"data row0 col4\" >21.80</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a46a49fa100>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-street-rates-feature"}}, "output_type": "display_data"}, "river-rate-per-feature": {"data": {"text/html": "<style type=\"text/css\">\n#T_28094 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_28094 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_28094 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_28094_row0_col0, #T_28094_row1_col0, #T_28094_row2_col4, #T_28094_row3_col2, #T_28094_row4_col2, #T_28094_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_28094\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_28094_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_28094_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_28094_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_28094_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_28094_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_28094_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_28094_row0_col0\" class=\"data row0 col0\" >1.62</td>\n      <td id=\"T_28094_row0_col1\" class=\"data row0 col1\" >0.09</td>\n      <td id=\"T_28094_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_28094_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_28094_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_28094_row1_col0\" class=\"data row1 col0\" >1.64</td>\n      <td id=\"T_28094_row1_col1\" class=\"data row1 col1\" >0.56</td>\n      <td id=\"T_28094_row1_col2\" class=\"data row1 col2\" >0.00</td>\n      <td id=\"T_28094_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_28094_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_28094_row2_col0\" class=\"data row2 col0\" >1.50</td>\n      <td id=\"T_28094_row2_col1\" class=\"data row2 col1\" >1.93</td>\n      <td id=\"T_28094_row2_col2\" class=\"data row2 col2\" >1.17</td>\n      <td id=\"T_28094_row2_col3\" class=\"data row2 col3\" >0.43</td>\n      <td id=\"T_28094_row2_col4\" class=\"data row2 col4\" >4.06</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_28094_row3_col0\" class=\"data row3 col0\" >1.82</td>\n      <td id=\"T_28094_row3_col1\" class=\"data row3 col1\" >1.01</td>\n      <td id=\"T_28094_row3_col2\" class=\"data row3 col2\" >2.38</td>\n      <td id=\"T_28094_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_28094_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_28094_row4_col0\" class=\"data row4 col0\" >1.64</td>\n      <td id=\"T_28094_row4_col1\" class=\"data row4 col1\" >1.19</td>\n      <td id=\"T_28094_row4_col2\" class=\"data row4 col2\" >2.46</td>\n      <td id=\"T_28094_row4_col3\" class=\"data row4 col3\" >1.50</td>\n      <td id=\"T_28094_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_28094_row5_col0\" class=\"data row5 col0\" >1.67</td>\n      <td id=\"T_28094_row5_col1\" class=\"data row5 col1\" >0.07</td>\n      <td id=\"T_28094_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_28094_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_28094_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a46a7f0beb0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-rate-per-feature"}}, "output_type": "display_data"}, "river-sampling-profile": {"data": {"text/html": "<style type=\"text/css\">\n#T_2c804 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_2c804 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_2c804 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_2c804_row0_col0, #T_2c804_row1_col0, #T_2c804_row2_col2, #T_2c804_row3_col0, #T_2c804_row4_col0, #T_2c804_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_2c804\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_2c804_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_2c804_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_2c804_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_2c804_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_2c804_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_2c804_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_2c804_row0_col0\" class=\"data row0 col0\" >97%</td>\n      <td id=\"T_2c804_row0_col1\" class=\"data row0 col1\" >3%</td>\n      <td id=\"T_2c804_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_2c804_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_2c804_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_2c804_row1_col0\" class=\"data row1 col0\" >94%</td>\n      <td id=\"T_2c804_row1_col1\" class=\"data row1 col1\" >6%</td>\n      <td id=\"T_2c804_row1_col2\" class=\"data row1 col2\" >0%</td>\n      <td id=\"T_2c804_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_2c804_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_2c804_row2_col0\" class=\"data row2 col0\" >24%</td>\n      <td id=\"T_2c804_row2_col1\" class=\"data row2 col1\" >9%</td>\n      <td id=\"T_2c804_row2_col2\" class=\"data row2 col2\" >27%</td>\n      <td id=\"T_2c804_row2_col3\" class=\"data row2 col3\" >24%</td>\n      <td id=\"T_2c804_row2_col4\" class=\"data row2 col4\" >15%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_2c804_row3_col0\" class=\"data row3 col0\" >55%</td>\n      <td id=\"T_2c804_row3_col1\" class=\"data row3 col1\" >36%</td>\n      <td id=\"T_2c804_row3_col2\" class=\"data row3 col2\" >9%</td>\n      <td id=\"T_2c804_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_2c804_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_2c804_row4_col0\" class=\"data row4 col0\" >45%</td>\n      <td id=\"T_2c804_row4_col1\" class=\"data row4 col1\" >33%</td>\n      <td id=\"T_2c804_row4_col2\" class=\"data row4 col2\" >12%</td>\n      <td id=\"T_2c804_row4_col3\" class=\"data row4 col3\" >9%</td>\n      <td id=\"T_2c804_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_2c804_row5_col0\" class=\"data row5 col0\" >94%</td>\n      <td id=\"T_2c804_row5_col1\" class=\"data row5 col1\" >6%</td>\n      <td id=\"T_2c804_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_2c804_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_2c804_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb76f10>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-sampling-profile"}}, "output_type": "display_data"}, "river-street-profile": {"data": {"text/html": "<style type=\"text/css\">\n#T_0b35c caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_0b35c caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_0b35c td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_0b35c_row0_col2 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_0b35c\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_0b35c_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_0b35c_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_0b35c_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_0b35c_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_0b35c_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_0b35c_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_0b35c_row0_col0\" class=\"data row0 col0\" >3%</td>\n      <td id=\"T_0b35c_row0_col1\" class=\"data row0 col1\" >30%</td>\n      <td id=\"T_0b35c_row0_col2\" class=\"data row0 col2\" >52%</td>\n      <td id=\"T_0b35c_row0_col3\" class=\"data row0 col3\" >12%</td>\n      <td id=\"T_0b35c_row0_col4\" class=\"data row0 col4\" >3%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a46a48a7d00>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-street-profile"}}, "output_type": "display_data"}, "river-street-rates-feature": {"data": {"text/html": "<style type=\"text/css\">\n#T_af839 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_af839 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_af839 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_af839_row0_col3 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_af839\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_af839_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_af839_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_af839_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_af839_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_af839_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_af839_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_af839_row0_col0\" class=\"data row0 col0\" >0.22</td>\n      <td id=\"T_af839_row0_col1\" class=\"data row0 col1\" >1.36</td>\n      <td id=\"T_af839_row0_col2\" class=\"data row0 col2\" >1.27</td>\n      <td id=\"T_af839_row0_col3\" class=\"data row0 col3\" >4.12</td>\n      <td id=\"T_af839_row0_col4\" class=\"data row0 col4\" >0.08</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a46a5326490>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-street-rates-feature"}}, "output_type": "display_data"}, "lake-municipal-results": {"data": {"text/html": "<style type=\"text/css\">\n#T_bb25a tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_bb25a tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_bb25a tr {\n  font-size: 10px;\n}\n#T_bb25a th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_bb25a td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_bb25a caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n#T_bb25a_row1_col1, #T_bb25a_row2_col1, #T_bb25a_row3_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_bb25a\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_bb25a_level0_col0\" class=\"col_heading level0 col0\" >quantity</th>\n      <th id=\"T_bb25a_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n      <th id=\"T_bb25a_level0_col2\" class=\"col_heading level0 col2\" >samples</th>\n      <th id=\"T_bb25a_level0_col3\" class=\"col_heading level0 col3\" >orchards</th>\n      <th id=\"T_bb25a_level0_col4\" class=\"col_heading level0 col4\" >vineyards</th>\n      <th id=\"T_bb25a_level0_col5\" class=\"col_heading level0 col5\" >buildings</th>\n      <th id=\"T_bb25a_level0_col6\" class=\"col_heading level0 col6\" >forest</th>\n      <th id=\"T_bb25a_level0_col7\" class=\"col_heading level0 col7\" >undefined</th>\n      <th id=\"T_bb25a_level0_col8\" class=\"col_heading level0 col8\" >public services</th>\n      <th id=\"T_bb25a_level0_col9\" class=\"col_heading level0 col9\" >streets</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >canton</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n      <th class=\"blank col3\" >&nbsp;</th>\n      <th class=\"blank col4\" >&nbsp;</th>\n      <th class=\"blank col5\" >&nbsp;</th>\n      <th class=\"blank col6\" >&nbsp;</th>\n      <th class=\"blank col7\" >&nbsp;</th>\n      <th class=\"blank col8\" >&nbsp;</th>\n      <th class=\"blank col9\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_bb25a_level0_row0\" class=\"row_heading level0 row0\" >Bern</th>\n      <td id=\"T_bb25a_row0_col0\" class=\"data row0 col0\" >9889</td>\n      <td id=\"T_bb25a_row0_col1\" class=\"data row0 col1\" >2.81</td>\n      <td id=\"T_bb25a_row0_col2\" class=\"data row0 col2\" >98</td>\n      <td id=\"T_bb25a_row0_col3\" class=\"data row0 col3\" >1</td>\n      <td id=\"T_bb25a_row0_col4\" class=\"data row0 col4\" >1</td>\n      <td id=\"T_bb25a_row0_col5\" class=\"data row0 col5\" >2</td>\n      <td id=\"T_bb25a_row0_col6\" class=\"data row0 col6\" >2</td>\n      <td id=\"T_bb25a_row0_col7\" class=\"data row0 col7\" >3</td>\n      <td id=\"T_bb25a_row0_col8\" class=\"data row0 col8\" >1</td>\n      <td id=\"T_bb25a_row0_col9\" class=\"data row0 col9\" >2</td>\n    </tr>\n    <tr>\n      <th id=\"T_bb25a_level0_row1\" class=\"row_heading level0 row1\" >Gen\u00e8ve</th>\n      <td id=\"T_bb25a_row1_col0\" class=\"data row1 col0\" >9559</td>\n      <td id=\"T_bb25a_row1_col1\" class=\"data row1 col1\" >5.88</td>\n      <td id=\"T_bb25a_row1_col2\" class=\"data row1 col2\" >33</td>\n      <td id=\"T_bb25a_row1_col3\" class=\"data row1 col3\" >1</td>\n      <td id=\"T_bb25a_row1_col4\" class=\"data row1 col4\" >1</td>\n      <td id=\"T_bb25a_row1_col5\" class=\"data row1 col5\" >5</td>\n      <td id=\"T_bb25a_row1_col6\" class=\"data row1 col6\" >1</td>\n      <td id=\"T_bb25a_row1_col7\" class=\"data row1 col7\" >1</td>\n      <td id=\"T_bb25a_row1_col8\" class=\"data row1 col8\" >1</td>\n      <td id=\"T_bb25a_row1_col9\" class=\"data row1 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_bb25a_level0_row2\" class=\"row_heading level0 row2\" >Valais</th>\n      <td id=\"T_bb25a_row2_col0\" class=\"data row2 col0\" >8902</td>\n      <td id=\"T_bb25a_row2_col1\" class=\"data row2 col1\" >23.70</td>\n      <td id=\"T_bb25a_row2_col2\" class=\"data row2 col2\" >13</td>\n      <td id=\"T_bb25a_row2_col3\" class=\"data row2 col3\" >1</td>\n      <td id=\"T_bb25a_row2_col4\" class=\"data row2 col4\" >1</td>\n      <td id=\"T_bb25a_row2_col5\" class=\"data row2 col5\" >2</td>\n      <td id=\"T_bb25a_row2_col6\" class=\"data row2 col6\" >3</td>\n      <td id=\"T_bb25a_row2_col7\" class=\"data row2 col7\" >2</td>\n      <td id=\"T_bb25a_row2_col8\" class=\"data row2 col8\" >1</td>\n      <td id=\"T_bb25a_row2_col9\" class=\"data row2 col9\" >1</td>\n    </tr>\n    <tr>\n      <th id=\"T_bb25a_level0_row3\" class=\"row_heading level0 row3\" >Vaud</th>\n      <td id=\"T_bb25a_row3_col0\" class=\"data row3 col0\" >62468</td>\n      <td id=\"T_bb25a_row3_col1\" class=\"data row3 col1\" >7.78</td>\n      <td id=\"T_bb25a_row3_col2\" class=\"data row3 col2\" >226</td>\n      <td id=\"T_bb25a_row3_col3\" class=\"data row3 col3\" >1</td>\n      <td id=\"T_bb25a_row3_col4\" class=\"data row3 col4\" >1</td>\n      <td id=\"T_bb25a_row3_col5\" class=\"data row3 col5\" >4</td>\n      <td id=\"T_bb25a_row3_col6\" class=\"data row3 col6\" >1</td>\n      <td id=\"T_bb25a_row3_col7\" class=\"data row3 col7\" >1</td>\n      <td id=\"T_bb25a_row3_col8\" class=\"data row3 col8\" >1</td>\n      <td id=\"T_bb25a_row3_col9\" class=\"data row3 col9\" >2</td>\n    </tr>\n    <tr>\n      <th id=\"T_bb25a_level0_row4\" class=\"row_heading level0 row4\" >Z\u00fcrich</th>\n      <td id=\"T_bb25a_row4_col0\" class=\"data row4 col0\" >27228</td>\n      <td id=\"T_bb25a_row4_col1\" class=\"data row4 col1\" >3.99</td>\n      <td id=\"T_bb25a_row4_col2\" class=\"data row4 col2\" >171</td>\n      <td id=\"T_bb25a_row4_col3\" class=\"data row4 col3\" >1</td>\n      <td id=\"T_bb25a_row4_col4\" class=\"data row4 col4\" >1</td>\n      <td id=\"T_bb25a_row4_col5\" class=\"data row4 col5\" >4</td>\n      <td id=\"T_bb25a_row4_col6\" class=\"data row4 col6\" >1</td>\n      <td id=\"T_bb25a_row4_col7\" class=\"data row4 col7\" >2</td>\n      <td id=\"T_bb25a_row4_col8\" class=\"data row4 col8\" >1</td>\n      <td id=\"T_bb25a_row4_col9\" class=\"data row4 col9\" >2</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a46a7b903d0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-municipal-results"}}, "output_type": "display_data"}, "river-municipal-results": {"data": {"text/html": "<style type=\"text/css\">\n#T_795cb tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_795cb tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_795cb tr {\n  font-size: 10px;\n}\n#T_795cb th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_795cb td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_795cb caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_795cb\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_795cb_level0_col0\" class=\"col_heading level0 col0\" >quantity</th>\n      <th id=\"T_795cb_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n      <th id=\"T_795cb_level0_col2\" class=\"col_heading level0 col2\" >samples</th>\n      <th id=\"T_795cb_level0_col3\" class=\"col_heading level0 col3\" >orchards</th>\n      <th id=\"T_795cb_level0_col4\" class=\"col_heading level0 col4\" >vineyards</th>\n      <th id=\"T_795cb_level0_col5\" class=\"col_heading level0 col5\" >buildings</th>\n      <th id=\"T_795cb_level0_col6\" class=\"col_heading level0 col6\" >forest</th>\n      <th id=\"T_795cb_level0_col7\" class=\"col_heading level0 col7\" >undefined</th>\n      <th id=\"T_795cb_level0_col8\" class=\"col_heading level0 col8\" >public services</th>\n      <th id=\"T_795cb_level0_col9\" class=\"col_heading level0 col9\" >streets</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >canton</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n      <th class=\"blank col3\" >&nbsp;</th>\n      <th class=\"blank col4\" >&nbsp;</th>\n      <th class=\"blank col5\" >&nbsp;</th>\n      <th class=\"blank col6\" >&nbsp;</th>\n      <th class=\"blank col7\" >&nbsp;</th>\n      <th class=\"blank col8\" >&nbsp;</th>\n      <th class=\"blank col9\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_795cb_level0_row0\" class=\"row_heading level0 row0\" >Bern</th>\n      <td id=\"T_795cb_row0_col0\" class=\"data row0 col0\" >3701</td>\n      <td id=\"T_795cb_row0_col1\" class=\"data row0 col1\" >1.21</td>\n      <td id=\"T_795cb_row0_col2\" class=\"data row0 col2\" >97</td>\n      <td id=\"T_795cb_row0_col3\" class=\"data row0 col3\" >1</td>\n      <td id=\"T_795cb_row0_col4\" class=\"data row0 col4\" >1</td>\n      <td id=\"T_795cb_row0_col5\" class=\"data row0 col5\" >3</td>\n      <td id=\"T_795cb_row0_col6\" class=\"data row0 col6\" >1</td>\n      <td id=\"T_795cb_row0_col7\" class=\"data row0 col7\" >2</td>\n      <td id=\"T_795cb_row0_col8\" class=\"data row0 col8\" >1</td>\n      <td id=\"T_795cb_row0_col9\" class=\"data row0 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_795cb_level0_row1\" class=\"row_heading level0 row1\" >Gen\u00e8ve</th>\n      <td id=\"T_795cb_row1_col0\" class=\"data row1 col0\" >1616</td>\n      <td id=\"T_795cb_row1_col1\" class=\"data row1 col1\" >4.38</td>\n      <td id=\"T_795cb_row1_col2\" class=\"data row1 col2\" >9</td>\n      <td id=\"T_795cb_row1_col3\" class=\"data row1 col3\" >1</td>\n      <td id=\"T_795cb_row1_col4\" class=\"data row1 col4\" >1</td>\n      <td id=\"T_795cb_row1_col5\" class=\"data row1 col5\" >4</td>\n      <td id=\"T_795cb_row1_col6\" class=\"data row1 col6\" >1</td>\n      <td id=\"T_795cb_row1_col7\" class=\"data row1 col7\" >1</td>\n      <td id=\"T_795cb_row1_col8\" class=\"data row1 col8\" >1</td>\n      <td id=\"T_795cb_row1_col9\" class=\"data row1 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_795cb_level0_row2\" class=\"row_heading level0 row2\" >Valais</th>\n      <td id=\"T_795cb_row2_col0\" class=\"data row2 col0\" >78</td>\n      <td id=\"T_795cb_row2_col1\" class=\"data row2 col1\" >0.40</td>\n      <td id=\"T_795cb_row2_col2\" class=\"data row2 col2\" >4</td>\n      <td id=\"T_795cb_row2_col3\" class=\"data row2 col3\" >1</td>\n      <td id=\"T_795cb_row2_col4\" class=\"data row2 col4\" >1</td>\n      <td id=\"T_795cb_row2_col5\" class=\"data row2 col5\" >2</td>\n      <td id=\"T_795cb_row2_col6\" class=\"data row2 col6\" >2</td>\n      <td id=\"T_795cb_row2_col7\" class=\"data row2 col7\" >2</td>\n      <td id=\"T_795cb_row2_col8\" class=\"data row2 col8\" >1</td>\n      <td id=\"T_795cb_row2_col9\" class=\"data row2 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_795cb_level0_row3\" class=\"row_heading level0 row3\" >Vaud</th>\n      <td id=\"T_795cb_row3_col0\" class=\"data row3 col0\" >738</td>\n      <td id=\"T_795cb_row3_col1\" class=\"data row3 col1\" >2.64</td>\n      <td id=\"T_795cb_row3_col2\" class=\"data row3 col2\" >5</td>\n      <td id=\"T_795cb_row3_col3\" class=\"data row3 col3\" >1</td>\n      <td id=\"T_795cb_row3_col4\" class=\"data row3 col4\" >1</td>\n      <td id=\"T_795cb_row3_col5\" class=\"data row3 col5\" >3</td>\n      <td id=\"T_795cb_row3_col6\" class=\"data row3 col6\" >2</td>\n      <td id=\"T_795cb_row3_col7\" class=\"data row3 col7\" >2</td>\n      <td id=\"T_795cb_row3_col8\" class=\"data row3 col8\" >1</td>\n      <td id=\"T_795cb_row3_col9\" class=\"data row3 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_795cb_level0_row4\" class=\"row_heading level0 row4\" >Z\u00fcrich</th>\n      <td id=\"T_795cb_row4_col0\" class=\"data row4 col0\" >6557</td>\n      <td id=\"T_795cb_row4_col1\" class=\"data row4 col1\" >1.15</td>\n      <td id=\"T_795cb_row4_col2\" class=\"data row4 col2\" >175</td>\n      <td id=\"T_795cb_row4_col3\" class=\"data row4 col3\" >1</td>\n      <td id=\"T_795cb_row4_col4\" class=\"data row4 col4\" >1</td>\n      <td id=\"T_795cb_row4_col5\" class=\"data row4 col5\" >4</td>\n      <td id=\"T_795cb_row4_col6\" class=\"data row4 col6\" >1</td>\n      <td id=\"T_795cb_row4_col7\" class=\"data row4 col7\" >2</td>\n      <td id=\"T_795cb_row4_col8\" class=\"data row4 col8\" >1</td>\n      <td id=\"T_795cb_row4_col9\" class=\"data row4 col9\" >3</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a4699f34340>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-municipal-results"}}, "output_type": "display_data"}, "lakes-i-summary": {"data": {"text/html": "<style type=\"text/css\">\n#T_ecb27 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_ecb27 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_ecb27 tr {\n  font-size: 10px;\n}\n#T_ecb27 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_ecb27 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_ecb27 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_ecb27\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_ecb27_level0_col0\" class=\"col_heading level0 col0\" >samples</th>\n      <th id=\"T_ecb27_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_ecb27_level0_row0\" class=\"row_heading level0 row0\" >Bielersee</th>\n      <td id=\"T_ecb27_row0_col0\" class=\"data row0 col0\" >50</td>\n      <td id=\"T_ecb27_row0_col1\" class=\"data row0 col1\" >4.08</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row1\" class=\"row_heading level0 row1\" >Brienzersee</th>\n      <td id=\"T_ecb27_row1_col0\" class=\"data row1 col0\" >5</td>\n      <td id=\"T_ecb27_row1_col1\" class=\"data row1 col1\" >3.96</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row2\" class=\"row_heading level0 row2\" >Greifensee</th>\n      <td id=\"T_ecb27_row2_col0\" class=\"data row2 col0\" >34</td>\n      <td id=\"T_ecb27_row2_col1\" class=\"data row2 col1\" >4.71</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row3\" class=\"row_heading level0 row3\" >Katzensee</th>\n      <td id=\"T_ecb27_row3_col0\" class=\"data row3 col0\" >12</td>\n      <td id=\"T_ecb27_row3_col1\" class=\"data row3 col1\" >1.77</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row4\" class=\"row_heading level0 row4\" >Lac-leman</th>\n      <td id=\"T_ecb27_row4_col0\" class=\"data row4 col0\" >249</td>\n      <td id=\"T_ecb27_row4_col1\" class=\"data row4 col1\" >8.91</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row5\" class=\"row_heading level0 row5\" >Neuenburgersee</th>\n      <td id=\"T_ecb27_row5_col0\" class=\"data row5 col0\" >23</td>\n      <td id=\"T_ecb27_row5_col1\" class=\"data row5 col1\" >1.78</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row6\" class=\"row_heading level0 row6\" >Thunersee</th>\n      <td id=\"T_ecb27_row6_col0\" class=\"data row6 col0\" >43</td>\n      <td id=\"T_ecb27_row6_col1\" class=\"data row6 col1\" >1.21</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row7\" class=\"row_heading level0 row7\" >Zurichsee</th>\n      <td id=\"T_ecb27_row7_col0\" class=\"data row7 col0\" >125</td>\n      <td id=\"T_ecb27_row7_col1\" class=\"data row7 col1\" >4.01</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a4699f138b0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lakes-i-summary"}}, "output_type": "display_data"}, "rivers-i-summary": {"data": {"text/html": "<style type=\"text/css\">\n#T_e63cc tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_e63cc tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_e63cc tr {\n  font-size: 10px;\n}\n#T_e63cc th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_e63cc td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_e63cc caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_e63cc\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_e63cc_level0_col0\" class=\"col_heading level0 col0\" >samples</th>\n      <th id=\"T_e63cc_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_e63cc_level0_row0\" class=\"row_heading level0 row0\" >Aare</th>\n      <td id=\"T_e63cc_row0_col0\" class=\"data row0 col0\" >61</td>\n      <td id=\"T_e63cc_row0_col1\" class=\"data row0 col1\" >1.27</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row1\" class=\"row_heading level0 row1\" >Aarenidau-buren-kanal</th>\n      <td id=\"T_e63cc_row1_col0\" class=\"data row1 col0\" >3</td>\n      <td id=\"T_e63cc_row1_col1\" class=\"data row1 col1\" >1.26</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row2\" class=\"row_heading level0 row2\" >Arve</th>\n      <td id=\"T_e63cc_row2_col0\" class=\"data row2 col0\" >8</td>\n      <td id=\"T_e63cc_row2_col1\" class=\"data row2 col1\" >2.92</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row3\" class=\"row_heading level0 row3\" >Chriesbach</th>\n      <td id=\"T_e63cc_row3_col0\" class=\"data row3 col0\" >12</td>\n      <td id=\"T_e63cc_row3_col1\" class=\"data row3 col1\" >0.92</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row4\" class=\"row_heading level0 row4\" >Dorfbach</th>\n      <td id=\"T_e63cc_row4_col0\" class=\"data row4 col0\" >1</td>\n      <td id=\"T_e63cc_row4_col1\" class=\"data row4 col1\" >0.11</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row5\" class=\"row_heading level0 row5\" >Emme</th>\n      <td id=\"T_e63cc_row5_col0\" class=\"data row5 col0\" >9</td>\n      <td id=\"T_e63cc_row5_col1\" class=\"data row5 col1\" >0.60</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row6\" class=\"row_heading level0 row6\" >Glatt</th>\n      <td id=\"T_e63cc_row6_col0\" class=\"data row6 col0\" >7</td>\n      <td id=\"T_e63cc_row6_col1\" class=\"data row6 col1\" >1.67</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row7\" class=\"row_heading level0 row7\" >Grandelbach</th>\n      <td id=\"T_e63cc_row7_col0\" class=\"data row7 col0\" >4</td>\n      <td id=\"T_e63cc_row7_col1\" class=\"data row7 col1\" >1.33</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row8\" class=\"row_heading level0 row8\" >Jona</th>\n      <td id=\"T_e63cc_row8_col0\" class=\"data row8 col0\" >12</td>\n      <td id=\"T_e63cc_row8_col1\" class=\"data row8 col1\" >2.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row9\" class=\"row_heading level0 row9\" >La-thiele</th>\n      <td id=\"T_e63cc_row9_col0\" class=\"data row9 col0\" >1</td>\n      <td id=\"T_e63cc_row9_col1\" class=\"data row9 col1\" >0.46</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row10\" class=\"row_heading level0 row10\" >Langeten</th>\n      <td id=\"T_e63cc_row10_col0\" class=\"data row10 col0\" >11</td>\n      <td id=\"T_e63cc_row10_col1\" class=\"data row10 col1\" >1.94</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row11\" class=\"row_heading level0 row11\" >Limmat</th>\n      <td id=\"T_e63cc_row11_col0\" class=\"data row11 col0\" >61</td>\n      <td id=\"T_e63cc_row11_col1\" class=\"data row11 col1\" >1.13</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row12\" class=\"row_heading level0 row12\" >Ognonnaz</th>\n      <td id=\"T_e63cc_row12_col0\" class=\"data row12 col0\" >1</td>\n      <td id=\"T_e63cc_row12_col1\" class=\"data row12 col1\" >2.85</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row13\" class=\"row_heading level0 row13\" >Rhein</th>\n      <td id=\"T_e63cc_row13_col0\" class=\"data row13 col0\" >10</td>\n      <td id=\"T_e63cc_row13_col1\" class=\"data row13 col1\" >1.52</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row14\" class=\"row_heading level0 row14\" >Rhone</th>\n      <td id=\"T_e63cc_row14_col0\" class=\"data row14 col0\" >8</td>\n      <td id=\"T_e63cc_row14_col1\" class=\"data row14 col1\" >3.43</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row15\" class=\"row_heading level0 row15\" >Schuss</th>\n      <td id=\"T_e63cc_row15_col0\" class=\"data row15 col0\" >2</td>\n      <td id=\"T_e63cc_row15_col1\" class=\"data row15 col1\" >1.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row16\" class=\"row_heading level0 row16\" >Sihl</th>\n      <td id=\"T_e63cc_row16_col0\" class=\"data row16 col0\" >51</td>\n      <td id=\"T_e63cc_row16_col1\" class=\"data row16 col1\" >1.08</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row17\" class=\"row_heading level0 row17\" >Toss</th>\n      <td id=\"T_e63cc_row17_col0\" class=\"data row17 col0\" >17</td>\n      <td id=\"T_e63cc_row17_col1\" class=\"data row17 col1\" >0.59</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row18\" class=\"row_heading level0 row18\" >Zulg</th>\n      <td id=\"T_e63cc_row18_col0\" class=\"data row18 col0\" >11</td>\n      <td id=\"T_e63cc_row18_col1\" class=\"data row18 col1\" >0.63</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7a4699ebd310>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "rivers-i-summary"}}, "output_type": "display_data"}}
\ No newline at end of file
+{"prior-likelihood": {"data": {"image/png": 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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "prior-likelihood"}}, "output_type": "display_data"}, "scatter-prior-likelihood": {"data": {"image/png": 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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "scatter-prior-likelihood"}}, "output_type": "display_data"}, "cumumlative-dist-forecast-prior": {"data": {"image/png": 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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "cumumlative-dist-forecast-prior"}}, "output_type": "display_data"}, "caption-histo": {"data": {"text/markdown": "Survey total pcs/m 2020 - 2021 versus 2015 - 2019. All locations considered.", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "caption-histo"}}, "output_type": "display_data"}, "material-report": {"data": {"text/html": "<style type=\"text/css\">\n#T_92ad7 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_92ad7 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_92ad7 tr {\n  font-size: 10px;\n}\n#T_92ad7 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_92ad7 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_92ad7 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_92ad7\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_92ad7_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_92ad7_level0_row0\" class=\"row_heading level0 row0\" >glass</th>\n      <td id=\"T_92ad7_row0_col0\" class=\"data row0 col0\" >6%</td>\n    </tr>\n    <tr>\n      <th id=\"T_92ad7_level0_row1\" class=\"row_heading level0 row1\" >metal</th>\n      <td id=\"T_92ad7_row1_col0\" class=\"data row1 col0\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_92ad7_level0_row2\" class=\"row_heading level0 row2\" >paper</th>\n      <td id=\"T_92ad7_row2_col0\" class=\"data row2 col0\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_92ad7_level0_row3\" class=\"row_heading level0 row3\" >plastic</th>\n      <td id=\"T_92ad7_row3_col0\" class=\"data row3 col0\" >81%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e8bafd30>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "material-report"}}, "output_type": "display_data"}, "forecast-weighted-prior": {"data": {"text/markdown": "__Given the weighted prior__\n* Average: 5.2\n* HDI 95%: 0.1 - 18.7\n* 90% Range: 0.2 - 16.51", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-weighted-prior"}}, "output_type": "display_data"}, "forecast-max-val": {"data": {"text/markdown": "__Given the observed max__\n* Average: 7.55\n* HDI 95%: 0.02 - 42.66\n* 90% Range: 0.16 - 34.52", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-max-val"}}, "output_type": "display_data"}, "forecast-99-max": {"data": {"text/markdown": "__Given the 99th percentile__\n* Average: 3.84\n* HDI 95%: 0.1 - 13.7\n* 90% Range: 0.2 - 13.03", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-99-max"}}, "output_type": "display_data"}, "ratio-most-common": {"data": {"text/markdown": "__The most common objects account for 72% of all objects__", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "ratio-most-common"}}, "output_type": "display_data"}, "most_common_objects": {"data": {"text/html": "<style type=\"text/css\">\n#T_3adc8 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_3adc8 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_3adc8 tr {\n  font-size: 10px;\n}\n#T_3adc8 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_3adc8 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_3adc8 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_3adc8 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_3adc8 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_3adc8\">\n  <thead>\n    <tr>\n      <th id=\"T_3adc8_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_3adc8_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_3adc8_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_3adc8_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_3adc8_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_3adc8_row0_col0\" class=\"data row0 col0\" >Fragmented plastics</td>\n      <td id=\"T_3adc8_row0_col1\" class=\"data row0 col1\" >6'796</td>\n      <td id=\"T_3adc8_row0_col2\" class=\"data row0 col2\" >0,83</td>\n      <td id=\"T_3adc8_row0_col3\" class=\"data row0 col3\" >0,15</td>\n      <td id=\"T_3adc8_row0_col4\" class=\"data row0 col4\" >0,88</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_3adc8_row1_col1\" class=\"data row1 col1\" >6'306</td>\n      <td id=\"T_3adc8_row1_col2\" class=\"data row1 col2\" >0,60</td>\n      <td id=\"T_3adc8_row1_col3\" class=\"data row1 col3\" >0,14</td>\n      <td id=\"T_3adc8_row1_col4\" class=\"data row1 col4\" >0,87</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row2_col0\" class=\"data row2 col0\" >Expanded polystyrene</td>\n      <td id=\"T_3adc8_row2_col1\" class=\"data row2 col1\" >4'319</td>\n      <td id=\"T_3adc8_row2_col2\" class=\"data row2 col2\" >0,55</td>\n      <td id=\"T_3adc8_row2_col3\" class=\"data row2 col3\" >0,10</td>\n      <td id=\"T_3adc8_row2_col4\" class=\"data row2 col4\" >0,67</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row3_col0\" class=\"data row3 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_3adc8_row3_col1\" class=\"data row3 col1\" >2'890</td>\n      <td id=\"T_3adc8_row3_col2\" class=\"data row3 col2\" >0,31</td>\n      <td id=\"T_3adc8_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_3adc8_row3_col4\" class=\"data row3 col4\" >0,88</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row4_col0\" class=\"data row4 col0\" >Industrial sheeting</td>\n      <td id=\"T_3adc8_row4_col1\" class=\"data row4 col1\" >2'031</td>\n      <td id=\"T_3adc8_row4_col2\" class=\"data row4 col2\" >0,24</td>\n      <td id=\"T_3adc8_row4_col3\" class=\"data row4 col3\" >0,05</td>\n      <td id=\"T_3adc8_row4_col4\" class=\"data row4 col4\" >0,69</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row5_col0\" class=\"data row5 col0\" >Industrial pellets (nurdles)</td>\n      <td id=\"T_3adc8_row5_col1\" class=\"data row5 col1\" >1'715</td>\n      <td id=\"T_3adc8_row5_col2\" class=\"data row5 col2\" >0,18</td>\n      <td id=\"T_3adc8_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_3adc8_row5_col4\" class=\"data row5 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row6_col0\" class=\"data row6 col0\" >plastic caps, lid rings: G21, G22, G23, G24</td>\n      <td id=\"T_3adc8_row6_col1\" class=\"data row6 col1\" >1'704</td>\n      <td id=\"T_3adc8_row6_col2\" class=\"data row6 col2\" >0,19</td>\n      <td id=\"T_3adc8_row6_col3\" class=\"data row6 col3\" >0,04</td>\n      <td id=\"T_3adc8_row6_col4\" class=\"data row6 col4\" >0,71</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row7_col0\" class=\"data row7 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_3adc8_row7_col1\" class=\"data row7 col1\" >1'502</td>\n      <td id=\"T_3adc8_row7_col2\" class=\"data row7 col2\" >0,19</td>\n      <td id=\"T_3adc8_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_3adc8_row7_col4\" class=\"data row7 col4\" >0,65</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row8_col0\" class=\"data row8 col0\" >Foam packaging/insulation/polyurethane</td>\n      <td id=\"T_3adc8_row8_col1\" class=\"data row8 col1\" >1'435</td>\n      <td id=\"T_3adc8_row8_col2\" class=\"data row8 col2\" >0,09</td>\n      <td id=\"T_3adc8_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_3adc8_row8_col4\" class=\"data row8 col4\" >0,87</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row9_col0\" class=\"data row9 col0\" >Cotton bud/swab sticks</td>\n      <td id=\"T_3adc8_row9_col1\" class=\"data row9 col1\" >1'385</td>\n      <td id=\"T_3adc8_row9_col2\" class=\"data row9 col2\" >0,16</td>\n      <td id=\"T_3adc8_row9_col3\" class=\"data row9 col3\" >0,03</td>\n      <td id=\"T_3adc8_row9_col4\" class=\"data row9 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row10_col0\" class=\"data row10 col0\" >Plastic construction waste</td>\n      <td id=\"T_3adc8_row10_col1\" class=\"data row10 col1\" >836</td>\n      <td id=\"T_3adc8_row10_col2\" class=\"data row10 col2\" >0,09</td>\n      <td id=\"T_3adc8_row10_col3\" class=\"data row10 col3\" >0,02</td>\n      <td id=\"T_3adc8_row10_col4\" class=\"data row10 col4\" >0,53</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row11_col0\" class=\"data row11 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_3adc8_row11_col1\" class=\"data row11 col1\" >551</td>\n      <td id=\"T_3adc8_row11_col2\" class=\"data row11 col2\" >0,05</td>\n      <td id=\"T_3adc8_row11_col3\" class=\"data row11 col3\" >0,01</td>\n      <td id=\"T_3adc8_row11_col4\" class=\"data row11 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row12_col0\" class=\"data row12 col0\" >Tobacco; plastic packaging, containers</td>\n      <td id=\"T_3adc8_row12_col1\" class=\"data row12 col1\" >529</td>\n      <td id=\"T_3adc8_row12_col2\" class=\"data row12 col2\" >0,06</td>\n      <td id=\"T_3adc8_row12_col3\" class=\"data row12 col3\" >0,01</td>\n      <td id=\"T_3adc8_row12_col4\" class=\"data row12 col4\" >0,50</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row13_col0\" class=\"data row13 col0\" >Foil wrappers, aluminum foil</td>\n      <td id=\"T_3adc8_row13_col1\" class=\"data row13 col1\" >386</td>\n      <td id=\"T_3adc8_row13_col2\" class=\"data row13 col2\" >0,04</td>\n      <td id=\"T_3adc8_row13_col3\" class=\"data row13 col3\" >0,01</td>\n      <td id=\"T_3adc8_row13_col4\" class=\"data row13 col4\" >0,50</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row14_col0\" class=\"data row14 col0\" >Straws and stirrers</td>\n      <td id=\"T_3adc8_row14_col1\" class=\"data row14 col1\" >382</td>\n      <td id=\"T_3adc8_row14_col2\" class=\"data row14 col2\" >0,04</td>\n      <td id=\"T_3adc8_row14_col3\" class=\"data row14 col3\" >0,01</td>\n      <td id=\"T_3adc8_row14_col4\" class=\"data row14 col4\" >0,50</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e657d229580>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "most_common_objects"}}, "output_type": "display_data"}, "l-sampling-summary": {"data": {"text/markdown": "<font color=#1e90ff>2020 - 2021 </font>\n* Number of samples: 263\n* Total objects: 45003\n* Average pcs/m: 5.01\n* Standard deviation: 8.24\n* Maximum pcs/m: 66.17\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "l-sampling-summary"}}, "output_type": "display_data"}, "prior-sampling-summary": {"data": {"text/markdown": "<font color=#daa520>2015 - 2019</font>\n* Number of samples: 568\n* Total objects: 85733\n* Average pcs/m: 4.01\n* Standard deviation: 6.73\n* Maximum pcs/m: 77.1\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "prior-sampling-summary"}}, "output_type": "display_data"}, "sampling-summary": {"data": {"text/markdown": "2015 - 2021\n* Number of samples: 831\n* Total objects: 130736\n* Average pcs/m: 4.32\n* Standard deviation: 7.25\n* Maximum pcs/m: 77.1\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "sampling-summary"}}, "output_type": "display_data"}, "feature-inventory": {"data": {"text/markdown": "__Features surveyed__\n* Rivers: 19\n* Lakes: 8\n\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "feature-inventory"}}, "output_type": "display_data"}, "administrative-boundaries": {"data": {"text/markdown": "__Administrative boundaries__\n* Survey locations: 131\n* Cities: 70\n\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "administrative-boundaries"}}, "output_type": "display_data"}, "lake-prior-likelihood": {"data": {"image/png": 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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-prior-likelihood"}}, "output_type": "display_data"}, "lake-cumumlative-dist-forecast-prior": {"data": {"image/png": 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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-cumumlative-dist-forecast-prior"}}, "output_type": "display_data"}, "caption-histo-l": {"data": {"text/markdown": "Survey total pcs/m 2020 - 2021 v/s 2015 - 2019. All locations considered.", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "caption-histo-l"}}, "output_type": "display_data"}, "material-report-l": {"data": {"text/html": "<style type=\"text/css\">\n#T_020a9 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_020a9 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_020a9 tr {\n  font-size: 10px;\n}\n#T_020a9 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_020a9 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_020a9 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_020a9\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_020a9_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_020a9_level0_row0\" class=\"row_heading level0 row0\" >glass</th>\n      <td id=\"T_020a9_row0_col0\" class=\"data row0 col0\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_020a9_level0_row1\" class=\"row_heading level0 row1\" >metal</th>\n      <td id=\"T_020a9_row1_col0\" class=\"data row1 col0\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_020a9_level0_row2\" class=\"row_heading level0 row2\" >paper</th>\n      <td id=\"T_020a9_row2_col0\" class=\"data row2 col0\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_020a9_level0_row3\" class=\"row_heading level0 row3\" >plastic</th>\n      <td id=\"T_020a9_row3_col0\" class=\"data row3 col0\" >83%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e62423a0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "material-report-l"}}, "output_type": "display_data"}, "forecast-weighted-prior-l": {"data": {"text/markdown": "__Given the weighted prior__\n* Average: 3.97\n* HDI 95%: 0.3 - 10.7\n* 90% Range: 0.3 - 10.7", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-weighted-prior-l"}}, "output_type": "display_data"}, "forecast-max-val-l": {"data": {"text/markdown": "__Given the observed max__\n* Average: 8.0\n* HDI 95%: 0.14 - 58.18\n* 90% Range: 0.64 - 54.9", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-max-val-l"}}, "output_type": "display_data"}, "forecast-99-max-l": {"data": {"text/markdown": "__Given the 99th percentile__\n* Average: 4.6\n* HDI 95%: 0.1 - 15.6\n* 90% Range: 0.5 - 14.84", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-99-max-l"}}, "output_type": "display_data"}, "ratio-most-common-l": {"data": {"text/markdown": "__The most common objects account for 74% of all objects__", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "ratio-most-common-l"}}, "output_type": "display_data"}, "most_common_objects-l": {"data": {"text/html": "<style type=\"text/css\">\n#T_c1178 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_c1178 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_c1178 tr {\n  font-size: 10px;\n}\n#T_c1178 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_c1178 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_c1178 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_c1178 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_c1178 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_c1178\">\n  <thead>\n    <tr>\n      <th id=\"T_c1178_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_c1178_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_c1178_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_c1178_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_c1178_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c1178_row0_col0\" class=\"data row0 col0\" >Fragmented plastics</td>\n      <td id=\"T_c1178_row0_col1\" class=\"data row0 col1\" >6'735</td>\n      <td id=\"T_c1178_row0_col2\" class=\"data row0 col2\" >0,94</td>\n      <td id=\"T_c1178_row0_col3\" class=\"data row0 col3\" >0,16</td>\n      <td id=\"T_c1178_row0_col4\" class=\"data row0 col4\" >0,93</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_c1178_row1_col1\" class=\"data row1 col1\" >6'081</td>\n      <td id=\"T_c1178_row1_col2\" class=\"data row1 col2\" >0,66</td>\n      <td id=\"T_c1178_row1_col3\" class=\"data row1 col3\" >0,14</td>\n      <td id=\"T_c1178_row1_col4\" class=\"data row1 col4\" >0,91</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row2_col0\" class=\"data row2 col0\" >Expanded polystyrene</td>\n      <td id=\"T_c1178_row2_col1\" class=\"data row2 col1\" >4'302</td>\n      <td id=\"T_c1178_row2_col2\" class=\"data row2 col2\" >0,63</td>\n      <td id=\"T_c1178_row2_col3\" class=\"data row2 col3\" >0,10</td>\n      <td id=\"T_c1178_row2_col4\" class=\"data row2 col4\" >0,75</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row3_col0\" class=\"data row3 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_c1178_row3_col1\" class=\"data row3 col1\" >2'789</td>\n      <td id=\"T_c1178_row3_col2\" class=\"data row3 col2\" >0,34</td>\n      <td id=\"T_c1178_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_c1178_row3_col4\" class=\"data row3 col4\" >0,91</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row4_col0\" class=\"data row4 col0\" >Industrial sheeting</td>\n      <td id=\"T_c1178_row4_col1\" class=\"data row4 col1\" >1'882</td>\n      <td id=\"T_c1178_row4_col2\" class=\"data row4 col2\" >0,26</td>\n      <td id=\"T_c1178_row4_col3\" class=\"data row4 col3\" >0,04</td>\n      <td id=\"T_c1178_row4_col4\" class=\"data row4 col4\" >0,74</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row5_col0\" class=\"data row5 col0\" >Industrial pellets (nurdles)</td>\n      <td id=\"T_c1178_row5_col1\" class=\"data row5 col1\" >1'705</td>\n      <td id=\"T_c1178_row5_col2\" class=\"data row5 col2\" >0,21</td>\n      <td id=\"T_c1178_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_c1178_row5_col4\" class=\"data row5 col4\" >0,40</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row6_col0\" class=\"data row6 col0\" >plastic caps, lid rings: G21, G22, G23, G24</td>\n      <td id=\"T_c1178_row6_col1\" class=\"data row6 col1\" >1'677</td>\n      <td id=\"T_c1178_row6_col2\" class=\"data row6 col2\" >0,21</td>\n      <td id=\"T_c1178_row6_col3\" class=\"data row6 col3\" >0,04</td>\n      <td id=\"T_c1178_row6_col4\" class=\"data row6 col4\" >0,78</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row7_col0\" class=\"data row7 col0\" >Foam packaging/insulation/polyurethane</td>\n      <td id=\"T_c1178_row7_col1\" class=\"data row7 col1\" >1'422</td>\n      <td id=\"T_c1178_row7_col2\" class=\"data row7 col2\" >0,10</td>\n      <td id=\"T_c1178_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_c1178_row7_col4\" class=\"data row7 col4\" >0,98</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row8_col0\" class=\"data row8 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_c1178_row8_col1\" class=\"data row8 col1\" >1'400</td>\n      <td id=\"T_c1178_row8_col2\" class=\"data row8 col2\" >0,20</td>\n      <td id=\"T_c1178_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_c1178_row8_col4\" class=\"data row8 col4\" >0,69</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row9_col0\" class=\"data row9 col0\" >Cotton bud/swab sticks</td>\n      <td id=\"T_c1178_row9_col1\" class=\"data row9 col1\" >1'379</td>\n      <td id=\"T_c1178_row9_col2\" class=\"data row9 col2\" >0,18</td>\n      <td id=\"T_c1178_row9_col3\" class=\"data row9 col3\" >0,03</td>\n      <td id=\"T_c1178_row9_col4\" class=\"data row9 col4\" >0,64</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row10_col0\" class=\"data row10 col0\" >Plastic construction waste</td>\n      <td id=\"T_c1178_row10_col1\" class=\"data row10 col1\" >804</td>\n      <td id=\"T_c1178_row10_col2\" class=\"data row10 col2\" >0,10</td>\n      <td id=\"T_c1178_row10_col3\" class=\"data row10 col3\" >0,02</td>\n      <td id=\"T_c1178_row10_col4\" class=\"data row10 col4\" >0,59</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row11_col0\" class=\"data row11 col0\" >Tobacco; plastic packaging, containers</td>\n      <td id=\"T_c1178_row11_col1\" class=\"data row11 col1\" >522</td>\n      <td id=\"T_c1178_row11_col2\" class=\"data row11 col2\" >0,07</td>\n      <td id=\"T_c1178_row11_col3\" class=\"data row11 col3\" >0,01</td>\n      <td id=\"T_c1178_row11_col4\" class=\"data row11 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row12_col0\" class=\"data row12 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_c1178_row12_col1\" class=\"data row12 col1\" >488</td>\n      <td id=\"T_c1178_row12_col2\" class=\"data row12 col2\" >0,05</td>\n      <td id=\"T_c1178_row12_col3\" class=\"data row12 col3\" >0,01</td>\n      <td id=\"T_c1178_row12_col4\" class=\"data row12 col4\" >0,60</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row13_col0\" class=\"data row13 col0\" >Straws and stirrers</td>\n      <td id=\"T_c1178_row13_col1\" class=\"data row13 col1\" >374</td>\n      <td id=\"T_c1178_row13_col2\" class=\"data row13 col2\" >0,05</td>\n      <td id=\"T_c1178_row13_col3\" class=\"data row13 col3\" >0,01</td>\n      <td id=\"T_c1178_row13_col4\" class=\"data row13 col4\" >0,54</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row14_col0\" class=\"data row14 col0\" >Foil wrappers, aluminum foil</td>\n      <td id=\"T_c1178_row14_col1\" class=\"data row14 col1\" >371</td>\n      <td id=\"T_c1178_row14_col2\" class=\"data row14 col2\" >0,04</td>\n      <td id=\"T_c1178_row14_col3\" class=\"data row14 col3\" >0,01</td>\n      <td id=\"T_c1178_row14_col4\" class=\"data row14 col4\" >0,53</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row15_col0\" class=\"data row15 col0\" >Lollypop sticks</td>\n      <td id=\"T_c1178_row15_col1\" class=\"data row15 col1\" >354</td>\n      <td id=\"T_c1178_row15_col2\" class=\"data row15 col2\" >0,04</td>\n      <td id=\"T_c1178_row15_col3\" class=\"data row15 col3\" >0,01</td>\n      <td id=\"T_c1178_row15_col4\" class=\"data row15 col4\" >0,52</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e39df4f0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "most_common_objects-l"}}, "output_type": "display_data"}, "l-sampling-summary-l": {"data": {"text/markdown": "<font color=#1e90ff>2020 - 2021 </font>\n* Number of samples: 230\n* Total objects: 43151\n* Average pcs/m: 5.5\n* Standard deviation: 8.63\n* Maximum pcs/m: 66.17\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "l-sampling-summary-l"}}, "output_type": "display_data"}, "prior-sampling-summary-l": {"data": {"text/markdown": "<font color=#daa520>2015 - 2019</font>\n* Number of samples: 311\n* Total objects: 74895\n* Average pcs/m: 6.28\n* Standard deviation: 8.34\n* Maximum pcs/m: 77.1\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "prior-sampling-summary-l"}}, "output_type": "display_data"}, "sampling-summary-l": {"data": {"text/markdown": "2015 - 2021\n* Number of samples: 541\n* Total objects: 118046\n* Average pcs/m: 5.95\n* Standard deviation: 8.47\n* Maximum pcs/m: 77.1\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "sampling-summary-l"}}, "output_type": "display_data"}, "feature-inventory-l": {"data": {"text/markdown": "__Features surveyed__\n* Lakes: 8\n\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "feature-inventory-l"}}, "output_type": "display_data"}, "administrative-boundaries-l": {"data": {"text/markdown": "__Administrative boundaries__\n* Survey locations: 78\n* Cities: 40\n\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "administrative-boundaries-l"}}, "output_type": "display_data"}, "river-prior-likelihood": {"data": {"image/png": 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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-prior-likelihood"}}, "output_type": "display_data"}, "river-cumumlative-dist-forecast-prior": {"data": {"image/png": 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", "text/plain": "<Figure size 640x480 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-cumumlative-dist-forecast-prior"}}, "output_type": "display_data"}, "caption-histo-r": {"data": {"text/markdown": "Survey total pcs/m 2020 - 2021 v/s 2017 - 2018. All locations considered.", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "caption-histo-r"}}, "output_type": "display_data"}, "material-report-r": {"data": {"text/html": "<style type=\"text/css\">\n#T_39019 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_39019 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_39019 tr {\n  font-size: 10px;\n}\n#T_39019 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_39019 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_39019 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_39019\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_39019_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_39019_level0_row0\" class=\"row_heading level0 row0\" >cloth</th>\n      <td id=\"T_39019_row0_col0\" class=\"data row0 col0\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row1\" class=\"row_heading level0 row1\" >glass</th>\n      <td id=\"T_39019_row1_col0\" class=\"data row1 col0\" >7%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row2\" class=\"row_heading level0 row2\" >metal</th>\n      <td id=\"T_39019_row2_col0\" class=\"data row2 col0\" >9%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row3\" class=\"row_heading level0 row3\" >paper</th>\n      <td id=\"T_39019_row3_col0\" class=\"data row3 col0\" >7%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row4\" class=\"row_heading level0 row4\" >plastic</th>\n      <td id=\"T_39019_row4_col0\" class=\"data row4 col0\" >65%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row5\" class=\"row_heading level0 row5\" >wood</th>\n      <td id=\"T_39019_row5_col0\" class=\"data row5 col0\" >2%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e6257160>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "material-report-r"}}, "output_type": "display_data"}, "forecast-weighted-prior-r": {"data": {"text/markdown": "__Given the weighted prior__\n* Average: 1.45\n* HDI 95%: 0.1 - 5.5\n* 90% Range: 0.1 - 4.64", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-weighted-prior-r"}}, "output_type": "display_data"}, "forecast-max-val-r": {"data": {"text/markdown": "__Given the observed max__\n* Average: 1.45\n* HDI 95%: 0.02 - 4.55\n* 90% Range: 0.1 - 6.08", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-max-val-r"}}, "output_type": "display_data"}, "forecast-99-max-r": {"data": {"text/markdown": "__Given the 99th percentile__\n* Average: 1.14\n* HDI 95%: 0.1 - 3.3\n* 90% Range: 0.1 - 3.11", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "forecast-99-max-r"}}, "output_type": "display_data"}, "ratio-most-common-r": {"data": {"text/markdown": "__The most common objects account for 62% of all objects__", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "ratio-most-common-r"}}, "output_type": "display_data"}, "most_common_objects-r": {"data": {"text/html": "<style type=\"text/css\">\n#T_22ca4 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_22ca4 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_22ca4 tr {\n  font-size: 10px;\n}\n#T_22ca4 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_22ca4 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_22ca4 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_22ca4 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_22ca4 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_22ca4\">\n  <thead>\n    <tr>\n      <th id=\"T_22ca4_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_22ca4_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_22ca4_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_22ca4_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_22ca4_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_22ca4_row0_col0\" class=\"data row0 col0\" >Diapers - wipes</td>\n      <td id=\"T_22ca4_row0_col1\" class=\"data row0 col1\" >299</td>\n      <td id=\"T_22ca4_row0_col2\" class=\"data row0 col2\" >0,21</td>\n      <td id=\"T_22ca4_row0_col3\" class=\"data row0 col3\" >0,16</td>\n      <td id=\"T_22ca4_row0_col4\" class=\"data row0 col4\" >0,30</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_22ca4_row1_col1\" class=\"data row1 col1\" >225</td>\n      <td id=\"T_22ca4_row1_col2\" class=\"data row1 col2\" >0,21</td>\n      <td id=\"T_22ca4_row1_col3\" class=\"data row1 col3\" >0,12</td>\n      <td id=\"T_22ca4_row1_col4\" class=\"data row1 col4\" >0,64</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row2_col0\" class=\"data row2 col0\" >Industrial sheeting</td>\n      <td id=\"T_22ca4_row2_col1\" class=\"data row2 col1\" >149</td>\n      <td id=\"T_22ca4_row2_col2\" class=\"data row2 col2\" >0,09</td>\n      <td id=\"T_22ca4_row2_col3\" class=\"data row2 col3\" >0,08</td>\n      <td id=\"T_22ca4_row2_col4\" class=\"data row2 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row3_col0\" class=\"data row3 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_22ca4_row3_col1\" class=\"data row3 col1\" >102</td>\n      <td id=\"T_22ca4_row3_col2\" class=\"data row3 col2\" >0,11</td>\n      <td id=\"T_22ca4_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_22ca4_row3_col4\" class=\"data row3 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row4_col0\" class=\"data row4 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_22ca4_row4_col1\" class=\"data row4 col1\" >101</td>\n      <td id=\"T_22ca4_row4_col2\" class=\"data row4 col2\" >0,11</td>\n      <td id=\"T_22ca4_row4_col3\" class=\"data row4 col3\" >0,05</td>\n      <td id=\"T_22ca4_row4_col4\" class=\"data row4 col4\" >0,67</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row5_col0\" class=\"data row5 col0\" >Packaging films nonfood or unknown</td>\n      <td id=\"T_22ca4_row5_col1\" class=\"data row5 col1\" >68</td>\n      <td id=\"T_22ca4_row5_col2\" class=\"data row5 col2\" >0,09</td>\n      <td id=\"T_22ca4_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_22ca4_row5_col4\" class=\"data row5 col4\" >0,27</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row6_col0\" class=\"data row6 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_22ca4_row6_col1\" class=\"data row6 col1\" >63</td>\n      <td id=\"T_22ca4_row6_col2\" class=\"data row6 col2\" >0,08</td>\n      <td id=\"T_22ca4_row6_col3\" class=\"data row6 col3\" >0,03</td>\n      <td id=\"T_22ca4_row6_col4\" class=\"data row6 col4\" >0,30</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row7_col0\" class=\"data row7 col0\" >Fragmented plastics</td>\n      <td id=\"T_22ca4_row7_col1\" class=\"data row7 col1\" >61</td>\n      <td id=\"T_22ca4_row7_col2\" class=\"data row7 col2\" >0,05</td>\n      <td id=\"T_22ca4_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_22ca4_row7_col4\" class=\"data row7 col4\" >0,52</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row8_col0\" class=\"data row8 col0\" >Bags; plastic shopping/carrier/grocery and pieces</td>\n      <td id=\"T_22ca4_row8_col1\" class=\"data row8 col1\" >55</td>\n      <td id=\"T_22ca4_row8_col2\" class=\"data row8 col2\" >0,05</td>\n      <td id=\"T_22ca4_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_22ca4_row8_col4\" class=\"data row8 col4\" >0,24</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row9_col0\" class=\"data row9 col0\" >Construction material; bricks, pipes, cement</td>\n      <td id=\"T_22ca4_row9_col1\" class=\"data row9 col1\" >42</td>\n      <td id=\"T_22ca4_row9_col2\" class=\"data row9 col2\" >0,02</td>\n      <td id=\"T_22ca4_row9_col3\" class=\"data row9 col3\" >0,02</td>\n      <td id=\"T_22ca4_row9_col4\" class=\"data row9 col4\" >0,12</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e2ed2ac0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "most_common_objects-r"}}, "output_type": "display_data"}, "l-sampling-summary-r": {"data": {"text/markdown": "<font color=#1e90ff>2020 - 2021 </font>\n* Number of samples: 33\n* Total objects: 1852\n* Average pcs/m: 1.58\n* Standard deviation: 2.82\n* Maximum pcs/m: 16.0\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "l-sampling-summary-r"}}, "output_type": "display_data"}, "prior-sampling-summary-r": {"data": {"text/markdown": "<font color=#daa520>2015 - 2019</font>\n* Number of samples: 257\n* Total objects: 10838\n* Average pcs/m: 1.25\n* Standard deviation: 1.39\n* Maximum pcs/m: 11.1\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "prior-sampling-summary-r"}}, "output_type": "display_data"}, "sampling-summary-r": {"data": {"text/markdown": "2017 - 2021\n* Number of samples: 290\n* Total objects: 12690\n* Average pcs/m: 1.29\n* Standard deviation: 1.62\n* Maximum pcs/m: 16.0\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "sampling-summary-r"}}, "output_type": "display_data"}, "feature-inventory-r": {"data": {"text/markdown": "__Features surveyed__\n* Rivers: 19\n\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "feature-inventory-r"}}, "output_type": "display_data"}, "administrative-boundaries-r": {"data": {"text/markdown": "__Administrative boundaries__\n* Survey locations: 53\n* Cities: 36\n\n", "text/plain": "<IPython.core.display.Markdown object>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "administrative-boundaries-r"}}, "output_type": "display_data"}, "map-of-survey-locations": {"data": {"image/png": 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", "text/plain": "<Figure size 1200x1000 with 1 Axes>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "map-of-survey-locations"}}, "output_type": "display_data"}, "rate-per-feature": {"data": {"text/html": "<style type=\"text/css\">\n#T_87bf0 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_87bf0 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_87bf0 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_87bf0_row0_col0, #T_87bf0_row1_col0, #T_87bf0_row2_col1, #T_87bf0_row3_col2, #T_87bf0_row4_col1, #T_87bf0_row5_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_87bf0\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_87bf0_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_87bf0_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_87bf0_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_87bf0_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_87bf0_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_87bf0_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_87bf0_row0_col0\" class=\"data row0 col0\" >5.03</td>\n      <td id=\"T_87bf0_row0_col1\" class=\"data row0 col1\" >0.09</td>\n      <td id=\"T_87bf0_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_87bf0_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_87bf0_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_87bf0_row1_col0\" class=\"data row1 col0\" >5.04</td>\n      <td id=\"T_87bf0_row1_col1\" class=\"data row1 col1\" >0.56</td>\n      <td id=\"T_87bf0_row1_col2\" class=\"data row1 col2\" >5.01</td>\n      <td id=\"T_87bf0_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_87bf0_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_87bf0_row2_col0\" class=\"data row2 col0\" >2.62</td>\n      <td id=\"T_87bf0_row2_col1\" class=\"data row2 col1\" >7.46</td>\n      <td id=\"T_87bf0_row2_col2\" class=\"data row2 col2\" >3.80</td>\n      <td id=\"T_87bf0_row2_col3\" class=\"data row2 col3\" >2.92</td>\n      <td id=\"T_87bf0_row2_col4\" class=\"data row2 col4\" >6.27</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_87bf0_row3_col0\" class=\"data row3 col0\" >4.86</td>\n      <td id=\"T_87bf0_row3_col1\" class=\"data row3 col1\" >2.73</td>\n      <td id=\"T_87bf0_row3_col2\" class=\"data row3 col2\" >13.13</td>\n      <td id=\"T_87bf0_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_87bf0_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_87bf0_row4_col0\" class=\"data row4 col0\" >5.45</td>\n      <td id=\"T_87bf0_row4_col1\" class=\"data row4 col1\" >6.22</td>\n      <td id=\"T_87bf0_row4_col2\" class=\"data row4 col2\" >2.95</td>\n      <td id=\"T_87bf0_row4_col3\" class=\"data row4 col3\" >1.84</td>\n      <td id=\"T_87bf0_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_87bf0_row5_col0\" class=\"data row5 col0\" >4.26</td>\n      <td id=\"T_87bf0_row5_col1\" class=\"data row5 col1\" >14.60</td>\n      <td id=\"T_87bf0_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_87bf0_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_87bf0_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e657d26e310>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "rate-per-feature"}}, "output_type": "display_data"}, "sampling-profile": {"data": {"text/html": "<style type=\"text/css\">\n#T_685fe caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_685fe caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_685fe td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_685fe_row0_col0, #T_685fe_row1_col0, #T_685fe_row2_col4, #T_685fe_row3_col0, #T_685fe_row4_col0, #T_685fe_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_685fe\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_685fe_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_685fe_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_685fe_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_685fe_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_685fe_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_685fe_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_685fe_row0_col0\" class=\"data row0 col0\" >100%</td>\n      <td id=\"T_685fe_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_685fe_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_685fe_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_685fe_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_685fe_row1_col0\" class=\"data row1 col0\" >98%</td>\n      <td id=\"T_685fe_row1_col1\" class=\"data row1 col1\" >1%</td>\n      <td id=\"T_685fe_row1_col2\" class=\"data row1 col2\" >1%</td>\n      <td id=\"T_685fe_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_685fe_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_685fe_row2_col0\" class=\"data row2 col0\" >14%</td>\n      <td id=\"T_685fe_row2_col1\" class=\"data row2 col1\" >18%</td>\n      <td id=\"T_685fe_row2_col2\" class=\"data row2 col2\" >10%</td>\n      <td id=\"T_685fe_row2_col3\" class=\"data row2 col3\" >22%</td>\n      <td id=\"T_685fe_row2_col4\" class=\"data row2 col4\" >36%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_685fe_row3_col0\" class=\"data row3 col0\" >68%</td>\n      <td id=\"T_685fe_row3_col1\" class=\"data row3 col1\" >24%</td>\n      <td id=\"T_685fe_row3_col2\" class=\"data row3 col2\" >8%</td>\n      <td id=\"T_685fe_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_685fe_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_685fe_row4_col0\" class=\"data row4 col0\" >57%</td>\n      <td id=\"T_685fe_row4_col1\" class=\"data row4 col1\" >22%</td>\n      <td id=\"T_685fe_row4_col2\" class=\"data row4 col2\" >16%</td>\n      <td id=\"T_685fe_row4_col3\" class=\"data row4 col3\" >6%</td>\n      <td id=\"T_685fe_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_685fe_row5_col0\" class=\"data row5 col0\" >93%</td>\n      <td id=\"T_685fe_row5_col1\" class=\"data row5 col1\" >7%</td>\n      <td id=\"T_685fe_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_685fe_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_685fe_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e39db3a0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "sampling-profile"}}, "output_type": "display_data"}, "street-profile": {"data": {"text/html": "<style type=\"text/css\">\n#T_30111 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_30111 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_30111 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_30111_row0_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_30111\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_30111_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_30111_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_30111_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_30111_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_30111_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_30111_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_30111_row0_col0\" class=\"data row0 col0\" >31%</td>\n      <td id=\"T_30111_row0_col1\" class=\"data row0 col1\" >46%</td>\n      <td id=\"T_30111_row0_col2\" class=\"data row0 col2\" >21%</td>\n      <td id=\"T_30111_row0_col3\" class=\"data row0 col3\" >2%</td>\n      <td id=\"T_30111_row0_col4\" class=\"data row0 col4\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e25d1e50>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "street-profile"}}, "output_type": "display_data"}, "street-rates-feature": {"data": {"text/html": "<style type=\"text/css\">\n#T_6a0e0 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_6a0e0 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_6a0e0 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_6a0e0_row0_col4 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_6a0e0\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_6a0e0_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_6a0e0_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_6a0e0_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_6a0e0_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_6a0e0_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_6a0e0_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_6a0e0_row0_col0\" class=\"data row0 col0\" >5.26</td>\n      <td id=\"T_6a0e0_row0_col1\" class=\"data row0 col1\" >4.33</td>\n      <td id=\"T_6a0e0_row0_col2\" class=\"data row0 col2\" >5.66</td>\n      <td id=\"T_6a0e0_row0_col3\" class=\"data row0 col3\" >4.12</td>\n      <td id=\"T_6a0e0_row0_col4\" class=\"data row0 col4\" >14.56</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e4240a90>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "street-rates-feature"}}, "output_type": "display_data"}, "lake-rate-per-feature": {"data": {"text/html": "<style type=\"text/css\">\n#T_b413e caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_b413e caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_b413e td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_b413e_row0_col0, #T_b413e_row1_col0, #T_b413e_row2_col1, #T_b413e_row3_col2, #T_b413e_row4_col1, #T_b413e_row5_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_b413e\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_b413e_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_b413e_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_b413e_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_b413e_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_b413e_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_b413e_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_b413e_row0_col0\" class=\"data row0 col0\" >5.50</td>\n      <td id=\"T_b413e_row0_col1\" class=\"data row0 col1\" >0.00</td>\n      <td id=\"T_b413e_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_b413e_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_b413e_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_b413e_row1_col0\" class=\"data row1 col0\" >5.51</td>\n      <td id=\"T_b413e_row1_col1\" class=\"data row1 col1\" >0.00</td>\n      <td id=\"T_b413e_row1_col2\" class=\"data row1 col2\" >5.01</td>\n      <td id=\"T_b413e_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_b413e_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_b413e_row2_col0\" class=\"data row2 col0\" >2.94</td>\n      <td id=\"T_b413e_row2_col1\" class=\"data row2 col1\" >7.83</td>\n      <td id=\"T_b413e_row2_col2\" class=\"data row2 col2\" >5.12</td>\n      <td id=\"T_b413e_row2_col3\" class=\"data row2 col3\" >3.33</td>\n      <td id=\"T_b413e_row2_col4\" class=\"data row2 col4\" >6.39</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_b413e_row3_col0\" class=\"data row3 col0\" >5.20</td>\n      <td id=\"T_b413e_row3_col1\" class=\"data row3 col1\" >3.13</td>\n      <td id=\"T_b413e_row3_col2\" class=\"data row3 col2\" >14.92</td>\n      <td id=\"T_b413e_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_b413e_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_b413e_row4_col0\" class=\"data row4 col0\" >5.88</td>\n      <td id=\"T_b413e_row4_col1\" class=\"data row4 col1\" >7.42</td>\n      <td id=\"T_b413e_row4_col2\" class=\"data row4 col2\" >3.00</td>\n      <td id=\"T_b413e_row4_col3\" class=\"data row4 col3\" >1.91</td>\n      <td id=\"T_b413e_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_b413e_row5_col0\" class=\"data row5 col0\" >4.64</td>\n      <td id=\"T_b413e_row5_col1\" class=\"data row5 col1\" >16.31</td>\n      <td id=\"T_b413e_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_b413e_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_b413e_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e65562c37f0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-rate-per-feature"}}, "output_type": "display_data"}, "lake-sampling-profile": {"data": {"text/html": "<style type=\"text/css\">\n#T_1d812 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_1d812 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_1d812 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_1d812_row0_col0, #T_1d812_row1_col0, #T_1d812_row2_col4, #T_1d812_row3_col0, #T_1d812_row4_col0, #T_1d812_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_1d812\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_1d812_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_1d812_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_1d812_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_1d812_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_1d812_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_1d812_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_1d812_row0_col0\" class=\"data row0 col0\" >100%</td>\n      <td id=\"T_1d812_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_1d812_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_1d812_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_1d812_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_1d812_row1_col0\" class=\"data row1 col0\" >99%</td>\n      <td id=\"T_1d812_row1_col1\" class=\"data row1 col1\" >0%</td>\n      <td id=\"T_1d812_row1_col2\" class=\"data row1 col2\" >1%</td>\n      <td id=\"T_1d812_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_1d812_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_1d812_row2_col0\" class=\"data row2 col0\" >12%</td>\n      <td id=\"T_1d812_row2_col1\" class=\"data row2 col1\" >20%</td>\n      <td id=\"T_1d812_row2_col2\" class=\"data row2 col2\" >8%</td>\n      <td id=\"T_1d812_row2_col3\" class=\"data row2 col3\" >21%</td>\n      <td id=\"T_1d812_row2_col4\" class=\"data row2 col4\" >39%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_1d812_row3_col0\" class=\"data row3 col0\" >70%</td>\n      <td id=\"T_1d812_row3_col1\" class=\"data row3 col1\" >22%</td>\n      <td id=\"T_1d812_row3_col2\" class=\"data row3 col2\" >8%</td>\n      <td id=\"T_1d812_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_1d812_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_1d812_row4_col0\" class=\"data row4 col0\" >58%</td>\n      <td id=\"T_1d812_row4_col1\" class=\"data row4 col1\" >20%</td>\n      <td id=\"T_1d812_row4_col2\" class=\"data row4 col2\" >16%</td>\n      <td id=\"T_1d812_row4_col3\" class=\"data row4 col3\" >6%</td>\n      <td id=\"T_1d812_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_1d812_row5_col0\" class=\"data row5 col0\" >93%</td>\n      <td id=\"T_1d812_row5_col1\" class=\"data row5 col1\" >7%</td>\n      <td id=\"T_1d812_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_1d812_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_1d812_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e6242c10>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-sampling-profile"}}, "output_type": "display_data"}, "lake-street-profile": {"data": {"text/html": "<style type=\"text/css\">\n#T_9a253 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_9a253 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_9a253 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_9a253_row0_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_9a253\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_9a253_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_9a253_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_9a253_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_9a253_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_9a253_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_9a253_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_9a253_row0_col0\" class=\"data row0 col0\" >35%</td>\n      <td id=\"T_9a253_row0_col1\" class=\"data row0 col1\" >48%</td>\n      <td id=\"T_9a253_row0_col2\" class=\"data row0 col2\" >16%</td>\n      <td id=\"T_9a253_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_9a253_row0_col4\" class=\"data row0 col4\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e6257910>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-street-profile"}}, "output_type": "display_data"}, "lake-street-rates-feature": {"data": {"text/html": "<style type=\"text/css\">\n#T_a1639 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_a1639 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_a1639 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_a1639_row0_col4 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_a1639\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a1639_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_a1639_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_a1639_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_a1639_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_a1639_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a1639_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_a1639_row0_col0\" class=\"data row0 col0\" >5.33</td>\n      <td id=\"T_a1639_row0_col1\" class=\"data row0 col1\" >4.60</td>\n      <td id=\"T_a1639_row0_col2\" class=\"data row0 col2\" >7.68</td>\n      <td id=\"T_a1639_row0_col3\" class=\"data row0 col3\" >0</td>\n      <td id=\"T_a1639_row0_col4\" class=\"data row0 col4\" >21.80</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e4266460>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-street-rates-feature"}}, "output_type": "display_data"}, "river-rate-per-feature": {"data": {"text/html": "<style type=\"text/css\">\n#T_f4453 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_f4453 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_f4453 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_f4453_row0_col0, #T_f4453_row1_col0, #T_f4453_row2_col4, #T_f4453_row3_col2, #T_f4453_row4_col2, #T_f4453_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_f4453\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_f4453_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_f4453_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_f4453_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_f4453_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_f4453_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_f4453_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_f4453_row0_col0\" class=\"data row0 col0\" >1.62</td>\n      <td id=\"T_f4453_row0_col1\" class=\"data row0 col1\" >0.09</td>\n      <td id=\"T_f4453_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_f4453_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_f4453_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_f4453_row1_col0\" class=\"data row1 col0\" >1.64</td>\n      <td id=\"T_f4453_row1_col1\" class=\"data row1 col1\" >0.56</td>\n      <td id=\"T_f4453_row1_col2\" class=\"data row1 col2\" >0.00</td>\n      <td id=\"T_f4453_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_f4453_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_f4453_row2_col0\" class=\"data row2 col0\" >1.50</td>\n      <td id=\"T_f4453_row2_col1\" class=\"data row2 col1\" >1.93</td>\n      <td id=\"T_f4453_row2_col2\" class=\"data row2 col2\" >1.17</td>\n      <td id=\"T_f4453_row2_col3\" class=\"data row2 col3\" >0.43</td>\n      <td id=\"T_f4453_row2_col4\" class=\"data row2 col4\" >4.06</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_f4453_row3_col0\" class=\"data row3 col0\" >1.82</td>\n      <td id=\"T_f4453_row3_col1\" class=\"data row3 col1\" >1.01</td>\n      <td id=\"T_f4453_row3_col2\" class=\"data row3 col2\" >2.38</td>\n      <td id=\"T_f4453_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_f4453_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_f4453_row4_col0\" class=\"data row4 col0\" >1.64</td>\n      <td id=\"T_f4453_row4_col1\" class=\"data row4 col1\" >1.19</td>\n      <td id=\"T_f4453_row4_col2\" class=\"data row4 col2\" >2.46</td>\n      <td id=\"T_f4453_row4_col3\" class=\"data row4 col3\" >1.50</td>\n      <td id=\"T_f4453_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_f4453_row5_col0\" class=\"data row5 col0\" >1.67</td>\n      <td id=\"T_f4453_row5_col1\" class=\"data row5 col1\" >0.07</td>\n      <td id=\"T_f4453_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_f4453_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_f4453_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e2f69820>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-rate-per-feature"}}, "output_type": "display_data"}, "river-sampling-profile": {"data": {"text/html": "<style type=\"text/css\">\n#T_b4d9b caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_b4d9b caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_b4d9b td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_b4d9b_row0_col0, #T_b4d9b_row1_col0, #T_b4d9b_row2_col2, #T_b4d9b_row3_col0, #T_b4d9b_row4_col0, #T_b4d9b_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_b4d9b\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_b4d9b_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_b4d9b_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_b4d9b_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_b4d9b_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_b4d9b_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_b4d9b_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_b4d9b_row0_col0\" class=\"data row0 col0\" >97%</td>\n      <td id=\"T_b4d9b_row0_col1\" class=\"data row0 col1\" >3%</td>\n      <td id=\"T_b4d9b_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_b4d9b_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_b4d9b_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_b4d9b_row1_col0\" class=\"data row1 col0\" >94%</td>\n      <td id=\"T_b4d9b_row1_col1\" class=\"data row1 col1\" >6%</td>\n      <td id=\"T_b4d9b_row1_col2\" class=\"data row1 col2\" >0%</td>\n      <td id=\"T_b4d9b_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_b4d9b_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_b4d9b_row2_col0\" class=\"data row2 col0\" >24%</td>\n      <td id=\"T_b4d9b_row2_col1\" class=\"data row2 col1\" >9%</td>\n      <td id=\"T_b4d9b_row2_col2\" class=\"data row2 col2\" >27%</td>\n      <td id=\"T_b4d9b_row2_col3\" class=\"data row2 col3\" >24%</td>\n      <td id=\"T_b4d9b_row2_col4\" class=\"data row2 col4\" >15%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_b4d9b_row3_col0\" class=\"data row3 col0\" >55%</td>\n      <td id=\"T_b4d9b_row3_col1\" class=\"data row3 col1\" >36%</td>\n      <td id=\"T_b4d9b_row3_col2\" class=\"data row3 col2\" >9%</td>\n      <td id=\"T_b4d9b_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_b4d9b_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_b4d9b_row4_col0\" class=\"data row4 col0\" >45%</td>\n      <td id=\"T_b4d9b_row4_col1\" class=\"data row4 col1\" >33%</td>\n      <td id=\"T_b4d9b_row4_col2\" class=\"data row4 col2\" >12%</td>\n      <td id=\"T_b4d9b_row4_col3\" class=\"data row4 col3\" >9%</td>\n      <td id=\"T_b4d9b_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_b4d9b_row5_col0\" class=\"data row5 col0\" >94%</td>\n      <td id=\"T_b4d9b_row5_col1\" class=\"data row5 col1\" >6%</td>\n      <td id=\"T_b4d9b_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_b4d9b_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_b4d9b_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e2f6e4f0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-sampling-profile"}}, "output_type": "display_data"}, "river-street-profile": {"data": {"text/html": "<style type=\"text/css\">\n#T_1e644 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_1e644 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_1e644 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_1e644_row0_col2 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_1e644\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_1e644_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_1e644_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_1e644_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_1e644_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_1e644_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_1e644_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_1e644_row0_col0\" class=\"data row0 col0\" >3%</td>\n      <td id=\"T_1e644_row0_col1\" class=\"data row0 col1\" >30%</td>\n      <td id=\"T_1e644_row0_col2\" class=\"data row0 col2\" >52%</td>\n      <td id=\"T_1e644_row0_col3\" class=\"data row0 col3\" >12%</td>\n      <td id=\"T_1e644_row0_col4\" class=\"data row0 col4\" >3%</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e626cfa0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-street-profile"}}, "output_type": "display_data"}, "river-street-rates-feature": {"data": {"text/html": "<style type=\"text/css\">\n#T_dbbe5 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_dbbe5 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_dbbe5 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_dbbe5_row0_col3 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_dbbe5\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_dbbe5_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_dbbe5_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_dbbe5_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_dbbe5_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_dbbe5_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_dbbe5_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_dbbe5_row0_col0\" class=\"data row0 col0\" >0.22</td>\n      <td id=\"T_dbbe5_row0_col1\" class=\"data row0 col1\" >1.36</td>\n      <td id=\"T_dbbe5_row0_col2\" class=\"data row0 col2\" >1.27</td>\n      <td id=\"T_dbbe5_row0_col3\" class=\"data row0 col3\" >4.12</td>\n      <td id=\"T_dbbe5_row0_col4\" class=\"data row0 col4\" >0.08</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e251b430>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-street-rates-feature"}}, "output_type": "display_data"}, "lake-municipal-results": {"data": {"text/html": "<style type=\"text/css\">\n#T_190e1 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_190e1 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_190e1 tr {\n  font-size: 10px;\n}\n#T_190e1 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_190e1 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_190e1 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n#T_190e1_row1_col1, #T_190e1_row2_col1, #T_190e1_row3_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_190e1\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_190e1_level0_col0\" class=\"col_heading level0 col0\" >quantity</th>\n      <th id=\"T_190e1_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n      <th id=\"T_190e1_level0_col2\" class=\"col_heading level0 col2\" >samples</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >canton</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_190e1_level0_row0\" class=\"row_heading level0 row0\" >Bern</th>\n      <td id=\"T_190e1_row0_col0\" class=\"data row0 col0\" >9889</td>\n      <td id=\"T_190e1_row0_col1\" class=\"data row0 col1\" >2.81</td>\n      <td id=\"T_190e1_row0_col2\" class=\"data row0 col2\" >98</td>\n    </tr>\n    <tr>\n      <th id=\"T_190e1_level0_row1\" class=\"row_heading level0 row1\" >Gen\u00e8ve</th>\n      <td id=\"T_190e1_row1_col0\" class=\"data row1 col0\" >9559</td>\n      <td id=\"T_190e1_row1_col1\" class=\"data row1 col1\" >5.88</td>\n      <td id=\"T_190e1_row1_col2\" class=\"data row1 col2\" >33</td>\n    </tr>\n    <tr>\n      <th id=\"T_190e1_level0_row2\" class=\"row_heading level0 row2\" >Valais</th>\n      <td id=\"T_190e1_row2_col0\" class=\"data row2 col0\" >8902</td>\n      <td id=\"T_190e1_row2_col1\" class=\"data row2 col1\" >23.70</td>\n      <td id=\"T_190e1_row2_col2\" class=\"data row2 col2\" >13</td>\n    </tr>\n    <tr>\n      <th id=\"T_190e1_level0_row3\" class=\"row_heading level0 row3\" >Vaud</th>\n      <td id=\"T_190e1_row3_col0\" class=\"data row3 col0\" >62468</td>\n      <td id=\"T_190e1_row3_col1\" class=\"data row3 col1\" >7.78</td>\n      <td id=\"T_190e1_row3_col2\" class=\"data row3 col2\" >226</td>\n    </tr>\n    <tr>\n      <th id=\"T_190e1_level0_row4\" class=\"row_heading level0 row4\" >Z\u00fcrich</th>\n      <td id=\"T_190e1_row4_col0\" class=\"data row4 col0\" >27228</td>\n      <td id=\"T_190e1_row4_col1\" class=\"data row4 col1\" >3.99</td>\n      <td id=\"T_190e1_row4_col2\" class=\"data row4 col2\" >171</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e60b4a30>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lake-municipal-results"}}, "output_type": "display_data"}, "river-municipal-results": {"data": {"text/html": "<style type=\"text/css\">\n#T_1cfe3 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_1cfe3 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_1cfe3 tr {\n  font-size: 10px;\n}\n#T_1cfe3 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_1cfe3 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_1cfe3 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_1cfe3\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_1cfe3_level0_col0\" class=\"col_heading level0 col0\" >quantity</th>\n      <th id=\"T_1cfe3_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n      <th id=\"T_1cfe3_level0_col2\" class=\"col_heading level0 col2\" >samples</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >canton</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_1cfe3_level0_row0\" class=\"row_heading level0 row0\" >Bern</th>\n      <td id=\"T_1cfe3_row0_col0\" class=\"data row0 col0\" >3701</td>\n      <td id=\"T_1cfe3_row0_col1\" class=\"data row0 col1\" >1.21</td>\n      <td id=\"T_1cfe3_row0_col2\" class=\"data row0 col2\" >97</td>\n    </tr>\n    <tr>\n      <th id=\"T_1cfe3_level0_row1\" class=\"row_heading level0 row1\" >Gen\u00e8ve</th>\n      <td id=\"T_1cfe3_row1_col0\" class=\"data row1 col0\" >1616</td>\n      <td id=\"T_1cfe3_row1_col1\" class=\"data row1 col1\" >4.38</td>\n      <td id=\"T_1cfe3_row1_col2\" class=\"data row1 col2\" >9</td>\n    </tr>\n    <tr>\n      <th id=\"T_1cfe3_level0_row2\" class=\"row_heading level0 row2\" >Valais</th>\n      <td id=\"T_1cfe3_row2_col0\" class=\"data row2 col0\" >78</td>\n      <td id=\"T_1cfe3_row2_col1\" class=\"data row2 col1\" >0.40</td>\n      <td id=\"T_1cfe3_row2_col2\" class=\"data row2 col2\" >4</td>\n    </tr>\n    <tr>\n      <th id=\"T_1cfe3_level0_row3\" class=\"row_heading level0 row3\" >Vaud</th>\n      <td id=\"T_1cfe3_row3_col0\" class=\"data row3 col0\" >738</td>\n      <td id=\"T_1cfe3_row3_col1\" class=\"data row3 col1\" >2.64</td>\n      <td id=\"T_1cfe3_row3_col2\" class=\"data row3 col2\" >5</td>\n    </tr>\n    <tr>\n      <th id=\"T_1cfe3_level0_row4\" class=\"row_heading level0 row4\" >Z\u00fcrich</th>\n      <td id=\"T_1cfe3_row4_col0\" class=\"data row4 col0\" >6557</td>\n      <td id=\"T_1cfe3_row4_col1\" class=\"data row4 col1\" >1.15</td>\n      <td id=\"T_1cfe3_row4_col2\" class=\"data row4 col2\" >175</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e2f1fa60>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "river-municipal-results"}}, "output_type": "display_data"}, "lakes-i-summary": {"data": {"text/html": "<style type=\"text/css\">\n#T_a9b8d tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_a9b8d tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_a9b8d tr {\n  font-size: 10px;\n}\n#T_a9b8d th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_a9b8d td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_a9b8d caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_a9b8d\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a9b8d_level0_col0\" class=\"col_heading level0 col0\" >samples</th>\n      <th id=\"T_a9b8d_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a9b8d_level0_row0\" class=\"row_heading level0 row0\" >Bielersee</th>\n      <td id=\"T_a9b8d_row0_col0\" class=\"data row0 col0\" >50</td>\n      <td id=\"T_a9b8d_row0_col1\" class=\"data row0 col1\" >4.08</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row1\" class=\"row_heading level0 row1\" >Brienzersee</th>\n      <td id=\"T_a9b8d_row1_col0\" class=\"data row1 col0\" >5</td>\n      <td id=\"T_a9b8d_row1_col1\" class=\"data row1 col1\" >3.96</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row2\" class=\"row_heading level0 row2\" >Greifensee</th>\n      <td id=\"T_a9b8d_row2_col0\" class=\"data row2 col0\" >34</td>\n      <td id=\"T_a9b8d_row2_col1\" class=\"data row2 col1\" >4.71</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row3\" class=\"row_heading level0 row3\" >Katzensee</th>\n      <td id=\"T_a9b8d_row3_col0\" class=\"data row3 col0\" >12</td>\n      <td id=\"T_a9b8d_row3_col1\" class=\"data row3 col1\" >1.77</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row4\" class=\"row_heading level0 row4\" >Lac-leman</th>\n      <td id=\"T_a9b8d_row4_col0\" class=\"data row4 col0\" >249</td>\n      <td id=\"T_a9b8d_row4_col1\" class=\"data row4 col1\" >8.91</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row5\" class=\"row_heading level0 row5\" >Neuenburgersee</th>\n      <td id=\"T_a9b8d_row5_col0\" class=\"data row5 col0\" >23</td>\n      <td id=\"T_a9b8d_row5_col1\" class=\"data row5 col1\" >1.78</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row6\" class=\"row_heading level0 row6\" >Thunersee</th>\n      <td id=\"T_a9b8d_row6_col0\" class=\"data row6 col0\" >43</td>\n      <td id=\"T_a9b8d_row6_col1\" class=\"data row6 col1\" >1.21</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row7\" class=\"row_heading level0 row7\" >Zurichsee</th>\n      <td id=\"T_a9b8d_row7_col0\" class=\"data row7 col0\" >125</td>\n      <td id=\"T_a9b8d_row7_col1\" class=\"data row7 col1\" >4.01</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e626c4c0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "lakes-i-summary"}}, "output_type": "display_data"}, "rivers-i-summary": {"data": {"text/html": "<style type=\"text/css\">\n#T_90020 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_90020 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_90020 tr {\n  font-size: 10px;\n}\n#T_90020 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_90020 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_90020 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_90020\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_90020_level0_col0\" class=\"col_heading level0 col0\" >samples</th>\n      <th id=\"T_90020_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_90020_level0_row0\" class=\"row_heading level0 row0\" >Aare</th>\n      <td id=\"T_90020_row0_col0\" class=\"data row0 col0\" >61</td>\n      <td id=\"T_90020_row0_col1\" class=\"data row0 col1\" >1.27</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row1\" class=\"row_heading level0 row1\" >Aarenidau-buren-kanal</th>\n      <td id=\"T_90020_row1_col0\" class=\"data row1 col0\" >3</td>\n      <td id=\"T_90020_row1_col1\" class=\"data row1 col1\" >1.26</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row2\" class=\"row_heading level0 row2\" >Arve</th>\n      <td id=\"T_90020_row2_col0\" class=\"data row2 col0\" >8</td>\n      <td id=\"T_90020_row2_col1\" class=\"data row2 col1\" >2.92</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row3\" class=\"row_heading level0 row3\" >Chriesbach</th>\n      <td id=\"T_90020_row3_col0\" class=\"data row3 col0\" >12</td>\n      <td id=\"T_90020_row3_col1\" class=\"data row3 col1\" >0.92</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row4\" class=\"row_heading level0 row4\" >Dorfbach</th>\n      <td id=\"T_90020_row4_col0\" class=\"data row4 col0\" >1</td>\n      <td id=\"T_90020_row4_col1\" class=\"data row4 col1\" >0.11</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row5\" class=\"row_heading level0 row5\" >Emme</th>\n      <td id=\"T_90020_row5_col0\" class=\"data row5 col0\" >9</td>\n      <td id=\"T_90020_row5_col1\" class=\"data row5 col1\" >0.60</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row6\" class=\"row_heading level0 row6\" >Glatt</th>\n      <td id=\"T_90020_row6_col0\" class=\"data row6 col0\" >7</td>\n      <td id=\"T_90020_row6_col1\" class=\"data row6 col1\" >1.67</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row7\" class=\"row_heading level0 row7\" >Grandelbach</th>\n      <td id=\"T_90020_row7_col0\" class=\"data row7 col0\" >4</td>\n      <td id=\"T_90020_row7_col1\" class=\"data row7 col1\" >1.33</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row8\" class=\"row_heading level0 row8\" >Jona</th>\n      <td id=\"T_90020_row8_col0\" class=\"data row8 col0\" >12</td>\n      <td id=\"T_90020_row8_col1\" class=\"data row8 col1\" >2.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row9\" class=\"row_heading level0 row9\" >La-thiele</th>\n      <td id=\"T_90020_row9_col0\" class=\"data row9 col0\" >1</td>\n      <td id=\"T_90020_row9_col1\" class=\"data row9 col1\" >0.46</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row10\" class=\"row_heading level0 row10\" >Langeten</th>\n      <td id=\"T_90020_row10_col0\" class=\"data row10 col0\" >11</td>\n      <td id=\"T_90020_row10_col1\" class=\"data row10 col1\" >1.94</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row11\" class=\"row_heading level0 row11\" >Limmat</th>\n      <td id=\"T_90020_row11_col0\" class=\"data row11 col0\" >61</td>\n      <td id=\"T_90020_row11_col1\" class=\"data row11 col1\" >1.13</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row12\" class=\"row_heading level0 row12\" >Ognonnaz</th>\n      <td id=\"T_90020_row12_col0\" class=\"data row12 col0\" >1</td>\n      <td id=\"T_90020_row12_col1\" class=\"data row12 col1\" >2.85</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row13\" class=\"row_heading level0 row13\" >Rhein</th>\n      <td id=\"T_90020_row13_col0\" class=\"data row13 col0\" >10</td>\n      <td id=\"T_90020_row13_col1\" class=\"data row13 col1\" >1.52</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row14\" class=\"row_heading level0 row14\" >Rhone</th>\n      <td id=\"T_90020_row14_col0\" class=\"data row14 col0\" >8</td>\n      <td id=\"T_90020_row14_col1\" class=\"data row14 col1\" >3.43</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row15\" class=\"row_heading level0 row15\" >Schuss</th>\n      <td id=\"T_90020_row15_col0\" class=\"data row15 col0\" >2</td>\n      <td id=\"T_90020_row15_col1\" class=\"data row15 col1\" >1.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row16\" class=\"row_heading level0 row16\" >Sihl</th>\n      <td id=\"T_90020_row16_col0\" class=\"data row16 col0\" >51</td>\n      <td id=\"T_90020_row16_col1\" class=\"data row16 col1\" >1.08</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row17\" class=\"row_heading level0 row17\" >Toss</th>\n      <td id=\"T_90020_row17_col0\" class=\"data row17 col0\" >17</td>\n      <td id=\"T_90020_row17_col1\" class=\"data row17 col1\" >0.59</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row18\" class=\"row_heading level0 row18\" >Zulg</th>\n      <td id=\"T_90020_row18_col0\" class=\"data row18 col0\" >11</td>\n      <td id=\"T_90020_row18_col1\" class=\"data row18 col1\" >0.63</td>\n    </tr>\n  </tbody>\n</table>\n", "text/plain": "<pandas.io.formats.style.Styler at 0x7e64e624d0a0>"}, "metadata": {"scrapbook": {"mime_prefix": "application/papermill.record/", "name": "rivers-i-summary"}}, "output_type": "display_data"}}
\ No newline at end of file
diff --git a/_build/jupyter_execute/combined.ipynb b/_build/jupyter_execute/combined.ipynb
index 6ab5214..a0eb0cf 100644
--- a/_build/jupyter_execute/combined.ipynb
+++ b/_build/jupyter_execute/combined.ipynb
@@ -6,6 +6,9 @@
    "id": "5bf92247-ef77-4aae-b62f-9388cba6765e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -180,6 +183,9 @@
    "id": "d504b355-6ec4-4d6d-b9d6-3459215fcb8b",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -406,6 +412,9 @@
    "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -532,6 +541,9 @@
    "id": "12c52a87-8340-419f-bfd9-75ca85260a97",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1094,6 +1106,9 @@
    "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1121,6 +1136,9 @@
    "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1152,6 +1170,9 @@
    "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1180,6 +1201,9 @@
    "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1212,6 +1236,9 @@
    "id": "82f55461-c497-483a-8c38-fbd509809afb",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1241,6 +1268,9 @@
    "id": "9b396025-1fa6-4661-9116-593fa1ed741d",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1607,19 +1637,18 @@
     "dxlc = dxl[['location', 'canton', 'feature_type']].drop_duplicates('location')\n",
     "dxlc.set_index(['location'], inplace=True, drop=True)\n",
     "dxf['canton'] = dxf.location.apply(lambda x : dxlc.loc[x, 'canton'])\n",
-    "sumlu = {x:'sum' for x in session_config.feature_variables}\n",
-    "dxf = dxf.groupby(['sample_id', 'canton', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n",
+    "# sumlu = {x:'sum' for x in session_config.feature_variables}\n",
+    "dxf = dxf.groupby(['sample_id', 'canton'], as_index=False).agg(session_config.unit_agg)\n",
     "\n",
-    "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n",
+    "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique'})\n",
     "\n",
-    "for alabel in session_config.feature_variables:\n",
-    "    dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
+    "# for alabel in session_config.feature_variables:\n",
+    "#     dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
     "    \n",
-    "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
-    "dxfc = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
-    "dxfc.rename(columns={'sample_id':'samples'}, inplace=True)\n",
-    "dxfc.drop('check', axis=1, inplace=True)\n",
-    "dxfc = dxfc.style.set_table_styles(userdisplay.table_css_styles)\n",
+    "# dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
+    "# dxfc = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
+    "dxf.rename(columns={'sample_id':'samples'}, inplace=True)\n",
+    "dxfc = dxf.style.set_table_styles(userdisplay.table_css_styles)\n",
     "dxfc = dxfc.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n",
     "dxfc = dxfc.format(userdisplay.format_kwargs, precision=2)\n",
     "\n",
@@ -1647,19 +1676,19 @@
     "dxlc = dxl[['location', 'canton', 'feature_type']].drop_duplicates('location')\n",
     "dxlc.set_index(['location'], inplace=True, drop=True)\n",
     "dxf['canton'] = dxf.location.apply(lambda x : dxlc.loc[x, 'canton'])\n",
-    "sumlu = {x:'sum' for x in session_config.feature_variables}\n",
-    "dxf = dxf.groupby(['sample_id', 'canton', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n",
+    "# sumlu = {x:'sum' for x in session_config.feature_variables}\n",
+    "dxf = dxf.groupby(['sample_id', 'canton'], as_index=False).agg(session_config.unit_agg)\n",
     "\n",
-    "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n",
+    "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique'})\n",
     "\n",
-    "for alabel in session_config.feature_variables:\n",
-    "    dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
+    "# for alabel in session_config.feature_variables:\n",
+    "#     dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
     "    \n",
-    "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
-    "dxfcr = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
-    "dxfcr.rename(columns={'sample_id':'samples'}, inplace=True)\n",
-    "dxfcr.drop('check', axis=1, inplace=True)\n",
-    "dxfcr = dxfcr.style.set_table_styles(userdisplay.table_css_styles)\n",
+    "# dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
+    "# dxfcr = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
+    "dxf.rename(columns={'sample_id':'samples'}, inplace=True)\n",
+    "# dxfcr.drop('check', axis=1, inplace=True)\n",
+    "dxfcr = dxf.style.set_table_styles(userdisplay.table_css_styles)\n",
     "dxfcr = dxfcr.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n",
     "dxfcr = dxfcr.format(userdisplay.format_kwargs, precision=2)\n",
     "# glue('all-data-municipal-results', i , display=False)\n",
@@ -1728,18 +1757,22 @@
     ":::\n",
     "::::\n",
     "\n",
-    "## Municipal Results - all data\n",
+    "## Cantonal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each canton.\n",
+    "The average pieces per meter for each canton.\n",
     "\n",
-    "::::::::::{tab-set}\n",
+    "::::::::::{grid}\n",
     "\n",
-    ":::::::::{tab-item} Lakes\n",
+    ":::::::::{grid-item-card}\n",
+    "Lakes\n",
+    "^^^\n",
     "```{glue} lake-municipal-results\n",
     "```\n",
     ":::::::::\n",
     "\n",
-    ":::::::::{tab-item} Rivers\n",
+    ":::::::::{grid-item-card}\n",
+    "Rivers\n",
+    "^^^\n",
     "```{glue} river-municipal-results\n",
     "``` \n",
     ":::::::::\n",
diff --git a/_build/jupyter_execute/d0ad3252da2fb520f54bd04cbfcaed0f3b5d93a0bc136a375a20fe05efbfd62f.png b/_build/jupyter_execute/d0ad3252da2fb520f54bd04cbfcaed0f3b5d93a0bc136a375a20fe05efbfd62f.png
new file mode 100644
index 0000000..6ab3e3c
Binary files /dev/null and b/_build/jupyter_execute/d0ad3252da2fb520f54bd04cbfcaed0f3b5d93a0bc136a375a20fe05efbfd62f.png differ
diff --git a/_build/jupyter_execute/geneve.ipynb b/_build/jupyter_execute/geneve.ipynb
index 5d8afee..7c86ab1 100644
--- a/_build/jupyter_execute/geneve.ipynb
+++ b/_build/jupyter_execute/geneve.ipynb
@@ -6,6 +6,9 @@
    "id": "5bf92247-ef77-4aae-b62f-9388cba6765e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -113,6 +116,9 @@
    "id": "fb4d8635-eb6f-402a-9cd6-8be9c76cce30",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -178,6 +184,9 @@
    "id": "d504b355-6ec4-4d6d-b9d6-3459215fcb8b",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -277,6 +286,9 @@
    "id": "c016aff6-dc3f-49a1-be4d-48b6448c9d22",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -402,6 +414,9 @@
    "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -527,6 +542,9 @@
    "id": "12c52a87-8340-419f-bfd9-75ca85260a97",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1099,6 +1117,9 @@
    "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1126,6 +1147,9 @@
    "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1157,6 +1181,9 @@
    "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1185,6 +1212,9 @@
    "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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     },
@@ -1217,6 +1247,9 @@
    "id": "82f55461-c497-483a-8c38-fbd509809afb",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1246,6 +1279,9 @@
    "id": "9b396025-1fa6-4661-9116-593fa1ed741d",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1597,6 +1633,9 @@
    "id": "b0bda4c0-1b4a-4011-9ad1-fb3a52aac885",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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     },
@@ -1637,6 +1676,9 @@
    "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1677,6 +1719,9 @@
    "id": "2d5b8904-044b-4aed-916c-5e36018f4087",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1735,7 +1780,7 @@
     "\n",
     "## Municipal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
     "\n",
     "::::::::::{tab-set}\n",
     "\n",
diff --git a/_build/jupyter_execute/valais.ipynb b/_build/jupyter_execute/valais.ipynb
index 9e0bddf..9b11fc5 100644
--- a/_build/jupyter_execute/valais.ipynb
+++ b/_build/jupyter_execute/valais.ipynb
@@ -6,6 +6,9 @@
    "id": "5bf92247-ef77-4aae-b62f-9388cba6765e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
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@@ -112,6 +115,9 @@
    "id": "fb4d8635-eb6f-402a-9cd6-8be9c76cce30",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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     },
@@ -407,6 +413,9 @@
    "id": "671460d2-c096-49b6-ab55-7cc9a003da78",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -604,6 +613,9 @@
    "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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     },
@@ -620,6 +632,9 @@
    "id": "12c52a87-8340-419f-bfd9-75ca85260a97",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
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@@ -1187,6 +1202,9 @@
    "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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@@ -1214,6 +1232,9 @@
    "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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@@ -1245,6 +1266,9 @@
    "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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@@ -1273,6 +1297,9 @@
    "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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     },
@@ -1305,6 +1332,9 @@
    "id": "82f55461-c497-483a-8c38-fbd509809afb",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1334,6 +1364,9 @@
    "id": "9b396025-1fa6-4661-9116-593fa1ed741d",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1685,6 +1718,9 @@
    "id": "b0bda4c0-1b4a-4011-9ad1-fb3a52aac885",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1725,6 +1761,9 @@
    "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1765,6 +1804,9 @@
    "id": "2d5b8904-044b-4aed-916c-5e36018f4087",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1823,7 +1865,7 @@
     "\n",
     "## Municipal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
     "\n",
     "::::::::::{tab-set}\n",
     "\n",
diff --git a/_build/jupyter_execute/vaud.ipynb b/_build/jupyter_execute/vaud.ipynb
index 337cf59..1790863 100644
--- a/_build/jupyter_execute/vaud.ipynb
+++ b/_build/jupyter_execute/vaud.ipynb
@@ -6,6 +6,9 @@
    "id": "5bf92247-ef77-4aae-b62f-9388cba6765e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -113,6 +116,9 @@
    "id": "fb4d8635-eb6f-402a-9cd6-8be9c76cce30",
    "metadata": {
     "editable": true,
+    "jupyter": {
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@@ -178,6 +184,9 @@
    "id": "d504b355-6ec4-4d6d-b9d6-3459215fcb8b",
    "metadata": {
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+    "jupyter": {
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@@ -277,6 +286,9 @@
    "id": "c016aff6-dc3f-49a1-be4d-48b6448c9d22",
    "metadata": {
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+    "jupyter": {
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+    },
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@@ -402,6 +414,9 @@
    "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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     },
@@ -527,6 +542,9 @@
    "id": "12c52a87-8340-419f-bfd9-75ca85260a97",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1099,6 +1117,9 @@
    "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1126,6 +1147,9 @@
    "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1157,6 +1181,9 @@
    "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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@@ -1185,6 +1212,9 @@
    "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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     },
@@ -1217,6 +1247,9 @@
    "id": "82f55461-c497-483a-8c38-fbd509809afb",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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@@ -1246,6 +1279,9 @@
    "id": "9b396025-1fa6-4661-9116-593fa1ed741d",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1597,6 +1633,9 @@
    "id": "b0bda4c0-1b4a-4011-9ad1-fb3a52aac885",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1637,6 +1676,9 @@
    "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1677,6 +1719,9 @@
    "id": "2d5b8904-044b-4aed-916c-5e36018f4087",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1735,7 +1780,7 @@
     "\n",
     "## Municipal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
     "\n",
     "::::::::::{tab-set}\n",
     "\n",
diff --git a/_build/jupyter_execute/zurich.ipynb b/_build/jupyter_execute/zurich.ipynb
index ae71a2a..d6f4bd1 100644
--- a/_build/jupyter_execute/zurich.ipynb
+++ b/_build/jupyter_execute/zurich.ipynb
@@ -1735,7 +1735,7 @@
     "\n",
     "## Municipal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
     "\n",
     "::::::::::{tab-set}\n",
     "\n",
diff --git a/bern.ipynb b/bern.ipynb
index 44ab057..04f3bf9 100644
--- a/bern.ipynb
+++ b/bern.ipynb
@@ -6,6 +6,9 @@
    "id": "5bf92247-ef77-4aae-b62f-9388cba6765e",
    "metadata": {
     "editable": true,
+    "jupyter": {
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@@ -113,6 +116,9 @@
    "id": "fb4d8635-eb6f-402a-9cd6-8be9c76cce30",
    "metadata": {
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+    "jupyter": {
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@@ -178,6 +184,9 @@
    "id": "d504b355-6ec4-4d6d-b9d6-3459215fcb8b",
    "metadata": {
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+    "jupyter": {
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@@ -473,6 +482,9 @@
    "id": "c016aff6-dc3f-49a1-be4d-48b6448c9d22",
    "metadata": {
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+    "jupyter": {
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@@ -781,6 +793,9 @@
    "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f",
    "metadata": {
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+    "jupyter": {
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@@ -1089,6 +1104,9 @@
    "id": "12c52a87-8340-419f-bfd9-75ca85260a97",
    "metadata": {
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+    "jupyter": {
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@@ -1675,6 +1693,9 @@
    "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3",
    "metadata": {
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+    "jupyter": {
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@@ -1729,6 +1750,9 @@
    "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9",
    "metadata": {
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+    "jupyter": {
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@@ -1787,6 +1811,9 @@
    "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86",
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+    "jupyter": {
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@@ -1842,6 +1869,9 @@
    "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3",
    "metadata": {
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+    "jupyter": {
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@@ -1901,6 +1931,9 @@
    "id": "82f55461-c497-483a-8c38-fbd509809afb",
    "metadata": {
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+    "jupyter": {
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+    },
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@@ -1957,6 +1990,9 @@
    "id": "9b396025-1fa6-4661-9116-593fa1ed741d",
    "metadata": {
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+    "jupyter": {
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@@ -2335,6 +2371,9 @@
    "id": "b0bda4c0-1b4a-4011-9ad1-fb3a52aac885",
    "metadata": {
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+    "jupyter": {
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@@ -2389,6 +2428,9 @@
    "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e",
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@@ -2443,6 +2485,9 @@
    "id": "2d5b8904-044b-4aed-916c-5e36018f4087",
    "metadata": {
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+    "jupyter": {
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      "slide_type": ""
     },
@@ -2528,7 +2573,7 @@
     "\n",
     "## Municipal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
     "\n",
     "::::::::::{tab-set}\n",
     "\n",
diff --git a/combined.ipynb b/combined.ipynb
index 11cf7ab..5ae758e 100644
--- a/combined.ipynb
+++ b/combined.ipynb
@@ -6,6 +6,9 @@
    "id": "5bf92247-ef77-4aae-b62f-9388cba6765e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -180,6 +183,9 @@
    "id": "d504b355-6ec4-4d6d-b9d6-3459215fcb8b",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -216,7 +222,7 @@
     },
     {
      "data": {
-      "application/papermill.record/image/png": 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",
+      "application/papermill.record/image/png": 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MZdehw7eu3M3OkYy/fTzJTSLhzy8InX41S7xdeC/lBf57ybEAWM+f4L9FkdtTbh9Vr7uQrNUv4SnaQfa6d6gNzXtNxvLPkLWISDiR0KikyrtBf/b9T8Hy6ez+agKmOJeIlLNpcnHgvpEZU3viycmo4AaBGp33KFE9SveNdG1bS+bzpwMQaodjHtvXlbB48WISExP9xy+//DKTJk0KjCM9fc87bFhLSohIw6DETuRg2musXFWVtBatCU2hc6Rvc3ivuwhX3gZceVtI//GGCq8La9J9P3c1xLQdSkzbIeXO2B1xWLby/zkwBnYWVq0btPCbCbgzfV24AxN38fbNgec7PJbB1uzKx7BNOrmAq68tPV69ys3xU3zXeYD93cHjCdx7My8vb597VcbExFQai4hIfaPETuRQuX0dBTh4fE9XLACdz8V11xa6e+GYMvuhMnol2zf9SPPCVLLWvAQYdv5ecVNVk26jCYmIJ/aIiypMzmrCGN+4tmEz8HWx7rG/btCOj+WSD9hsNuKbNSlXL7FFi3K7pVakUUxkwLUxESEkJSVVKW67PXDMYXR0dIXXxsTE8OCDWm9ORA4/2itWpJYVON1gDBEUY7kK4fH2ANx31Dfce37PCic8GK+Hoh1L2bnySYzXhad4F66cfyq8vxUShWXZadTxSpp0H1Pre17uq+u1ZM/CfY0923uvVBERqR3VyV3UYidSy1ImzGKG4wF62v4KKJ+xZAujenspdgdu2m48RWT+Om6f94s94iL/64jmxxN7xLAqx2IMvPP+dB56YAK5ebkAfPjRZxx7XA9/na9nfsnIf48IuCazzO5lJTNEV0VHY10UOFv2jjvu4L333gMgPb1u7TIhItIQKbETqWURFJdL6rbGdKN96C52zbu90tmWse2HE5V4KgDhzY4jJKJ5lZ9tTOkG58b4ulK/HVM69g3g/95zEvZL6TUFywvZsZ/12zzA1qyKx6RlZWWVG8OmsWsiIsGjxE7kABW7PUz5YhkeVy6394lm/r/i4XXfuYKr3idj0R0UOdOZapvsT+oiEk4ud5/o5MHEtR9e43Fy++pCNcW+ljosG/bYRKyQwGVLrNAI//ptZYXafYvw+uOLLr/9VuPGjQPGsGnsmohIcGmMnUh1laxLFxIONju7d6yj5+OrCMXNhynjibA5ab8uC4B17RtjyuzlGhrTjqTT3yE0MnFfd6+xfCekvFC+fNsDrSjOSqNlUhJ/p1Z97JvWchMRqRs0xk7kYDEGM20Q1uZFuC9+E9O6F1tnncHURkcwpOhXcnc1ZueR7djQox2w5x+YMUQknECTbqMPSkK35xEMm1F6PO+SXE7u0QUAV7bWbRMRaSiU2IlUUUHGfJxZf9Fo8yIAtv10CwXRDkJt0Dt2JRRBTMtTiTn3rUMeW6G7dFmSlHhoHG7YqrFvIiINjhI7kUoU7/6LbQvHUpy1CstraLSnvCgiFMvmwGAoaNUWc90XtbaOXGXKTpIAAvZjnTEMvMWWxr6JiDRASuxEynAVpOPcvba0wBjSf7gG8HVlut2la9AdefEqcERhjGFnvpNCy05EiL1Ki/BWx95JXEm3a9mFg8uyLF8ip7XkREQaHiV2InsUZ//NppmDSguModXmXNoXufd9EbAz30nPh74FYPWkwUQ6au+fVaX7tO4lbskTPPJQDnFxsYwdO7bW4hARkfpBiZ00WMYYXLn/YLwuMhaMwbm7dK23sMZdsbxeIv6eX+G1W2K60yrUtwVYpMPXitezTWMiQsvvKlHz+Pa/T2tKvK/btezM1Y7tnmDSnt0flNiJiDQ8SuykwfG6C8nd8AmZi+6p8HzjLjfR7Nhx4MyHxS19hbevA0ckxW4PHq+heVi0P6OKCLWzetJgIkLtWLW0PkhFLXV779Oq5UhERGRvSuzkwJSs6VaWI6r0tasIjAfsDrDvyUq8HnAXVf9ZoZGlmYy7GLxusIVCyYK7Xi+4CwMu8XqKfc/bo2Dbz2QsGA3gHwtnLLCHNyU09ggSjp+CcTSnoCCfELyULOVrQiMoJIzIyPL/ZCzL2m/3695j5KqiwBWY1PVM9C0WrERORET2R4md1JwxMG0wbP41sPz+7NLXn9wIqz+Dsx6HXjf4yjYugDfPqf7z7lgPUc18r2ffDYtfhb7joP94ALzbfsf2Ur+AS2x73SIaaL9XmfemH7ElHu07+GEKzHuYt9wDyek/mX/fsR6ALXkWfR6bDZQfR7e/xK2yiQ5VseQGJXUiIlI1Suyk5lwF5ZO6Q8zrzmfH4gl4XbkUr51Bmxrcw2Z3VHzCsvyJZKQpBsqPo6vu5IbqUkudiIhUh7YUk5pz5sPkwDFowEHtinXlb2bz7PMxHjcWBo8rpzTrMQZrz29zRPNeANhComh6zB2EhCf4b2PZQgLXmwuJANuetj23k2JnER7shDgicITY9tzaUOD0gBU4jq7ABT1eqTz0iiY6VEV1x9G1atWKtD2TJ7TciYjI4UFbisnBZww4y4ytc0QGJnQlQsPLl9nsFdetxI7fHydr1XOBhXuyntDotsR1uBR7WGOi25yLzR6239ALynadevb8AOCAUF8LntuA21VyjcWwj0L226W69+SGsjTRQUREDgUldlJ9+xpbV8uKs9aw7Zc7AIMzJxXjKZ0YEdNmCE263QqAZY8gNKplle65365TrxsyfevR0XwAVGMXCXWZiohIXaDETqpv77F1ySf6ZqzWsk1fn1VhefIZXxLe5KhKr69oUsPes033Zhlf092+xifsq0tVLXIiIlIXKLGTA3P7Ot8Eg1rOanL++cj/OqbNEGKOuACwiGh2LLbQ6Eqv9xo4+739z0bdu+vU67WTnt0fgMQ4u3/YXVl1PYE77rjjSE5OJj4+PtihiIhIECixkwPjiKy1TMd43aR9fzmFmYFdvC1Ofqq0jjEUON047DZC7L7My+M1FLs9WFhEOOyYkqQu0+NrtrNsvh/fDcB4OLYFNI0I8Yde5PKAZWjbNJxQewUZXT3x+eefBzsEEREJovr7F0yCxx4GF77h+9nPJIXq2vjVGeWSulaDPva/NsYw7MWFpEyYzZcrSvtT/0jLJmXCbAY8MQ/wdbeu3g5kLcPaNpsHem1hzc2w5mb4fFge1rbZbFr3Q0A+etdHK0iZMJu3Fm6stfdzKE2fPp3LL7882GGIiEiQqcVOqs8eAkedf8C3MV4PrtwNgGHbovG4ctb7z7U970fsEc0DZrcWu73+NeSK3aW7SZQs2GOAfKevta6ssJDSLteIfcxare8mTJhAo0aN2LBhA23btg12OCIiEiRax06qx+OGP7/wve58ri/Jq+4tnDnkbvyc7Yvvq/D8kRf+gS1038uhlO2KNQYu+NCwdKvH1yVslS4e3Lmph48vNDhCbP7uVa/XULQnKSy7e0SRy4PXGELttnrZFduqVSsKCgpITk7m999/D3Y4IiJSi7SOnRw8nmKYfrXv9d1bq5XYGWMwniLSvruU4qxVAedsjkbYQqNoc9asCpO6knF0GAtsITi94PT6ul2XZljlliZJiYeZw+3Y9hr+Z7NVvK9reJndJOqrrKwsIiNrf3ayiIjUH0rspHosG7Q5pfR1JbyeYgozfsbrKSTj55FgvAHnm3QbQ5Ou/8aq5F6LUncx/JVfCA+PprBx3wrrlJ3lWtdnr4qIiBwMSuykekIj4JqZVa6etfoldq18ssJzbYfMJzQqqVqP33tduhJaIFhERESJnRwExhic2X9j3PkBSV1E8xNwxHWg2bHjsWyOwP1aK9GrXRN+u28wPV7xZW57r0GnFjoREREldlIdznx4qpvv9eiV+9zvdefvj5O1+vmAshYnPU1M23Or/UiX28unv6cTFmKn15HN/d2/kaH73pdVRESkoVJiJ9VTsLPSKmWTupCoVoQ1TqlRUmcMDJvu5fffl/uOEwZT4XYQDcD06dOZMGECubm5rFmzhpiYGP+5J554gvT0/eyTJiIiDYYSO6kVrvw0CjLmB0yOaNTpWuJ7VLykSVUUumF5ZgiENvYV7FnKpGeir+u1IZkwYQJ//vkn4OvqLisnJwev1/e5l034RESk4Wlgfx6ltjmz17Prj2fI3fhZuXNxHa+o0T2N8SV1Ba49BU1P4LcbbEQ5fIPoGuJ4utzcXP9ra683HxsbS1JSEjExMTz44IOHOjQREalDlNhJ1e3VUuTK28zGmQNwGxsu48COl+jGRxAanUxE8xPwhCVT4HQTFmLHvmdBObfHi9PjxWZZAWvHlSwQ7LDbuegjiyXp+Fr/jBdsIUQ5NKYO8CdwZY0dO5axY8cGKSIREalLlNhJ1RgDr5/hPyzatZLN3w8H4Lus43l8yxWc2jaEty4f7K/T77Ef2LCzgBkjetOzbRMAZq/axi3vLuWEdk344Kbe/rrnP7+ANek5vHpVL5akx+95yDas3UuJjIzDMr2Bmi8i/O6773LnnXdWWq9Fixb89ttvAWU33XQTM2dWvsTL8OHDeeyxxwLKOnfuTF5eXqXXvvjii5xzzjn+4yVLljBkyBD/scbQiYhIVSixk6pxFUDGSt/rFt3Y/N0l/v5QR1xH2AL2sMYH/JiiMuvU/fcMG6Pfh5R4G+GhBzZpoqCggLS0tBpdu2vXripdm5WVVa5s69atAd2o+1JYWBhw7HQ6K3ymxtCJiMj+KLGTass/8174+V8ARLc+m2t638/le7pXy/p61KkYDGEhpS1tg49KYPWkweXqfvyvk7jkI8Mt39lhz6nTOsezetJgIkLt5caVVVdkZCRJSZUvhtyiRYtyZU2aNKnStY0bl09sW7ZsWaUWu4iIiIBjh8NR7pkaQyciIpWxzN5T7A5z1dlIV/YwBvJ3wOPtAdjQLQVXcQYeY5HbewGR4RF0bhHrH0dX3VuXTJTo8Uppec9EmHHhgU2SWLt2LW63m5CQEDp16lTzG4mIiARRdXIXtdjJ/hkD0wbD5l/9Re7CdLBZRHW+mTNeXgLA6kmDiXTs/9epJIkrezxsBqzeHlhvyQ3V2x6s7BpvZZV0ZSYlJbFly5aq3UxERKQeU2In++d1Q7cLIWcrZG9mrZXEQ5su4e4OX9Ki86UkxK4muXEkEaH7n9hgDPzfdHyzXfejJnu+ll3jrSIalyYiIg2FEjvZP3so9LoBjrmUv+dcw+CfroRCi9bn3kFoeARzb08gPNS23zFwxsDOwn0ndSnxMGOYL5mryRp1JS11NpuNxMTEgHMalyYiIg2JEjspZQy4CsERWVrmLAAMBVl/4Mxaxr9bRmAPa4wj9EwAIhzVb6lbckPgmnS1teBwYmKiulxFRKRBU2InPiVj6Yqy4ZbS8XTe53pSTCEP5AzljORGDGn2I02PvpNQe+XLj1TUUleTrtbKLF68GI/Hg91e83XuREREDgdK7MTHVVA6QcKZD44ovO4iXIUZRDg9DPPOZ9r2Adx9elMadbqmwluUnRxR0cSI6k6KqKq9u19FREQaKiV2sk87V0wlu3Ushd5Qrvn7Pkx+KFO7D8QWUv7XprLJEQejpU5EREQCKbGTCnk9xez+81WwWYTb3Pzx4Hn7rV/gqjipK5kYERmqpE5ERORgU2In5bjytrBhVum+sK0GfVxhvZKuV2Pg7PdKy8tOjqitiRElKlqzLi0tjVtvvZWuXbty44031t7DRERE6hntPCE+znyY3BKAde0bY/bsIhEaeyRtz/m2XPV9db2mxMNXww9e61yXLl32uWZd586dWbNmzcF5sIiISJBo5wmpFZEt+9Py1Jcodnu4++M/AJh8QVccdnuF69KlxMPMg5jUwb7XrNN6dSIiIkrsBHzNb86CgKKQqGSS+k0DwON289FS3/pwk4YcxfCPK16Xrra7XfdHa9aJiIiUp8SuoatgL1iAdkN+9L8OsdkYf2ZnAFxe20Ffl05ERERqRoldQ1d2/TqgMDyEthf4jr1eQ2ZuMQA39DkCm80i31l66cFal25/OnbsSFxcHAkJCYfuoSIiIvWEErsGzhgvJXnZP0c0wmO36BDpS5qK3B5OnPIdAKsnDSY8NCRg9mswljD5/vvvD+0DRURE6hEldg1c3pZvcTYJB8Bjt2h+wmSMMTg9XgBC9syOLVnSJHW377qUeN+YOhEREak79Ke5AcvfOo+MX8dCs0gA2pw7l9Do1gx7cSEpibE8OLQr6yafBfgWIC7ZHqxdo4M/+1VERESqr/Kd3OWwVLh9KVt/uApHsRtHsZvmPR/EEdOGQpeHJRuz+N8vGylwuv31y652OHM42JTUiYiI1DlqsWuAXPlpbJnzf1gG2mzM8RVedj7gmwE76vQOOD1eQmy+vN8YGDaj9PpgttRddtll7Nixg2bNmvHOO+8ELxAREZE6SIldQ2IM3oLtFGz6Bsvra4LzOsKxQqL8EyhC7RajB3TAKpO9le2GDfbYunnz5pGWlkZSUlLwghAREamjgt4V+/zzz9OuXTvCw8Pp0aMHP/30037rv/POOxx99NFERkaSmJjINddcw86dOw9RtPWYMZjXBmB7rANxH4ymZVou9phW2O7ehnXnP+CIwhjDsBcXklMU2AVbtrVuxjCNrRMREamrgprYffDBB4wePZp77rmHZcuW0adPH84880w2bdpUYf358+dz5ZVXct1117Fq1SqmT5/O4sWLuf766w9x5PWQqwBry2/+Q8uy07jzdQFVSsbXvTRvvX98XaE7sLUuMvSQRSwiIiLVFNTE7oknnuC6667j+uuvp0uXLjz11FMkJyfzwgsvVFj/l19+oW3btowcOZJ27dpxyimncNNNN/Hbb79VWF8q9s8RjYgYs5FGna6p8Pzzc9f7X5edNKHWOhERkbotaImd0+lkyZIlDBo0KKB80KBBLFiwoMJrTjrpJLZs2cJXX32FMYZt27YxY8YMzj777EMR8mEjusNwCI3wHxtjKHC6sdss/v7Pmfz9nzMJD7GT7yRgQWIldSIiInVb0BK7HTt24PF4ym0NlZCQQEZGRoXXnHTSSbzzzjtcfPHFOBwOWrRoQaNGjXjmmWf2+Zzi4mJycnICfhoiV95m/+uY1mf5X5eMq0uZMJup3/xFqN1GiM3GsBkWKS/UjQWJp0+fTpcuXWjVqhXp6emVXyAiItJABX3yhLVXM5AxplxZidWrVzNy5EgmTJjAkiVLmDVrFqmpqYwYMWKf958yZQpxcXH+n+Tk5FqNv75w5qT6X4c3O87/2uUx9GjT2FfH7dttosAFS8rkTynxwV2QePPmzZx55pl06dIFr9cXY0xMTHCCERERqcMsY8qOojp0nE4nkZGRTJ8+nfPPP99fPmrUKJYvX868efPKXXPFFVdQVFTE9OnT/WXz58+nT58+bN26lcTExHLXFBcXU1xc7D/OyckhOTmZ7OxsYmNja/ld1V1bvj6fVr/u2Wf17q3giAo4X+B0E2KzEWq3cdZ7pRMmltwATSOC3w27YcMGhgwZws6dO4mJieHBBx9k2LBhwQ1KRETkEMjJySEuLq5KuUvQViRzOBz06NGDOXPmBCR2c+bMYciQIRVeU1BQQEhIYMh2ux3wtfRVJCwsjLCwsFqKun7yeoop2rmsXLkxhkKXB4BIh+9z3XvNurqQ1AG0bduW33//PdhhiIiI1GlBXaB47NixXHHFFfTs2ZPevXvz8ssvs2nTJn/X6vjx40lLS+Ott94C4Nxzz+WGG27ghRdeYPDgwaSnpzN69Gh69epFy5Ytg/lW6iyvu5CtP95QrrxkbN2SjVncPqgjN/U9khCbhTGlWdzBngU7d+5cLr/88irV3bJly8ELRERE5DAR1MTu4osvZufOnUyaNIn09HS6du3KV199RZs2bQBIT08PWNPu6quvJjc3l2effZbbbruNRo0acdppp/HII48E6y3UaXlbviV9T1JXUX427erjeWneeh7/5i/mrt3OBzf1PqSzYIuLi0lLSzu4DxEREWlAgjbGLliq009dnxXuWMaWby7wH1teQ/t1Wb6DMmPsShYiDg+xc/b7VkA37FcHecKEWuxEREQqVy/G2MnBY4wJSOriOlxO/DH3wDf3+ApsIRS7PXi8xj+2Lt9ZOrauXaODNwt27ty5FBcXExYWRr9+/ZSwiYiI1CIldoch5+41/teNu9xIk25jsELC4eyp/vKJH63g0+Vp3HTqkdzcrz3DZpSufDNzONgOIKlr1arVPs+VdL0mJSUpqRMREallSuwOQ5mL7/O/bnbseN++YPk7fAWRTcGyCLXbKHJ5+XndDm44tUOt7gdblXFzWodORESk9imxOwy58nwTTmyhe5InVwE8dqTv9Z7xdfee04XxZ3X2j60rUd2ZsA888ADZ2dnExcUxceJEwNcatz8l69CJiIhI7VJidxjyFO0AY4g/+h5wFVVYJyzEt/7f3uvWVbe17pVXXiEtLY2kpCR/YqcuVhERkeBQYneYce9J6lptziXi7xshZSZc9Bbcnw34ZsF2v/srAFbcPwhT5legsta66dOnM2HCBHJzc/1l2rtVRESk7lBid5hJ/fh4LAMRRe591nF7fSvc5Dvhwo9Kyyvrgp0wYQJ//vlnhec0Zk5ERCT4lNgdRlx5FXSBnvNUwGGY3U7XrqezMhN6vmr3r1ycEg8Rlfw2lLTU2Wy2gH15NWZORESkblBidxjZ8NkpWAZs3jJrTocE7pNb5IGVOxxgL13eJCW+auvW9e3blx07dtCsWTPeeeed2gxdREREaoESu8OE113kG1e3ny5YYwyXvbIQ8hMg+kiW3OCbLBERUrWZsErmRERE6jZb5VWkPijc8kP5pC75RAiN9B8WOD0s35wF+f+A101kqC+xO9h7woqIiMihoRa7w4CneDfp8/9F+5KC29eBI9KX1JXJ2pweCxPRCoyXzvFWpWPqREREpH7Rn/Z6zl24ndRPTiCg0c0RCY6ogHrGwKWf2qHR0QB8fJFa6kRERA436oqtx9wFGaR+0gswldYtdNd8IeLp06fTpUsXLMsiKiqK0047rWYBi4iIyEGlFrt6LO2HK/2vHY1TMJ3jsGyhYNnL1S1wuiHjOwDeuu50LKvqX33Z9esKCgq0KLGIiEgdpcSuHjLGkJ/2Hc7svwGwhzel9dlfVXINWMY3uaK6XbBl16/r2LGj1qwTERGpo5TY1UNFO5aR/uMN/uOWfV8HZ77vYK+xdeBL6q74zI6J7wdAeEj5Fr2qSExMZM2aNTW6VkRERA4+jbGrhzzFu/yvm3QbQ3h4IkxuCYteqbB+gQvW7LAgJIqUxCiiHFVrsisZW6euVxERkfpBiV095HXlARDe9BiadhtZeuLbifDaYF8T3R7GwP9N90L+BsjfwHvne6vcFVsyts7r9QLaD1ZERKSuU1dsPVO8ey3bFo4BwF2001cY2RTu3up7vdfadYVuWJPpxcpZ5Ttta0VV8/kxY8bwxx9/8Mwzz9C5c2eNrRMREanjlNjVdcaAqwAAT3E2m78c7F+zLiIuBWbe5jsYPLncvrBer2FXgRcsCxPegsFHgt1W9ZkTN954IwBPP/30Ab8NEREROfiU2NVlxsC0wbD5VwDsULq7BMC690pfD5wElCZ2Xq+h238WkldYAAkDoHEPnroEwquwft306dOZMGEC33//PYmJibXxTkREROQQ0Bi7usxV4E/q9muvPWEBdhV6yM/PwvIWg9dNz0SqvIVYydi69evX1yBoERERCRa12NUXt69j3afHA5DY5yWiEk8pPbfXuDoAh92OaXYKGA+LbrDTPKrq69eVrFt3wQUXkJmZWSvhi4iIyMGnxK6e8Fpg9oyPs0U1r3C9uhKFTg/nvGcgJAYsGzFhNdsX1uFw1DRcERERCQIldnWZZYM2vpa5wh1L/MXhTY/e5yXGQM/HlpCfux3iupPSJrnKXbDgG1+XlpZW45BFREQkeJTY1WWhEXDNTIp2rWLrrHP8xZZVOjSy0OnhzP/+CMDXo07Fi508J1hAs0iYObx6rXUTJkzwv9a6dSIiIvWLErt6YHOZpC6i+YkB5wyGDTt9y6F4jWHYR0DjHhhj+O5GG9VY3QQoHV8HaN06ERGRekaJXV3lzIenumG8LqxWdozNIq79ZcT3nAhAkcuD1xjsNosZI3oD4MXO6u2AZSelOcSF1/zxSUlJDBs2rBbeiIiIiBwqWu6kLivYiVWU4z9s3ushLJtvIbqrpi0iZcJsvl2dSc+2TejZtknA4sMzhu27C7ZkD9hWrVrx7rvvBpzbuXNn7b8PEREROSSU2NVVIRF4b/qRjW1iMRZEtz5nv9WNgQJX6fH+xtWVrFOXlpZGQUFBwLkZM2YAGl8nIiJSH6krtq6y2didtRBnmO8rimk3NOD0m9f2wmsMDrsNY+D/psOS9Krduuw4usjIwIWN27dvr31hRURE6ikldnXRnv1hdy19mJLZD9FJp1Pk8jDk2Z+x2Sw+ufkkIh2+r6/AFZjUVXWXiaSkJC699NKAsk6dOrFmzZpaeysiIiJy6Cixq2vK7A97hAVZjcNx9RgO+Ga9xseEMX/dDrzGVHj5khugaUTNFiQWERGR+k2JXV1TZn9Ym4HIAjeOng8AEOkI4aa+R1Dk8hARaq/w8shQJXUiIiINlRK7OuyfIxrhDQ2jfVicv+ykI5txSvtmWHuyt70nTYiIiEjDpcSurinTxeq1WRjjy9rcHi/frN5GeKiNUzvEE2K3qj1pQkRERA5vWu6kLjEGXj8joCisSTcAnB4vN7+zlGvf+A2nxwtAobtmkyaaNm1aayGLiIhI3aEWu7rEVQAZKwEoCrNjLEjoPRXwja/r3ioOh91W4fi66kya+OyzzzjllFO0Vp2IiMhhRoldHbUlOZbwDjdw6bvbge28c/0JfHhTb8JCbP7xdWVVZ9JE27Zt6dixIzfffHPtBi0iIiJBpcSuDmvSfSxLPv4e8C11UrJuXW34/vvva+1eIiIiUjcosaujQqNaExYaxktX9ADAYQ8cDqnZsCIiIrI3JXZ1iCsvjdA9r9c3up6tG7IY0CUBuy2wj/VAZsPedNNN7Nq1iyZNmvDSSy8deNAiIiJSZyixq0PchRl4HXYMFjfPjqaIX1g9aXC5LtiabiEGMHPmTNLS0khKSqrFyEVERKQuUGJXh+RnLiCrbRyeyA4k/+NbksSitLWupPv17PdKr9EWYiIiIlJCiV0dsvuvtwCwF/zNnLF9A85V1P2aEl+9pG769OmkpaXVVrgiIiJSxyixqyOMMRS53EzY8G/sYU34n8tDeJn16vbufk2Jh5nDq9dSN2HCBP9rrWEnIiJy+FFiV0cUZ63GeOFh52vgBK/z/yA0tla7X3Nzc/2vH3zwwVqKXEREROoKJXZ1RPHO33FYbjratgHgtlW8F2x1u18rkpSUxLBhww4wYhEREalrtFdsHZG5+B5sNi9bWsXAVV8SEhZZo+7X6dOn06VLFzp37lzunMbXiYiIHN7UYlcHeF35eIzFusLWxLQ9n8Q2p2CzLIbNKK1T1e7XCRMm8Oeff1Z47vTTT+e7777T+DoREZHDlBK7OiBv8yycJpR/r7sT1sHqUz1ghbB6u+98dbpfy46j29uxxx5LWlqaxteJiIgcppTY1QFFO5ZhYWgZmsn/ha0i5LctuI69FvbsQzFjWPXH1FW0APFjjz3GY489VgsRi4iISF2kxK4OyF73DuE2+LD3p7RaNBe+gYv+uZySxG5/Sd2XX37JiBEj/Mfp6TXYZ0xEREQOC0rs6gLLDsZDdLvzYNFcAP7cAVi+btj9bRdWWFhY4aQIjaMTERFpeJTYBVnRjuVgPABEJ/Yvd76ybtiIiIhy3a4xMTEaRyciItIAKbELss3fnA/G4PKEMGb6Xzyz1/nKxtadc845bNmy5aDFJyIiIvWHErsgMl4XGEOrzblEFLl5houDHZKIiIjUY0rsgihv0ywsAxFF7oDyxfYTKSSy0vF1S5Yswel04nA46NGjx0GOVkREROo6JXZBYrweMhaMpGxPq2vsXzhtUQx7NQosq9LxdUOGDCEtLY2kpCR1x4qIiIi2FAsWV25qubINOWBCo/zZ3IHsBysiIiINjxK7INm+bHK5svOenU+hy1OuvGT/11atWgX8aM06ERERKUtdsUFSsPUHAKzwxpgIG8sL44mIjOHyT8vX3d/+r6A160RERMRHiV0QGOP1v25y1L+wzruMY0Mjme+26PK8r7zsxIlFixaRmprK+eefT3FxccC9tGadiIiIlFBiFwReVx6W15C0JZfwok8wR15OsdsL2P11yk6ciImJoXv37qxfvz44AYuIiEi9oDF2QWA8vlY3yxisTQu47NVfuOujFQF1LKt0bN0TTzwRjDBFRESknlFidyh53LDqE5wLp2IsyGoSgafVCSzYVMBny7dS4Axcz65kbN2KFSv2cUMRERGRUuqKPZQ8xTD9aiIBq31j8qJDaXbx19z3yyacbi+h9sA8Ozc3F4A333yTN95449DHKyIiIvWKErsgCo1pS4jdxjUntcVmsyhwVVwvKSnp0AYmIiIi9VLQu2Kff/552rVrR3h4OD169OCnn37ab/3i4mLuuece2rRpQ1hYGEceeSTTpk07RNHWrui2FzDsxYWk7S4E4OMZ09k6uQtpE1vRoZ3WqRMREZHqCWqL3QcffMDo0aN5/vnnOfnkk3nppZc488wzWb16Na1bt67wmosuuoht27bx2muv0b59ezIzM3G73RXWreuspr1YsnEnL/24nrvP6sKDD0zAnelbr25rdmk9rVMnIiIiVRHUxO6JJ57guuuu4/rrrwfgqaeeYvbs2bzwwgtMmTKlXP1Zs2Yxb948/vnnH5o0aQJA27ZtD2XItSq8WQ8iQr9j+m9bGH9mF7KiOmCL3Y03J4OWSUlYaJ06ERERqbqgJXZOp5MlS5Ywbty4gPJBgwaxYMGCCq/5/PPP6dmzJ48++ij/+9//iIqK4rzzzuPBBx8kIiLiUIRdc8aAs8B/GBrVmqiwUNY8eAYABS6IuPJzWuFbnPir4dorVkRERKonaIndjh078Hg8JCQkBJQnJCSQkZFR4TX//PMP8+fPJzw8nE8++YQdO3Zw8803s2vXrn2OsysuLg7YrSEnJ6f23kRVGQPTBsPmX/1FTbqN3Gf1sosTi4iIiFRV0CdPWHtlMMaYcmUlvF4vlmXxzjvv0KtXL8466yyeeOIJ3njjDQoLCyu8ZsqUKcTFxfl/kpOTa/09VMpVEJDUFYaHYItsvs/qSupERESkJoKW2DVr1gy73V6udS4zM7NcK16JxMREkpKSiIuL85d16dIFYwxbtmyp8Jrx48eTnZ3t/9m8eXPtvYka+OeIRmxJjiGyRW+K3R7GfbSCcR+twOn2BDUuERERqf+Cltg5HA569OjBnDlzAsrnzJnDSSedVOE1J598Mlu3biUvL89f9tdff2Gz2WjVqlWF14SFhREbGxvwE0xem0WzHhOwbKF4vIb3F2/m/cWbcXsN2185j4wne3Ph+ecFNUYRERGpn4LaFTt27FheffVVpk2bxpo1axgzZgybNm1ixIgRgK+17corr/TXv/TSS2natCnXXHMNq1ev5scff+SOO+7g2muvrfuTJ8qIiO8JwGcff0TReyPJfu06ju7YlsLVM3Fu/IXly5cGOUIRERGpj4K63MnFF1/Mzp07mTRpEunp6XTt2pWvvvqKNm3aAJCens6mTZv89aOjo5kzZw633norPXv2pGnTplx00UU89NBDwXoLVeOI4u+OTfyHYU26ATDpgfvZtukfAHaXqR4TrXXrREREpPosY4wJdhCHUk5ODnFxcWRnZx+yblmvu4j1H3YBIDKxH0n9X8cYQ1KrZNK3pmGz2QiNS8TtBSsshjf++yCXXTLskMQmIiIidVt1chftFXsIGE8xltdgbBaJpzwDQKHLQ2ZOEQAJLRIJvdM3+SMlHi69OGihioiISD2mxO5gMwZePZ3227PIjQ7FqqR9VGvYiYiISE3VKLHLz8/n4Ycf5rvvviMzMxOv1xtw/p9//qmV4A4LXjcFCa0Jz1pPqNuLFRoFQKQjhEcfuJvs7Bze/iuWkt1uldSJiIhITdUosbv++uuZN28eV1xxBYmJiftcUFjAazxkOJdhtY2DkAgS8vL8izCPGTOWs94D9555FSnxEKE2VBEREamhGqURX3/9NTNnzuTkk0+u7XgOO15Xrn98XVL/aXTs0oW0tDSSkpJYvnYDq7f7voJ2jWCm9ocVERGRA1CjdewaN25MkyZNKq/Y0BmD/X8Xkrg1j4gCFzPnbiItLQ2AnflOej44G7JXgfEwczjYlNSJiIjIAahRYvfggw8yYcIECgoKajuew4urAGvrMqIKXDTbXsCE++/3n4qJiQEscGUBNrXUiYiIyAGrUVfs1KlTWb9+PQkJCbRt25bQ0NCA80uXaueEvW1JjiU3N9d//MSjj3BP+iCw7KQ0tzS2TkRERA5YjdKJoUOH1nIYh6l9rP3cMimJ/7mHgs338WuJExEREakNNUrsJk6cWNtxHH6MgdfP8B9+tzaZtLT5AGzLLiJkwz8QdQRdmtuJDFVWJyIiIgfugDoAlyxZwpo1a7Asi5SUFI499tjaiqv+cxVAxkoAisLsPPFq6dp+TRrHYeX9jSnewYybe6u1TkRERGpFjRK7zMxMLrnkEubOnUujRo0wxpCdnU3//v15//33iY+Pr+0467UtybHkF7v8xw9PnswD2weDZcemqbAiIiJSS2o0K/bWW28lJyeHVatWsWvXLrKysvjjjz/Iyclh5MiRtR3jYeGzzz7n559/Zv7CX7j4oot94+vUVCciIiK1qEYtdrNmzeLbb7+lS5cu/rKUlBSee+45Bg0aVGvB1Weeoizse16HRrfhuOOOY+hzP7Nq626ifvoTQjuCVaO8WkRERKRCNcosvF5vuSVOAEJDQ8vtG9tQbV/2oP910mn/w+nxclzrxri9ht05OwBLW4iJiIhIrapRYnfaaacxatQotm7d6i9LS0tjzJgxnH766bUWXH2Wt3m2/3VIeFPCQuzcceZRmITB0PRk2jW2tIWYiIiI1KoatRc9++yzDBkyhLZt25KcnIxlWWzatIlu3brx9ttv13aM9ZIVEgFk+Y+/+OILMnfnU7gmmoijztEWYiIiIlLrapTYJScns3TpUubMmcOff/6JMYaUlBQGDBhQ2/HVSx5nDl5PIf8c0YiEU55j5idfcvEllwBgj0si6YEtaqkTERGRWndAI7wGDhzIwIEDayuWw4a7IB0sC0+IRURyfyac1dN/zgqLCWJkIiIicjircmL39NNPc+ONNxIeHs7TTz+937oNfcmTgrS5xG/Lx7KHY7NCyMsr3SO20ZmTghiZiIiIHM6qnNg9+eSTXHbZZYSHh/Pkk0/us55lWQ0+sSvevoQW2cVAMXjd/vKWSUmEHHth8AITERGRw1qVE7vU1NQKX0t5eelz2dkkHEdcB2Js5ZeFERERETkYarTcyaRJkygoKChXXlhYyKRJ6mo0xsWuZpEUpvSHEIe/fHueG4zW+RMREZGDo0aJ3QMPPEBeXl658oKCAh544IEDDqo+8ziz/a+jk88EwOw59np8iZ0WJhYREZGDoUbphTEGq4L1On7//XeaNGlywEHVZ4XbfgVjcDg9RFiNwOvF/0lZdrAsZgzTwsQiIiJS+6qV2DVu3BjLsrAsi44dOwYkdx6Ph7y8PEaMGFHrQdYneZu+wjLQZmMOvNAb7t5KdHQ0MTExFEY0BsuupE5EREQOimoldk899RTGGK699loeeOAB4uLi/OccDgdt27ald+/etR5kfWG8HnI3fsbeeduaNX9y1nuwentQwhIREZEGolqJ3VVXXYXb7Vu+Y8CAAbRq1eqgBFVf5W78rFxZgdNNjwdnUeAGmg8gJSFE4+tERETkoKj25ImQkBBuvvlmPB7PwYinXsvd8AWW12DzmoDyQpcHy/g+L42vExERkYOlRrNiTzjhBJYtW1bbsdRvxtDk1y9ovy6LI/7Z7S/+4rPPafrDf3DZj9D4OhERETmoatQpePPNN3PbbbexZcsWevToQVRUVMD57t2710pw9YqrgIgiV2BZ8onc/9BkYuMasXPGrYQfcSrcMiE48YmIiMhhr0aJ3cUXXwwE7glrWZZ/GZSG2E1bvHstYXte513yPNFHDMVlC2fb+Fb8+eefAMSc1LBnDIuIiMjBVaPETluKlZfzz3Ti97wOTzwZHFG4nG5yCn2tePa4JCKPGRa8AEVEROSwV6PErk2bNrUdR/1mDI1/+cR/GBLhS/E++egjPHk7gxWViIiINDA1Xnhj/fr1PPXUU6xZswbLsujSpQujRo3iyCOPrM346gdjyOrcg7DV3xBua4wjNBKAhybd769ihcUEJzYRERFpMGo0K3b27NmkpKSwaNEiunfvTteuXfn111856qijmDNnTm3HWPfZbOzetYDM5lHs6nmmfz2T3Nxcf5VGZz2oPWJFRETkoKpRmjFu3DjGjBnDww8/XK78rrvuYuDAgbUSXH1RtH0ZltdgbBaRrU4rd75kfJ3WsBMREZGDqUYtdmvWrOG6664rV37ttdeyevXqAw6qXjEG+9sXc8T6LJrsKCAmaUDpqZIX3mLwupXUiYiIyEFVo8QuPj6e5cuXlytfvnw5zZs3P9CY6hePE2d0NDYD0cWhWCGR/lODzziTyE4nE3FEzyAGKCIiIg1Fjbpib7jhBm688Ub++ecfTjrpJCzLYv78+TzyyCPcdttttR1j3RYSxrZmoXgjGtMo5UbCyjTLPf3cy3z3TB4AXZrbNb5OREREDqoapRr33XcfMTExTJ06lfHjxwPQsmVL7r///oBFixsEY/Dmb8PYLGxhjQJO2WwWhPpmw350ocbXiYiIyMFlGWNM5dX2rWTmZ0xM/VjOIycnh7i4OLKzs4mNjT2wmxkD0waTlbeSrMYOfi64isn/ne7/TJontGDnlb8BsOZmiAw90OhFRESkoalO7nJAnYOZmZmsXbsWy7Lo1KkT8fHxlV90OHEVwOZfaQxEFBTznzfeY+1f6/ynjYGQgk2+qp5WEFqjIY0iIiIiVVKjTCMnJ4crrriCli1b0rdvX0499VRatmzJ5ZdfTnZ2dm3HWC9sSY4lL883ns5ms5GUlER8QgJW9kqs7JW4PN4gRygiIiKHuxoldtdffz2//vorM2fOZPfu3WRnZ/Pll1/y22+/ccMNN9R2jHVXuV5s3yC6xMREtmzZwg/zF2HCEjBhCdg1wE5EREQOshp1xc6cOZPZs2dzyimn+MsGDx7MK6+8whlnnFFrwdVpxsDre71XKzBPDgu1Q5Oee14fqsBERESkoapRi13Tpk2Ji4srVx4XF0fjxo0POKh6wVUAGSsBKAqzY9QgJyIiIkFWo8Tu3nvvZezYsaSnp/vLMjIyuOOOO7jvvvtqLbj6YktyLFZoVEBZodPDgKnfQ+b3YDxBikxEREQakhp1xb7wwgusW7eONm3a0Lp1awA2bdpEWFgY27dv56WXXvLXXbp0ae1EWscZd0HgMYatuwuxgANcUUZERESkSmqU2A0dOrSWw6j/QqPb8uijN1BQUEBkZCRhIXY+uOlkLpoBWPZghyciIiINQI0Su4kTJ9Z2HPVeVFJ/Lu1xaUBZt1aNwBGceERERKThOaAFipcsWcKaNWuwLIuUlBSOPfbY2oqr3gmNPbJcmXpgRURE5FCqUWKXmZnJJZdcwty5c2nUqBHGGLKzs+nfvz/vv/9+w9uBAti0M4Y05ypCQkLo1KkTLreXAS9vgUI7hCdSw3kqIiIiIlVWo2zj1ltvJScnh1WrVrFr1y6ysrL4448/yMnJYeTIkbUdY90UEo5n+NtsaRXDV4vyObr3ELp27crpp58OQHaxl/QtK7F2L6dzMy8RB9Q2KiIiIlK5GqUbs2bN4ttvv6VLly7+spSUFJ577jkGDRpUa8HVaTY7+aHFFEaG8t+PMv3FMTExAEQ6QjCOZmA8fHShHW08ISIiIgdbjVrsvF4voaHlt1IIDQ3F620Ae6J6XLDoFWzLPwBjyC8qHUwX2usSBj4xj0Knx7frRNPe2GzK6kREROTgq1Fid9pppzFq1Ci2bt3qL0tLS2PMmDH+rsjDmscJX91O9IpvsAyU7BHbMimJnMSe/J2Zh8H4ljlRU52IiIgcIjXqin322WcZMmQIbdu2JTk5Gcuy2LRpE926dePtt9+u7RjrHssOKUMoyFwEFGHZ7IALC3jvhhMBCAvR2nUiIiJyaNUosUtOTmbp0qXMmTOHP//8E2MMKSkpDBgwoLbjq5tCw+Git9j94wjMltlY9nCgCIDeRzYFoMAVxPhERESkQap2Yud2uwkPD2f58uUMHDiQgQMHHoy46oXCzF8BsPZ0xRa7vcxelcHpnZtjjJY3ERERkUOr2tlHSEgIbdq0wePRxvYhkS0A376wAFn5Tm763xKK3V6GzQhmZCIiItIQ1ahZ6d5772X8+PHs2rWrtuOpH5z5cH8cbRYtwPIaLMvX8BkaYqNHm8YUeyxWb/dVTYlHa9iJiIjIIVGjlOPpp59m3bp1tGzZkjZt2hAVFRVwfunSpbUSXH3x0zdv4Gh8NHa7ncTExIDxdTOGaWKsiIiIHBo1SuyGDh2KZVkYbYYKQJtOfbA7YgHf/rBlEzsldSIiInKoVCuxKygo4I477uDTTz/F5XJx+umn88wzz9CsWbODFV+9YNkcGGMocHq44rMQlqQHOyIRERFpiKqV2E2cOJE33niDyy67jIiICN59913+9a9/MX369IMVX533/ve5RGx7jjcXZ5DTth8mNA6a9gbLTs9Eja8TERGRQ6daacfHH3/Ma6+9xiWXXALAZZddxsknn4zH48Fub5gL8j77aTbbXr+dZgmJ9L3/AhaneQEbS26AphHqihUREZFDp1qzYjdv3kyfPn38x7169SIkJCRga7GGKizExotXHA9NTwLLIjJUSZ2IiIgcWtVqsfN4PDgcjsAbhITgdrtrNah6pUz2FukIKdk2VkREROSQq1ZiZ4zh6quvJiwszF9WVFTEiBEjApY8+fjjj2svwjrOZg8H8nC6vfz893YwzdRUJyIiIkFRra7Yq666iubNmxMXF+f/ufzyy2nZsmVAWXU8//zztGvXjvDwcHr06MFPP/1Upet+/vlnQkJCOOaYY6r1vNpmjG8Hjl35Tm54axEY7cghIiIiwVGtFrvXX3+9Vh/+wQcfMHr0aJ5//nlOPvlkXnrpJc4880xWr15N69at93lddnY2V155Jaeffjrbtm2r1Ziqy7KFAYWE2G2YkFiwLO02ISIiIkER1J3qn3jiCa677jquv/56unTpwlNPPUVycjIvvPDCfq+76aabuPTSS+ndu/chijSQpyjL/9oWEg5A02gHxPcBy67dJkRERCQogpbYOZ1OlixZwqBBgwLKBw0axIIFC/Z53euvv8769euZOHFilZ5TXFxMTk5OwM+Byt9WJj6r/EeopE5ERESCIWiJ3Y4dO/B4PCQkJASUJyQkkJGRUeE1f//9N+PGjeOdd94hJKRqfZ1TpkwJGP+XnJx8wLF7XDmkJ0aTnhiN2TMNNivfqfF1IiIiElRB7YoFsPZq3jLGlCsD31Irl156KQ888AAdO3as8v3Hjx9Pdna2/2fz5s0HHLO7KIO8GAeuNj1o36EDoU1b441N9G0UKyIiIhIkQRvi36xZM+x2e7nWuczMzHKteAC5ubn89ttvLFu2jH//+98AeL1ejDGEhITwzTffcNppp5W7LiwsLGB5ltqwe82rWAZsIRF89+2nzF61jWIPjJkX9DxZREREGrCgJXYOh4MePXowZ84czj//fH/5nDlzGDJkSLn6sbGxrFy5MqDs+eef5/vvv2fGjBm0a9fuoMcMgDG0TivGeIpxNYkhBC9nd08k3wn8eGhCEBEREalIUBflGDt2LFdccQU9e/akd+/evPzyy2zatIkRI0YAvm7UtLQ03nrrLWw2G127dg24vnnz5oSHh5crP6jcRXiMk8hiD6Gpy/ltUzYGi/sWNEbbToiIiEgwBTWxu/jii9m5cyeTJk0iPT2drl278tVXX9GmTRsA0tPT2bRpUzBDLC80grTkWCyvYfxHzfjy8YHYImNpdv23YAvRGnYiIiISNJYxDWvEf05ODnFxcWRnZxMbG1vt690F20j99EQA+t1RxNatGThim9HigQyw7Kz+F0Q5KrmJiIiISBVVJ3fRaP9qchdt97+2LDsAzWLCYM9rrWEnIiIiwaJOw2oyRbtptz4LCxsQEexwRERERPyU2FVT0c7lRHgM4GF3gRPQ8nUiIiJSN6grtrrKJHHFLm/w4hARERHZixK76jCGyK8f8h/GRoQCWuRERERE6gYldtXhKiCssBAAZ2Q0EQ5fT/aOwmAGJSIiIuKjxK6GioY86n/t2dMjqzXsREREJJiU2FWDMaVj6hzxJ+B0lx63awQzh2u5ExEREQketS9VlTHw2kD/odveCGf7/sQk52NrejQzh4NNSZ2IiIgEkRK7qnIVYGWuAaAozE5UTFOa9bsGlxUJTXurpU5ERESCToldDWxJjqWlx8LVrD9gI6W5pbF1IiIiEnRKR2ogNKat78WebcRmDNPYOhEREQk+TZ6oKruD3R2O5dUtNk7792KaJySQPqUj7p3rldSJiIhInaDErqrsoeS3bMeUr/P5Z0sB+VmZuLb9zdYH23Ncty7Bjk5EREREXbHVUbx7DfmFviVOLJsNW3RzrPBG3Hf/g0GOTERERESJXZUZjxvHzi1YxrdZrCMukYSJWwA4//+CGZmIiIiIj7piq8qVx8LZu8jY7Wuxc2u3CREREaljlNhVkTN3E/fNK/YfW2FR4M7nwwuMJk+IiIhInaDErorcrix2u0szuEa9L8DaPpdijyeIUYmIiIiUUidiFeVtmsnjNzfDbcXQ+KQXGfmjGxPsoERERETKUGJXRR5nNiekRABukgacDn/5yiMdQQ1LRERExE9dsVXk3v0PyRuyif7HwcQZv4JRF6yIiIjULUrsqigkKpFwp4dEdwZf/r4FjDpiRUREpG5RYldVBuZucDN7nZsz4nPA0kcnIiIidYvG2FWFMTRZ/CWXf1xIWq6h5Q/3EnLnNcGOSkRERCSAmp2qwJ29gfDismPqtHCdiIiI1D1K7Kpg9/p3A441uk5ERETqIiV2VbB7zSvBDkFERESkUkrsKuF1FwEwfZWLtFxfW92uwmBGJCIiIlIxJXaVMO4CACbMLd0n1hMaA0BKPERo+omIiIjUEUrsqii3uHRkXaOzHgRgxjCwNI9CRERE6ggldpVwF2UGHMc0aU7k0ecDSupERESkblFiVwlX7qaA40KnW7tOiIiISJ2kEWKV2PXH0xgLlj2YwF9Rl3FV+jW4LJvG14mIiEidoxa7SrgLM8CyyG3dmmOufBxX7FFg2TS+TkREROocJXaV8BTtBKD5CY9zwYduf7mSOhEREalr1Jm4H57i3QAYr2Hc++vx5OViRZ9O5wQHESHK7ERERKRuUWK3H7kbPgOg2BtK/OcjcBQblodezIw5b6rFTkSkAfN6vTidzmCHIYeJ0NBQ7HZ7rdxLid1+7F77BgDhlotXlrlJy/EQEvcdNpuyOhGRhsrpdJKamorX6w12KHIYadSoES1atMA6wJYjJXb7YQ9viis3lfCmx2Ki3ZCThkFJnYhIQ2WMIT09HbvdTnJyMjabhqrLgTHGUFBQQGamb93cxMTEA7qfErv9cO5eS6vNuaz7J43cYnflF4iIyGHN7XZTUFBAy5YtiYyMDHY4cpiIiIgAIDMzk+bNmx9Qt6z+r8Z+OKLaEFHkpptnLYXFrmCHIyIiQebxeABwOBxBjkQONyX/R8HlOrB8Q4ldVVm+xs0QmxYmFhFp6A50HJTI3mrrd0qJ3X6V2TrM5svmmkZqDTsRERGpm5TY7YsxxK9YCMD0VS7c2VuDHJCIiEjNTJkyheOPP56YmBiaN2/O0KFDWbt2bUAdYwz3338/LVu2JCIign79+rFq1aqAOi+//DL9+vUjNjYWy7LYvXt3uWe1bdsWy7ICfsaNG3fA7+GVV16hT58+NG7cmMaNGzNgwAAWLVpUrt7zzz9Pu3btCA8Pp0ePHvz0008B5z/++GMGDx5Ms2bNsCyL5cuXl7vH+vXrOf/884mPjyc2NpaLLrqIbdu2HfB7OBSU2O2Lq4DwYt9YinvmlU5pj4mOCVZEIiIiNTJv3jxuueUWfvnlF+bMmYPb7WbQoEHk5+f76zz66KM88cQTPPvssyxevJgWLVowcOBAcnNz/XUKCgo444wzuPvuu/f7vEmTJpGenu7/uffeew/4PcydO5fhw4fzww8/sHDhQlq3bs2gQYNIS0vz1/nggw8YPXo099xzD8uWLaNPnz6ceeaZbNq0yV8nPz+fk08+mYcffrjC5+Tn5zNo0CAsy+L777/n559/xul0cu6559aPJW5MA5OdnW0Ak52dvf+KxXnGTIw1ZmKsiU05xYTEdzSAefu96YcmUBERqXMKCwvN6tWrTWFhYbBDOSCZmZkGMPPmzTPGGOP1ek2LFi3Mww8/7K9TVFRk4uLizIsvvlju+h9++MEAJisrq9y5Nm3amCeffPJghe7ndrtNTEyMefPNN/1lvXr1MiNGjAio17lzZzNu3Lhy16emphrALFu2LKB89uzZxmazBeQJu3btMoCZM2dO7b6JMvb3u1Xl3MUYoxa7Kvjj8ydpefeftH7KcP7/DQt2OCIiIgckOzsbgCZNmgCQmppKRkYGgwYN8tcJCwujb9++LFiwoNr3f+SRR2jatCnHHHMM//nPfw7KLh0FBQW4XC7/e3A6nSxZsiTgPQAMGjSoWu+huLgYy7IICwvzl4WHh2Oz2Zg/f37tBH8QaX7nPhivx78UcUxkhGZMiIhIOcYYjKcwKM+27BE1mklpjGHs2LGccsopdO3aFYCMjAwAEhISAuomJCSwcePGat1/1KhRHHfccTRu3JhFixYxfvx4UlNTefXVV6sd6/6MGzeOpKQkBgwYAMCOHTvweDwVvoeS91cVJ554IlFRUdx1111MnjwZYwx33XUXXq+X9PT0Wn0PB4MSu33wegopWR7QHt4kqLGIiEjdZDyFrP/wqKA8+8iLVmGFVH+R5H//+9+sWLGiwtanvRNFY0y1k8cxY8b4X3fv3p3GjRszbNgwfyve3iZPnszkyZP9x6tXr6Z169b7fcajjz7Ke++9x9y5cwkPD6/V9xAfH8/06dP517/+xdNPP43NZmP48OEcd9xxtbaf68GkxG4firPWUPLP5dUF28AkgKWeaxERqb9uvfVWPv/8c3788UdatWrlL2/RogXga7kru6VVZmZmuRaw6jrxxBMBWLduXYWJ3YgRI7jooov8xy1bttzv/R5//HEmT57Mt99+S/fu3f3lzZo1w263l2udq8l7GDRoEOvXr2fHjh2EhIT493Ft165dte4TDErs9qFw+2/+xO7em6/AVezFFpsAN38f1LhERKTusOwRHHnRqsorHqRnV5UxhltvvZVPPvmEuXPnlktQ2rVrR4sWLZgzZw7HHnss4BuzNm/ePB555JEDinPZsmXAvvdAbdKkiX+cXGUee+wxHnroIWbPnk3Pnj0DzjkcDnr06MGcOXM4//zz/eVz5sxhyJAhNYq9WbNmAHz//fdkZmZy3nnn1eg+h5ISu30o2vU7u+IieObXOAo2/QGAvSg7yFGJiEhdYllWjbpDD7VbbrmFd999l88++4yYmBh/q1ZcXBwREb6xeqNHj2by5Ml06NCBDh06MHnyZCIjI7n00kv998nIyCAjI4N169YBsHLlSmJiYmjdujVNmjRh4cKF/PLLL/Tv35+4uDgWL17MmDFjOO+88yrtXq3Mo48+yn333ce7775L27Zt/e8hOjqa6OhoAMaOHcsVV1xBz5496d27Ny+//DKbNm1ixIgR/vvs2rWLTZs2sXWrb33akvX8WrRo4W+5fP311+nSpQvx8fEsXLiQUaNGMWbMGDp16nRA7+GQqN3JunVfVacMr5t+tPnrnbamfetGBt8WFCakeWeT7zxEgYqISJ1TX5c7Kfk7tvfP66+/7q/j9XrNxIkTTYsWLUxYWJg59dRTzcqVKwPuM3HixP3eZ8mSJeaEE04wcXFxJjw83HTq1MlMnDjR5OfnH/B7aNOmTYXPnjhxYkC95557zrRp08Y4HA5z3HHH+Zd0KfH6669Xep+77rrLJCQkmNDQUNOhQwczdepU4/V6D/g97E9tLXdiGWPK7Jt1+MvJySEuLo7s7GxiY2P3We/vd33N1H3HZJGe6Wupa3b1dDa+PIzI0EMSqoiI1DFFRUWkpqb6dzYQqS37+92qau4C6ordN2OwewxYvo/IHpdEz4HDiNAnJiIiInWU0pQKeN2FWAaO+Gc3toLSrVRmDNNydiIiIlJ3af2OChRmlt9UGJTUiYiISN2mxK4CrtxUjM1ibYemeKIrnp4tIiIiUtcosatA8e4/wRjCYztiUzOdiIiI1BMaY1eBkPDmtNqcy5LifPpc8i9mr4/AFhYd7LBERERE9kuJXQVy/n6fpkVuTmEFd8TeSWzfM8Gmj0pERETqNnXFVsDrzvO/PvHIppo1ISIiIvWCErsKGHeB/3UqR4NlD2I0IiIiIlWjxK4Clt0BwPRVLr4a3Y2if+ZzREi6FicWERGROk2J3V6MMRiPE4AJc4tx7Uwl8+k+bH3mNPXIiohIvTRlyhSOP/54YmJiaN68OUOHDmXt2rUBdYwx3H///bRs2ZKIiAj69evHqlWr/Od37drFrbfeSqdOnYiMjKR169aMHDmS7OzsgPtkZWVxxRVXEBcXR1xcHFdccQW7d+8+4Pfwyiuv0KdPHxo3bkzjxo0ZMGAAixaVX3f2+eef92/L1aNHD3766Sf/OZfLxV133UW3bt2IioqiZcuWXHnllWzdujXgHi+//DL9+vUjNjYWy7JqJf5DRYndXrzO3f7XucWl2+hOuP/BIEQjIiJy4ObNm8ctt9zCL7/8wpw5c3C73QwaNIj8/Hx/nUcffZQnnniCZ599lsWLF9OiRQsGDhxIbq5vB6atW7eydetWHn/8cVauXMkbb7zBrFmzuO666wKedemll7J8+XJmzZrFrFmzWL58OVdcccUBv4e5c+cyfPhwfvjhBxYuXEjr1q0ZNGgQaWlp/joffPABo0eP5p577mHZsmX06dOHM888k02bNgFQUFDA0qVLue+++1i6dCkff/wxf/31F+edd17AswoKCjjjjDO4++67DzjuQ840MNnZ2QYw2dnZFZ4vylpr/v5fG2MmxpqkGMsAxh6XZPKdhzZOERGpewoLC83q1atNYWFhsEM5IJmZmQYw8+bNM8YY4/V6TYsWLczDDz/sr1NUVGTi4uLMiy++uM/7fPjhh8bhcBiXy2WMMWb16tUGML/88ou/zsKFCw1g/vzzz1p9D26328TExJg333zTX9arVy8zYsSIgHqdO3c248aN2+d9Fi1aZACzcePGcud++OEHA5isrKxai3tf9ve7VVnuUpZa7PbiLthaeSUREZF6rKT7tEmTJgCkpqaSkZHBoEGD/HXCwsLo27cvCxYs2O99YmNjCQnxDUJfuHAhcXFxnHDCCf46J554InFxcfu9T00UFBTgcrn878HpdLJkyZKA9wAwaNCgSt+DZVk0atSoVuMLlqAndvvrC9/bxx9/zMCBA4mPjyc2NpbevXsze/bsWo3HeN21ej8RETl8GQMFruD8GFN5fBXHbBg7diynnHIKXbt2BSAjIwOAhISEgLoJCQn+c3vbuXMnDz74IDfddJO/LCMjg+bNm5er27x5833ep6bGjRtHUlISAwYMAGDHjh14PJ5qvYeioiLGjRvHpZdeSmxsbK3GFyxBnedZ0hf+/PPPc/LJJ/PSSy9x5plnsnr1alq3bl2u/o8//sjAgQOZPHkyjRo14vXXX+fcc8/l119/5dhjj62VmFy5G8qVhdjQjFgRESmn0A1dng/Os9fcDJGh1b/u3//+NytWrGD+/Pnlzll7zRI0xpQrA8jJyeHss88mJSWFiRMn7vce+7sPwOTJk5k8ebL/eF85QFmPPvoo7733HnPnziU8PLxG78HlcnHJJZfg9Xp5/vkgfYkHQVDTlSeeeILrrruO66+/HoCnnnqK2bNn88ILLzBlypRy9Z966qmA48mTJ/PZZ5/xxRdf1Fpi53XllStrGqE1ikVEpP679dZb+fzzz/nxxx9p1aqVv7xFixaAr8UtMTHRX56ZmVmuBSw3N5czzjiD6OhoPvnkE0JDQwPus23btnLP3b59e7n7lBgxYgQXXXSR/7hly5b7fQ+PP/44kydP5ttvv6V79+7+8mbNmmG328u1zlX0HlwuFxdddBGpqal8//33h01rHQQxsSvpCx83blxAeWV94WV5vV5yc3P9/esVKS4upri42H+ck5Oz33tmr3/f/3pnoa+du4at3SIicpiLCPG1nAXr2VVljOHWW2/lk08+Ye7cubRr1y7gfLt27WjRogVz5szxN5Q4nU7mzZvHI4884q+Xk5PD4MGDCQsL4/PPPy/XWta7d2+ys7NZtGgRvXr1AuDXX38lOzubk046qcLYmjRpst+/42U99thjPPTQQ8yePZuePXsGnHM4HPTo0YM5c+Zw/vnn+8vnzJnDkCFD/MclSd3ff//NDz/8QNOmTav07PoiaIldTfrC9zZ16lTy8/MDMv29TZkyhQceeKDKcXldeRibRW7TJCZf2JKx7/1JTExMla8XEZGGw7Jq1h16qN1yyy28++67fPbZZ8TExPj/zsbFxREREYFlWYwePZrJkyfToUMHOnTowOTJk4mMjOTSSy8FfC11gwYNoqCggLfffpucnBx/Y0l8fDx2u50uXbpwxhlncMMNN/DSSy8BcOONN3LOOefQqVOnA3oPjz76KPfddx/vvvsubdu29b+H6OhooqOjARg7dixXXHEFPXv2pHfv3rz88sts2rSJESNGAOB2uxk2bBhLly7lyy+/xOPx+O/TpEkTHA7fBgUZGRlkZGSwbt06AFauXElMTAytW7euchIaNLU8W7fK0tLSDGAWLFgQUP7QQw+ZTp06VXr9u+++ayIjI82cOXP2W6+oqMhkZ2f7fzZv3rzfKcN/vdPW/PVOW5Px1+cmcdwfJqR5Z/P2e9Or/sZEROSwVV+XO8HX+VTu5/XXX/fX8Xq9ZuLEiaZFixYmLCzMnHrqqWblypX+8yVLf1T0k5qa6q+3c+dOc9lll5mYmBgTExNjLrvsslpZLqRNmzYVPnvixIkB9Z577jnTpk0b43A4zHHHHedf0sUYY1JTU/f5Hn744Qd/vYkTJ1b6edW22lruxDKmpvNqDozT6SQyMpLp06cHNJmOGjWK5cuXM2/evH1e+8EHH3DNNdcwffp0zj777Go9Nycnh7i4OP8U7bLcRTtI/fh4PMai8ZmL6PW/ZkDNB6iKiMjhpaioiNTUVP9qDiK1ZX+/W/vLXfYWtOVOyvaFlzVnzpx99sMDvPfee1x99dW8++671U7qKuMpzMTyGjr/vZOEp48kYtuXYDy1+gwRERGRgyWos2Ir6wsfP348aWlpvPXWW4Avqbvyyiv573//y4knnujvF4+IiCAuLu6A4zGe0kkW7650sbv4B9yNs4HLDvjeIiIiIgdbUBO7iy++mJ07dzJp0iTS09Pp2rUrX331FW3atAEgPT3dv78bwEsvvYTb7eaWW27hlltu8ZdfddVVvPHGG7USk7Hgn65duf3lVLalP4E9LgkldiIiIlIfBH3Z3Ztvvpmbb654rvjeydrcuXMPaixFhdnMzjoRe1gTLGvjQX2WiIiISG0LemJXl+SkL+DxLVcAYPg8yNGIiIiIVI8SuzIKM+ZxUrThKu+3XJafHexwRERERKpFiV0Z0TGJTGr7Cu3XZRHuzQe0T6yIiIjUH0Fb7qQuchdsLVfWNFL7xIqIiEj9oMSuDGf238EOQURERKTGlNiVUeQJwft3ZLDDEBERkQMwd+5cLMti9+7dwQ7lkFNit4fxOMFARysNALelPcREREQOlUOdjK1fv57zzz+f+Ph4YmNjueiii9i2bVtAnaVLlzJw4EAaNWpE06ZNufHGG8nLy/Of37VrF+eeey7R0dEcd9xx/P777wHX33zzzUydOvWQvJ8SSuz2yNs8C4fl9h9nxx6FPS6JhIQWQYxKREREalt+fj6DBg3Csiy+//57fv75Z5xOJ+eeey5erxeArVu3MmDAANq3b8+vv/7KrFmzWLVqFVdffbX/Pv/5z3/Izc1l6dKl9O3bl+uvv95/buHChSxatIjRo0cf0vemxG4Pd9FObBj/cZvbfiLpgS3M/+W3IEYlIiJy4IwxPProoxxxxBFERERw9NFHM2PGDP+5AQMGcMYZZ2CM7+/g7t27ad26Nffccw9Q2po2c+ZMjj76aMLDwznhhBNYuXJlwHMWLFjAqaeeSkREBMnJyYwcOZL8/Hz/+eLiYu68806Sk5MJCwujQ4cOvPbaa2zYsIH+/fsD0LhxYyzL8idQ+4u9xFdffUXHjh2JiIigf//+bNiwYb+fx88//8yGDRt444036NatG926deP1119n8eLFfP/99wB8+eWXhIaG8txzz9GpUyeOP/54nnvuOT766CPWrVsHwJo1a7jkkkvo2LEjN954I6tXrwbA5XLxr3/9ixdffBG73V7dr+uAKLHbw124nTv/+TfTV7m4/ZuiYIcjIiL1SIHTTYHT7U+MAJxuLwVON8VuT4V1vd7Sui6Pr26Rq2p1q+vee+/l9ddf54UXXmDVqlWMGTOGyy+/nHnz5mFZFm+++SaLFi3i6aefBmDEiBEkJCRw//33B9znjjvu4PHHH2fx4sU0b96c8847D5fLBcDKlSsZPHgwF1xwAStWrOCDDz5g/vz5/Pvf//Zff+WVV/L+++/z9NNPs2bNGl588UWio6NJTk7mo48+AmDt2rWkp6fz3//+t9LYATZv3swFF1zAWWedxfLly7n++usZN27cfj+P4uJiLMsiLCzMXxYeHo7NZmP+/Pn+Og6HA5utNFWKiIgA8Nc5+uij+f7773G73cyePZvu3bsD8Mgjj9CvXz969uxZjW+plpgGJjs72wAmOzs7oHzDggmm810zTOdmNjMsJcQccd8qc8Y7xni9QQpURETqnMLCQrN69WpTWFgYUN7mri9Nm7u+NDtyi/xlz3z3l2lz15fmrhm/B9TtfO/Xps1dX5pNO/P9Za/+9I9pc9eXZuR7SwPqHjvpG9Pmri/N2owcf9m7v26sVsx5eXkmPDzcLFiwIKD8uuuuM8OHD/cff/jhhyYsLMyMHz/eREZGmrVr1/rP/fDDDwYw77//vr9s586dJiIiwnzwwQfGGGOuuOIKc+ONNwY846effjI2m80UFhaatWvXGsDMmTOnwjhLnpGVlVWt2MePH2+6dOlivGX+YN91113l7lVWZmamiY2NNaNGjTL5+fkmLy/P3HLLLQbwv4c//vjDhISEmEcffdQUFxebXbt2mQsuuMAAZvLkycYYY3bv3m2GDx9uWrdubU499VSzatUq89dff5kOHTqYHTt2mJtuusm0a9fOXHjhhWb37t0VxlJiX79bxuw7d6mIWuz2yF3/Hl0j15NbbFi93UvaaxczY5jWsBMRkfpt9erVFBUVMXDgQKKjo/0/b731FuvXr/fXu/DCC7nggguYMmUKU6dOpWPHjuXu1bt3b//rJk2a0KlTJ9asWQPAkiVLeOONNwKeMXjwYLxeL6mpqSxfvhy73U7fvn1rNfY1a9Zw4oknYpX5g102zorEx8czffp0vvjiC6Kjo4mLiyM7O5vjjjvO33V61FFH8eabbzJ16lQiIyNp0aIFRxxxBAkJCf46cXFxvPvuu2zcuJF58+aRkpLCTTfdxGOPPcY777zDP//8w9q1a4mMjGTSpElVft8HQnsq7BFmczHliOfpB6ze7iUkbpeSOhERqZLVkwYDEBFaOp7qxlOP5NpT2mG3Bf4xWXLfAADCQ0rrXtm7DcN7JWPb6w/P/Lv6l6s7rEerasVWMhlg5syZJCUlBZwr2xVZUFDAkiVLsNvt/P131dd1LUmovF4vN910EyNHjixXp3Xr1v5xabUduynT/V0dgwYNYv369ezYsYOQkBAaNWpEixYtaNeunb/OpZdeyqWXXsq2bduIiorCsiyeeOKJgDplTZs2jUaNGjFkyBAuuOAChg4dSmhoKBdeeCETJkyoUZzVpcRub/ZQwBnsKEREpB6JdJT/c+oIseGoYCh7RXVD7TZC7VWvWx0pKSmEhYWxadOm/baW3XbbbdhsNr7++mvOOusszj77bE477bSAOr/88gutW7cGICsri7/++ovOnTsDcNxxx7Fq1Srat29f4f27deuG1+tl3rx5DBgwoNx5h8MBgMdTOs6wKrGnpKTw6aeflouzqpo1awbA999/T2ZmJuedd165OgkJCYAvcQsPD2fgwIHl6mzfvp0HH3zQP/7O4/H4xx+6XK6A93UwKbEDCgpyuOkv30BLb8T9sDsDg5rrRESk/ouJieH2229nzJgxeL1eTjnlFHJycliwYAHR0dFcddVVzJw5k2nTprFw4UKOO+44xo0bx1VXXcWKFSto3Lix/16TJk2iadOmJCQkcM8999CsWTOGDh0KwF133cWJJ57ILbfcwg033EBUVBRr1qxhzpw5PPPMM7Rt25arrrqKa6+9lqeffpqjjz6ajRs3kpmZyUUXXUSbNm2wLIsvv/ySs846i4iIiCrFPmLECKZOncrYsWO56aab/F3ClXn99dfp0qUL8fHxLFy4kFGjRjFmzBg6derkr/Pss89y0kknER0dzZw5c7jjjjt4+OGHadSoUbn7jRo1ittuu83fsnjyySfzv//9j0GDBvHyyy9z8sknH9D3WGWVjsI7zFQ0AHFX5p/+ga+JLZMMYOxxSSbfGcRARUSkztnfAPe6zOv1mv/+97+mU6dOJjQ01MTHx5vBgwebefPmmczMTJOQkOCfEGCMMS6Xy/Tq1ctcdNFFxpjSiQ1ffPGFOeqoo4zD4TDHH3+8Wb58ecBzFi1aZAYOHGiio6NNVFSU6d69u/nPf/7jP19YWGjGjBljEhMTjcPhMO3btzfTpk3zn580aZJp0aKFsSzLXHXVVZXGXuKLL74w7du3N2FhYaZPnz5m2rRp+508YYxvgkVCQoIJDQ01HTp0MFOnTg2YgGGMb0JIkyZNjMPhMN27dzdvvfVWhfeaNWuW6dWrl/F4PP6y/Px8c+GFF5qYmBhz+umnm23btu0zlpLPpjYmT1jG1LBzup7KycnxD5KMjY0FYNffH/DN929gvIZr71lJUdZW7HFJ5GzfQqQ2oBARkT2KiopITU2lXbt2hIeHBzucQ2bu3Ln079+frKysClur5MDt73erotxlXzQrFrDjoUf0Gobu/o2m7nQAQm2GCHVUi4iISD2ixA7A5ptttC2lH4VWJABNIi3NihUREZF6RYkdkL9zNb/kdGX4P/eTY8UFOxwREZE6pV+/fhhj1A1bD6izEfCGNOa+jf8CNhJx7CXEkc3FPRpXep2IiIhIXaLEDnDuWkHn8BAGe1fBoFbcOHoKURGOYIclIiIiUi1K7AByVvLYkQvotj4diqHAOxpQYiciIiL1i8bYAWGNOgc7BBEREZEDpsQOcBXupNgbSudn84idksOxxx4b7JBEREREqq3Bd8UaY7jht3PZUticPOeF5DohJj8/2GGJiIiIVFuDb7FzFmZxWfNZdIzYFOxQREREpBbMnTsXy7LYvXt3sEM55Bp8YufJ+ZOT41ZwX9tpwQ5FRESkwWrIyVhtavCJXf7W7wEIt3uCHImIiIjIgWnwiZ0XB7/ntmfxV9tJyzXBDkdERKTWGWN49NFHOeKII4iIiODoo49mxowZ/nMDBgzgjDPOwBjf38Hdu3fTunVr7rnnHqC0NW3mzJkcffTRhIeHc8IJJ7By5cqA5yxYsIBTTz2ViIgIkpOTGTlyJPllxq0XFxdz5513kpycTFhYGB06dOC1115jw4YN9O/fH4DGjRtjWRZXX311pbGX+Oqrr+jYsSMRERH079+fDRs2VPqZWJbFSy+9xDnnnENkZCRdunRh4cKFrFu3jn79+hEVFUXv3r1Zv369/5r169czZMgQEhISiI6O5vjjj+fbb7/1n//zzz+JjIzk3Xff9Zd9/PHHhIeHl/usDhrTwGRnZxvAZGdnG2OM2bT4UdP5rhmmczObAQxgOnXqHOQoRUSkLiosLDSrV682hYWFgSeK86r/43aVXu92+cqcBVW7bzXdfffdpnPnzmbWrFlm/fr15vXXXzdhYWFm7ty5xhhjtmzZYho3bmyeeuopY4wxF198senZs6dxOp3GGGN++OEHA5guXbqYb775xqxYscKcc845pm3btv46K1asMNHR0ebJJ580f/31l/n555/Nsccea66++mp/HBdddJFJTk42H3/8sVm/fr359ttvzfvvv2/cbrf56KOPDGDWrl1r0tPTze7du6sU+6ZNm0xYWJgZNWqU+fPPP83bb79tEhISDGCysrL2+ZkAJikpyXzwwQdm7dq1ZujQoaZt27bmtNNOM7NmzTKrV682J554ojnjjDP81yxfvty8+OKLZsWKFeavv/4y99xzjwkPDzcbN27013nuuedMXFyc2bBhg0lLSzNNmjQxTz75ZKXf0T5/t0z53GV/Gnxit3nxo2bQhGdNUozlT+zefm96kKMUEZG6aJ9/fCfGVv/nj49Lr//jY1/ZtLMC7/tIu4qvrYa8vDwTHh5uFixYEFB+3XXXmeHDh/uPP/zwQxMWFmbGjx9vIiMjzdq1a/3nShK7999/31+2c+dOExERYT744ANjjDFXXHGFufHGGwOe8dNPPxmbzWYKCwvN2rVrDWDmzJlTYZwlzyibjFUl9vHjx5suXboYr9frP3/XXXdVKbG79957/ccLFy40gHnttdf8Ze+9954JDw/f5z2MMSYlJcU888wzAWVnn3226dOnjzn99NPNwIEDA2Lbl9pK7Br8cidW7mqebf8D/fcch8S15Pz/GxbUmERERGrL6tWrKSoqYuDAgQHlTqczYN3WCy+8kE8++YQpU6bwwgsv0LFjx3L36t27t/91kyZN6NSpE2vWrAFgyZIlrFu3jnfeecdfxxiD1+slNTWVlStXYrfb6du3b63GvmbNGk488UQsy6owzv3p3r27/3VCQgIA3bp1CygrKioiJyeH2NhY8vPzeeCBB/jyyy/ZunUrbrebwsJCNm0KXFlj2rRpdOzYEZvNxh9//BEQ28HW4BM7d2E6RYRz37ntKXIb3mv1MBEN/lMREZFquXtr9a+xh5W+7nyu7x7WXkPfRx/4uCyv1wvAzJkzSUpKCjgXFlYaQ0FBAUuWLMFut/P3339X+f4lSYvX6+Wmm25i5MiR5eq0bt2adevWHZTYjan5+PjQ0FD/65L3UVFZSRx33HEHs2fP5vHHH6d9+/ZEREQwbNgwnE5nwH1///138vPzsdlsZGRk0LJlyxrHWF0NPoXxFO3E2GxM7vUnAKv/BYcwsRYRkcOBI+rArreH+H5q+75ASkoKYWFhbNq0ab+tZbfddhs2m42vv/6as846i7PPPpvTTjstoM4vv/xC69atAcjKyuKvv/6ic2fftpzHHXccq1aton379hXev1u3bni9XubNm8eAAQPKnXc4fHu0ezylq1RUJfaUlBQ+/fTTcnEeDD/99BNXX301559/PgB5eXnlJmrs2rWLq6++mnvuuYeMjAwuu+wyli5dSkRExEGJaW8NflZsQUE296XeADt/BeNRUiciIoeVmJgYbr/9dsaMGcObb77J+vXrWbZsGc899xxvvvkm4GsRmzZtGu+88w4DBw5k3LhxXHXVVWRlZQXca9KkSXz33Xf88ccfXH311TRr1oyhQ4cCcNddd7Fw4UJuueUWli9fzt9//83nn3/OrbfeCkDbtm256qqruPbaa/n0009JTU1l7ty5fPjhhwC0adMGy7L48ssv2b59O3l5eVWKfcSIEaxfv56xY8eydu1a3n33Xd54442D8lm2b9+ejz/+mOXLl/P7779z6aWX+lvzSowYMYLk5GTuvfdennjiCYwx3H777QclngpVOgrvMLP3AMQVb3Ywa+/rYtbe18V0nJpt8p1BDlBEROqs/Q1wr8u8Xq/573//azp16mRCQ0NNfHy8GTx4sJk3b57JzMw0CQkJZvLkyf76LpfL9OrVy1x00UXGmNKJDV988YU56qijjMPhMMcff7xZvnx5wHMWLVpkBg4caKKjo01UVJTp3r27+c9//uM/X1hYaMaMGWMSExONw+Ew7du3N9OmTfOfnzRpkmnRooWxLMtcddVVlcZe4osvvjDt27c3YWFhpk+fPmbatGlVmjzxySef+I9TU1MNYJYtW+Yv23tCR2pqqunfv7+JiIgwycnJ5tlnnzV9+/Y1o0aNMsYY8+abb5qoqCjz119/+e/x22+/GYfDYWbOnLnf76i2Jk9Ye95cg5GTk0NcXBzZ2dnExsay9u0OrPgsjas+LaTRJa8x/d9Hc/IJPYIdpoiI1EFFRUWkpqbSrl07wsPDgx3OITN37lz69+9PVlYWjRo1CnY4h6X9/W7tnbvsT4PuivW6i7BZLsbPd1PohvS3r+O6qy4PdlgiIiIiNdKgE7virFVgWeQWlzZa3nf/g0GMSERERKTmGvSsWFfeRjzGwo0dcGOPS9IadiIiInvp16/fAS0rIodOg26xc+VuwukNweUuyW/1SysiIiL1V4NO7GyOGGzGEEsBAFrpREREROqzBp3YFe1cQbjNFewwRERERGpFg07sQsKbBTsEERERkVrToBM7jKHV5pxgRyEiIiJSKxp0Yle49SfCi0v3pLPbLCIa9DxhERERqc8adGLniG0HwJpbouk4+i3WrFqpvWJFREQq0bZtW5566qkq19+wYQOWZbF8+fKDFlNZb7zxxkHbIeP+++/nmGOOOSj3rg0NOrHLT58HQEyYhRV/PE0axwU5IhERkbpv8eLF3HjjjbV6z4OZjNWm22+/ne+++y7YYexTg+54NJ7i0oPIZELtDTrPFRERqZL4+Phgh3DIGWPweDxER0cTHR19QPdyuVyEhobWUmSBGnQm4/FaPLGwmPvnFrHrh6eDHY6IiEit++KLL2jUqBFerxeA5cuXY1kWd9xxh7/OTTfdxPDhw/3HCxYs4NRTTyUiIoLk5GRGjhxJfn6+//zeXbF//vknp5xyCuHh4aSkpPDtt99iWRaffvppQCz//PMP/fv3JzIykqOPPpqFCxcCMHfuXK655hqys7OxLAvLsrj//vsBcDqd3HnnnSQlJREVFcUJJ5zA3LlzA+77xhtv0Lp1ayIjIzn//PPZuXPnfj+Tkq7h999/n5NOOonw8HCOOuqogPvOnTsXy7KYPXs2PXv2JCwsjJ9++qlcV6zX62XSpEm0atWKsLAwjjnmGGbNmlXuWR9++CH9+vUjPDyct99+e7/xHYgGm9gZY8g10Tyx0MkD85zk/vSsJk6IiMhh59RTTyU3N5dly5YBMG/ePJo1a8a8efP8debOnUvfvn0BWLlyJYMHD+aCCy5gxYoVfPDBB8yfP59///vfFd7f6/UydOhQIiMj+fXXX3n55Ze55557Kqx7zz33cPvtt7N8+XI6duzI8OHDcbvdnHTSSTz11FPExsaSnp5Oeno6t99+OwDXXHMNP//8M++//z4rVqzgwgsv5IwzzuDvv/8G4Ndff+Xaa6/l5ptvZvny5fTv35+HHnqoSp/NHXfcwW233cayZcs46aSTOO+888olhXfeeSdTpkxhzZo1dO/evdw9/vvf/zJ16lQef/xxVqxYweDBgznvvPP88ZW46667GDlyJGvWrGHw4MFViq9GTAOTnZ1tALM7a5dZ/EZXkxRjGcAkJrYMdmgiIlLHFRYWmtWrV5vCwsKA8qlTp5qkpKRKf84999xy9zz33HOrdO3UqVNrHPdxxx1nHn/8cWOMMUOHDjX/+c9/jMPhMDk5OSY9Pd0AZs2aNcYYY6644gpz4403Blz/008/GZvN5n/fbdq0MU8++aQxxpivv/7ahISEmPT0dH/9OXPmGMB88sknxhhjUlNTDWBeffVVf51Vq1YFPPf11183cXFxAc9dt26dsSzLpKWlBZSffvrpZvz48cYYY4YPH27OOOOMgPMXX3xxuXuVVRLPww8/7C9zuVymVatW5pFHHjHGGPPDDz8YwHz66acB106cONEcffTR/uOWLVua//znPwF1jj/+eHPzzTcHPOupp57aZzzG7Pt3y5jS3CU7O3u/9zDGmAbdRhVmc+HFBng0G1ZERGosJyeHtLS0SuslJyeXK9u+fXuVrs3Jqfm6q/369WPu3LmMHTuWn376iYceeoiPPvqI+fPns3v3bhISEujcuTMAS5YsYd26dbzzzjv+640xeL1eUlNT6dKlS8C9165dS3JyMi1atPCX9erVq8I4yrZ4JSYmApCZmel/9t6WLl2KMYaOHTsGlBcXF9O0aVMA1qxZw/nnnx9wvnfv3gHdofvSu3dv/+uQkBB69uzJmjVrAur07Nlzn9fn5OSwdetWTj755IDyk08+md9//73K96lNDTax87pyMDYbmbYWQBrK7EREpKZiY2NJSkqqtF5Fkw7i4+OrdG1sbGyNYgNfYvfaa6/x+++/Y7PZSElJoW/fvsybN4+srCx/Nyz4ulZvuukmRo4cWe4+rVu3LldmjMGq4t/QshMGSq4pGftXEa/Xi91uZ8mSJdjt9oBzJRMYjDFVenZV7f1eoqKiqn1NRZ9JVe5TGxpwYlcQ7BBEROQwMXbsWMaOHVujaz///PNajqa8knF2Tz31FH379sWyLPr27cuUKVPIyspi1KhR/rrHHXccq1aton379lW6d+fOndm0aRPbtm0jISEB8C2HUl0OhwOPxxNQduyxx+LxeMjMzKRPnz4VXpeSksIvv/wSULb38b788ssvnHrqqQC43W6WLFmyz7GEFYmNjaVly5bMnz/ffx/wTT7ZV6vlwdZgJ08AOD0hWF4nALWc8IuIiNQZcXFxHHPMMbz99tv069cP8CV7S5cu5a+//vKXgW+Q/8KFC7nllltYvnw5f//9N59//jm33nprhfceOHAgRx55JFdddRUrVqzg559/9k+eqGpLHvhm2ubl5fHdd9+xY8cOCgoK6NixI5dddhlXXnklH3/8MampqSxevJhHHnmEr776CoCRI0cya9YsHn30Uf766y+effbZKnXDAjz33HN88skn/Pnnn9xyyy1kZWVx7bXXVjlm8E3AeOSRR/jggw9Yu3Yt48aNY/ny5QHJ8qHUYBM74zXEbAwjgR0lJUGNR0RE5GDq378/Ho/Hn8Q1btyYlJQU4uPjA8bNde/enXnz5vH333/Tp08fjj32WO677z7/mLi92e12Pv30U/Ly8jj++OO5/vrruffeewEIDw+vcnwnnXQSI0aM4OKLLyY+Pp5HH30UgNdff50rr7yS2267jU6dOnHeeefx66+/+scrnnjiibz66qs888wzHHPMMXzzzTf+51fm4Ycf5pFHHuHoo4/mp59+4rPPPqNZs2ZVjhl8ieVtt93GbbfdRrdu3Zg1axaff/45HTp0qNZ9aotlartzuo7LyckhLi6O1LU/8sin2/n4oeFk5jpJSkpiy5YtwQ5PRETqsKKiIlJTU2nXrl21kpaG5ueff+aUU05h3bp1HHnkkcEOp5wNGzbQrl07li1bVme2B9vf71ZJ7pKdnV3pWMsGO8YO4+arsAvYZWsGbA12NCIiIvXWJ598QnR0NB06dGDdunWMGjWKk08+uU4mdYe7BpvYufLTAQht1YPjOrcmoXnD2x5FRESkNuTm5nLnnXeyefNmmjVrxoABA5g6dWqww2qQGmxihzGcVfwJXH4ZY68+kyObxwQ7IhERkXrpyiuv5Morrwx2GFXWtm3bWl8mpa5osJMnLK+bFwqu5oWiGznvqTkUuT2VXyQiIiJShzXYxK6siFB75ZVERERE6rgG2xXrzt/Kee8VsL3AkNj9FSIdQ4MdkoiI1BOHazeeBE9t/U412MSu8Q9TWZruIS3X0NK7PNjhiIhIPVCyrZXT6SQiIiLI0cjhpKDAtyNW2W3XaqLBJnYRTmeZI+0TKyIilQsJCSEyMpLt27cTGhqKzaYRTXJgjDEUFBSQmZlJo0aNyu2JW10NNrErSw3qIiJSFZZlkZiYSGpqKhs3bgx2OHIYadSoES1atDjg+yixQ+11IiJSdQ6Hgw4dOuAM6PkRqbnQ0NADbqkrEfTE7vnnn+exxx4jPT2do446iqeeeoo+ffrss/68efMYO3Ysq1atomXLltx5552MGDHiwIJQZiciItVgs9m0pZjUSUEdHPDBBx8wevRo7rnnHpYtW0afPn0488wz2bRpU4X1U1NTOeuss+jTpw/Lli3j7rvvZuTIkXz00UeHOHIRERGRuscyQZyzfcIJJ3Dcccfxwgsv+Mu6dOnC0KFDmTJlSrn6d911F59//jlr1qzxl40YMYLff/+dhQsXVumZ/o10x8WQ8lweabmGpKQktmzZcuBvSERERKSW+XOX7GxiY2P3WzdoLXZOp5MlS5YwaNCggPJBgwaxYMGCCq9ZuHBhufqDBw/mt99+w+Vy1TgWTZ4QERGRw0HQxtjt2LEDj8dDQkJCQHlCQgIZGRkVXpORkVFhfbfbzY4dO0hMTCx3TXFxMcXFxf7j7OxsAHKKjT+hM14vOTk5B/BuRERERA6OkhylKp2sQZ88YVmBMxeMMeXKKqtfUXmJKVOm8MADD5QrT34yz/86PT2duLi4KscsIiIicqjl5uZWmq8ELbFr1qwZdru9XOtcZmZmuVa5Ei1atKiwfkhICE2bNq3wmvHjxzN27Fj/sdfrZdeuXYSGhtK6dWs2b95caX+11F05OTkkJyfre6zn9D0eHvQ9Hh70PdY9xhhyc3Np2bJlpXWDltg5HA569OjBnDlzOP/88/3lc+bMYciQIRVe07t3b7744ouAsm+++YaePXvucwuOsLAwwsLCAsoaNWrkb9aMjY3VL+5hQN/j4UHf4+FB3+PhQd9j3VLVnsWgLncyduxYXn31VaZNm8aaNWsYM2YMmzZt8q9LN378eK688kp//REjRrBx40bGjh3LmjVrmDZtGq+99hq33357sN6CiIiISJ0R1DF2F198MTt37mTSpEmkp6fTtWtXvvrqK9q0aQP4xr6VXdOuXbt2fPXVV4wZM4bnnnuOli1b8vTTT/N///d/wXoLIiIiInVG0CdP3Hzzzdx8880VnnvjjTfKlfXt25elS5ce8HPDwsKYOHFiuW5aqV/0PR4e9D0eHvQ9Hh70PdZvQV2gWERERERqT1DH2ImIiIhI7VFiJyIiInKYUGInIiIicphokInd888/T7t27QgPD6dHjx789NNPwQ5J9uPHH3/k3HPPpWXLlliWxaeffhpw3hjD/fffT8uWLYmIiKBfv36sWrUqOMHKPk2ZMoXjjz+emJgYmjdvztChQ1m7dm1AHX2Xdd8LL7xA9+7d/Wuc9e7dm6+//tp/Xt9h/TRlyhQsy2L06NH+Mn2X9VODS+w++OADRo8ezT333MOyZcvo06cPZ555ZsCyKlK35Ofnc/TRR/Pss89WeP7RRx/liSee4Nlnn2Xx4sW0aNGCgQMHkpube4gjlf2ZN28et9xyC7/88gtz5szB7XYzaNAg8vPz/XX0XdZ9rVq14uGHH+a3337jt99+47TTTmPIkCH+P/j6DuufxYsX8/LLL9O9e/eAcn2X9ZRpYHr16mVGjBgRUNa5c2czbty4IEUk1QGYTz75xH/s9XpNixYtzMMPP+wvKyoqMnFxcebFF18MQoRSVZmZmQYw8+bNM8bou6zPGjdubF599VV9h/VQbm6u6dChg5kzZ47p27evGTVqlDFG/x7rswbVYud0OlmyZAmDBg0KKB80aBALFiw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       "application/papermill.record/text/plain": "<Figure size 640x480 with 1 Axes>"
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@@ -242,8 +248,8 @@
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     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_d466c tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_d466c tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_d466c tr {\n  font-size: 10px;\n}\n#T_d466c th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_d466c td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_d466c caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_d466c\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_d466c_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_d466c_level0_row0\" class=\"row_heading level0 row0\" >glass</th>\n      <td id=\"T_d466c_row0_col0\" class=\"data row0 col0\" >6%</td>\n    </tr>\n    <tr>\n      <th id=\"T_d466c_level0_row1\" class=\"row_heading level0 row1\" >metal</th>\n      <td id=\"T_d466c_row1_col0\" class=\"data row1 col0\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_d466c_level0_row2\" class=\"row_heading level0 row2\" >paper</th>\n      <td id=\"T_d466c_row2_col0\" class=\"data row2 col0\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_d466c_level0_row3\" class=\"row_heading level0 row3\" >plastic</th>\n      <td id=\"T_d466c_row3_col0\" class=\"data row3 col0\" >81%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb2c9a0>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_92ad7 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_92ad7 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_92ad7 tr {\n  font-size: 10px;\n}\n#T_92ad7 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_92ad7 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_92ad7 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_92ad7\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_92ad7_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_92ad7_level0_row0\" class=\"row_heading level0 row0\" >glass</th>\n      <td id=\"T_92ad7_row0_col0\" class=\"data row0 col0\" >6%</td>\n    </tr>\n    <tr>\n      <th id=\"T_92ad7_level0_row1\" class=\"row_heading level0 row1\" >metal</th>\n      <td id=\"T_92ad7_row1_col0\" class=\"data row1 col0\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_92ad7_level0_row2\" class=\"row_heading level0 row2\" >paper</th>\n      <td id=\"T_92ad7_row2_col0\" class=\"data row2 col0\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_92ad7_level0_row3\" class=\"row_heading level0 row3\" >plastic</th>\n      <td id=\"T_92ad7_row3_col0\" class=\"data row3 col0\" >81%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e8bafd30>"
      },
      "metadata": {
       "scrapbook": {
@@ -255,7 +261,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 3.33\n* HDI 95%: 0.1 - 8.1\n* 90% Range: 0.4 - 7.05",
+      "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 5.2\n* HDI 95%: 0.1 - 18.7\n* 90% Range: 0.2 - 16.51",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -268,7 +274,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 6.38\n* HDI 95%: 0.02 - 31.4\n* 90% Range: 0.14 - 38.0",
+      "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 7.55\n* HDI 95%: 0.02 - 42.66\n* 90% Range: 0.16 - 34.52",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -281,7 +287,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 3.63\n* HDI 95%: 0.1 - 10.4\n* 90% Range: 0.2 - 9.83",
+      "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 3.84\n* HDI 95%: 0.1 - 13.7\n* 90% Range: 0.2 - 13.03",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -307,8 +313,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_b04cc tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_b04cc tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_b04cc tr {\n  font-size: 10px;\n}\n#T_b04cc th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_b04cc td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_b04cc caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_b04cc caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_b04cc td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_b04cc\">\n  <thead>\n    <tr>\n      <th id=\"T_b04cc_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_b04cc_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_b04cc_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_b04cc_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_b04cc_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_b04cc_row0_col0\" class=\"data row0 col0\" >Fragmented plastics</td>\n      <td id=\"T_b04cc_row0_col1\" class=\"data row0 col1\" >6'796</td>\n      <td id=\"T_b04cc_row0_col2\" class=\"data row0 col2\" >0,83</td>\n      <td id=\"T_b04cc_row0_col3\" class=\"data row0 col3\" >0,15</td>\n      <td id=\"T_b04cc_row0_col4\" class=\"data row0 col4\" >0,88</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_b04cc_row1_col1\" class=\"data row1 col1\" >6'306</td>\n      <td id=\"T_b04cc_row1_col2\" class=\"data row1 col2\" >0,60</td>\n      <td id=\"T_b04cc_row1_col3\" class=\"data row1 col3\" >0,14</td>\n      <td id=\"T_b04cc_row1_col4\" class=\"data row1 col4\" >0,87</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row2_col0\" class=\"data row2 col0\" >Expanded polystyrene</td>\n      <td id=\"T_b04cc_row2_col1\" class=\"data row2 col1\" >4'319</td>\n      <td id=\"T_b04cc_row2_col2\" class=\"data row2 col2\" >0,55</td>\n      <td id=\"T_b04cc_row2_col3\" class=\"data row2 col3\" >0,10</td>\n      <td id=\"T_b04cc_row2_col4\" class=\"data row2 col4\" >0,67</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row3_col0\" class=\"data row3 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_b04cc_row3_col1\" class=\"data row3 col1\" >2'890</td>\n      <td id=\"T_b04cc_row3_col2\" class=\"data row3 col2\" >0,31</td>\n      <td id=\"T_b04cc_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_b04cc_row3_col4\" class=\"data row3 col4\" >0,88</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row4_col0\" class=\"data row4 col0\" >Industrial sheeting</td>\n      <td id=\"T_b04cc_row4_col1\" class=\"data row4 col1\" >2'031</td>\n      <td id=\"T_b04cc_row4_col2\" class=\"data row4 col2\" >0,24</td>\n      <td id=\"T_b04cc_row4_col3\" class=\"data row4 col3\" >0,05</td>\n      <td id=\"T_b04cc_row4_col4\" class=\"data row4 col4\" >0,69</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row5_col0\" class=\"data row5 col0\" >Industrial pellets (nurdles)</td>\n      <td id=\"T_b04cc_row5_col1\" class=\"data row5 col1\" >1'715</td>\n      <td id=\"T_b04cc_row5_col2\" class=\"data row5 col2\" >0,18</td>\n      <td id=\"T_b04cc_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_b04cc_row5_col4\" class=\"data row5 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row6_col0\" class=\"data row6 col0\" >plastic caps, lid rings: G21, G22, G23, G24</td>\n      <td id=\"T_b04cc_row6_col1\" class=\"data row6 col1\" >1'704</td>\n      <td id=\"T_b04cc_row6_col2\" class=\"data row6 col2\" >0,19</td>\n      <td id=\"T_b04cc_row6_col3\" class=\"data row6 col3\" >0,04</td>\n      <td id=\"T_b04cc_row6_col4\" class=\"data row6 col4\" >0,71</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row7_col0\" class=\"data row7 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_b04cc_row7_col1\" class=\"data row7 col1\" >1'502</td>\n      <td id=\"T_b04cc_row7_col2\" class=\"data row7 col2\" >0,19</td>\n      <td id=\"T_b04cc_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_b04cc_row7_col4\" class=\"data row7 col4\" >0,65</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row8_col0\" class=\"data row8 col0\" >Foam packaging/insulation/polyurethane</td>\n      <td id=\"T_b04cc_row8_col1\" class=\"data row8 col1\" >1'435</td>\n      <td id=\"T_b04cc_row8_col2\" class=\"data row8 col2\" >0,09</td>\n      <td id=\"T_b04cc_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_b04cc_row8_col4\" class=\"data row8 col4\" >0,87</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row9_col0\" class=\"data row9 col0\" >Cotton bud/swab sticks</td>\n      <td id=\"T_b04cc_row9_col1\" class=\"data row9 col1\" >1'385</td>\n      <td id=\"T_b04cc_row9_col2\" class=\"data row9 col2\" >0,16</td>\n      <td id=\"T_b04cc_row9_col3\" class=\"data row9 col3\" >0,03</td>\n      <td id=\"T_b04cc_row9_col4\" class=\"data row9 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row10_col0\" class=\"data row10 col0\" >Plastic construction waste</td>\n      <td id=\"T_b04cc_row10_col1\" class=\"data row10 col1\" >836</td>\n      <td id=\"T_b04cc_row10_col2\" class=\"data row10 col2\" >0,09</td>\n      <td id=\"T_b04cc_row10_col3\" class=\"data row10 col3\" >0,02</td>\n      <td id=\"T_b04cc_row10_col4\" class=\"data row10 col4\" >0,53</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row11_col0\" class=\"data row11 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_b04cc_row11_col1\" class=\"data row11 col1\" >551</td>\n      <td id=\"T_b04cc_row11_col2\" class=\"data row11 col2\" >0,05</td>\n      <td id=\"T_b04cc_row11_col3\" class=\"data row11 col3\" >0,01</td>\n      <td id=\"T_b04cc_row11_col4\" class=\"data row11 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row12_col0\" class=\"data row12 col0\" >Tobacco; plastic packaging, containers</td>\n      <td id=\"T_b04cc_row12_col1\" class=\"data row12 col1\" >529</td>\n      <td id=\"T_b04cc_row12_col2\" class=\"data row12 col2\" >0,06</td>\n      <td id=\"T_b04cc_row12_col3\" class=\"data row12 col3\" >0,01</td>\n      <td id=\"T_b04cc_row12_col4\" class=\"data row12 col4\" >0,50</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row13_col0\" class=\"data row13 col0\" >Foil wrappers, aluminum foil</td>\n      <td id=\"T_b04cc_row13_col1\" class=\"data row13 col1\" >386</td>\n      <td id=\"T_b04cc_row13_col2\" class=\"data row13 col2\" >0,04</td>\n      <td id=\"T_b04cc_row13_col3\" class=\"data row13 col3\" >0,01</td>\n      <td id=\"T_b04cc_row13_col4\" class=\"data row13 col4\" >0,50</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row14_col0\" class=\"data row14 col0\" >Straws and stirrers</td>\n      <td id=\"T_b04cc_row14_col1\" class=\"data row14 col1\" >382</td>\n      <td id=\"T_b04cc_row14_col2\" class=\"data row14 col2\" >0,04</td>\n      <td id=\"T_b04cc_row14_col3\" class=\"data row14 col3\" >0,01</td>\n      <td id=\"T_b04cc_row14_col4\" class=\"data row14 col4\" >0,50</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb18f40>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_3adc8 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_3adc8 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_3adc8 tr {\n  font-size: 10px;\n}\n#T_3adc8 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_3adc8 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_3adc8 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_3adc8 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_3adc8 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_3adc8\">\n  <thead>\n    <tr>\n      <th id=\"T_3adc8_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_3adc8_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_3adc8_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_3adc8_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_3adc8_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_3adc8_row0_col0\" class=\"data row0 col0\" >Fragmented plastics</td>\n      <td id=\"T_3adc8_row0_col1\" class=\"data row0 col1\" >6'796</td>\n      <td id=\"T_3adc8_row0_col2\" class=\"data row0 col2\" >0,83</td>\n      <td id=\"T_3adc8_row0_col3\" class=\"data row0 col3\" >0,15</td>\n      <td id=\"T_3adc8_row0_col4\" class=\"data row0 col4\" >0,88</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_3adc8_row1_col1\" class=\"data row1 col1\" >6'306</td>\n      <td id=\"T_3adc8_row1_col2\" class=\"data row1 col2\" >0,60</td>\n      <td id=\"T_3adc8_row1_col3\" class=\"data row1 col3\" >0,14</td>\n      <td id=\"T_3adc8_row1_col4\" class=\"data row1 col4\" >0,87</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row2_col0\" class=\"data row2 col0\" >Expanded polystyrene</td>\n      <td id=\"T_3adc8_row2_col1\" class=\"data row2 col1\" >4'319</td>\n      <td id=\"T_3adc8_row2_col2\" class=\"data row2 col2\" >0,55</td>\n      <td id=\"T_3adc8_row2_col3\" class=\"data row2 col3\" >0,10</td>\n      <td id=\"T_3adc8_row2_col4\" class=\"data row2 col4\" >0,67</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row3_col0\" class=\"data row3 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_3adc8_row3_col1\" class=\"data row3 col1\" >2'890</td>\n      <td id=\"T_3adc8_row3_col2\" class=\"data row3 col2\" >0,31</td>\n      <td id=\"T_3adc8_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_3adc8_row3_col4\" class=\"data row3 col4\" >0,88</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row4_col0\" class=\"data row4 col0\" >Industrial sheeting</td>\n      <td id=\"T_3adc8_row4_col1\" class=\"data row4 col1\" >2'031</td>\n      <td id=\"T_3adc8_row4_col2\" class=\"data row4 col2\" >0,24</td>\n      <td id=\"T_3adc8_row4_col3\" class=\"data row4 col3\" >0,05</td>\n      <td id=\"T_3adc8_row4_col4\" class=\"data row4 col4\" >0,69</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row5_col0\" class=\"data row5 col0\" >Industrial pellets (nurdles)</td>\n      <td id=\"T_3adc8_row5_col1\" class=\"data row5 col1\" >1'715</td>\n      <td id=\"T_3adc8_row5_col2\" class=\"data row5 col2\" >0,18</td>\n      <td id=\"T_3adc8_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_3adc8_row5_col4\" class=\"data row5 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row6_col0\" class=\"data row6 col0\" >plastic caps, lid rings: G21, G22, G23, G24</td>\n      <td id=\"T_3adc8_row6_col1\" class=\"data row6 col1\" >1'704</td>\n      <td id=\"T_3adc8_row6_col2\" class=\"data row6 col2\" >0,19</td>\n      <td id=\"T_3adc8_row6_col3\" class=\"data row6 col3\" >0,04</td>\n      <td id=\"T_3adc8_row6_col4\" class=\"data row6 col4\" >0,71</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row7_col0\" class=\"data row7 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_3adc8_row7_col1\" class=\"data row7 col1\" >1'502</td>\n      <td id=\"T_3adc8_row7_col2\" class=\"data row7 col2\" >0,19</td>\n      <td id=\"T_3adc8_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_3adc8_row7_col4\" class=\"data row7 col4\" >0,65</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row8_col0\" class=\"data row8 col0\" >Foam packaging/insulation/polyurethane</td>\n      <td id=\"T_3adc8_row8_col1\" class=\"data row8 col1\" >1'435</td>\n      <td id=\"T_3adc8_row8_col2\" class=\"data row8 col2\" >0,09</td>\n      <td id=\"T_3adc8_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_3adc8_row8_col4\" class=\"data row8 col4\" >0,87</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row9_col0\" class=\"data row9 col0\" >Cotton bud/swab sticks</td>\n      <td id=\"T_3adc8_row9_col1\" class=\"data row9 col1\" >1'385</td>\n      <td id=\"T_3adc8_row9_col2\" class=\"data row9 col2\" >0,16</td>\n      <td id=\"T_3adc8_row9_col3\" class=\"data row9 col3\" >0,03</td>\n      <td id=\"T_3adc8_row9_col4\" class=\"data row9 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row10_col0\" class=\"data row10 col0\" >Plastic construction waste</td>\n      <td id=\"T_3adc8_row10_col1\" class=\"data row10 col1\" >836</td>\n      <td id=\"T_3adc8_row10_col2\" class=\"data row10 col2\" >0,09</td>\n      <td id=\"T_3adc8_row10_col3\" class=\"data row10 col3\" >0,02</td>\n      <td id=\"T_3adc8_row10_col4\" class=\"data row10 col4\" >0,53</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row11_col0\" class=\"data row11 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_3adc8_row11_col1\" class=\"data row11 col1\" >551</td>\n      <td id=\"T_3adc8_row11_col2\" class=\"data row11 col2\" >0,05</td>\n      <td id=\"T_3adc8_row11_col3\" class=\"data row11 col3\" >0,01</td>\n      <td id=\"T_3adc8_row11_col4\" class=\"data row11 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row12_col0\" class=\"data row12 col0\" >Tobacco; plastic packaging, containers</td>\n      <td id=\"T_3adc8_row12_col1\" class=\"data row12 col1\" >529</td>\n      <td id=\"T_3adc8_row12_col2\" class=\"data row12 col2\" >0,06</td>\n      <td id=\"T_3adc8_row12_col3\" class=\"data row12 col3\" >0,01</td>\n      <td id=\"T_3adc8_row12_col4\" class=\"data row12 col4\" >0,50</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row13_col0\" class=\"data row13 col0\" >Foil wrappers, aluminum foil</td>\n      <td id=\"T_3adc8_row13_col1\" class=\"data row13 col1\" >386</td>\n      <td id=\"T_3adc8_row13_col2\" class=\"data row13 col2\" >0,04</td>\n      <td id=\"T_3adc8_row13_col3\" class=\"data row13 col3\" >0,01</td>\n      <td id=\"T_3adc8_row13_col4\" class=\"data row13 col4\" >0,50</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row14_col0\" class=\"data row14 col0\" >Straws and stirrers</td>\n      <td id=\"T_3adc8_row14_col1\" class=\"data row14 col1\" >382</td>\n      <td id=\"T_3adc8_row14_col2\" class=\"data row14 col2\" >0,04</td>\n      <td id=\"T_3adc8_row14_col3\" class=\"data row14 col3\" >0,01</td>\n      <td id=\"T_3adc8_row14_col4\" class=\"data row14 col4\" >0,50</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e657d229580>"
      },
      "metadata": {
       "scrapbook": {
@@ -498,7 +504,7 @@
     },
     {
      "data": {
-      "application/papermill.record/image/png": 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",
       "application/papermill.record/text/plain": "<Figure size 640x480 with 1 Axes>"
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@@ -524,8 +530,8 @@
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     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_0b524 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_0b524 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_0b524 tr {\n  font-size: 10px;\n}\n#T_0b524 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_0b524 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_0b524 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_0b524\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_0b524_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_0b524_level0_row0\" class=\"row_heading level0 row0\" >glass</th>\n      <td id=\"T_0b524_row0_col0\" class=\"data row0 col0\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_0b524_level0_row1\" class=\"row_heading level0 row1\" >metal</th>\n      <td id=\"T_0b524_row1_col0\" class=\"data row1 col0\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_0b524_level0_row2\" class=\"row_heading level0 row2\" >paper</th>\n      <td id=\"T_0b524_row2_col0\" class=\"data row2 col0\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_0b524_level0_row3\" class=\"row_heading level0 row3\" >plastic</th>\n      <td id=\"T_0b524_row3_col0\" class=\"data row3 col0\" >83%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb21c40>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_020a9 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_020a9 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_020a9 tr {\n  font-size: 10px;\n}\n#T_020a9 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_020a9 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_020a9 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_020a9\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_020a9_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_020a9_level0_row0\" class=\"row_heading level0 row0\" >glass</th>\n      <td id=\"T_020a9_row0_col0\" class=\"data row0 col0\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_020a9_level0_row1\" class=\"row_heading level0 row1\" >metal</th>\n      <td id=\"T_020a9_row1_col0\" class=\"data row1 col0\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_020a9_level0_row2\" class=\"row_heading level0 row2\" >paper</th>\n      <td id=\"T_020a9_row2_col0\" class=\"data row2 col0\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_020a9_level0_row3\" class=\"row_heading level0 row3\" >plastic</th>\n      <td id=\"T_020a9_row3_col0\" class=\"data row3 col0\" >83%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e62423a0>"
      },
      "metadata": {
       "scrapbook": {
@@ -537,7 +543,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 4.24\n* HDI 95%: 0.3 - 13.0\n* 90% Range: 0.5 - 13.0",
+      "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 3.97\n* HDI 95%: 0.3 - 10.7\n* 90% Range: 0.3 - 10.7",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -550,7 +556,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 10.81\n* HDI 95%: 0.14 - 48.54\n* 90% Range: 0.55 - 51.72",
+      "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 8.0\n* HDI 95%: 0.14 - 58.18\n* 90% Range: 0.64 - 54.9",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -563,7 +569,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 5.53\n* HDI 95%: 0.4 - 18.7\n* 90% Range: 0.7 - 17.94",
+      "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 4.6\n* HDI 95%: 0.1 - 15.6\n* 90% Range: 0.5 - 14.84",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -589,8 +595,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_0ece9 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_0ece9 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_0ece9 tr {\n  font-size: 10px;\n}\n#T_0ece9 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_0ece9 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_0ece9 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_0ece9 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_0ece9 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_0ece9\">\n  <thead>\n    <tr>\n      <th id=\"T_0ece9_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_0ece9_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_0ece9_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_0ece9_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_0ece9_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_0ece9_row0_col0\" class=\"data row0 col0\" >Fragmented plastics</td>\n      <td id=\"T_0ece9_row0_col1\" class=\"data row0 col1\" >6'735</td>\n      <td id=\"T_0ece9_row0_col2\" class=\"data row0 col2\" >0,94</td>\n      <td id=\"T_0ece9_row0_col3\" class=\"data row0 col3\" >0,16</td>\n      <td id=\"T_0ece9_row0_col4\" class=\"data row0 col4\" >0,93</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_0ece9_row1_col1\" class=\"data row1 col1\" >6'081</td>\n      <td id=\"T_0ece9_row1_col2\" class=\"data row1 col2\" >0,66</td>\n      <td id=\"T_0ece9_row1_col3\" class=\"data row1 col3\" >0,14</td>\n      <td id=\"T_0ece9_row1_col4\" class=\"data row1 col4\" >0,91</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row2_col0\" class=\"data row2 col0\" >Expanded polystyrene</td>\n      <td id=\"T_0ece9_row2_col1\" class=\"data row2 col1\" >4'302</td>\n      <td id=\"T_0ece9_row2_col2\" class=\"data row2 col2\" >0,63</td>\n      <td id=\"T_0ece9_row2_col3\" class=\"data row2 col3\" >0,10</td>\n      <td id=\"T_0ece9_row2_col4\" class=\"data row2 col4\" >0,75</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row3_col0\" class=\"data row3 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_0ece9_row3_col1\" class=\"data row3 col1\" >2'789</td>\n      <td id=\"T_0ece9_row3_col2\" class=\"data row3 col2\" >0,34</td>\n      <td id=\"T_0ece9_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_0ece9_row3_col4\" class=\"data row3 col4\" >0,91</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row4_col0\" class=\"data row4 col0\" >Industrial sheeting</td>\n      <td id=\"T_0ece9_row4_col1\" class=\"data row4 col1\" >1'882</td>\n      <td id=\"T_0ece9_row4_col2\" class=\"data row4 col2\" >0,26</td>\n      <td id=\"T_0ece9_row4_col3\" class=\"data row4 col3\" >0,04</td>\n      <td id=\"T_0ece9_row4_col4\" class=\"data row4 col4\" >0,74</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row5_col0\" class=\"data row5 col0\" >Industrial pellets (nurdles)</td>\n      <td id=\"T_0ece9_row5_col1\" class=\"data row5 col1\" >1'705</td>\n      <td id=\"T_0ece9_row5_col2\" class=\"data row5 col2\" >0,21</td>\n      <td id=\"T_0ece9_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_0ece9_row5_col4\" class=\"data row5 col4\" >0,40</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row6_col0\" class=\"data row6 col0\" >plastic caps, lid rings: G21, G22, G23, G24</td>\n      <td id=\"T_0ece9_row6_col1\" class=\"data row6 col1\" >1'677</td>\n      <td id=\"T_0ece9_row6_col2\" class=\"data row6 col2\" >0,21</td>\n      <td id=\"T_0ece9_row6_col3\" class=\"data row6 col3\" >0,04</td>\n      <td id=\"T_0ece9_row6_col4\" class=\"data row6 col4\" >0,78</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row7_col0\" class=\"data row7 col0\" >Foam packaging/insulation/polyurethane</td>\n      <td id=\"T_0ece9_row7_col1\" class=\"data row7 col1\" >1'422</td>\n      <td id=\"T_0ece9_row7_col2\" class=\"data row7 col2\" >0,10</td>\n      <td id=\"T_0ece9_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_0ece9_row7_col4\" class=\"data row7 col4\" >0,98</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row8_col0\" class=\"data row8 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_0ece9_row8_col1\" class=\"data row8 col1\" >1'400</td>\n      <td id=\"T_0ece9_row8_col2\" class=\"data row8 col2\" >0,20</td>\n      <td id=\"T_0ece9_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_0ece9_row8_col4\" class=\"data row8 col4\" >0,69</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row9_col0\" class=\"data row9 col0\" >Cotton bud/swab sticks</td>\n      <td id=\"T_0ece9_row9_col1\" class=\"data row9 col1\" >1'379</td>\n      <td id=\"T_0ece9_row9_col2\" class=\"data row9 col2\" >0,18</td>\n      <td id=\"T_0ece9_row9_col3\" class=\"data row9 col3\" >0,03</td>\n      <td id=\"T_0ece9_row9_col4\" class=\"data row9 col4\" >0,64</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row10_col0\" class=\"data row10 col0\" >Plastic construction waste</td>\n      <td id=\"T_0ece9_row10_col1\" class=\"data row10 col1\" >804</td>\n      <td id=\"T_0ece9_row10_col2\" class=\"data row10 col2\" >0,10</td>\n      <td id=\"T_0ece9_row10_col3\" class=\"data row10 col3\" >0,02</td>\n      <td id=\"T_0ece9_row10_col4\" class=\"data row10 col4\" >0,59</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row11_col0\" class=\"data row11 col0\" >Tobacco; plastic packaging, containers</td>\n      <td id=\"T_0ece9_row11_col1\" class=\"data row11 col1\" >522</td>\n      <td id=\"T_0ece9_row11_col2\" class=\"data row11 col2\" >0,07</td>\n      <td id=\"T_0ece9_row11_col3\" class=\"data row11 col3\" >0,01</td>\n      <td id=\"T_0ece9_row11_col4\" class=\"data row11 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row12_col0\" class=\"data row12 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_0ece9_row12_col1\" class=\"data row12 col1\" >488</td>\n      <td id=\"T_0ece9_row12_col2\" class=\"data row12 col2\" >0,05</td>\n      <td id=\"T_0ece9_row12_col3\" class=\"data row12 col3\" >0,01</td>\n      <td id=\"T_0ece9_row12_col4\" class=\"data row12 col4\" >0,60</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row13_col0\" class=\"data row13 col0\" >Straws and stirrers</td>\n      <td id=\"T_0ece9_row13_col1\" class=\"data row13 col1\" >374</td>\n      <td id=\"T_0ece9_row13_col2\" class=\"data row13 col2\" >0,05</td>\n      <td id=\"T_0ece9_row13_col3\" class=\"data row13 col3\" >0,01</td>\n      <td id=\"T_0ece9_row13_col4\" class=\"data row13 col4\" >0,54</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row14_col0\" class=\"data row14 col0\" >Foil wrappers, aluminum foil</td>\n      <td id=\"T_0ece9_row14_col1\" class=\"data row14 col1\" >371</td>\n      <td id=\"T_0ece9_row14_col2\" class=\"data row14 col2\" >0,04</td>\n      <td id=\"T_0ece9_row14_col3\" class=\"data row14 col3\" >0,01</td>\n      <td id=\"T_0ece9_row14_col4\" class=\"data row14 col4\" >0,53</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row15_col0\" class=\"data row15 col0\" >Lollypop sticks</td>\n      <td id=\"T_0ece9_row15_col1\" class=\"data row15 col1\" >354</td>\n      <td id=\"T_0ece9_row15_col2\" class=\"data row15 col2\" >0,04</td>\n      <td id=\"T_0ece9_row15_col3\" class=\"data row15 col3\" >0,01</td>\n      <td id=\"T_0ece9_row15_col4\" class=\"data row15 col4\" >0,52</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a534ea60>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_c1178 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_c1178 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_c1178 tr {\n  font-size: 10px;\n}\n#T_c1178 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_c1178 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_c1178 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_c1178 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_c1178 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_c1178\">\n  <thead>\n    <tr>\n      <th id=\"T_c1178_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_c1178_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_c1178_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_c1178_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_c1178_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c1178_row0_col0\" class=\"data row0 col0\" >Fragmented plastics</td>\n      <td id=\"T_c1178_row0_col1\" class=\"data row0 col1\" >6'735</td>\n      <td id=\"T_c1178_row0_col2\" class=\"data row0 col2\" >0,94</td>\n      <td id=\"T_c1178_row0_col3\" class=\"data row0 col3\" >0,16</td>\n      <td id=\"T_c1178_row0_col4\" class=\"data row0 col4\" >0,93</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_c1178_row1_col1\" class=\"data row1 col1\" >6'081</td>\n      <td id=\"T_c1178_row1_col2\" class=\"data row1 col2\" >0,66</td>\n      <td id=\"T_c1178_row1_col3\" class=\"data row1 col3\" >0,14</td>\n      <td id=\"T_c1178_row1_col4\" class=\"data row1 col4\" >0,91</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row2_col0\" class=\"data row2 col0\" >Expanded polystyrene</td>\n      <td id=\"T_c1178_row2_col1\" class=\"data row2 col1\" >4'302</td>\n      <td id=\"T_c1178_row2_col2\" class=\"data row2 col2\" >0,63</td>\n      <td id=\"T_c1178_row2_col3\" class=\"data row2 col3\" >0,10</td>\n      <td id=\"T_c1178_row2_col4\" class=\"data row2 col4\" >0,75</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row3_col0\" class=\"data row3 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_c1178_row3_col1\" class=\"data row3 col1\" >2'789</td>\n      <td id=\"T_c1178_row3_col2\" class=\"data row3 col2\" >0,34</td>\n      <td id=\"T_c1178_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_c1178_row3_col4\" class=\"data row3 col4\" >0,91</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row4_col0\" class=\"data row4 col0\" >Industrial sheeting</td>\n      <td id=\"T_c1178_row4_col1\" class=\"data row4 col1\" >1'882</td>\n      <td id=\"T_c1178_row4_col2\" class=\"data row4 col2\" >0,26</td>\n      <td id=\"T_c1178_row4_col3\" class=\"data row4 col3\" >0,04</td>\n      <td id=\"T_c1178_row4_col4\" class=\"data row4 col4\" >0,74</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row5_col0\" class=\"data row5 col0\" >Industrial pellets (nurdles)</td>\n      <td id=\"T_c1178_row5_col1\" class=\"data row5 col1\" >1'705</td>\n      <td id=\"T_c1178_row5_col2\" class=\"data row5 col2\" >0,21</td>\n      <td id=\"T_c1178_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_c1178_row5_col4\" class=\"data row5 col4\" >0,40</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row6_col0\" class=\"data row6 col0\" >plastic caps, lid rings: G21, G22, G23, G24</td>\n      <td id=\"T_c1178_row6_col1\" class=\"data row6 col1\" >1'677</td>\n      <td id=\"T_c1178_row6_col2\" class=\"data row6 col2\" >0,21</td>\n      <td id=\"T_c1178_row6_col3\" class=\"data row6 col3\" >0,04</td>\n      <td id=\"T_c1178_row6_col4\" class=\"data row6 col4\" >0,78</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row7_col0\" class=\"data row7 col0\" >Foam packaging/insulation/polyurethane</td>\n      <td id=\"T_c1178_row7_col1\" class=\"data row7 col1\" >1'422</td>\n      <td id=\"T_c1178_row7_col2\" class=\"data row7 col2\" >0,10</td>\n      <td id=\"T_c1178_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_c1178_row7_col4\" class=\"data row7 col4\" >0,98</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row8_col0\" class=\"data row8 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_c1178_row8_col1\" class=\"data row8 col1\" >1'400</td>\n      <td id=\"T_c1178_row8_col2\" class=\"data row8 col2\" >0,20</td>\n      <td id=\"T_c1178_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_c1178_row8_col4\" class=\"data row8 col4\" >0,69</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row9_col0\" class=\"data row9 col0\" >Cotton bud/swab sticks</td>\n      <td id=\"T_c1178_row9_col1\" class=\"data row9 col1\" >1'379</td>\n      <td id=\"T_c1178_row9_col2\" class=\"data row9 col2\" >0,18</td>\n      <td id=\"T_c1178_row9_col3\" class=\"data row9 col3\" >0,03</td>\n      <td id=\"T_c1178_row9_col4\" class=\"data row9 col4\" >0,64</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row10_col0\" class=\"data row10 col0\" >Plastic construction waste</td>\n      <td id=\"T_c1178_row10_col1\" class=\"data row10 col1\" >804</td>\n      <td id=\"T_c1178_row10_col2\" class=\"data row10 col2\" >0,10</td>\n      <td id=\"T_c1178_row10_col3\" class=\"data row10 col3\" >0,02</td>\n      <td id=\"T_c1178_row10_col4\" class=\"data row10 col4\" >0,59</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row11_col0\" class=\"data row11 col0\" >Tobacco; plastic packaging, containers</td>\n      <td id=\"T_c1178_row11_col1\" class=\"data row11 col1\" >522</td>\n      <td id=\"T_c1178_row11_col2\" class=\"data row11 col2\" >0,07</td>\n      <td id=\"T_c1178_row11_col3\" class=\"data row11 col3\" >0,01</td>\n      <td id=\"T_c1178_row11_col4\" class=\"data row11 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row12_col0\" class=\"data row12 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_c1178_row12_col1\" class=\"data row12 col1\" >488</td>\n      <td id=\"T_c1178_row12_col2\" class=\"data row12 col2\" >0,05</td>\n      <td id=\"T_c1178_row12_col3\" class=\"data row12 col3\" >0,01</td>\n      <td id=\"T_c1178_row12_col4\" class=\"data row12 col4\" >0,60</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row13_col0\" class=\"data row13 col0\" >Straws and stirrers</td>\n      <td id=\"T_c1178_row13_col1\" class=\"data row13 col1\" >374</td>\n      <td id=\"T_c1178_row13_col2\" class=\"data row13 col2\" >0,05</td>\n      <td id=\"T_c1178_row13_col3\" class=\"data row13 col3\" >0,01</td>\n      <td id=\"T_c1178_row13_col4\" class=\"data row13 col4\" >0,54</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row14_col0\" class=\"data row14 col0\" >Foil wrappers, aluminum foil</td>\n      <td id=\"T_c1178_row14_col1\" class=\"data row14 col1\" >371</td>\n      <td id=\"T_c1178_row14_col2\" class=\"data row14 col2\" >0,04</td>\n      <td id=\"T_c1178_row14_col3\" class=\"data row14 col3\" >0,01</td>\n      <td id=\"T_c1178_row14_col4\" class=\"data row14 col4\" >0,53</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row15_col0\" class=\"data row15 col0\" >Lollypop sticks</td>\n      <td id=\"T_c1178_row15_col1\" class=\"data row15 col1\" >354</td>\n      <td id=\"T_c1178_row15_col2\" class=\"data row15 col2\" >0,04</td>\n      <td id=\"T_c1178_row15_col3\" class=\"data row15 col3\" >0,01</td>\n      <td id=\"T_c1178_row15_col4\" class=\"data row15 col4\" >0,52</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e39df4f0>"
      },
      "metadata": {
       "scrapbook": {
@@ -785,6 +791,9 @@
    "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -808,7 +817,7 @@
     },
     {
      "data": {
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",
       "application/papermill.record/text/plain": "<Figure size 640x480 with 1 Axes>"
      },
      "metadata": {
@@ -834,8 +843,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_a28fd tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_a28fd tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_a28fd tr {\n  font-size: 10px;\n}\n#T_a28fd th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_a28fd td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_a28fd caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_a28fd\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a28fd_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a28fd_level0_row0\" class=\"row_heading level0 row0\" >cloth</th>\n      <td id=\"T_a28fd_row0_col0\" class=\"data row0 col0\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row1\" class=\"row_heading level0 row1\" >glass</th>\n      <td id=\"T_a28fd_row1_col0\" class=\"data row1 col0\" >7%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row2\" class=\"row_heading level0 row2\" >metal</th>\n      <td id=\"T_a28fd_row2_col0\" class=\"data row2 col0\" >9%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row3\" class=\"row_heading level0 row3\" >paper</th>\n      <td id=\"T_a28fd_row3_col0\" class=\"data row3 col0\" >7%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row4\" class=\"row_heading level0 row4\" >plastic</th>\n      <td id=\"T_a28fd_row4_col0\" class=\"data row4 col0\" >65%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row5\" class=\"row_heading level0 row5\" >wood</th>\n      <td id=\"T_a28fd_row5_col0\" class=\"data row5 col0\" >2%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a53f4bb0>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_39019 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_39019 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_39019 tr {\n  font-size: 10px;\n}\n#T_39019 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_39019 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_39019 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_39019\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_39019_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_39019_level0_row0\" class=\"row_heading level0 row0\" >cloth</th>\n      <td id=\"T_39019_row0_col0\" class=\"data row0 col0\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row1\" class=\"row_heading level0 row1\" >glass</th>\n      <td id=\"T_39019_row1_col0\" class=\"data row1 col0\" >7%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row2\" class=\"row_heading level0 row2\" >metal</th>\n      <td id=\"T_39019_row2_col0\" class=\"data row2 col0\" >9%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row3\" class=\"row_heading level0 row3\" >paper</th>\n      <td id=\"T_39019_row3_col0\" class=\"data row3 col0\" >7%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row4\" class=\"row_heading level0 row4\" >plastic</th>\n      <td id=\"T_39019_row4_col0\" class=\"data row4 col0\" >65%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row5\" class=\"row_heading level0 row5\" >wood</th>\n      <td id=\"T_39019_row5_col0\" class=\"data row5 col0\" >2%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e6257160>"
      },
      "metadata": {
       "scrapbook": {
@@ -847,7 +856,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 1.24\n* HDI 95%: 0.1 - 5.0\n* 90% Range: 0.1 - 4.62",
+      "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 1.45\n* HDI 95%: 0.1 - 5.5\n* 90% Range: 0.1 - 4.64",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -860,7 +869,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 1.76\n* HDI 95%: 0.02 - 10.96\n* 90% Range: 0.1 - 4.93",
+      "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 1.45\n* HDI 95%: 0.02 - 4.55\n* 90% Range: 0.1 - 6.08",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -873,7 +882,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 1.16\n* HDI 95%: 0.1 - 3.6\n* 90% Range: 0.1 - 3.41",
+      "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 1.14\n* HDI 95%: 0.1 - 3.3\n* 90% Range: 0.1 - 3.11",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -899,8 +908,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_0a3c6 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_0a3c6 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_0a3c6 tr {\n  font-size: 10px;\n}\n#T_0a3c6 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_0a3c6 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_0a3c6 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_0a3c6 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_0a3c6 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_0a3c6\">\n  <thead>\n    <tr>\n      <th id=\"T_0a3c6_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_0a3c6_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_0a3c6_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_0a3c6_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_0a3c6_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_0a3c6_row0_col0\" class=\"data row0 col0\" >Diapers - wipes</td>\n      <td id=\"T_0a3c6_row0_col1\" class=\"data row0 col1\" >299</td>\n      <td id=\"T_0a3c6_row0_col2\" class=\"data row0 col2\" >0,21</td>\n      <td id=\"T_0a3c6_row0_col3\" class=\"data row0 col3\" >0,16</td>\n      <td id=\"T_0a3c6_row0_col4\" class=\"data row0 col4\" >0,30</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_0a3c6_row1_col1\" class=\"data row1 col1\" >225</td>\n      <td id=\"T_0a3c6_row1_col2\" class=\"data row1 col2\" >0,21</td>\n      <td id=\"T_0a3c6_row1_col3\" class=\"data row1 col3\" >0,12</td>\n      <td id=\"T_0a3c6_row1_col4\" class=\"data row1 col4\" >0,64</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row2_col0\" class=\"data row2 col0\" >Industrial sheeting</td>\n      <td id=\"T_0a3c6_row2_col1\" class=\"data row2 col1\" >149</td>\n      <td id=\"T_0a3c6_row2_col2\" class=\"data row2 col2\" >0,09</td>\n      <td id=\"T_0a3c6_row2_col3\" class=\"data row2 col3\" >0,08</td>\n      <td id=\"T_0a3c6_row2_col4\" class=\"data row2 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row3_col0\" class=\"data row3 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_0a3c6_row3_col1\" class=\"data row3 col1\" >102</td>\n      <td id=\"T_0a3c6_row3_col2\" class=\"data row3 col2\" >0,11</td>\n      <td id=\"T_0a3c6_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_0a3c6_row3_col4\" class=\"data row3 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row4_col0\" class=\"data row4 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_0a3c6_row4_col1\" class=\"data row4 col1\" >101</td>\n      <td id=\"T_0a3c6_row4_col2\" class=\"data row4 col2\" >0,11</td>\n      <td id=\"T_0a3c6_row4_col3\" class=\"data row4 col3\" >0,05</td>\n      <td id=\"T_0a3c6_row4_col4\" class=\"data row4 col4\" >0,67</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row5_col0\" class=\"data row5 col0\" >Packaging films nonfood or unknown</td>\n      <td id=\"T_0a3c6_row5_col1\" class=\"data row5 col1\" >68</td>\n      <td id=\"T_0a3c6_row5_col2\" class=\"data row5 col2\" >0,09</td>\n      <td id=\"T_0a3c6_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_0a3c6_row5_col4\" class=\"data row5 col4\" >0,27</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row6_col0\" class=\"data row6 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_0a3c6_row6_col1\" class=\"data row6 col1\" >63</td>\n      <td id=\"T_0a3c6_row6_col2\" class=\"data row6 col2\" >0,08</td>\n      <td id=\"T_0a3c6_row6_col3\" class=\"data row6 col3\" >0,03</td>\n      <td id=\"T_0a3c6_row6_col4\" class=\"data row6 col4\" >0,30</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row7_col0\" class=\"data row7 col0\" >Fragmented plastics</td>\n      <td id=\"T_0a3c6_row7_col1\" class=\"data row7 col1\" >61</td>\n      <td id=\"T_0a3c6_row7_col2\" class=\"data row7 col2\" >0,05</td>\n      <td id=\"T_0a3c6_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_0a3c6_row7_col4\" class=\"data row7 col4\" >0,52</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row8_col0\" class=\"data row8 col0\" >Bags; plastic shopping/carrier/grocery and pieces</td>\n      <td id=\"T_0a3c6_row8_col1\" class=\"data row8 col1\" >55</td>\n      <td id=\"T_0a3c6_row8_col2\" class=\"data row8 col2\" >0,05</td>\n      <td id=\"T_0a3c6_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_0a3c6_row8_col4\" class=\"data row8 col4\" >0,24</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row9_col0\" class=\"data row9 col0\" >Construction material; bricks, pipes, cement</td>\n      <td id=\"T_0a3c6_row9_col1\" class=\"data row9 col1\" >42</td>\n      <td id=\"T_0a3c6_row9_col2\" class=\"data row9 col2\" >0,02</td>\n      <td id=\"T_0a3c6_row9_col3\" class=\"data row9 col3\" >0,02</td>\n      <td id=\"T_0a3c6_row9_col4\" class=\"data row9 col4\" >0,12</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a3fd0c10>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_22ca4 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_22ca4 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_22ca4 tr {\n  font-size: 10px;\n}\n#T_22ca4 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_22ca4 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_22ca4 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_22ca4 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_22ca4 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_22ca4\">\n  <thead>\n    <tr>\n      <th id=\"T_22ca4_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_22ca4_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_22ca4_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_22ca4_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_22ca4_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_22ca4_row0_col0\" class=\"data row0 col0\" >Diapers - wipes</td>\n      <td id=\"T_22ca4_row0_col1\" class=\"data row0 col1\" >299</td>\n      <td id=\"T_22ca4_row0_col2\" class=\"data row0 col2\" >0,21</td>\n      <td id=\"T_22ca4_row0_col3\" class=\"data row0 col3\" >0,16</td>\n      <td id=\"T_22ca4_row0_col4\" class=\"data row0 col4\" >0,30</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_22ca4_row1_col1\" class=\"data row1 col1\" >225</td>\n      <td id=\"T_22ca4_row1_col2\" class=\"data row1 col2\" >0,21</td>\n      <td id=\"T_22ca4_row1_col3\" class=\"data row1 col3\" >0,12</td>\n      <td id=\"T_22ca4_row1_col4\" class=\"data row1 col4\" >0,64</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row2_col0\" class=\"data row2 col0\" >Industrial sheeting</td>\n      <td id=\"T_22ca4_row2_col1\" class=\"data row2 col1\" >149</td>\n      <td id=\"T_22ca4_row2_col2\" class=\"data row2 col2\" >0,09</td>\n      <td id=\"T_22ca4_row2_col3\" class=\"data row2 col3\" >0,08</td>\n      <td id=\"T_22ca4_row2_col4\" class=\"data row2 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row3_col0\" class=\"data row3 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_22ca4_row3_col1\" class=\"data row3 col1\" >102</td>\n      <td id=\"T_22ca4_row3_col2\" class=\"data row3 col2\" >0,11</td>\n      <td id=\"T_22ca4_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_22ca4_row3_col4\" class=\"data row3 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row4_col0\" class=\"data row4 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_22ca4_row4_col1\" class=\"data row4 col1\" >101</td>\n      <td id=\"T_22ca4_row4_col2\" class=\"data row4 col2\" >0,11</td>\n      <td id=\"T_22ca4_row4_col3\" class=\"data row4 col3\" >0,05</td>\n      <td id=\"T_22ca4_row4_col4\" class=\"data row4 col4\" >0,67</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row5_col0\" class=\"data row5 col0\" >Packaging films nonfood or unknown</td>\n      <td id=\"T_22ca4_row5_col1\" class=\"data row5 col1\" >68</td>\n      <td id=\"T_22ca4_row5_col2\" class=\"data row5 col2\" >0,09</td>\n      <td id=\"T_22ca4_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_22ca4_row5_col4\" class=\"data row5 col4\" >0,27</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row6_col0\" class=\"data row6 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_22ca4_row6_col1\" class=\"data row6 col1\" >63</td>\n      <td id=\"T_22ca4_row6_col2\" class=\"data row6 col2\" >0,08</td>\n      <td id=\"T_22ca4_row6_col3\" class=\"data row6 col3\" >0,03</td>\n      <td id=\"T_22ca4_row6_col4\" class=\"data row6 col4\" >0,30</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row7_col0\" class=\"data row7 col0\" >Fragmented plastics</td>\n      <td id=\"T_22ca4_row7_col1\" class=\"data row7 col1\" >61</td>\n      <td id=\"T_22ca4_row7_col2\" class=\"data row7 col2\" >0,05</td>\n      <td id=\"T_22ca4_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_22ca4_row7_col4\" class=\"data row7 col4\" >0,52</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row8_col0\" class=\"data row8 col0\" >Bags; plastic shopping/carrier/grocery and pieces</td>\n      <td id=\"T_22ca4_row8_col1\" class=\"data row8 col1\" >55</td>\n      <td id=\"T_22ca4_row8_col2\" class=\"data row8 col2\" >0,05</td>\n      <td id=\"T_22ca4_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_22ca4_row8_col4\" class=\"data row8 col4\" >0,24</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row9_col0\" class=\"data row9 col0\" >Construction material; bricks, pipes, cement</td>\n      <td id=\"T_22ca4_row9_col1\" class=\"data row9 col1\" >42</td>\n      <td id=\"T_22ca4_row9_col2\" class=\"data row9 col2\" >0,02</td>\n      <td id=\"T_22ca4_row9_col3\" class=\"data row9 col3\" >0,02</td>\n      <td id=\"T_22ca4_row9_col4\" class=\"data row9 col4\" >0,12</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e2ed2ac0>"
      },
      "metadata": {
       "scrapbook": {
@@ -1094,6 +1103,9 @@
    "id": "12c52a87-8340-419f-bfd9-75ca85260a97",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1670,6 +1682,9 @@
    "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1680,8 +1695,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_1c1b9 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_1c1b9 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_1c1b9 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_1c1b9_row0_col0, #T_1c1b9_row1_col0, #T_1c1b9_row2_col1, #T_1c1b9_row3_col2, #T_1c1b9_row4_col1, #T_1c1b9_row5_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_1c1b9\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_1c1b9_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_1c1b9_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_1c1b9_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_1c1b9_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_1c1b9_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_1c1b9_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_1c1b9_row0_col0\" class=\"data row0 col0\" >5.03</td>\n      <td id=\"T_1c1b9_row0_col1\" class=\"data row0 col1\" >0.09</td>\n      <td id=\"T_1c1b9_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_1c1b9_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_1c1b9_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_1c1b9_row1_col0\" class=\"data row1 col0\" >5.04</td>\n      <td id=\"T_1c1b9_row1_col1\" class=\"data row1 col1\" >0.56</td>\n      <td id=\"T_1c1b9_row1_col2\" class=\"data row1 col2\" >5.01</td>\n      <td id=\"T_1c1b9_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_1c1b9_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_1c1b9_row2_col0\" class=\"data row2 col0\" >2.62</td>\n      <td id=\"T_1c1b9_row2_col1\" class=\"data row2 col1\" >7.46</td>\n      <td id=\"T_1c1b9_row2_col2\" class=\"data row2 col2\" >3.80</td>\n      <td id=\"T_1c1b9_row2_col3\" class=\"data row2 col3\" >2.92</td>\n      <td id=\"T_1c1b9_row2_col4\" class=\"data row2 col4\" >6.27</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_1c1b9_row3_col0\" class=\"data row3 col0\" >4.86</td>\n      <td id=\"T_1c1b9_row3_col1\" class=\"data row3 col1\" >2.73</td>\n      <td id=\"T_1c1b9_row3_col2\" class=\"data row3 col2\" >13.13</td>\n      <td id=\"T_1c1b9_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_1c1b9_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_1c1b9_row4_col0\" class=\"data row4 col0\" >5.45</td>\n      <td id=\"T_1c1b9_row4_col1\" class=\"data row4 col1\" >6.22</td>\n      <td id=\"T_1c1b9_row4_col2\" class=\"data row4 col2\" >2.95</td>\n      <td id=\"T_1c1b9_row4_col3\" class=\"data row4 col3\" >1.84</td>\n      <td id=\"T_1c1b9_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_1c1b9_row5_col0\" class=\"data row5 col0\" >4.26</td>\n      <td id=\"T_1c1b9_row5_col1\" class=\"data row5 col1\" >14.60</td>\n      <td id=\"T_1c1b9_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_1c1b9_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_1c1b9_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a53f4340>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_87bf0 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_87bf0 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_87bf0 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_87bf0_row0_col0, #T_87bf0_row1_col0, #T_87bf0_row2_col1, #T_87bf0_row3_col2, #T_87bf0_row4_col1, #T_87bf0_row5_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_87bf0\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_87bf0_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_87bf0_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_87bf0_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_87bf0_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_87bf0_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_87bf0_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_87bf0_row0_col0\" class=\"data row0 col0\" >5.03</td>\n      <td id=\"T_87bf0_row0_col1\" class=\"data row0 col1\" >0.09</td>\n      <td id=\"T_87bf0_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_87bf0_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_87bf0_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_87bf0_row1_col0\" class=\"data row1 col0\" >5.04</td>\n      <td id=\"T_87bf0_row1_col1\" class=\"data row1 col1\" >0.56</td>\n      <td id=\"T_87bf0_row1_col2\" class=\"data row1 col2\" >5.01</td>\n      <td id=\"T_87bf0_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_87bf0_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_87bf0_row2_col0\" class=\"data row2 col0\" >2.62</td>\n      <td id=\"T_87bf0_row2_col1\" class=\"data row2 col1\" >7.46</td>\n      <td id=\"T_87bf0_row2_col2\" class=\"data row2 col2\" >3.80</td>\n      <td id=\"T_87bf0_row2_col3\" class=\"data row2 col3\" >2.92</td>\n      <td id=\"T_87bf0_row2_col4\" class=\"data row2 col4\" >6.27</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_87bf0_row3_col0\" class=\"data row3 col0\" >4.86</td>\n      <td id=\"T_87bf0_row3_col1\" class=\"data row3 col1\" >2.73</td>\n      <td id=\"T_87bf0_row3_col2\" class=\"data row3 col2\" >13.13</td>\n      <td id=\"T_87bf0_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_87bf0_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_87bf0_row4_col0\" class=\"data row4 col0\" >5.45</td>\n      <td id=\"T_87bf0_row4_col1\" class=\"data row4 col1\" >6.22</td>\n      <td id=\"T_87bf0_row4_col2\" class=\"data row4 col2\" >2.95</td>\n      <td id=\"T_87bf0_row4_col3\" class=\"data row4 col3\" >1.84</td>\n      <td id=\"T_87bf0_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_87bf0_row5_col0\" class=\"data row5 col0\" >4.26</td>\n      <td id=\"T_87bf0_row5_col1\" class=\"data row5 col1\" >14.60</td>\n      <td id=\"T_87bf0_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_87bf0_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_87bf0_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e657d26e310>"
      },
      "metadata": {
       "scrapbook": {
@@ -1693,8 +1708,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_fc5d9 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_fc5d9 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_fc5d9 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_fc5d9_row0_col0, #T_fc5d9_row1_col0, #T_fc5d9_row2_col4, #T_fc5d9_row3_col0, #T_fc5d9_row4_col0, #T_fc5d9_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_fc5d9\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_fc5d9_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_fc5d9_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_fc5d9_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_fc5d9_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_fc5d9_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_fc5d9_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_fc5d9_row0_col0\" class=\"data row0 col0\" >100%</td>\n      <td id=\"T_fc5d9_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_fc5d9_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_fc5d9_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_fc5d9_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_fc5d9_row1_col0\" class=\"data row1 col0\" >98%</td>\n      <td id=\"T_fc5d9_row1_col1\" class=\"data row1 col1\" >1%</td>\n      <td id=\"T_fc5d9_row1_col2\" class=\"data row1 col2\" >1%</td>\n      <td id=\"T_fc5d9_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_fc5d9_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_fc5d9_row2_col0\" class=\"data row2 col0\" >14%</td>\n      <td id=\"T_fc5d9_row2_col1\" class=\"data row2 col1\" >18%</td>\n      <td id=\"T_fc5d9_row2_col2\" class=\"data row2 col2\" >10%</td>\n      <td id=\"T_fc5d9_row2_col3\" class=\"data row2 col3\" >22%</td>\n      <td id=\"T_fc5d9_row2_col4\" class=\"data row2 col4\" >36%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_fc5d9_row3_col0\" class=\"data row3 col0\" >68%</td>\n      <td id=\"T_fc5d9_row3_col1\" class=\"data row3 col1\" >24%</td>\n      <td id=\"T_fc5d9_row3_col2\" class=\"data row3 col2\" >8%</td>\n      <td id=\"T_fc5d9_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_fc5d9_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_fc5d9_row4_col0\" class=\"data row4 col0\" >57%</td>\n      <td id=\"T_fc5d9_row4_col1\" class=\"data row4 col1\" >22%</td>\n      <td id=\"T_fc5d9_row4_col2\" class=\"data row4 col2\" >16%</td>\n      <td id=\"T_fc5d9_row4_col3\" class=\"data row4 col3\" >6%</td>\n      <td id=\"T_fc5d9_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_fc5d9_row5_col0\" class=\"data row5 col0\" >93%</td>\n      <td id=\"T_fc5d9_row5_col1\" class=\"data row5 col1\" >7%</td>\n      <td id=\"T_fc5d9_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_fc5d9_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_fc5d9_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb76b80>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_685fe caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_685fe caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_685fe td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_685fe_row0_col0, #T_685fe_row1_col0, #T_685fe_row2_col4, #T_685fe_row3_col0, #T_685fe_row4_col0, #T_685fe_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_685fe\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_685fe_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_685fe_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_685fe_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_685fe_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_685fe_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_685fe_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_685fe_row0_col0\" class=\"data row0 col0\" >100%</td>\n      <td id=\"T_685fe_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_685fe_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_685fe_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_685fe_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_685fe_row1_col0\" class=\"data row1 col0\" >98%</td>\n      <td id=\"T_685fe_row1_col1\" class=\"data row1 col1\" >1%</td>\n      <td id=\"T_685fe_row1_col2\" class=\"data row1 col2\" >1%</td>\n      <td id=\"T_685fe_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_685fe_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_685fe_row2_col0\" class=\"data row2 col0\" >14%</td>\n      <td id=\"T_685fe_row2_col1\" class=\"data row2 col1\" >18%</td>\n      <td id=\"T_685fe_row2_col2\" class=\"data row2 col2\" >10%</td>\n      <td id=\"T_685fe_row2_col3\" class=\"data row2 col3\" >22%</td>\n      <td id=\"T_685fe_row2_col4\" class=\"data row2 col4\" >36%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_685fe_row3_col0\" class=\"data row3 col0\" >68%</td>\n      <td id=\"T_685fe_row3_col1\" class=\"data row3 col1\" >24%</td>\n      <td id=\"T_685fe_row3_col2\" class=\"data row3 col2\" >8%</td>\n      <td id=\"T_685fe_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_685fe_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_685fe_row4_col0\" class=\"data row4 col0\" >57%</td>\n      <td id=\"T_685fe_row4_col1\" class=\"data row4 col1\" >22%</td>\n      <td id=\"T_685fe_row4_col2\" class=\"data row4 col2\" >16%</td>\n      <td id=\"T_685fe_row4_col3\" class=\"data row4 col3\" >6%</td>\n      <td id=\"T_685fe_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_685fe_row5_col0\" class=\"data row5 col0\" >93%</td>\n      <td id=\"T_685fe_row5_col1\" class=\"data row5 col1\" >7%</td>\n      <td id=\"T_685fe_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_685fe_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_685fe_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e39db3a0>"
      },
      "metadata": {
       "scrapbook": {
@@ -1724,6 +1739,9 @@
    "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1734,8 +1752,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_34a15 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_34a15 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_34a15 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_34a15_row0_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_34a15\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_34a15_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_34a15_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_34a15_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_34a15_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_34a15_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_34a15_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_34a15_row0_col0\" class=\"data row0 col0\" >31%</td>\n      <td id=\"T_34a15_row0_col1\" class=\"data row0 col1\" >46%</td>\n      <td id=\"T_34a15_row0_col2\" class=\"data row0 col2\" >21%</td>\n      <td id=\"T_34a15_row0_col3\" class=\"data row0 col3\" >2%</td>\n      <td id=\"T_34a15_row0_col4\" class=\"data row0 col4\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a473ebe8520>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_30111 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_30111 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_30111 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_30111_row0_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_30111\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_30111_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_30111_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_30111_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_30111_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_30111_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_30111_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_30111_row0_col0\" class=\"data row0 col0\" >31%</td>\n      <td id=\"T_30111_row0_col1\" class=\"data row0 col1\" >46%</td>\n      <td id=\"T_30111_row0_col2\" class=\"data row0 col2\" >21%</td>\n      <td id=\"T_30111_row0_col3\" class=\"data row0 col3\" >2%</td>\n      <td id=\"T_30111_row0_col4\" class=\"data row0 col4\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e25d1e50>"
      },
      "metadata": {
       "scrapbook": {
@@ -1747,8 +1765,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_a1596 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_a1596 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_a1596 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_a1596_row0_col4 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_a1596\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a1596_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_a1596_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_a1596_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_a1596_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_a1596_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a1596_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_a1596_row0_col0\" class=\"data row0 col0\" >5.26</td>\n      <td id=\"T_a1596_row0_col1\" class=\"data row0 col1\" >4.33</td>\n      <td id=\"T_a1596_row0_col2\" class=\"data row0 col2\" >5.66</td>\n      <td id=\"T_a1596_row0_col3\" class=\"data row0 col3\" >4.12</td>\n      <td id=\"T_a1596_row0_col4\" class=\"data row0 col4\" >14.56</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a5360130>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_6a0e0 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_6a0e0 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_6a0e0 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_6a0e0_row0_col4 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_6a0e0\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_6a0e0_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_6a0e0_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_6a0e0_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_6a0e0_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_6a0e0_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_6a0e0_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_6a0e0_row0_col0\" class=\"data row0 col0\" >5.26</td>\n      <td id=\"T_6a0e0_row0_col1\" class=\"data row0 col1\" >4.33</td>\n      <td id=\"T_6a0e0_row0_col2\" class=\"data row0 col2\" >5.66</td>\n      <td id=\"T_6a0e0_row0_col3\" class=\"data row0 col3\" >4.12</td>\n      <td id=\"T_6a0e0_row0_col4\" class=\"data row0 col4\" >14.56</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e4240a90>"
      },
      "metadata": {
       "scrapbook": {
@@ -1782,6 +1800,9 @@
    "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1792,8 +1813,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_13be6 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_13be6 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_13be6 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_13be6_row0_col0, #T_13be6_row1_col0, #T_13be6_row2_col1, #T_13be6_row3_col2, #T_13be6_row4_col1, #T_13be6_row5_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_13be6\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_13be6_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_13be6_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_13be6_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_13be6_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_13be6_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_13be6_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_13be6_row0_col0\" class=\"data row0 col0\" >5.50</td>\n      <td id=\"T_13be6_row0_col1\" class=\"data row0 col1\" >0.00</td>\n      <td id=\"T_13be6_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_13be6_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_13be6_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_13be6_row1_col0\" class=\"data row1 col0\" >5.51</td>\n      <td id=\"T_13be6_row1_col1\" class=\"data row1 col1\" >0.00</td>\n      <td id=\"T_13be6_row1_col2\" class=\"data row1 col2\" >5.01</td>\n      <td id=\"T_13be6_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_13be6_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_13be6_row2_col0\" class=\"data row2 col0\" >2.94</td>\n      <td id=\"T_13be6_row2_col1\" class=\"data row2 col1\" >7.83</td>\n      <td id=\"T_13be6_row2_col2\" class=\"data row2 col2\" >5.12</td>\n      <td id=\"T_13be6_row2_col3\" class=\"data row2 col3\" >3.33</td>\n      <td id=\"T_13be6_row2_col4\" class=\"data row2 col4\" >6.39</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_13be6_row3_col0\" class=\"data row3 col0\" >5.20</td>\n      <td id=\"T_13be6_row3_col1\" class=\"data row3 col1\" >3.13</td>\n      <td id=\"T_13be6_row3_col2\" class=\"data row3 col2\" >14.92</td>\n      <td id=\"T_13be6_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_13be6_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_13be6_row4_col0\" class=\"data row4 col0\" >5.88</td>\n      <td id=\"T_13be6_row4_col1\" class=\"data row4 col1\" >7.42</td>\n      <td id=\"T_13be6_row4_col2\" class=\"data row4 col2\" >3.00</td>\n      <td id=\"T_13be6_row4_col3\" class=\"data row4 col3\" >1.91</td>\n      <td id=\"T_13be6_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_13be6_row5_col0\" class=\"data row5 col0\" >4.64</td>\n      <td id=\"T_13be6_row5_col1\" class=\"data row5 col1\" >16.31</td>\n      <td id=\"T_13be6_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_13be6_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_13be6_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a536dd60>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_b413e caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_b413e caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_b413e td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_b413e_row0_col0, #T_b413e_row1_col0, #T_b413e_row2_col1, #T_b413e_row3_col2, #T_b413e_row4_col1, #T_b413e_row5_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_b413e\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_b413e_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_b413e_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_b413e_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_b413e_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_b413e_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_b413e_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_b413e_row0_col0\" class=\"data row0 col0\" >5.50</td>\n      <td id=\"T_b413e_row0_col1\" class=\"data row0 col1\" >0.00</td>\n      <td id=\"T_b413e_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_b413e_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_b413e_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_b413e_row1_col0\" class=\"data row1 col0\" >5.51</td>\n      <td id=\"T_b413e_row1_col1\" class=\"data row1 col1\" >0.00</td>\n      <td id=\"T_b413e_row1_col2\" class=\"data row1 col2\" >5.01</td>\n      <td id=\"T_b413e_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_b413e_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_b413e_row2_col0\" class=\"data row2 col0\" >2.94</td>\n      <td id=\"T_b413e_row2_col1\" class=\"data row2 col1\" >7.83</td>\n      <td id=\"T_b413e_row2_col2\" class=\"data row2 col2\" >5.12</td>\n      <td id=\"T_b413e_row2_col3\" class=\"data row2 col3\" >3.33</td>\n      <td id=\"T_b413e_row2_col4\" class=\"data row2 col4\" >6.39</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_b413e_row3_col0\" class=\"data row3 col0\" >5.20</td>\n      <td id=\"T_b413e_row3_col1\" class=\"data row3 col1\" >3.13</td>\n      <td id=\"T_b413e_row3_col2\" class=\"data row3 col2\" >14.92</td>\n      <td id=\"T_b413e_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_b413e_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_b413e_row4_col0\" class=\"data row4 col0\" >5.88</td>\n      <td id=\"T_b413e_row4_col1\" class=\"data row4 col1\" >7.42</td>\n      <td id=\"T_b413e_row4_col2\" class=\"data row4 col2\" >3.00</td>\n      <td id=\"T_b413e_row4_col3\" class=\"data row4 col3\" >1.91</td>\n      <td id=\"T_b413e_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_b413e_row5_col0\" class=\"data row5 col0\" >4.64</td>\n      <td id=\"T_b413e_row5_col1\" class=\"data row5 col1\" >16.31</td>\n      <td id=\"T_b413e_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_b413e_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_b413e_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e65562c37f0>"
      },
      "metadata": {
       "scrapbook": {
@@ -1805,8 +1826,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_81787 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_81787 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_81787 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_81787_row0_col0, #T_81787_row1_col0, #T_81787_row2_col4, #T_81787_row3_col0, #T_81787_row4_col0, #T_81787_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_81787\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_81787_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_81787_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_81787_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_81787_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_81787_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_81787_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_81787_row0_col0\" class=\"data row0 col0\" >100%</td>\n      <td id=\"T_81787_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_81787_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_81787_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_81787_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_81787_row1_col0\" class=\"data row1 col0\" >99%</td>\n      <td id=\"T_81787_row1_col1\" class=\"data row1 col1\" >0%</td>\n      <td id=\"T_81787_row1_col2\" class=\"data row1 col2\" >1%</td>\n      <td id=\"T_81787_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_81787_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_81787_row2_col0\" class=\"data row2 col0\" >12%</td>\n      <td id=\"T_81787_row2_col1\" class=\"data row2 col1\" >20%</td>\n      <td id=\"T_81787_row2_col2\" class=\"data row2 col2\" >8%</td>\n      <td id=\"T_81787_row2_col3\" class=\"data row2 col3\" >21%</td>\n      <td id=\"T_81787_row2_col4\" class=\"data row2 col4\" >39%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_81787_row3_col0\" class=\"data row3 col0\" >70%</td>\n      <td id=\"T_81787_row3_col1\" class=\"data row3 col1\" >22%</td>\n      <td id=\"T_81787_row3_col2\" class=\"data row3 col2\" >8%</td>\n      <td id=\"T_81787_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_81787_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_81787_row4_col0\" class=\"data row4 col0\" >58%</td>\n      <td id=\"T_81787_row4_col1\" class=\"data row4 col1\" >20%</td>\n      <td id=\"T_81787_row4_col2\" class=\"data row4 col2\" >16%</td>\n      <td id=\"T_81787_row4_col3\" class=\"data row4 col3\" >6%</td>\n      <td id=\"T_81787_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_81787_row5_col0\" class=\"data row5 col0\" >93%</td>\n      <td id=\"T_81787_row5_col1\" class=\"data row5 col1\" >7%</td>\n      <td id=\"T_81787_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_81787_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_81787_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a7f1a220>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_1d812 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_1d812 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_1d812 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_1d812_row0_col0, #T_1d812_row1_col0, #T_1d812_row2_col4, #T_1d812_row3_col0, #T_1d812_row4_col0, #T_1d812_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_1d812\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_1d812_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_1d812_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_1d812_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_1d812_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_1d812_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_1d812_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_1d812_row0_col0\" class=\"data row0 col0\" >100%</td>\n      <td id=\"T_1d812_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_1d812_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_1d812_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_1d812_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_1d812_row1_col0\" class=\"data row1 col0\" >99%</td>\n      <td id=\"T_1d812_row1_col1\" class=\"data row1 col1\" >0%</td>\n      <td id=\"T_1d812_row1_col2\" class=\"data row1 col2\" >1%</td>\n      <td id=\"T_1d812_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_1d812_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_1d812_row2_col0\" class=\"data row2 col0\" >12%</td>\n      <td id=\"T_1d812_row2_col1\" class=\"data row2 col1\" >20%</td>\n      <td id=\"T_1d812_row2_col2\" class=\"data row2 col2\" >8%</td>\n      <td id=\"T_1d812_row2_col3\" class=\"data row2 col3\" >21%</td>\n      <td id=\"T_1d812_row2_col4\" class=\"data row2 col4\" >39%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_1d812_row3_col0\" class=\"data row3 col0\" >70%</td>\n      <td id=\"T_1d812_row3_col1\" class=\"data row3 col1\" >22%</td>\n      <td id=\"T_1d812_row3_col2\" class=\"data row3 col2\" >8%</td>\n      <td id=\"T_1d812_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_1d812_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_1d812_row4_col0\" class=\"data row4 col0\" >58%</td>\n      <td id=\"T_1d812_row4_col1\" class=\"data row4 col1\" >20%</td>\n      <td id=\"T_1d812_row4_col2\" class=\"data row4 col2\" >16%</td>\n      <td id=\"T_1d812_row4_col3\" class=\"data row4 col3\" >6%</td>\n      <td id=\"T_1d812_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_1d812_row5_col0\" class=\"data row5 col0\" >93%</td>\n      <td id=\"T_1d812_row5_col1\" class=\"data row5 col1\" >7%</td>\n      <td id=\"T_1d812_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_1d812_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_1d812_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e6242c10>"
      },
      "metadata": {
       "scrapbook": {
@@ -1837,6 +1858,9 @@
    "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1847,8 +1871,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_edd53 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_edd53 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_edd53 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_edd53_row0_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_edd53\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_edd53_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_edd53_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_edd53_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_edd53_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_edd53_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_edd53_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_edd53_row0_col0\" class=\"data row0 col0\" >35%</td>\n      <td id=\"T_edd53_row0_col1\" class=\"data row0 col1\" >48%</td>\n      <td id=\"T_edd53_row0_col2\" class=\"data row0 col2\" >16%</td>\n      <td id=\"T_edd53_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_edd53_row0_col4\" class=\"data row0 col4\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a52ce190>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_9a253 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_9a253 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_9a253 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_9a253_row0_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_9a253\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_9a253_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_9a253_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_9a253_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_9a253_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_9a253_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_9a253_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_9a253_row0_col0\" class=\"data row0 col0\" >35%</td>\n      <td id=\"T_9a253_row0_col1\" class=\"data row0 col1\" >48%</td>\n      <td id=\"T_9a253_row0_col2\" class=\"data row0 col2\" >16%</td>\n      <td id=\"T_9a253_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_9a253_row0_col4\" class=\"data row0 col4\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e6257910>"
      },
      "metadata": {
       "scrapbook": {
@@ -1860,8 +1884,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_43d42 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_43d42 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_43d42 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_43d42_row0_col4 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_43d42\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_43d42_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_43d42_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_43d42_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_43d42_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_43d42_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_43d42_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_43d42_row0_col0\" class=\"data row0 col0\" >5.33</td>\n      <td id=\"T_43d42_row0_col1\" class=\"data row0 col1\" >4.60</td>\n      <td id=\"T_43d42_row0_col2\" class=\"data row0 col2\" >7.68</td>\n      <td id=\"T_43d42_row0_col3\" class=\"data row0 col3\" >0</td>\n      <td id=\"T_43d42_row0_col4\" class=\"data row0 col4\" >21.80</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a49fa100>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_a1639 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_a1639 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_a1639 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_a1639_row0_col4 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_a1639\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a1639_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_a1639_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_a1639_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_a1639_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_a1639_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a1639_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_a1639_row0_col0\" class=\"data row0 col0\" >5.33</td>\n      <td id=\"T_a1639_row0_col1\" class=\"data row0 col1\" >4.60</td>\n      <td id=\"T_a1639_row0_col2\" class=\"data row0 col2\" >7.68</td>\n      <td id=\"T_a1639_row0_col3\" class=\"data row0 col3\" >0</td>\n      <td id=\"T_a1639_row0_col4\" class=\"data row0 col4\" >21.80</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e4266460>"
      },
      "metadata": {
       "scrapbook": {
@@ -1896,6 +1920,9 @@
    "id": "82f55461-c497-483a-8c38-fbd509809afb",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1906,8 +1933,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_28094 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_28094 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_28094 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_28094_row0_col0, #T_28094_row1_col0, #T_28094_row2_col4, #T_28094_row3_col2, #T_28094_row4_col2, #T_28094_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_28094\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_28094_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_28094_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_28094_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_28094_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_28094_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_28094_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_28094_row0_col0\" class=\"data row0 col0\" >1.62</td>\n      <td id=\"T_28094_row0_col1\" class=\"data row0 col1\" >0.09</td>\n      <td id=\"T_28094_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_28094_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_28094_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_28094_row1_col0\" class=\"data row1 col0\" >1.64</td>\n      <td id=\"T_28094_row1_col1\" class=\"data row1 col1\" >0.56</td>\n      <td id=\"T_28094_row1_col2\" class=\"data row1 col2\" >0.00</td>\n      <td id=\"T_28094_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_28094_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_28094_row2_col0\" class=\"data row2 col0\" >1.50</td>\n      <td id=\"T_28094_row2_col1\" class=\"data row2 col1\" >1.93</td>\n      <td id=\"T_28094_row2_col2\" class=\"data row2 col2\" >1.17</td>\n      <td id=\"T_28094_row2_col3\" class=\"data row2 col3\" >0.43</td>\n      <td id=\"T_28094_row2_col4\" class=\"data row2 col4\" >4.06</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_28094_row3_col0\" class=\"data row3 col0\" >1.82</td>\n      <td id=\"T_28094_row3_col1\" class=\"data row3 col1\" >1.01</td>\n      <td id=\"T_28094_row3_col2\" class=\"data row3 col2\" >2.38</td>\n      <td id=\"T_28094_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_28094_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_28094_row4_col0\" class=\"data row4 col0\" >1.64</td>\n      <td id=\"T_28094_row4_col1\" class=\"data row4 col1\" >1.19</td>\n      <td id=\"T_28094_row4_col2\" class=\"data row4 col2\" >2.46</td>\n      <td id=\"T_28094_row4_col3\" class=\"data row4 col3\" >1.50</td>\n      <td id=\"T_28094_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_28094_row5_col0\" class=\"data row5 col0\" >1.67</td>\n      <td id=\"T_28094_row5_col1\" class=\"data row5 col1\" >0.07</td>\n      <td id=\"T_28094_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_28094_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_28094_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a7f0beb0>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_f4453 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_f4453 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_f4453 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_f4453_row0_col0, #T_f4453_row1_col0, #T_f4453_row2_col4, #T_f4453_row3_col2, #T_f4453_row4_col2, #T_f4453_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_f4453\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_f4453_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_f4453_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_f4453_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_f4453_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_f4453_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_f4453_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_f4453_row0_col0\" class=\"data row0 col0\" >1.62</td>\n      <td id=\"T_f4453_row0_col1\" class=\"data row0 col1\" >0.09</td>\n      <td id=\"T_f4453_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_f4453_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_f4453_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_f4453_row1_col0\" class=\"data row1 col0\" >1.64</td>\n      <td id=\"T_f4453_row1_col1\" class=\"data row1 col1\" >0.56</td>\n      <td id=\"T_f4453_row1_col2\" class=\"data row1 col2\" >0.00</td>\n      <td id=\"T_f4453_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_f4453_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_f4453_row2_col0\" class=\"data row2 col0\" >1.50</td>\n      <td id=\"T_f4453_row2_col1\" class=\"data row2 col1\" >1.93</td>\n      <td id=\"T_f4453_row2_col2\" class=\"data row2 col2\" >1.17</td>\n      <td id=\"T_f4453_row2_col3\" class=\"data row2 col3\" >0.43</td>\n      <td id=\"T_f4453_row2_col4\" class=\"data row2 col4\" >4.06</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_f4453_row3_col0\" class=\"data row3 col0\" >1.82</td>\n      <td id=\"T_f4453_row3_col1\" class=\"data row3 col1\" >1.01</td>\n      <td id=\"T_f4453_row3_col2\" class=\"data row3 col2\" >2.38</td>\n      <td id=\"T_f4453_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_f4453_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_f4453_row4_col0\" class=\"data row4 col0\" >1.64</td>\n      <td id=\"T_f4453_row4_col1\" class=\"data row4 col1\" >1.19</td>\n      <td id=\"T_f4453_row4_col2\" class=\"data row4 col2\" >2.46</td>\n      <td id=\"T_f4453_row4_col3\" class=\"data row4 col3\" >1.50</td>\n      <td id=\"T_f4453_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_f4453_row5_col0\" class=\"data row5 col0\" >1.67</td>\n      <td id=\"T_f4453_row5_col1\" class=\"data row5 col1\" >0.07</td>\n      <td id=\"T_f4453_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_f4453_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_f4453_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e2f69820>"
      },
      "metadata": {
       "scrapbook": {
@@ -1919,8 +1946,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_2c804 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_2c804 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_2c804 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_2c804_row0_col0, #T_2c804_row1_col0, #T_2c804_row2_col2, #T_2c804_row3_col0, #T_2c804_row4_col0, #T_2c804_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_2c804\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_2c804_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_2c804_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_2c804_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_2c804_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_2c804_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_2c804_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_2c804_row0_col0\" class=\"data row0 col0\" >97%</td>\n      <td id=\"T_2c804_row0_col1\" class=\"data row0 col1\" >3%</td>\n      <td id=\"T_2c804_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_2c804_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_2c804_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_2c804_row1_col0\" class=\"data row1 col0\" >94%</td>\n      <td id=\"T_2c804_row1_col1\" class=\"data row1 col1\" >6%</td>\n      <td id=\"T_2c804_row1_col2\" class=\"data row1 col2\" >0%</td>\n      <td id=\"T_2c804_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_2c804_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_2c804_row2_col0\" class=\"data row2 col0\" >24%</td>\n      <td id=\"T_2c804_row2_col1\" class=\"data row2 col1\" >9%</td>\n      <td id=\"T_2c804_row2_col2\" class=\"data row2 col2\" >27%</td>\n      <td id=\"T_2c804_row2_col3\" class=\"data row2 col3\" >24%</td>\n      <td id=\"T_2c804_row2_col4\" class=\"data row2 col4\" >15%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_2c804_row3_col0\" class=\"data row3 col0\" >55%</td>\n      <td id=\"T_2c804_row3_col1\" class=\"data row3 col1\" >36%</td>\n      <td id=\"T_2c804_row3_col2\" class=\"data row3 col2\" >9%</td>\n      <td id=\"T_2c804_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_2c804_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_2c804_row4_col0\" class=\"data row4 col0\" >45%</td>\n      <td id=\"T_2c804_row4_col1\" class=\"data row4 col1\" >33%</td>\n      <td id=\"T_2c804_row4_col2\" class=\"data row4 col2\" >12%</td>\n      <td id=\"T_2c804_row4_col3\" class=\"data row4 col3\" >9%</td>\n      <td id=\"T_2c804_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_2c804_row5_col0\" class=\"data row5 col0\" >94%</td>\n      <td id=\"T_2c804_row5_col1\" class=\"data row5 col1\" >6%</td>\n      <td id=\"T_2c804_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_2c804_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_2c804_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb76f10>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_b4d9b caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_b4d9b caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_b4d9b td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_b4d9b_row0_col0, #T_b4d9b_row1_col0, #T_b4d9b_row2_col2, #T_b4d9b_row3_col0, #T_b4d9b_row4_col0, #T_b4d9b_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_b4d9b\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_b4d9b_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_b4d9b_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_b4d9b_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_b4d9b_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_b4d9b_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_b4d9b_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_b4d9b_row0_col0\" class=\"data row0 col0\" >97%</td>\n      <td id=\"T_b4d9b_row0_col1\" class=\"data row0 col1\" >3%</td>\n      <td id=\"T_b4d9b_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_b4d9b_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_b4d9b_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_b4d9b_row1_col0\" class=\"data row1 col0\" >94%</td>\n      <td id=\"T_b4d9b_row1_col1\" class=\"data row1 col1\" >6%</td>\n      <td id=\"T_b4d9b_row1_col2\" class=\"data row1 col2\" >0%</td>\n      <td id=\"T_b4d9b_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_b4d9b_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_b4d9b_row2_col0\" class=\"data row2 col0\" >24%</td>\n      <td id=\"T_b4d9b_row2_col1\" class=\"data row2 col1\" >9%</td>\n      <td id=\"T_b4d9b_row2_col2\" class=\"data row2 col2\" >27%</td>\n      <td id=\"T_b4d9b_row2_col3\" class=\"data row2 col3\" >24%</td>\n      <td id=\"T_b4d9b_row2_col4\" class=\"data row2 col4\" >15%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_b4d9b_row3_col0\" class=\"data row3 col0\" >55%</td>\n      <td id=\"T_b4d9b_row3_col1\" class=\"data row3 col1\" >36%</td>\n      <td id=\"T_b4d9b_row3_col2\" class=\"data row3 col2\" >9%</td>\n      <td id=\"T_b4d9b_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_b4d9b_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_b4d9b_row4_col0\" class=\"data row4 col0\" >45%</td>\n      <td id=\"T_b4d9b_row4_col1\" class=\"data row4 col1\" >33%</td>\n      <td id=\"T_b4d9b_row4_col2\" class=\"data row4 col2\" >12%</td>\n      <td id=\"T_b4d9b_row4_col3\" class=\"data row4 col3\" >9%</td>\n      <td id=\"T_b4d9b_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_b4d9b_row5_col0\" class=\"data row5 col0\" >94%</td>\n      <td id=\"T_b4d9b_row5_col1\" class=\"data row5 col1\" >6%</td>\n      <td id=\"T_b4d9b_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_b4d9b_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_b4d9b_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e2f6e4f0>"
      },
      "metadata": {
       "scrapbook": {
@@ -1952,6 +1979,9 @@
    "id": "9b396025-1fa6-4661-9116-593fa1ed741d",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1962,8 +1992,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_0b35c caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_0b35c caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_0b35c td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_0b35c_row0_col2 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_0b35c\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_0b35c_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_0b35c_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_0b35c_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_0b35c_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_0b35c_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_0b35c_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_0b35c_row0_col0\" class=\"data row0 col0\" >3%</td>\n      <td id=\"T_0b35c_row0_col1\" class=\"data row0 col1\" >30%</td>\n      <td id=\"T_0b35c_row0_col2\" class=\"data row0 col2\" >52%</td>\n      <td id=\"T_0b35c_row0_col3\" class=\"data row0 col3\" >12%</td>\n      <td id=\"T_0b35c_row0_col4\" class=\"data row0 col4\" >3%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a48a7d00>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_1e644 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_1e644 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_1e644 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_1e644_row0_col2 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_1e644\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_1e644_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_1e644_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_1e644_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_1e644_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_1e644_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_1e644_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_1e644_row0_col0\" class=\"data row0 col0\" >3%</td>\n      <td id=\"T_1e644_row0_col1\" class=\"data row0 col1\" >30%</td>\n      <td id=\"T_1e644_row0_col2\" class=\"data row0 col2\" >52%</td>\n      <td id=\"T_1e644_row0_col3\" class=\"data row0 col3\" >12%</td>\n      <td id=\"T_1e644_row0_col4\" class=\"data row0 col4\" >3%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e626cfa0>"
      },
      "metadata": {
       "scrapbook": {
@@ -1975,8 +2005,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_af839 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_af839 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_af839 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_af839_row0_col3 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_af839\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_af839_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_af839_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_af839_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_af839_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_af839_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_af839_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_af839_row0_col0\" class=\"data row0 col0\" >0.22</td>\n      <td id=\"T_af839_row0_col1\" class=\"data row0 col1\" >1.36</td>\n      <td id=\"T_af839_row0_col2\" class=\"data row0 col2\" >1.27</td>\n      <td id=\"T_af839_row0_col3\" class=\"data row0 col3\" >4.12</td>\n      <td id=\"T_af839_row0_col4\" class=\"data row0 col4\" >0.08</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a5326490>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_dbbe5 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_dbbe5 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_dbbe5 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_dbbe5_row0_col3 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_dbbe5\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_dbbe5_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_dbbe5_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_dbbe5_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_dbbe5_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_dbbe5_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_dbbe5_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_dbbe5_row0_col0\" class=\"data row0 col0\" >0.22</td>\n      <td id=\"T_dbbe5_row0_col1\" class=\"data row0 col1\" >1.36</td>\n      <td id=\"T_dbbe5_row0_col2\" class=\"data row0 col2\" >1.27</td>\n      <td id=\"T_dbbe5_row0_col3\" class=\"data row0 col3\" >4.12</td>\n      <td id=\"T_dbbe5_row0_col4\" class=\"data row0 col4\" >0.08</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e251b430>"
      },
      "metadata": {
       "scrapbook": {
@@ -2340,8 +2370,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_bb25a tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_bb25a tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_bb25a tr {\n  font-size: 10px;\n}\n#T_bb25a th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_bb25a td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_bb25a caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n#T_bb25a_row1_col1, #T_bb25a_row2_col1, #T_bb25a_row3_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_bb25a\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_bb25a_level0_col0\" class=\"col_heading level0 col0\" >quantity</th>\n      <th id=\"T_bb25a_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n      <th id=\"T_bb25a_level0_col2\" class=\"col_heading level0 col2\" >samples</th>\n      <th id=\"T_bb25a_level0_col3\" class=\"col_heading level0 col3\" >orchards</th>\n      <th id=\"T_bb25a_level0_col4\" class=\"col_heading level0 col4\" >vineyards</th>\n      <th id=\"T_bb25a_level0_col5\" class=\"col_heading level0 col5\" >buildings</th>\n      <th id=\"T_bb25a_level0_col6\" class=\"col_heading level0 col6\" >forest</th>\n      <th id=\"T_bb25a_level0_col7\" class=\"col_heading level0 col7\" >undefined</th>\n      <th id=\"T_bb25a_level0_col8\" class=\"col_heading level0 col8\" >public services</th>\n      <th id=\"T_bb25a_level0_col9\" class=\"col_heading level0 col9\" >streets</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >canton</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n      <th class=\"blank col3\" >&nbsp;</th>\n      <th class=\"blank col4\" >&nbsp;</th>\n      <th class=\"blank col5\" >&nbsp;</th>\n      <th class=\"blank col6\" >&nbsp;</th>\n      <th class=\"blank col7\" >&nbsp;</th>\n      <th class=\"blank col8\" >&nbsp;</th>\n      <th class=\"blank col9\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_bb25a_level0_row0\" class=\"row_heading level0 row0\" >Bern</th>\n      <td id=\"T_bb25a_row0_col0\" class=\"data row0 col0\" >9889</td>\n      <td id=\"T_bb25a_row0_col1\" class=\"data row0 col1\" >2.81</td>\n      <td id=\"T_bb25a_row0_col2\" class=\"data row0 col2\" >98</td>\n      <td id=\"T_bb25a_row0_col3\" class=\"data row0 col3\" >1</td>\n      <td id=\"T_bb25a_row0_col4\" class=\"data row0 col4\" >1</td>\n      <td id=\"T_bb25a_row0_col5\" class=\"data row0 col5\" >2</td>\n      <td id=\"T_bb25a_row0_col6\" class=\"data row0 col6\" >2</td>\n      <td id=\"T_bb25a_row0_col7\" class=\"data row0 col7\" >3</td>\n      <td id=\"T_bb25a_row0_col8\" class=\"data row0 col8\" >1</td>\n      <td id=\"T_bb25a_row0_col9\" class=\"data row0 col9\" >2</td>\n    </tr>\n    <tr>\n      <th id=\"T_bb25a_level0_row1\" class=\"row_heading level0 row1\" >Genève</th>\n      <td id=\"T_bb25a_row1_col0\" class=\"data row1 col0\" >9559</td>\n      <td id=\"T_bb25a_row1_col1\" class=\"data row1 col1\" >5.88</td>\n      <td id=\"T_bb25a_row1_col2\" class=\"data row1 col2\" >33</td>\n      <td id=\"T_bb25a_row1_col3\" class=\"data row1 col3\" >1</td>\n      <td id=\"T_bb25a_row1_col4\" class=\"data row1 col4\" >1</td>\n      <td id=\"T_bb25a_row1_col5\" class=\"data row1 col5\" >5</td>\n      <td id=\"T_bb25a_row1_col6\" class=\"data row1 col6\" >1</td>\n      <td id=\"T_bb25a_row1_col7\" class=\"data row1 col7\" >1</td>\n      <td id=\"T_bb25a_row1_col8\" class=\"data row1 col8\" >1</td>\n      <td id=\"T_bb25a_row1_col9\" class=\"data row1 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_bb25a_level0_row2\" class=\"row_heading level0 row2\" >Valais</th>\n      <td id=\"T_bb25a_row2_col0\" class=\"data row2 col0\" >8902</td>\n      <td id=\"T_bb25a_row2_col1\" class=\"data row2 col1\" >23.70</td>\n      <td id=\"T_bb25a_row2_col2\" class=\"data row2 col2\" >13</td>\n      <td id=\"T_bb25a_row2_col3\" class=\"data row2 col3\" >1</td>\n      <td id=\"T_bb25a_row2_col4\" class=\"data row2 col4\" >1</td>\n      <td id=\"T_bb25a_row2_col5\" class=\"data row2 col5\" >2</td>\n      <td id=\"T_bb25a_row2_col6\" class=\"data row2 col6\" >3</td>\n      <td id=\"T_bb25a_row2_col7\" class=\"data row2 col7\" >2</td>\n      <td id=\"T_bb25a_row2_col8\" class=\"data row2 col8\" >1</td>\n      <td id=\"T_bb25a_row2_col9\" class=\"data row2 col9\" >1</td>\n    </tr>\n    <tr>\n      <th id=\"T_bb25a_level0_row3\" class=\"row_heading level0 row3\" >Vaud</th>\n      <td id=\"T_bb25a_row3_col0\" class=\"data row3 col0\" >62468</td>\n      <td id=\"T_bb25a_row3_col1\" class=\"data row3 col1\" >7.78</td>\n      <td id=\"T_bb25a_row3_col2\" class=\"data row3 col2\" >226</td>\n      <td id=\"T_bb25a_row3_col3\" class=\"data row3 col3\" >1</td>\n      <td id=\"T_bb25a_row3_col4\" class=\"data row3 col4\" >1</td>\n      <td id=\"T_bb25a_row3_col5\" class=\"data row3 col5\" >4</td>\n      <td id=\"T_bb25a_row3_col6\" class=\"data row3 col6\" >1</td>\n      <td id=\"T_bb25a_row3_col7\" class=\"data row3 col7\" >1</td>\n      <td id=\"T_bb25a_row3_col8\" class=\"data row3 col8\" >1</td>\n      <td id=\"T_bb25a_row3_col9\" class=\"data row3 col9\" >2</td>\n    </tr>\n    <tr>\n      <th id=\"T_bb25a_level0_row4\" class=\"row_heading level0 row4\" >Zürich</th>\n      <td id=\"T_bb25a_row4_col0\" class=\"data row4 col0\" >27228</td>\n      <td id=\"T_bb25a_row4_col1\" class=\"data row4 col1\" >3.99</td>\n      <td id=\"T_bb25a_row4_col2\" class=\"data row4 col2\" >171</td>\n      <td id=\"T_bb25a_row4_col3\" class=\"data row4 col3\" >1</td>\n      <td id=\"T_bb25a_row4_col4\" class=\"data row4 col4\" >1</td>\n      <td id=\"T_bb25a_row4_col5\" class=\"data row4 col5\" >4</td>\n      <td id=\"T_bb25a_row4_col6\" class=\"data row4 col6\" >1</td>\n      <td id=\"T_bb25a_row4_col7\" class=\"data row4 col7\" >2</td>\n      <td id=\"T_bb25a_row4_col8\" class=\"data row4 col8\" >1</td>\n      <td id=\"T_bb25a_row4_col9\" class=\"data row4 col9\" >2</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a7b903d0>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_190e1 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_190e1 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_190e1 tr {\n  font-size: 10px;\n}\n#T_190e1 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_190e1 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_190e1 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n#T_190e1_row1_col1, #T_190e1_row2_col1, #T_190e1_row3_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_190e1\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_190e1_level0_col0\" class=\"col_heading level0 col0\" >quantity</th>\n      <th id=\"T_190e1_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n      <th id=\"T_190e1_level0_col2\" class=\"col_heading level0 col2\" >samples</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >canton</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_190e1_level0_row0\" class=\"row_heading level0 row0\" >Bern</th>\n      <td id=\"T_190e1_row0_col0\" class=\"data row0 col0\" >9889</td>\n      <td id=\"T_190e1_row0_col1\" class=\"data row0 col1\" >2.81</td>\n      <td id=\"T_190e1_row0_col2\" class=\"data row0 col2\" >98</td>\n    </tr>\n    <tr>\n      <th id=\"T_190e1_level0_row1\" class=\"row_heading level0 row1\" >Genève</th>\n      <td id=\"T_190e1_row1_col0\" class=\"data row1 col0\" >9559</td>\n      <td id=\"T_190e1_row1_col1\" class=\"data row1 col1\" >5.88</td>\n      <td id=\"T_190e1_row1_col2\" class=\"data row1 col2\" >33</td>\n    </tr>\n    <tr>\n      <th id=\"T_190e1_level0_row2\" class=\"row_heading level0 row2\" >Valais</th>\n      <td id=\"T_190e1_row2_col0\" class=\"data row2 col0\" >8902</td>\n      <td id=\"T_190e1_row2_col1\" class=\"data row2 col1\" >23.70</td>\n      <td id=\"T_190e1_row2_col2\" class=\"data row2 col2\" >13</td>\n    </tr>\n    <tr>\n      <th id=\"T_190e1_level0_row3\" class=\"row_heading level0 row3\" >Vaud</th>\n      <td id=\"T_190e1_row3_col0\" class=\"data row3 col0\" >62468</td>\n      <td id=\"T_190e1_row3_col1\" class=\"data row3 col1\" >7.78</td>\n      <td id=\"T_190e1_row3_col2\" class=\"data row3 col2\" >226</td>\n    </tr>\n    <tr>\n      <th id=\"T_190e1_level0_row4\" class=\"row_heading level0 row4\" >Zürich</th>\n      <td id=\"T_190e1_row4_col0\" class=\"data row4 col0\" >27228</td>\n      <td id=\"T_190e1_row4_col1\" class=\"data row4 col1\" >3.99</td>\n      <td id=\"T_190e1_row4_col2\" class=\"data row4 col2\" >171</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e60b4a30>"
      },
      "metadata": {
       "scrapbook": {
@@ -2359,19 +2389,18 @@
     "dxlc = dxl[['location', 'canton', 'feature_type']].drop_duplicates('location')\n",
     "dxlc.set_index(['location'], inplace=True, drop=True)\n",
     "dxf['canton'] = dxf.location.apply(lambda x : dxlc.loc[x, 'canton'])\n",
-    "sumlu = {x:'sum' for x in session_config.feature_variables}\n",
-    "dxf = dxf.groupby(['sample_id', 'canton', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n",
+    "# sumlu = {x:'sum' for x in session_config.feature_variables}\n",
+    "dxf = dxf.groupby(['sample_id', 'canton'], as_index=False).agg(session_config.unit_agg)\n",
     "\n",
-    "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n",
+    "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique'})\n",
     "\n",
-    "for alabel in session_config.feature_variables:\n",
-    "    dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
+    "# for alabel in session_config.feature_variables:\n",
+    "#     dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
     "    \n",
-    "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
-    "dxfc = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
-    "dxfc.rename(columns={'sample_id':'samples'}, inplace=True)\n",
-    "dxfc.drop('check', axis=1, inplace=True)\n",
-    "dxfc = dxfc.style.set_table_styles(userdisplay.table_css_styles)\n",
+    "# dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
+    "# dxfc = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
+    "dxf.rename(columns={'sample_id':'samples'}, inplace=True)\n",
+    "dxfc = dxf.style.set_table_styles(userdisplay.table_css_styles)\n",
     "dxfc = dxfc.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n",
     "dxfc = dxfc.format(userdisplay.format_kwargs, precision=2)\n",
     "\n",
@@ -2394,8 +2423,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_795cb tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_795cb tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_795cb tr {\n  font-size: 10px;\n}\n#T_795cb th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_795cb td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_795cb caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_795cb\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_795cb_level0_col0\" class=\"col_heading level0 col0\" >quantity</th>\n      <th id=\"T_795cb_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n      <th id=\"T_795cb_level0_col2\" class=\"col_heading level0 col2\" >samples</th>\n      <th id=\"T_795cb_level0_col3\" class=\"col_heading level0 col3\" >orchards</th>\n      <th id=\"T_795cb_level0_col4\" class=\"col_heading level0 col4\" >vineyards</th>\n      <th id=\"T_795cb_level0_col5\" class=\"col_heading level0 col5\" >buildings</th>\n      <th id=\"T_795cb_level0_col6\" class=\"col_heading level0 col6\" >forest</th>\n      <th id=\"T_795cb_level0_col7\" class=\"col_heading level0 col7\" >undefined</th>\n      <th id=\"T_795cb_level0_col8\" class=\"col_heading level0 col8\" >public services</th>\n      <th id=\"T_795cb_level0_col9\" class=\"col_heading level0 col9\" >streets</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >canton</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n      <th class=\"blank col3\" >&nbsp;</th>\n      <th class=\"blank col4\" >&nbsp;</th>\n      <th class=\"blank col5\" >&nbsp;</th>\n      <th class=\"blank col6\" >&nbsp;</th>\n      <th class=\"blank col7\" >&nbsp;</th>\n      <th class=\"blank col8\" >&nbsp;</th>\n      <th class=\"blank col9\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_795cb_level0_row0\" class=\"row_heading level0 row0\" >Bern</th>\n      <td id=\"T_795cb_row0_col0\" class=\"data row0 col0\" >3701</td>\n      <td id=\"T_795cb_row0_col1\" class=\"data row0 col1\" >1.21</td>\n      <td id=\"T_795cb_row0_col2\" class=\"data row0 col2\" >97</td>\n      <td id=\"T_795cb_row0_col3\" class=\"data row0 col3\" >1</td>\n      <td id=\"T_795cb_row0_col4\" class=\"data row0 col4\" >1</td>\n      <td id=\"T_795cb_row0_col5\" class=\"data row0 col5\" >3</td>\n      <td id=\"T_795cb_row0_col6\" class=\"data row0 col6\" >1</td>\n      <td id=\"T_795cb_row0_col7\" class=\"data row0 col7\" >2</td>\n      <td id=\"T_795cb_row0_col8\" class=\"data row0 col8\" >1</td>\n      <td id=\"T_795cb_row0_col9\" class=\"data row0 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_795cb_level0_row1\" class=\"row_heading level0 row1\" >Genève</th>\n      <td id=\"T_795cb_row1_col0\" class=\"data row1 col0\" >1616</td>\n      <td id=\"T_795cb_row1_col1\" class=\"data row1 col1\" >4.38</td>\n      <td id=\"T_795cb_row1_col2\" class=\"data row1 col2\" >9</td>\n      <td id=\"T_795cb_row1_col3\" class=\"data row1 col3\" >1</td>\n      <td id=\"T_795cb_row1_col4\" class=\"data row1 col4\" >1</td>\n      <td id=\"T_795cb_row1_col5\" class=\"data row1 col5\" >4</td>\n      <td id=\"T_795cb_row1_col6\" class=\"data row1 col6\" >1</td>\n      <td id=\"T_795cb_row1_col7\" class=\"data row1 col7\" >1</td>\n      <td id=\"T_795cb_row1_col8\" class=\"data row1 col8\" >1</td>\n      <td id=\"T_795cb_row1_col9\" class=\"data row1 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_795cb_level0_row2\" class=\"row_heading level0 row2\" >Valais</th>\n      <td id=\"T_795cb_row2_col0\" class=\"data row2 col0\" >78</td>\n      <td id=\"T_795cb_row2_col1\" class=\"data row2 col1\" >0.40</td>\n      <td id=\"T_795cb_row2_col2\" class=\"data row2 col2\" >4</td>\n      <td id=\"T_795cb_row2_col3\" class=\"data row2 col3\" >1</td>\n      <td id=\"T_795cb_row2_col4\" class=\"data row2 col4\" >1</td>\n      <td id=\"T_795cb_row2_col5\" class=\"data row2 col5\" >2</td>\n      <td id=\"T_795cb_row2_col6\" class=\"data row2 col6\" >2</td>\n      <td id=\"T_795cb_row2_col7\" class=\"data row2 col7\" >2</td>\n      <td id=\"T_795cb_row2_col8\" class=\"data row2 col8\" >1</td>\n      <td id=\"T_795cb_row2_col9\" class=\"data row2 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_795cb_level0_row3\" class=\"row_heading level0 row3\" >Vaud</th>\n      <td id=\"T_795cb_row3_col0\" class=\"data row3 col0\" >738</td>\n      <td id=\"T_795cb_row3_col1\" class=\"data row3 col1\" >2.64</td>\n      <td id=\"T_795cb_row3_col2\" class=\"data row3 col2\" >5</td>\n      <td id=\"T_795cb_row3_col3\" class=\"data row3 col3\" >1</td>\n      <td id=\"T_795cb_row3_col4\" class=\"data row3 col4\" >1</td>\n      <td id=\"T_795cb_row3_col5\" class=\"data row3 col5\" >3</td>\n      <td id=\"T_795cb_row3_col6\" class=\"data row3 col6\" >2</td>\n      <td id=\"T_795cb_row3_col7\" class=\"data row3 col7\" >2</td>\n      <td id=\"T_795cb_row3_col8\" class=\"data row3 col8\" >1</td>\n      <td id=\"T_795cb_row3_col9\" class=\"data row3 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_795cb_level0_row4\" class=\"row_heading level0 row4\" >Zürich</th>\n      <td id=\"T_795cb_row4_col0\" class=\"data row4 col0\" >6557</td>\n      <td id=\"T_795cb_row4_col1\" class=\"data row4 col1\" >1.15</td>\n      <td id=\"T_795cb_row4_col2\" class=\"data row4 col2\" >175</td>\n      <td id=\"T_795cb_row4_col3\" class=\"data row4 col3\" >1</td>\n      <td id=\"T_795cb_row4_col4\" class=\"data row4 col4\" >1</td>\n      <td id=\"T_795cb_row4_col5\" class=\"data row4 col5\" >4</td>\n      <td id=\"T_795cb_row4_col6\" class=\"data row4 col6\" >1</td>\n      <td id=\"T_795cb_row4_col7\" class=\"data row4 col7\" >2</td>\n      <td id=\"T_795cb_row4_col8\" class=\"data row4 col8\" >1</td>\n      <td id=\"T_795cb_row4_col9\" class=\"data row4 col9\" >3</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a4699f34340>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_1cfe3 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_1cfe3 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_1cfe3 tr {\n  font-size: 10px;\n}\n#T_1cfe3 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_1cfe3 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_1cfe3 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_1cfe3\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_1cfe3_level0_col0\" class=\"col_heading level0 col0\" >quantity</th>\n      <th id=\"T_1cfe3_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n      <th id=\"T_1cfe3_level0_col2\" class=\"col_heading level0 col2\" >samples</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >canton</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_1cfe3_level0_row0\" class=\"row_heading level0 row0\" >Bern</th>\n      <td id=\"T_1cfe3_row0_col0\" class=\"data row0 col0\" >3701</td>\n      <td id=\"T_1cfe3_row0_col1\" class=\"data row0 col1\" >1.21</td>\n      <td id=\"T_1cfe3_row0_col2\" class=\"data row0 col2\" >97</td>\n    </tr>\n    <tr>\n      <th id=\"T_1cfe3_level0_row1\" class=\"row_heading level0 row1\" >Genève</th>\n      <td id=\"T_1cfe3_row1_col0\" class=\"data row1 col0\" >1616</td>\n      <td id=\"T_1cfe3_row1_col1\" class=\"data row1 col1\" >4.38</td>\n      <td id=\"T_1cfe3_row1_col2\" class=\"data row1 col2\" >9</td>\n    </tr>\n    <tr>\n      <th id=\"T_1cfe3_level0_row2\" class=\"row_heading level0 row2\" >Valais</th>\n      <td id=\"T_1cfe3_row2_col0\" class=\"data row2 col0\" >78</td>\n      <td id=\"T_1cfe3_row2_col1\" class=\"data row2 col1\" >0.40</td>\n      <td id=\"T_1cfe3_row2_col2\" class=\"data row2 col2\" >4</td>\n    </tr>\n    <tr>\n      <th id=\"T_1cfe3_level0_row3\" class=\"row_heading level0 row3\" >Vaud</th>\n      <td id=\"T_1cfe3_row3_col0\" class=\"data row3 col0\" >738</td>\n      <td id=\"T_1cfe3_row3_col1\" class=\"data row3 col1\" >2.64</td>\n      <td id=\"T_1cfe3_row3_col2\" class=\"data row3 col2\" >5</td>\n    </tr>\n    <tr>\n      <th id=\"T_1cfe3_level0_row4\" class=\"row_heading level0 row4\" >Zürich</th>\n      <td id=\"T_1cfe3_row4_col0\" class=\"data row4 col0\" >6557</td>\n      <td id=\"T_1cfe3_row4_col1\" class=\"data row4 col1\" >1.15</td>\n      <td id=\"T_1cfe3_row4_col2\" class=\"data row4 col2\" >175</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e2f1fa60>"
      },
      "metadata": {
       "scrapbook": {
@@ -2413,19 +2442,19 @@
     "dxlc = dxl[['location', 'canton', 'feature_type']].drop_duplicates('location')\n",
     "dxlc.set_index(['location'], inplace=True, drop=True)\n",
     "dxf['canton'] = dxf.location.apply(lambda x : dxlc.loc[x, 'canton'])\n",
-    "sumlu = {x:'sum' for x in session_config.feature_variables}\n",
-    "dxf = dxf.groupby(['sample_id', 'canton', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n",
+    "# sumlu = {x:'sum' for x in session_config.feature_variables}\n",
+    "dxf = dxf.groupby(['sample_id', 'canton'], as_index=False).agg(session_config.unit_agg)\n",
     "\n",
-    "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n",
+    "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique'})\n",
     "\n",
-    "for alabel in session_config.feature_variables:\n",
-    "    dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
+    "# for alabel in session_config.feature_variables:\n",
+    "#     dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
     "    \n",
-    "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
-    "dxfcr = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
-    "dxfcr.rename(columns={'sample_id':'samples'}, inplace=True)\n",
-    "dxfcr.drop('check', axis=1, inplace=True)\n",
-    "dxfcr = dxfcr.style.set_table_styles(userdisplay.table_css_styles)\n",
+    "# dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
+    "# dxfcr = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
+    "dxf.rename(columns={'sample_id':'samples'}, inplace=True)\n",
+    "# dxfcr.drop('check', axis=1, inplace=True)\n",
+    "dxfcr = dxf.style.set_table_styles(userdisplay.table_css_styles)\n",
     "dxfcr = dxfcr.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n",
     "dxfcr = dxfcr.format(userdisplay.format_kwargs, precision=2)\n",
     "# glue('all-data-municipal-results', i , display=False)\n",
@@ -2448,8 +2477,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_ecb27 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_ecb27 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_ecb27 tr {\n  font-size: 10px;\n}\n#T_ecb27 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_ecb27 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_ecb27 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_ecb27\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_ecb27_level0_col0\" class=\"col_heading level0 col0\" >samples</th>\n      <th id=\"T_ecb27_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_ecb27_level0_row0\" class=\"row_heading level0 row0\" >Bielersee</th>\n      <td id=\"T_ecb27_row0_col0\" class=\"data row0 col0\" >50</td>\n      <td id=\"T_ecb27_row0_col1\" class=\"data row0 col1\" >4.08</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row1\" class=\"row_heading level0 row1\" >Brienzersee</th>\n      <td id=\"T_ecb27_row1_col0\" class=\"data row1 col0\" >5</td>\n      <td id=\"T_ecb27_row1_col1\" class=\"data row1 col1\" >3.96</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row2\" class=\"row_heading level0 row2\" >Greifensee</th>\n      <td id=\"T_ecb27_row2_col0\" class=\"data row2 col0\" >34</td>\n      <td id=\"T_ecb27_row2_col1\" class=\"data row2 col1\" >4.71</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row3\" class=\"row_heading level0 row3\" >Katzensee</th>\n      <td id=\"T_ecb27_row3_col0\" class=\"data row3 col0\" >12</td>\n      <td id=\"T_ecb27_row3_col1\" class=\"data row3 col1\" >1.77</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row4\" class=\"row_heading level0 row4\" >Lac-leman</th>\n      <td id=\"T_ecb27_row4_col0\" class=\"data row4 col0\" >249</td>\n      <td id=\"T_ecb27_row4_col1\" class=\"data row4 col1\" >8.91</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row5\" class=\"row_heading level0 row5\" >Neuenburgersee</th>\n      <td id=\"T_ecb27_row5_col0\" class=\"data row5 col0\" >23</td>\n      <td id=\"T_ecb27_row5_col1\" class=\"data row5 col1\" >1.78</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row6\" class=\"row_heading level0 row6\" >Thunersee</th>\n      <td id=\"T_ecb27_row6_col0\" class=\"data row6 col0\" >43</td>\n      <td id=\"T_ecb27_row6_col1\" class=\"data row6 col1\" >1.21</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row7\" class=\"row_heading level0 row7\" >Zurichsee</th>\n      <td id=\"T_ecb27_row7_col0\" class=\"data row7 col0\" >125</td>\n      <td id=\"T_ecb27_row7_col1\" class=\"data row7 col1\" >4.01</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a4699f138b0>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_a9b8d tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_a9b8d tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_a9b8d tr {\n  font-size: 10px;\n}\n#T_a9b8d th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_a9b8d td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_a9b8d caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_a9b8d\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a9b8d_level0_col0\" class=\"col_heading level0 col0\" >samples</th>\n      <th id=\"T_a9b8d_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a9b8d_level0_row0\" class=\"row_heading level0 row0\" >Bielersee</th>\n      <td id=\"T_a9b8d_row0_col0\" class=\"data row0 col0\" >50</td>\n      <td id=\"T_a9b8d_row0_col1\" class=\"data row0 col1\" >4.08</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row1\" class=\"row_heading level0 row1\" >Brienzersee</th>\n      <td id=\"T_a9b8d_row1_col0\" class=\"data row1 col0\" >5</td>\n      <td id=\"T_a9b8d_row1_col1\" class=\"data row1 col1\" >3.96</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row2\" class=\"row_heading level0 row2\" >Greifensee</th>\n      <td id=\"T_a9b8d_row2_col0\" class=\"data row2 col0\" >34</td>\n      <td id=\"T_a9b8d_row2_col1\" class=\"data row2 col1\" >4.71</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row3\" class=\"row_heading level0 row3\" >Katzensee</th>\n      <td id=\"T_a9b8d_row3_col0\" class=\"data row3 col0\" >12</td>\n      <td id=\"T_a9b8d_row3_col1\" class=\"data row3 col1\" >1.77</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row4\" class=\"row_heading level0 row4\" >Lac-leman</th>\n      <td id=\"T_a9b8d_row4_col0\" class=\"data row4 col0\" >249</td>\n      <td id=\"T_a9b8d_row4_col1\" class=\"data row4 col1\" >8.91</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row5\" class=\"row_heading level0 row5\" >Neuenburgersee</th>\n      <td id=\"T_a9b8d_row5_col0\" class=\"data row5 col0\" >23</td>\n      <td id=\"T_a9b8d_row5_col1\" class=\"data row5 col1\" >1.78</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row6\" class=\"row_heading level0 row6\" >Thunersee</th>\n      <td id=\"T_a9b8d_row6_col0\" class=\"data row6 col0\" >43</td>\n      <td id=\"T_a9b8d_row6_col1\" class=\"data row6 col1\" >1.21</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row7\" class=\"row_heading level0 row7\" >Zurichsee</th>\n      <td id=\"T_a9b8d_row7_col0\" class=\"data row7 col0\" >125</td>\n      <td id=\"T_a9b8d_row7_col1\" class=\"data row7 col1\" >4.01</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e626c4c0>"
      },
      "metadata": {
       "scrapbook": {
@@ -2461,8 +2490,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_e63cc tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_e63cc tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_e63cc tr {\n  font-size: 10px;\n}\n#T_e63cc th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_e63cc td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_e63cc caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_e63cc\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_e63cc_level0_col0\" class=\"col_heading level0 col0\" >samples</th>\n      <th id=\"T_e63cc_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_e63cc_level0_row0\" class=\"row_heading level0 row0\" >Aare</th>\n      <td id=\"T_e63cc_row0_col0\" class=\"data row0 col0\" >61</td>\n      <td id=\"T_e63cc_row0_col1\" class=\"data row0 col1\" >1.27</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row1\" class=\"row_heading level0 row1\" >Aarenidau-buren-kanal</th>\n      <td id=\"T_e63cc_row1_col0\" class=\"data row1 col0\" >3</td>\n      <td id=\"T_e63cc_row1_col1\" class=\"data row1 col1\" >1.26</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row2\" class=\"row_heading level0 row2\" >Arve</th>\n      <td id=\"T_e63cc_row2_col0\" class=\"data row2 col0\" >8</td>\n      <td id=\"T_e63cc_row2_col1\" class=\"data row2 col1\" >2.92</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row3\" class=\"row_heading level0 row3\" >Chriesbach</th>\n      <td id=\"T_e63cc_row3_col0\" class=\"data row3 col0\" >12</td>\n      <td id=\"T_e63cc_row3_col1\" class=\"data row3 col1\" >0.92</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row4\" class=\"row_heading level0 row4\" >Dorfbach</th>\n      <td id=\"T_e63cc_row4_col0\" class=\"data row4 col0\" >1</td>\n      <td id=\"T_e63cc_row4_col1\" class=\"data row4 col1\" >0.11</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row5\" class=\"row_heading level0 row5\" >Emme</th>\n      <td id=\"T_e63cc_row5_col0\" class=\"data row5 col0\" >9</td>\n      <td id=\"T_e63cc_row5_col1\" class=\"data row5 col1\" >0.60</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row6\" class=\"row_heading level0 row6\" >Glatt</th>\n      <td id=\"T_e63cc_row6_col0\" class=\"data row6 col0\" >7</td>\n      <td id=\"T_e63cc_row6_col1\" class=\"data row6 col1\" >1.67</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row7\" class=\"row_heading level0 row7\" >Grandelbach</th>\n      <td id=\"T_e63cc_row7_col0\" class=\"data row7 col0\" >4</td>\n      <td id=\"T_e63cc_row7_col1\" class=\"data row7 col1\" >1.33</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row8\" class=\"row_heading level0 row8\" >Jona</th>\n      <td id=\"T_e63cc_row8_col0\" class=\"data row8 col0\" >12</td>\n      <td id=\"T_e63cc_row8_col1\" class=\"data row8 col1\" >2.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row9\" class=\"row_heading level0 row9\" >La-thiele</th>\n      <td id=\"T_e63cc_row9_col0\" class=\"data row9 col0\" >1</td>\n      <td id=\"T_e63cc_row9_col1\" class=\"data row9 col1\" >0.46</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row10\" class=\"row_heading level0 row10\" >Langeten</th>\n      <td id=\"T_e63cc_row10_col0\" class=\"data row10 col0\" >11</td>\n      <td id=\"T_e63cc_row10_col1\" class=\"data row10 col1\" >1.94</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row11\" class=\"row_heading level0 row11\" >Limmat</th>\n      <td id=\"T_e63cc_row11_col0\" class=\"data row11 col0\" >61</td>\n      <td id=\"T_e63cc_row11_col1\" class=\"data row11 col1\" >1.13</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row12\" class=\"row_heading level0 row12\" >Ognonnaz</th>\n      <td id=\"T_e63cc_row12_col0\" class=\"data row12 col0\" >1</td>\n      <td id=\"T_e63cc_row12_col1\" class=\"data row12 col1\" >2.85</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row13\" class=\"row_heading level0 row13\" >Rhein</th>\n      <td id=\"T_e63cc_row13_col0\" class=\"data row13 col0\" >10</td>\n      <td id=\"T_e63cc_row13_col1\" class=\"data row13 col1\" >1.52</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row14\" class=\"row_heading level0 row14\" >Rhone</th>\n      <td id=\"T_e63cc_row14_col0\" class=\"data row14 col0\" >8</td>\n      <td id=\"T_e63cc_row14_col1\" class=\"data row14 col1\" >3.43</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row15\" class=\"row_heading level0 row15\" >Schuss</th>\n      <td id=\"T_e63cc_row15_col0\" class=\"data row15 col0\" >2</td>\n      <td id=\"T_e63cc_row15_col1\" class=\"data row15 col1\" >1.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row16\" class=\"row_heading level0 row16\" >Sihl</th>\n      <td id=\"T_e63cc_row16_col0\" class=\"data row16 col0\" >51</td>\n      <td id=\"T_e63cc_row16_col1\" class=\"data row16 col1\" >1.08</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row17\" class=\"row_heading level0 row17\" >Toss</th>\n      <td id=\"T_e63cc_row17_col0\" class=\"data row17 col0\" >17</td>\n      <td id=\"T_e63cc_row17_col1\" class=\"data row17 col1\" >0.59</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row18\" class=\"row_heading level0 row18\" >Zulg</th>\n      <td id=\"T_e63cc_row18_col0\" class=\"data row18 col0\" >11</td>\n      <td id=\"T_e63cc_row18_col1\" class=\"data row18 col1\" >0.63</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a4699ebd310>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_90020 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_90020 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_90020 tr {\n  font-size: 10px;\n}\n#T_90020 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_90020 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_90020 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_90020\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_90020_level0_col0\" class=\"col_heading level0 col0\" >samples</th>\n      <th id=\"T_90020_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_90020_level0_row0\" class=\"row_heading level0 row0\" >Aare</th>\n      <td id=\"T_90020_row0_col0\" class=\"data row0 col0\" >61</td>\n      <td id=\"T_90020_row0_col1\" class=\"data row0 col1\" >1.27</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row1\" class=\"row_heading level0 row1\" >Aarenidau-buren-kanal</th>\n      <td id=\"T_90020_row1_col0\" class=\"data row1 col0\" >3</td>\n      <td id=\"T_90020_row1_col1\" class=\"data row1 col1\" >1.26</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row2\" class=\"row_heading level0 row2\" >Arve</th>\n      <td id=\"T_90020_row2_col0\" class=\"data row2 col0\" >8</td>\n      <td id=\"T_90020_row2_col1\" class=\"data row2 col1\" >2.92</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row3\" class=\"row_heading level0 row3\" >Chriesbach</th>\n      <td id=\"T_90020_row3_col0\" class=\"data row3 col0\" >12</td>\n      <td id=\"T_90020_row3_col1\" class=\"data row3 col1\" >0.92</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row4\" class=\"row_heading level0 row4\" >Dorfbach</th>\n      <td id=\"T_90020_row4_col0\" class=\"data row4 col0\" >1</td>\n      <td id=\"T_90020_row4_col1\" class=\"data row4 col1\" >0.11</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row5\" class=\"row_heading level0 row5\" >Emme</th>\n      <td id=\"T_90020_row5_col0\" class=\"data row5 col0\" >9</td>\n      <td id=\"T_90020_row5_col1\" class=\"data row5 col1\" >0.60</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row6\" class=\"row_heading level0 row6\" >Glatt</th>\n      <td id=\"T_90020_row6_col0\" class=\"data row6 col0\" >7</td>\n      <td id=\"T_90020_row6_col1\" class=\"data row6 col1\" >1.67</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row7\" class=\"row_heading level0 row7\" >Grandelbach</th>\n      <td id=\"T_90020_row7_col0\" class=\"data row7 col0\" >4</td>\n      <td id=\"T_90020_row7_col1\" class=\"data row7 col1\" >1.33</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row8\" class=\"row_heading level0 row8\" >Jona</th>\n      <td id=\"T_90020_row8_col0\" class=\"data row8 col0\" >12</td>\n      <td id=\"T_90020_row8_col1\" class=\"data row8 col1\" >2.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row9\" class=\"row_heading level0 row9\" >La-thiele</th>\n      <td id=\"T_90020_row9_col0\" class=\"data row9 col0\" >1</td>\n      <td id=\"T_90020_row9_col1\" class=\"data row9 col1\" >0.46</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row10\" class=\"row_heading level0 row10\" >Langeten</th>\n      <td id=\"T_90020_row10_col0\" class=\"data row10 col0\" >11</td>\n      <td id=\"T_90020_row10_col1\" class=\"data row10 col1\" >1.94</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row11\" class=\"row_heading level0 row11\" >Limmat</th>\n      <td id=\"T_90020_row11_col0\" class=\"data row11 col0\" >61</td>\n      <td id=\"T_90020_row11_col1\" class=\"data row11 col1\" >1.13</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row12\" class=\"row_heading level0 row12\" >Ognonnaz</th>\n      <td id=\"T_90020_row12_col0\" class=\"data row12 col0\" >1</td>\n      <td id=\"T_90020_row12_col1\" class=\"data row12 col1\" >2.85</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row13\" class=\"row_heading level0 row13\" >Rhein</th>\n      <td id=\"T_90020_row13_col0\" class=\"data row13 col0\" >10</td>\n      <td id=\"T_90020_row13_col1\" class=\"data row13 col1\" >1.52</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row14\" class=\"row_heading level0 row14\" >Rhone</th>\n      <td id=\"T_90020_row14_col0\" class=\"data row14 col0\" >8</td>\n      <td id=\"T_90020_row14_col1\" class=\"data row14 col1\" >3.43</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row15\" class=\"row_heading level0 row15\" >Schuss</th>\n      <td id=\"T_90020_row15_col0\" class=\"data row15 col0\" >2</td>\n      <td id=\"T_90020_row15_col1\" class=\"data row15 col1\" >1.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row16\" class=\"row_heading level0 row16\" >Sihl</th>\n      <td id=\"T_90020_row16_col0\" class=\"data row16 col0\" >51</td>\n      <td id=\"T_90020_row16_col1\" class=\"data row16 col1\" >1.08</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row17\" class=\"row_heading level0 row17\" >Toss</th>\n      <td id=\"T_90020_row17_col0\" class=\"data row17 col0\" >17</td>\n      <td id=\"T_90020_row17_col1\" class=\"data row17 col1\" >0.59</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row18\" class=\"row_heading level0 row18\" >Zulg</th>\n      <td id=\"T_90020_row18_col0\" class=\"data row18 col0\" >11</td>\n      <td id=\"T_90020_row18_col1\" class=\"data row18 col1\" >0.63</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e624d0a0>"
      },
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@@ -2521,18 +2550,22 @@
     ":::\n",
     "::::\n",
     "\n",
-    "## Municipal Results - all data\n",
+    "## Cantonal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each canton.\n",
+    "The average pieces per meter for each canton.\n",
     "\n",
-    "::::::::::{tab-set}\n",
+    "::::::::::{grid}\n",
     "\n",
-    ":::::::::{tab-item} Lakes\n",
+    ":::::::::{grid-item-card}\n",
+    "Lakes\n",
+    "^^^\n",
     "```{glue} lake-municipal-results\n",
     "```\n",
     ":::::::::\n",
     "\n",
-    ":::::::::{tab-item} Rivers\n",
+    ":::::::::{grid-item-card}\n",
+    "Rivers\n",
+    "^^^\n",
     "```{glue} river-municipal-results\n",
     "``` \n",
     ":::::::::\n",
diff --git a/docs/_images/0a9ec2d2feba1c6d1a60449ef39dcd379748b26ade040ebe37bbf600e0d5de7b.png b/docs/_images/0a9ec2d2feba1c6d1a60449ef39dcd379748b26ade040ebe37bbf600e0d5de7b.png
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diff --git a/docs/_sources/bern.ipynb b/docs/_sources/bern.ipynb
index 44ab057..04f3bf9 100644
--- a/docs/_sources/bern.ipynb
+++ b/docs/_sources/bern.ipynb
@@ -6,6 +6,9 @@
    "id": "5bf92247-ef77-4aae-b62f-9388cba6765e",
    "metadata": {
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+    "jupyter": {
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+    },
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@@ -113,6 +116,9 @@
    "id": "fb4d8635-eb6f-402a-9cd6-8be9c76cce30",
    "metadata": {
     "editable": true,
+    "jupyter": {
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+    },
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@@ -178,6 +184,9 @@
    "id": "d504b355-6ec4-4d6d-b9d6-3459215fcb8b",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -473,6 +482,9 @@
    "id": "c016aff6-dc3f-49a1-be4d-48b6448c9d22",
    "metadata": {
     "editable": true,
+    "jupyter": {
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+    },
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@@ -781,6 +793,9 @@
    "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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     },
@@ -1089,6 +1104,9 @@
    "id": "12c52a87-8340-419f-bfd9-75ca85260a97",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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@@ -1675,6 +1693,9 @@
    "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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@@ -1729,6 +1750,9 @@
    "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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@@ -1787,6 +1811,9 @@
    "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1842,6 +1869,9 @@
    "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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     },
@@ -1901,6 +1931,9 @@
    "id": "82f55461-c497-483a-8c38-fbd509809afb",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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     },
@@ -1957,6 +1990,9 @@
    "id": "9b396025-1fa6-4661-9116-593fa1ed741d",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -2335,6 +2371,9 @@
    "id": "b0bda4c0-1b4a-4011-9ad1-fb3a52aac885",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -2389,6 +2428,9 @@
    "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
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@@ -2443,6 +2485,9 @@
    "id": "2d5b8904-044b-4aed-916c-5e36018f4087",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -2528,7 +2573,7 @@
     "\n",
     "## Municipal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
     "\n",
     "::::::::::{tab-set}\n",
     "\n",
diff --git a/docs/_sources/combined.ipynb b/docs/_sources/combined.ipynb
index 11cf7ab..5ae758e 100644
--- a/docs/_sources/combined.ipynb
+++ b/docs/_sources/combined.ipynb
@@ -6,6 +6,9 @@
    "id": "5bf92247-ef77-4aae-b62f-9388cba6765e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -180,6 +183,9 @@
    "id": "d504b355-6ec4-4d6d-b9d6-3459215fcb8b",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -216,7 +222,7 @@
     },
     {
      "data": {
-      "application/papermill.record/image/png": 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",
+      "application/papermill.record/image/png": 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       "application/papermill.record/text/plain": "<Figure size 640x480 with 1 Axes>"
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@@ -242,8 +248,8 @@
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     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_d466c tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_d466c tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_d466c tr {\n  font-size: 10px;\n}\n#T_d466c th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_d466c td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_d466c caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_d466c\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_d466c_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_d466c_level0_row0\" class=\"row_heading level0 row0\" >glass</th>\n      <td id=\"T_d466c_row0_col0\" class=\"data row0 col0\" >6%</td>\n    </tr>\n    <tr>\n      <th id=\"T_d466c_level0_row1\" class=\"row_heading level0 row1\" >metal</th>\n      <td id=\"T_d466c_row1_col0\" class=\"data row1 col0\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_d466c_level0_row2\" class=\"row_heading level0 row2\" >paper</th>\n      <td id=\"T_d466c_row2_col0\" class=\"data row2 col0\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_d466c_level0_row3\" class=\"row_heading level0 row3\" >plastic</th>\n      <td id=\"T_d466c_row3_col0\" class=\"data row3 col0\" >81%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb2c9a0>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_92ad7 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_92ad7 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_92ad7 tr {\n  font-size: 10px;\n}\n#T_92ad7 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_92ad7 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_92ad7 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_92ad7\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_92ad7_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_92ad7_level0_row0\" class=\"row_heading level0 row0\" >glass</th>\n      <td id=\"T_92ad7_row0_col0\" class=\"data row0 col0\" >6%</td>\n    </tr>\n    <tr>\n      <th id=\"T_92ad7_level0_row1\" class=\"row_heading level0 row1\" >metal</th>\n      <td id=\"T_92ad7_row1_col0\" class=\"data row1 col0\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_92ad7_level0_row2\" class=\"row_heading level0 row2\" >paper</th>\n      <td id=\"T_92ad7_row2_col0\" class=\"data row2 col0\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_92ad7_level0_row3\" class=\"row_heading level0 row3\" >plastic</th>\n      <td id=\"T_92ad7_row3_col0\" class=\"data row3 col0\" >81%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e8bafd30>"
      },
      "metadata": {
       "scrapbook": {
@@ -255,7 +261,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 3.33\n* HDI 95%: 0.1 - 8.1\n* 90% Range: 0.4 - 7.05",
+      "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 5.2\n* HDI 95%: 0.1 - 18.7\n* 90% Range: 0.2 - 16.51",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -268,7 +274,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 6.38\n* HDI 95%: 0.02 - 31.4\n* 90% Range: 0.14 - 38.0",
+      "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 7.55\n* HDI 95%: 0.02 - 42.66\n* 90% Range: 0.16 - 34.52",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -281,7 +287,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 3.63\n* HDI 95%: 0.1 - 10.4\n* 90% Range: 0.2 - 9.83",
+      "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 3.84\n* HDI 95%: 0.1 - 13.7\n* 90% Range: 0.2 - 13.03",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -307,8 +313,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_b04cc tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_b04cc tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_b04cc tr {\n  font-size: 10px;\n}\n#T_b04cc th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_b04cc td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_b04cc caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_b04cc caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_b04cc td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_b04cc\">\n  <thead>\n    <tr>\n      <th id=\"T_b04cc_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_b04cc_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_b04cc_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_b04cc_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_b04cc_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_b04cc_row0_col0\" class=\"data row0 col0\" >Fragmented plastics</td>\n      <td id=\"T_b04cc_row0_col1\" class=\"data row0 col1\" >6'796</td>\n      <td id=\"T_b04cc_row0_col2\" class=\"data row0 col2\" >0,83</td>\n      <td id=\"T_b04cc_row0_col3\" class=\"data row0 col3\" >0,15</td>\n      <td id=\"T_b04cc_row0_col4\" class=\"data row0 col4\" >0,88</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_b04cc_row1_col1\" class=\"data row1 col1\" >6'306</td>\n      <td id=\"T_b04cc_row1_col2\" class=\"data row1 col2\" >0,60</td>\n      <td id=\"T_b04cc_row1_col3\" class=\"data row1 col3\" >0,14</td>\n      <td id=\"T_b04cc_row1_col4\" class=\"data row1 col4\" >0,87</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row2_col0\" class=\"data row2 col0\" >Expanded polystyrene</td>\n      <td id=\"T_b04cc_row2_col1\" class=\"data row2 col1\" >4'319</td>\n      <td id=\"T_b04cc_row2_col2\" class=\"data row2 col2\" >0,55</td>\n      <td id=\"T_b04cc_row2_col3\" class=\"data row2 col3\" >0,10</td>\n      <td id=\"T_b04cc_row2_col4\" class=\"data row2 col4\" >0,67</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row3_col0\" class=\"data row3 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_b04cc_row3_col1\" class=\"data row3 col1\" >2'890</td>\n      <td id=\"T_b04cc_row3_col2\" class=\"data row3 col2\" >0,31</td>\n      <td id=\"T_b04cc_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_b04cc_row3_col4\" class=\"data row3 col4\" >0,88</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row4_col0\" class=\"data row4 col0\" >Industrial sheeting</td>\n      <td id=\"T_b04cc_row4_col1\" class=\"data row4 col1\" >2'031</td>\n      <td id=\"T_b04cc_row4_col2\" class=\"data row4 col2\" >0,24</td>\n      <td id=\"T_b04cc_row4_col3\" class=\"data row4 col3\" >0,05</td>\n      <td id=\"T_b04cc_row4_col4\" class=\"data row4 col4\" >0,69</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row5_col0\" class=\"data row5 col0\" >Industrial pellets (nurdles)</td>\n      <td id=\"T_b04cc_row5_col1\" class=\"data row5 col1\" >1'715</td>\n      <td id=\"T_b04cc_row5_col2\" class=\"data row5 col2\" >0,18</td>\n      <td id=\"T_b04cc_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_b04cc_row5_col4\" class=\"data row5 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row6_col0\" class=\"data row6 col0\" >plastic caps, lid rings: G21, G22, G23, G24</td>\n      <td id=\"T_b04cc_row6_col1\" class=\"data row6 col1\" >1'704</td>\n      <td id=\"T_b04cc_row6_col2\" class=\"data row6 col2\" >0,19</td>\n      <td id=\"T_b04cc_row6_col3\" class=\"data row6 col3\" >0,04</td>\n      <td id=\"T_b04cc_row6_col4\" class=\"data row6 col4\" >0,71</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row7_col0\" class=\"data row7 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_b04cc_row7_col1\" class=\"data row7 col1\" >1'502</td>\n      <td id=\"T_b04cc_row7_col2\" class=\"data row7 col2\" >0,19</td>\n      <td id=\"T_b04cc_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_b04cc_row7_col4\" class=\"data row7 col4\" >0,65</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row8_col0\" class=\"data row8 col0\" >Foam packaging/insulation/polyurethane</td>\n      <td id=\"T_b04cc_row8_col1\" class=\"data row8 col1\" >1'435</td>\n      <td id=\"T_b04cc_row8_col2\" class=\"data row8 col2\" >0,09</td>\n      <td id=\"T_b04cc_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_b04cc_row8_col4\" class=\"data row8 col4\" >0,87</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row9_col0\" class=\"data row9 col0\" >Cotton bud/swab sticks</td>\n      <td id=\"T_b04cc_row9_col1\" class=\"data row9 col1\" >1'385</td>\n      <td id=\"T_b04cc_row9_col2\" class=\"data row9 col2\" >0,16</td>\n      <td id=\"T_b04cc_row9_col3\" class=\"data row9 col3\" >0,03</td>\n      <td id=\"T_b04cc_row9_col4\" class=\"data row9 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row10_col0\" class=\"data row10 col0\" >Plastic construction waste</td>\n      <td id=\"T_b04cc_row10_col1\" class=\"data row10 col1\" >836</td>\n      <td id=\"T_b04cc_row10_col2\" class=\"data row10 col2\" >0,09</td>\n      <td id=\"T_b04cc_row10_col3\" class=\"data row10 col3\" >0,02</td>\n      <td id=\"T_b04cc_row10_col4\" class=\"data row10 col4\" >0,53</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row11_col0\" class=\"data row11 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_b04cc_row11_col1\" class=\"data row11 col1\" >551</td>\n      <td id=\"T_b04cc_row11_col2\" class=\"data row11 col2\" >0,05</td>\n      <td id=\"T_b04cc_row11_col3\" class=\"data row11 col3\" >0,01</td>\n      <td id=\"T_b04cc_row11_col4\" class=\"data row11 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row12_col0\" class=\"data row12 col0\" >Tobacco; plastic packaging, containers</td>\n      <td id=\"T_b04cc_row12_col1\" class=\"data row12 col1\" >529</td>\n      <td id=\"T_b04cc_row12_col2\" class=\"data row12 col2\" >0,06</td>\n      <td id=\"T_b04cc_row12_col3\" class=\"data row12 col3\" >0,01</td>\n      <td id=\"T_b04cc_row12_col4\" class=\"data row12 col4\" >0,50</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row13_col0\" class=\"data row13 col0\" >Foil wrappers, aluminum foil</td>\n      <td id=\"T_b04cc_row13_col1\" class=\"data row13 col1\" >386</td>\n      <td id=\"T_b04cc_row13_col2\" class=\"data row13 col2\" >0,04</td>\n      <td id=\"T_b04cc_row13_col3\" class=\"data row13 col3\" >0,01</td>\n      <td id=\"T_b04cc_row13_col4\" class=\"data row13 col4\" >0,50</td>\n    </tr>\n    <tr>\n      <td id=\"T_b04cc_row14_col0\" class=\"data row14 col0\" >Straws and stirrers</td>\n      <td id=\"T_b04cc_row14_col1\" class=\"data row14 col1\" >382</td>\n      <td id=\"T_b04cc_row14_col2\" class=\"data row14 col2\" >0,04</td>\n      <td id=\"T_b04cc_row14_col3\" class=\"data row14 col3\" >0,01</td>\n      <td id=\"T_b04cc_row14_col4\" class=\"data row14 col4\" >0,50</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb18f40>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_3adc8 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_3adc8 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_3adc8 tr {\n  font-size: 10px;\n}\n#T_3adc8 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_3adc8 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_3adc8 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_3adc8 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_3adc8 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_3adc8\">\n  <thead>\n    <tr>\n      <th id=\"T_3adc8_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_3adc8_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_3adc8_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_3adc8_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_3adc8_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_3adc8_row0_col0\" class=\"data row0 col0\" >Fragmented plastics</td>\n      <td id=\"T_3adc8_row0_col1\" class=\"data row0 col1\" >6'796</td>\n      <td id=\"T_3adc8_row0_col2\" class=\"data row0 col2\" >0,83</td>\n      <td id=\"T_3adc8_row0_col3\" class=\"data row0 col3\" >0,15</td>\n      <td id=\"T_3adc8_row0_col4\" class=\"data row0 col4\" >0,88</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_3adc8_row1_col1\" class=\"data row1 col1\" >6'306</td>\n      <td id=\"T_3adc8_row1_col2\" class=\"data row1 col2\" >0,60</td>\n      <td id=\"T_3adc8_row1_col3\" class=\"data row1 col3\" >0,14</td>\n      <td id=\"T_3adc8_row1_col4\" class=\"data row1 col4\" >0,87</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row2_col0\" class=\"data row2 col0\" >Expanded polystyrene</td>\n      <td id=\"T_3adc8_row2_col1\" class=\"data row2 col1\" >4'319</td>\n      <td id=\"T_3adc8_row2_col2\" class=\"data row2 col2\" >0,55</td>\n      <td id=\"T_3adc8_row2_col3\" class=\"data row2 col3\" >0,10</td>\n      <td id=\"T_3adc8_row2_col4\" class=\"data row2 col4\" >0,67</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row3_col0\" class=\"data row3 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_3adc8_row3_col1\" class=\"data row3 col1\" >2'890</td>\n      <td id=\"T_3adc8_row3_col2\" class=\"data row3 col2\" >0,31</td>\n      <td id=\"T_3adc8_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_3adc8_row3_col4\" class=\"data row3 col4\" >0,88</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row4_col0\" class=\"data row4 col0\" >Industrial sheeting</td>\n      <td id=\"T_3adc8_row4_col1\" class=\"data row4 col1\" >2'031</td>\n      <td id=\"T_3adc8_row4_col2\" class=\"data row4 col2\" >0,24</td>\n      <td id=\"T_3adc8_row4_col3\" class=\"data row4 col3\" >0,05</td>\n      <td id=\"T_3adc8_row4_col4\" class=\"data row4 col4\" >0,69</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row5_col0\" class=\"data row5 col0\" >Industrial pellets (nurdles)</td>\n      <td id=\"T_3adc8_row5_col1\" class=\"data row5 col1\" >1'715</td>\n      <td id=\"T_3adc8_row5_col2\" class=\"data row5 col2\" >0,18</td>\n      <td id=\"T_3adc8_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_3adc8_row5_col4\" class=\"data row5 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row6_col0\" class=\"data row6 col0\" >plastic caps, lid rings: G21, G22, G23, G24</td>\n      <td id=\"T_3adc8_row6_col1\" class=\"data row6 col1\" >1'704</td>\n      <td id=\"T_3adc8_row6_col2\" class=\"data row6 col2\" >0,19</td>\n      <td id=\"T_3adc8_row6_col3\" class=\"data row6 col3\" >0,04</td>\n      <td id=\"T_3adc8_row6_col4\" class=\"data row6 col4\" >0,71</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row7_col0\" class=\"data row7 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_3adc8_row7_col1\" class=\"data row7 col1\" >1'502</td>\n      <td id=\"T_3adc8_row7_col2\" class=\"data row7 col2\" >0,19</td>\n      <td id=\"T_3adc8_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_3adc8_row7_col4\" class=\"data row7 col4\" >0,65</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row8_col0\" class=\"data row8 col0\" >Foam packaging/insulation/polyurethane</td>\n      <td id=\"T_3adc8_row8_col1\" class=\"data row8 col1\" >1'435</td>\n      <td id=\"T_3adc8_row8_col2\" class=\"data row8 col2\" >0,09</td>\n      <td id=\"T_3adc8_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_3adc8_row8_col4\" class=\"data row8 col4\" >0,87</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row9_col0\" class=\"data row9 col0\" >Cotton bud/swab sticks</td>\n      <td id=\"T_3adc8_row9_col1\" class=\"data row9 col1\" >1'385</td>\n      <td id=\"T_3adc8_row9_col2\" class=\"data row9 col2\" >0,16</td>\n      <td id=\"T_3adc8_row9_col3\" class=\"data row9 col3\" >0,03</td>\n      <td id=\"T_3adc8_row9_col4\" class=\"data row9 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row10_col0\" class=\"data row10 col0\" >Plastic construction waste</td>\n      <td id=\"T_3adc8_row10_col1\" class=\"data row10 col1\" >836</td>\n      <td id=\"T_3adc8_row10_col2\" class=\"data row10 col2\" >0,09</td>\n      <td id=\"T_3adc8_row10_col3\" class=\"data row10 col3\" >0,02</td>\n      <td id=\"T_3adc8_row10_col4\" class=\"data row10 col4\" >0,53</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row11_col0\" class=\"data row11 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_3adc8_row11_col1\" class=\"data row11 col1\" >551</td>\n      <td id=\"T_3adc8_row11_col2\" class=\"data row11 col2\" >0,05</td>\n      <td id=\"T_3adc8_row11_col3\" class=\"data row11 col3\" >0,01</td>\n      <td id=\"T_3adc8_row11_col4\" class=\"data row11 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row12_col0\" class=\"data row12 col0\" >Tobacco; plastic packaging, containers</td>\n      <td id=\"T_3adc8_row12_col1\" class=\"data row12 col1\" >529</td>\n      <td id=\"T_3adc8_row12_col2\" class=\"data row12 col2\" >0,06</td>\n      <td id=\"T_3adc8_row12_col3\" class=\"data row12 col3\" >0,01</td>\n      <td id=\"T_3adc8_row12_col4\" class=\"data row12 col4\" >0,50</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row13_col0\" class=\"data row13 col0\" >Foil wrappers, aluminum foil</td>\n      <td id=\"T_3adc8_row13_col1\" class=\"data row13 col1\" >386</td>\n      <td id=\"T_3adc8_row13_col2\" class=\"data row13 col2\" >0,04</td>\n      <td id=\"T_3adc8_row13_col3\" class=\"data row13 col3\" >0,01</td>\n      <td id=\"T_3adc8_row13_col4\" class=\"data row13 col4\" >0,50</td>\n    </tr>\n    <tr>\n      <td id=\"T_3adc8_row14_col0\" class=\"data row14 col0\" >Straws and stirrers</td>\n      <td id=\"T_3adc8_row14_col1\" class=\"data row14 col1\" >382</td>\n      <td id=\"T_3adc8_row14_col2\" class=\"data row14 col2\" >0,04</td>\n      <td id=\"T_3adc8_row14_col3\" class=\"data row14 col3\" >0,01</td>\n      <td id=\"T_3adc8_row14_col4\" class=\"data row14 col4\" >0,50</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e657d229580>"
      },
      "metadata": {
       "scrapbook": {
@@ -498,7 +504,7 @@
     },
     {
      "data": {
-      "application/papermill.record/image/png": 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OsheOph2FRfPLb8z2j4pl5fm2aoYE2cjK83Lq9L+YvQfuPK8tES574OOAwxh2fnv0DkEvaao1rGUPHMHNFbiJ1AfGQF4m5B56LknpFNiJVJGZM2cyc+bMYuX1ff1bSaNp17/2K8u37GPmlT0YfLxv9+qKv1P556vLiG0ayoKxhbnDRv03nu837uaRy7pzWQ/f0UvrktP5x/M/0DLcxZdj+xdOke5fhZW9ExNxHDRq7yvzZGLtXoyxHNBqUGEn0v7AytqOCe8KYcf6yrw5WCnfYLCg9QWFddPXYWX+TYvGjdn/+T9Js+CgJ4TL1vrOI/3fCaMxxuKK5hcDsPWjMf6Dw2tKadOpGo0TqSeMgdcHwbZfIKf8k6sK7ESqSHp6eoNb/2YMXPa+h5V/+A7OpvkZvp2Ye8ECxi6Esd/nV871lf2ddsjRPvt85fd+S+HxQnm+sl0ZgVOk/TvAN+vggbPh6vxzzbfshQtmQYQLfrm1sO69H8EnK+GuM+Cm/Py6O9MM5z7hWwu3akRhrrOHv3Dz3m8wyPlOqcvOgmweRrT5pFzfl6Oxhk0BnEg9l5fpC+oqSGvsRKpIaSN2ULfXv5W1vi0zD3q+5Mba5dshaqIG+aY4qyHXWa/W8PYQD14qd5SUMV7+/t/FpO/bBECILddfN9frwIMNBx6CbB46XL4cyx4SMBVbkSBKQZeIHLHcDJjmO084fcRKIqOPVR67kiiwEykfk3+KwD/mGtallBWQWZC7j+fOh3M6N8dus/y7NqvSEaWDMIatCy4i78CWw1YNbtGLtv0/UGAmNatgbVVRdhfY8//x4nGDJ8f3j6GgIufMVmAtVmG7TrDnb8H2esCdDVjgLHIiRm4mUMFwwRYEDmd+u15w5/9/grPIxp68LF+Oxgq16wCHy/e66PcpoN3swvW05WXZISi48LrgexkUWvh/Pu6cwlyT5W63lJ+RIwRs+f+YdeeC95A1s7mZ8ERHANL/vZ7IFm20eULkaDhw4ADGGCzLqjfTrf70HzsM7F2GlZeKCWrim2otsHcZVl4apkkvenWI4sLj83PkGi/bFlx81DYQVFRQeHtiB88vNUrUaJvUuKJrq4q6Yg4cd5nv9frPYe4N0O4s+NcXhXVmnQCZeyv2fhc8Aafe4nv990/wxkXQoivcVuT9X+kHuyt4sOw5E6DfRN/rPRvg+dMhtBnc81dhnbeGwt8/VKzdU26GC5/0vc7cC4/nr6F9MK2wzscjIOHTirUbdykMe7PwOn+0jLs3Q6PmvtcL74Xlr1as3dJ+Rrf+DC3zsxwsfRKWPFqxdkuhwE7kCHXr1q1enfpgDOzNKsjpZiDsGEJyNtOxieH9IieUDXsR/kiCFy+EwccVBHWmVgd1riZxxAz+HMuyHb6ySE2p5NoqqadiTveNGpaTpmJFjlB9Os6rpES98bdAiN2N3eZLPVLAn6bEZmEnBwCvO5PEj04BDj8yVhM0Gid1QpG1Vdy1qXBKVFOx+e02kKnYAkGhpB84oDx2IlLxxL6ZeYFBXc9WhhC7h0auwP+rMMbgtHIAw/ZFJedtix08H1sZSXJFjqqS1qwdqqQ/tKUFERXhCAZb/j+KPHngyS09iID8ICqfMzQwYClgdxQGeUWVVLcibPaS23CWf8So5HZtJbdbNOCpDMsqpd3g4mUVVVK7DhfgqoZ2nYDzyNotaKpKWhGpRebOncukSZM4cOAAAMOHD+fxxx8PqNO1a1cOHjx42LZefPFFLrroIv91fHw8l156aUCd2pqn7kiPyfr1hgxGvbWCW992MPKsWE5p3xi7zQJj/CcflCa4RS8sxxH+IRCpKqWtWTtUSWueSlvPVRHXz4cOfXyv4+fAl3eVvp5L5AgpsJN6Z9KkSaxfX7jINzU1tVidHTt2+AO/smRlBe7szM3NLTVXXU1vnDh0dO7Q0beKOM6xnF2fXcO+lHtYmdOaHdtW8VzHGeU+e1RTnlKr1NU1axVcWyUCCuykHioI2Gw2G61bt6ZJkybF6rRp06ZcI3YhIYHTBE6nM+BosAIFeepqitfAhe9Cwu6S78ffUvJB84emJfG6M9ny2dkEk4VlwatdHiHL6yTYyi0W1BUN5hTISZ1RdM3aoRxF/vfe5044c3Tg0XOhzeDeHRV/T0eRacGeN8BJVwemDoKS2y26tkuknBTYSb3VunXrUjczFB3Rq4iePXvWug0S5jBBXa/W0DTYgDer2IPbvyk+pRqS/3ekw+XLsZUxnapgrg4oWM9VUSUtbi9p4f4RtZu/uL3EhfsVVNLi9qIL94suzi9tzdqhSlrzVNp6roqwBxV+3qKOtF2RfArspM6ZO3cuL7zwAt9++21A+ciRI/niiy9q7Zq3qlJ0ytUYQ0Z2Fgm7fQFY+0gv84dlB/wjP8Ru2L6wYgfTB7fohd3VTIFbXVewnqsiSsszVlIOtYoqKc9YSTnUKqqkPGNFc6jt3ljxNkXqKAV2UucUbIzYsmUL7du395fv27cvYP1bTa95qw7FN0RYQOGo2n+CTmDnJxXbtVfSuaaWPYQct5eb3/gNgFev7xWQ6kRqKa/HFxwBtDuj7LoNScHUq9asSQOgPHZS5xTkjevevTu///67v7xgxA7q9tmsRZW0IaLnKyXXPc6xnFmNh5V7g0OB0qZUM3PdxE3ynQGbMGVQwDmsUksVzX927w7fNKemYgtzqGnNmtRRFYld9P/UUmft3Rt4bM5LL71UQz2pHodLVzK3WS+CrUxcjbsS3e+/hAQdh9u7hjyPwW6Bq8gIW2auB8seDHYHNpvvD1uex0uex4vNeANG47JyPRgMdpvFrCtPAsBprwcnNVQ0B1ltTVZaesO+rxZdC69LW89VESXlGSsth1qF2i0hz1hpOdQqoqQ8Y6XlUBOphxTYyVFxaG650nzzzTd06dLFf/3OO+9wzz33BNSp72vo4NBjvYo7zrGcxtZejvnH8oC1cHN+SGTq/AQuPakNT191sr/+2dMXsS8jl6/Gnk3nKN8U9bz47Uz8aA0D4qJ45bpe/rr9Zy4haX8Wn952JkNOLr4DuE4qbx6zourauZEFZ3veVgfTeohIlVFgJ0fFobnlSuN2B45+ZGZm1tq8ceVR0ZMfCp4ZOteQsKdwyuizY67GSl/lvw4mi+CmcdrgUF51NY+ZiEgFaY2dHBUF6+KAEvPAFSjPiB3UjTV0R3ryQ4GS1s4VPcze6zUk7felMmkZ4cLjNdiswHNdM3N90WWww158KvaQugVTsS6H3XfSRH2Ql+3bhQlw0azCI6LKUhenYo/02CcRqZUqErsosJOjIjk5GY/Hg91up3Xr1jXdnSpV2qhcWRsdyuNYx1pmNR6Gy2RiWRDc1Lf5Iddj8FguHHYbLoe9dmxyqGsHhIuI1CHaPCE1orR1dJMmTWLEiBE11KvqVd5RudJOfvC1YTDuTJK+u5ac/YXT1cFk0eHyX7lq9npevOYEmkdEYlkWry79kye+2shVp8Tw6D+6AxBS06lIXukHuyuY9LlonrE9G+D500vPoVYRpZ3tWTSHmohIPaXATqpMaevoynN0V21X1qjc4YK6Xq2hWQhA4PFdBQ0XnP5go/DUB/AlCc61NSZ+azqv/JjMHf3DShyNC3U6WDd1cEU/UuXlZvoCOYBbvtP0n4hILaLATqrMoWe0FggLC6upLlWJIx2VC3EAeNm24OLDnv5w6PmrWXkeAF7+/i/u6N8JgBFnH8uNZ3WowfVvpsjoXP706y3fUamp2ALNu5R8VuY/51VuKrZAZc/2FBGpoxTYSZUr64zW2qCiO1UrMipX0gZVY8xhg7qCgM5yhGJZFsYYsvI82G0WWx69MKCu02HDSQXzyhUklC1t4X5FWHa4fr7vdcHh5kc6aldanrGScqhVRFWc7SkiUocosJMqU1pakppQWvBmDAydBwm7K9duWaNypQV1npy9/qAuKLw9sYPnl3n6gzGGoS8uI/7vVP51ZnsmX3xc5TpbVMHZnqXlUKuIomd7iohIraLATqpMaGgomZmZNZ5frqrSjByqtFE5Y3xr54pNRBrD9q+HBYzUxQ6ejy2o7BGkPI/h9GOa8vu2/eR5KjgNKSIiDZoCO6kyp512GsnJyUydOrVG+5HlPnxQF9cC5g2t2LGRJY3KGWPYvugKsvfEH/b54Ba9sByHn7J0OmzcPagrt/XreGTr6Dxu30gdQOfzfWvNrEOmcO9YU/F27c7K90lERKqVAjupMt9++21Nd6GYik6dlsYYg9trCMo/M9XrNWS7PXjdWQFBXa7XgQcbDjwE2XwbH5yN42h2zrtYjpCAUyJy3B48XoPDZsPpsPnfp2DDRIXy0ZV0Dmpupm/6FXxBXUlrzbT+TESkXlFgJ5VWkLfO6XTy6aef0r59+5ruEuCLcQqEBpWeP6787fnWvF1wQmtuOqsDAEn7s+gz4ztCgmx8ln+8Z4fLl3Pf51t5Pz6ZO/t34La+7QHYl2Xj+KnfAARshHj0f+uZ/eMWbut3LHcP8h3cnpXnIW7SQkaf14lR5xxDSJD98EeGVeYcVBERqZcquLVOpFBB3rr9+/czcuRIRo0aVdNd8m+OqEpZeR7i/07lpSWbycjJw+vO9OejM54cfz2bIxQrP9WGZQvC5gj1lVXiLNdnvvmT6177tXyVD3cOaszpvmOwRESk3tOInVRaQd667du3Exoayi23HP2dkofufs3MK9zxGteiIIfckbFZFpee2IY8dx47vv4HpK3Bayw+O75wKNDVJA7LHsJDlx7HpIvjcNgK/83UtJGThCmDirU74fyu3D2oS0DdkCC7v265RusOddem4qlHip5tKiIi9ZoCOzlirVu3Zt26dUflvYoGcodLXVLRzRGlcTls3NXsEd9auvxTqWyWIcTK9d1vEkfM4M+xLAuXo/jRXpZllbherlx1jfEl6LXl1/XkgSe3+AH1BZyhWjcnItKAKbCTCitYW5ecXMX5RA6jImlMerU+8rV1/vd1ZwZskCh6OgQE5qCrUgVr5859ADr08ZXFz4Ev74K4S2HYm1X/niIiUqcpsJMKO/RM2KOVt660NCYlpS6p6K7X0mTk5HHGw/8D73T+220ycUN/xO5qVj2B3KEK1s79+DRE9yh9JK6gXGvpREQaPAV2UmFFz4Tt3LlzjeStK5rGpKqCuIJEw0V5cg6Qluc71srVuOvRC+qK2rSo8HXPG+Ckq31TsUXdu0Nr6URERIGdVNwtt9xCWloakZGRTJ48+ai9b1WnMQlsu+REw15j8UrnKACOHbDo6AV1xvjy0B3KHuT7OpTW1YmICArspBKOZjBXoDrSmAS078kq8fQIm2VoH7yT4Ba9sB+t4El56UREpJIU2EmtVrALtjrSmBS+h8HrLhwd63D5cmyHHP1VbRskSnJoXjqtnRMRkXJSYCe1ltfAhe8WT2dSVWlMjDEYdybbvx5GTmqCv7wgsTBAnsfLvPjtAAzt2dZ/pNhRc9cmaNRca+dERKRcFNhJrWRKCeoOl8YkO8+D1xicdhuO/CDM4zXkuD1YWIQ47f6AbvPC4WTt34DTcmPPj5uCmp9ClicIm9dDiNNOnsfLxI/WAHDpSW2qN7ArOO+16No6pzZEiIhI+elIMamQuXPnYlkWlmXRtm3bankPY2BvVmFQ16ExJPwfrLsV5l1Rdpxzzau/EDdpId+sT/GX/Zq4j7hJC7nk2R/8myQ2zz2e8avO4ZI/ZrJ4fw9cTeI49oo/2N/1JY6b/BX9Zy4BfKdODIiLYkBcFLbqDrAy98K0NvBEx+p9HxERqbc0YicVMmnSJP/r6shfV1IS4i+GQyNn8brZeR6GvbQMgA9G9iY4KDAFSEH6EuPJzi/x4snZW2yTRMvTHiOmZ4f8gDUv4F5wkJ1Xrut1xJ+rUrS2TkREKkiBnVRIQQ47oFry12XmBQZ1ZU29eo1h9fY0/2tjDP+94QS8Bpw22LbgInJSE2iaf66rhSHxo8LA7c0x12I5m+ByFJ7Jenx0JAlTBmFxlKY/3Tmw8F7f64GP+PLRFVBeOhERqSAFdlIp0dHRDB06tErbPDSlSfwt0CykeGxTsI7ObrN47fpeGG8ODm8W2xZcFbAJooC9yLmuBYJb9KJRWItiO13ttpLPdQ3oZF4m2ILAkT+M6PWCO6v0Z0rjCAGvG5a/6rseMEX56ERE5IgosJNymzt3LklJSdXW/qEpTUoK6owxXPbcD6zbeZA3ru9Opy2jyElN4O/fS27z0HNdC1QqfUnR/HLnTIB+E33lezbA86dXrC2AW3+Gpsf62gJfsCgiInIEFNhJuVXH+rqCPHUFu2ALlJTSpGDjQ87+AUBbkr8fQZvw9QF1Dg3kqjT/nCcXWp3gC+w8OVXTpsNZGCCKiIgcIQV2Um5Vvb6upI0S4ButK2ldnXFnkr0nnmc6rsaLhTN/o0PRYK5aEwk7XHDhk74p06Kja827BK6NK3d7IVXXNxERERTYSSVUdn1dwehcgUM3SoAvqPtiePHRuqzcPK54/C1s3jFM6/A8cUN/9CcRrnQw5/WAO/vw9Q516Do4m01r40REpFZQYCfl9tZbb5GTk4PL5arws6WNzhWIv8U3ShfiKAzqCtKVYAxbvryMXPcQNmZ1IqhxN+yuZkc+Mvf3T/DGRRV75ozb4cw7ILSZdqyKiEito8BOyq1v376Veq4g4XBpQV2v1sU3Shhj+PurYazcug+AbqGJDGvxNZ+kDqLTBR8dvXNbD/XTf2DbcrhxQc28v4iISBkU2Em1KmmkrmB0rkDRUTr/c54s0nevZuzmmQB8dvw4zm2fy42jbsZmC0xEXCGePIif43t98rWVWxun/HIiIlJLKbCTalPSSF1Jo3NF5Xm85Hm8WG43FoY2zhSCwmI49vLfCA0Jq9hIXUHOuQLORr6drV/e5bs+6WqtjRMRkXpFgZ2UaO7cuUyaNClgJ2xSUhIvvvgiXbp0KXFatujmiIJkwwV56aD0hMNFfbIyibvnreb0pn8ztW0eb3SdwrHD1vo3SpRb0ZxzBR5MA8sOcZf6rq0jGPkTERGphRTYSYkmTZrE+vXri5WPGjWKrl27sm7duoDyw22OONxI3aG8eb6A0tUkDsteibQgeZmBQV2BoGAY9mbF2xMREakDFNhJiQpG6mw2G61bt/aXh4eHl5jDrqTUJeBLXzJvqG9N3eGCOmMMvfaM47PjV2PHC0Db/h8c+UaJuzaBs4IjfiIiInWQAjspU+vWrdm+fXuZdfxnvBoDeIkfYfdvjjBeNxZgjN0foBWso7NZFsFBvulQj9ewIjGZPUnJdAjOw24Zglv0wirPFKzHXfwkiNyia+tCtZZOREQaBAV2UqJmzZod9lzYgjV1mXmQkGJg7zIahzWiWciJ/tG5Ex78hgPZbr67qy8dmvuCq3d/3cqkT9dywQmteP6angBk5+VxxSsrgYl8dvw44v7xY/lz1a3/HObecASfVkREpH5QYCcl+vTTT+nQoUOpZ8IWW1NnPFh5qaSlppKVdxyhzvL/ahlj2PvtZTRzXE0r514im3asmgTEADGn+9KTiIiINACWMcbUdCeOpvT0dCIjI0lLSyMiIqKmu1OrdevWjalTpxY7PqwgjUnPV4oWemlrT+TKOC+jzjkWp8MGQGaub5tssMOOzVbyVKzXncnmD44j2xtEWEQ07S/+Bsuylb+jJU3FFlDOORERqeMqErtoxE5KdejOV/CthbvgHS/r9xamCvnhejchQdAk+Bjs9sAgqqSRuyC7jSC7DWMMXncmXrdvPVywLY92588vf1DnccOaub6drl0vBrt+nUVEpGGrwLBI9Xj++efp0KEDwcHB9OzZk6VLl5ZZ/+233+bEE08kNDSU1q1b869//Yu9e/cepd7Wfw899BDjxo3joYceKnbP6zUc//Ay1v9VGPD1ag39ZnxFr6kL2X2wlFGzQxhj8OZlsG3BRWz+4DgSPzql8GZFRtc8OfDJKN/6utJG7ERERBqQGg3s3n//fe644w7uu+8+Vq5cSZ8+fTj//PPZunVrifV/+OEHrrvuOm666SbWrl3L3LlzWb58OTfffPNR7nn99corr/DUU0/xyiuvFLu3L8tDVmYqVubftItwk/B/MO+KirVvjGH7oivYPPd4clITAu4Ft+hVds663AyYcYzvKzfDt9O13VlaRyciIpKvRueuZs6cyU033eQPzGbNmsXChQt54YUXmD59erH6P//8M+3bt2f06NEAdOjQgZEjRzJjxoyj2u+GymGzYcI6gfHy6ZU2Gjl95asfHAj41tGVxRiDJ2cv2Xvi/WWuJnG07f8BWBaWPeTwGyYyDxmd/ec8cARrHZ2IiAg1OGKXm5tLfHw8AwcODCgfOHAgP/30U4nPnHHGGWzfvp0vv/wSYwy7du1i3rx5XHjhhUejyw2ew2ZBWCeI6IorqPBXJ9TpINTp8G+OKEnBSF3RadcOly8nZvB8bEGNsDlCyw7qjAnMTVcgqALHWYiIiNRzNRbY7dmzB4/HQ1RUVEB5VFQUO3fuLPGZM844g7fffpsrr7wSp9NJq1ataNy4Mf/5z39KfZ+cnBzS09MDvqRkc+fOLTV3ncdr6PnoMjDuCrVZsEHi0JG64Ba9yp/SpODc1yc6Vui9RUREGpoa3zxx6B92Y0ypf+wTEhIYPXo0kyZNIj4+ngULFpCYmMioUaNKbX/69OlERkb6v2JiYqq0//XJpEmT/K+L5q8zBi54x0NOVioc3EzXZm5CyjGJ719Pd8gGiQ6XL6/YUWGHnvuqNXUiIiIlqrE1ds2bN8dutxcbnUtJSSk2ildg+vTpnHnmmdx9990AdO/enUaNGtGnTx8efvjhgDNNC0ycOJFx48b5r9PT0xXclaLgfFgg4DzYLDes3w0WYGVs5sMrOpY5+2mMwXiy8LozA0bpoIIjdSW5axM0aq7pVxERkRLUWGDndDrp2bMnixYt4rLLLvOXL1q0iEsvvbTEZzIzM3E4Artst/sW7JeWZ9nlcuFyuaqo1w1DdHS0PylxrttLZq4XLAvT6nx+HwmNnIdfS3doQNfh8uW+dXSlbZDIzQSMbyOELX8ThicPPLnFz31VUCciIlKiGt0VO27cOK699lp69epF7969efnll9m6dat/anXixIkkJSXx5ptvAnDxxRdzyy238MILLzBo0CCSk5O54447OPXUU2nTpk1NfpR6oVmzZtjtdjwej7/sia828PL3f0GjYyCiG05H2XGV8WRVbpTulX6wez1cPx869PGVxc+BL+86gk8kIiLSsNRoYHfllVeyd+9epkyZQnJyMscffzxffvkl7dq1AyA5OTkgp90NN9zAgQMHePbZZ7nzzjtp3Lgx5557Lo899lhNfYR65dNPP2XkyJEB3/MjOXDusKN0FaW1dSIiImXSWbESYNSoUfTv398/Fbs/y8uJL3rBshHX0saXw8sesSs49xXg2GFrsTnKGYiVNRVbQOe+iohIA6SzYqXSXnzxRcC3Vs7tzY/5LTtYFvOGVlFcZYxvp2teNsy5wFd2y3e+kySKsgf5vkRERKRcFNg1QHPnzmXSpEn+XbBJSUmEhoZy2mmn8e233wKQlechbtJC3wNRg8BylHsnbJkKctIVTV/iu1HJTyMiIiIFFNg1QJMmTWL9+vUBZZmZmSQnJ/uvD52gj2tBibnrjDEYdybbvx5W7OzXEh2akw60dk5ERKSKKLBrgApG6mw2mz/3X3h4uD93nTHwz0/smKj8494se4nTsKWlNgHfTljLHlJ2R+7a5EtforVzIiIiVUKBXQPWunVrtm/fXqx8f5aHFTtM/to6G71aQ+ghS92MMcWOCXM1iaNt/w/AsgJ3whasqYPiOekOXVcnIiIilabArgEq7TxY8MVg5z63Dmvv35iwTsSP60yzkMABtZJG6jpcvrzkXHWlrqkTERGRqlbjZ8XK0Xf77bcDgefBFshyw74s3+sWoRQL6qB4EuIyExB78uCYfuA4ZFpW6+pERESqnEbsGqDjjz+erl27BpwHC77Btcw8IKIbJrwri0baDrv0rdSRugIOJ/SbCGeODizXujoREZEqp8CuARoxYgQjRowIKDMG/jEX4pPJX1sHrtJSyBXZMmtzhAaupXNnQ1CR0bncDN9/ay2diIhItdNUbAMyd+5cunXrFpDWpECWG+KT3JD8JSR/yckt3aWmN9n+9bCSbvjW0r01NLD8qePgizshZR14vVX0SURERKQkCuwakIL8dZs3by61joXBwvD25SXPlBpPlj9fnatJXGFKk4L8dH//UDhKB2DZ4Pd34fM7NPUqIiJSzTQV24AU5K+7/PLLSUlJCbhnDGDZMS3PY/H1EBJkP2x7bft/UPraugJ3rPH9t9bUiYiIVDsFdg2Q0+kMuPatrzNgPGBzEhVhw1baWG7RIyksqzBHXW4pR4lpbZ2IiMhRo8BOyMg1rFu/DCsvlZatuhLiOLbEesZ42brgoqIFylEnIiJSi2iNXQPnNXDhO15w+EbWrozzlry2zhi2LbiYvANbgPz1dV6jc19FRERqEY3YNWDGwIXvwpZ0OzQ+kS7HHse/+5Uc6xfdNBEU3p6YwZ9j5WUVVtC5ryIiIjVOgV0DluWGhN2AMbSL9PC/axzYyorJ8tfXxQ6ej2XZwJNbeE/nvoqIiNQ4TcU2EHPnzi35jFhjYO8yUv/+puygzuul7bYD2D2mcETu24erpa8iIiJSOQrsGohJkyb5X4eHh5OZl398mPFg5aVyINtNZq67xGeNMSR9NZSQbDdNUrOL74DVujoREZFaQVOxDcQnn3zCpk2b+Ne//kXE+VPp9nz+DcuOadGXj65wE+wonrvOGIMnZy85+9cD0CQ1G2MP9t0cNA0GTNG6OhERkVpCgV0D0aVLF7p06cKWpJTCoA7AsugV04geMb5TJ4qeGmGMIenba8neuxKAPzs35dgr/sAWlL+WzuECXEfvQ4iIiEiZFNjVM3PnzmXSpEn+UyYKzJgxg6uvvjogv3D8LRAaBCGO/KDukJx0FtA2//WBsCD2n9gPy6EpVxERkdpKgV09U3Ae7KEyMzMxBobOKywLDYIgm5f//rwVhzuTq8tINNwouj9h/d89/BFiIiIiUmMU2NUzBSN1NpuN1q1b+8tDQ0PJzMtPbwLEtfCN1GXleZn06VpCyObq/KVz3LUJr83ir496AeBq3JW2g2YrqBMREanlFNjVU61bt2b79u1k53nwGoPDZuPSD/JvGsObl3jIzrOwWRYXnNAKpzcLNuffd4aCzcLk5z+JHvQhVlBIjXwOERERKT8FdvXMobnq/u+teL7bsJtHLutOwu4YAI4JT+eUh38gKsLFL/f25/lrevo2TUwr8mDRxXgaqRMREakTlMeungoPDy/13uP9y37WGC9bF1xUxT0SERGR6qYRu3qmZ8+eZGRkMHXqVABe+GdPvMaQ67Fx78++Ol1aRZAwZRAWJY/EbV90BXkHtgDgahKHZdc0rIiISF2gEbt65rfffmPF73/w9F9N6fv4dxgDIUEOhn9c+KO22yxCnQ5CnEUSElt2iLsU0+0ifzLioPD2xAz+XJsmRERE6ggFdvWQwdAlKoItezMxGLLcxXfDFhMUDMPexFz+sn/TROzg+ViWfkVERETqCk3F1hMjR45k3759NG3alJdeeolR5xzD/sxcQoLsvjNh880bWvpeiGJr6zRSJyIiUqcosKsnvvjiC5KSkoiOjgbg5NgmzB3VG7ACkhKXGKvlZsC0NliAu2MTsFlaWyciIlIHKbCrhz6M305wkJ1Bx0WR47GKT8MaA3mZvkJb8V8Bra0TERGpmxTY1TMGuHPu7wCsfWgQQz8sXCM3b2gJZ8KecjNc8ATeO9aw5bM+GEtr60REROoqBXb1jAWc1qEpHq/BYA8YrQsNwjdSd8iZsAbYvuz/8DjygzmN1ImIiNRJCuzqoTduPBWn3caF7xUGaCVumrhrEwRHYNyZ5KQmAMpbJyIiUpdpvq2eMUBwkJ3sQ9bWhQaVUNkZirE72f71MH9R2/4faG2diIhIHaXArh6YO3eu/4zYXWnZnP/0UrJyPf77ZaY4OXS0zhFa7f0VERGR6qHArh6YNGmS/7XlDKGR0841nxT+aEvPW2c0WiciIlKPKLCrBy666CLOO+88AGY/+wQvXNebdXt8AVqpJ00Axp2l0ToREZF6xDLGmJruxNGUnp5OZGQkaWlpRERE1HR3Kszt8ZLr8WKzLIKD7P6yrxJ24XLYeHZ1C1bsLIzXE/4PGjmLNJCfjBhgW6/eZKdvAODYK/7AFtToqH0OERERKZ+KxC7aFVvHfLM+hZH/jSdq70rSf3gbYwwrVv/BrW+vAMBEDQKbL7Dr1bqUTRP5cvavLzxlQqN1IiIidZ4Cuzpq3eevcmDX3wCEOh10bxuJw2YjPtc3ihd/CzQLKV9KOq2tExERqR+0xq6OOa9rSxKmDCLc4Q4o/2Bkb/57c29/JBcaVIE8wwrqRERE6gUFdnWEx2tYtnkvy7ek4nLYKQjFoqOjAfLX25UjQLNsmNjeZObvqFBCYhERkfpDgV0dkeP2cMmdT3D2qScRGxtDcnJywH1jYOi8cjQUFIK57iOSYiIwNkvTsCIiIvWI1tjVERYWmcvewb1vOzuKlIeHh2MM7M0i4KSJUlOcGIPXnVmkYQV1IiIi9YUCuzoixGmnSZCHgpAsOjqa8PBwpkyZyj/mQnyRAbzSTpowxsu2BRf7c9eJiIhI/aLArg4oyF33zLPP4cnLJSQkhIsuugiAzDy46/nCuqWlODHGsG3BxeTuXcsxf+3HWLDzlH5aXyciIlKPKLCrA+avTuaO91dxVsdWvHXzaQH3iqaXLivFifH4TpmwgKzGTQjbt4/ofv/V+joREZF6RJsn6rBDN0yUJ8WJsVmEDv0QYk7HcuqkCRERkfpEI3Z1wEXdWzPwuChsh0RtWe7ybZgAAof2WnSGGxdo44SIiEg9oxG7WszjNfy+bT9rd6TjcthZu3oVy5YtIz4+vljd0jZMgG993favh2F5Dc13Z2J9PxM8edXcexERETnaNGJXi+W4PVz63I8AJEwZxKWXXkpSUhLR0dFs3749oG5Zg2/Gk0XOvrXEbEsnOMcDS5+EPncCzmrsvYiIiBxtCuxqMQuL6MYh/tdH1JbBF9QBtDoBgkKPtHsiIiJSyyiwq8VCnHZ+nHBu1Tf8L62vExERqY+0xq6OMsaXw65SFNSJiIjUSwrs6oC5c+fSrVu3gPNh/zEXer5SzgaK7ogVERGRektTsbVYdp6H299dydwJ95CWvMVfHhoWHnCEWK/WZZ8Nu/3rYdXbUREREakVFNjVYl5jWJSwi4MHDvgKLBuOFp1J7T2Vgq0PZZ02AYEnToiIiEj9pqnYWsoYg91mMf3yE4gI8R3+ao9oTZt71xF60lDAN1JXZlBnDF53JpbXYPNqOlZERKS+04hdLWSMYeiLy5h++QkMPzWWc9dvpNcrBrCIv8V3dBj4pl/LTEq86Aqy98QTuzUdV67nqPVfREREaoZG7GqhrDwP8X+n8uKSzWTkuLEHh2MLjsAWHE5oEP6vMpMSuzPJ3lP8hApiTlcOOxERkXpKI3a1VNNGTr5dl8LaXFi/t2LPmtwM3E91JNaTw7bYCOy3/45xNsGyLF9Qp3QnIiIi9ZICu1oo1Okg/v4BXPAuJOwuLC9r92tRxp2FMysTAFfjrtjD2vqCOhEREanXNBVbS2Xm+YK6zFVz2XqHRZ/4Gzhj28zyDbY5XGxvG872tuFED/hAQZ2IiEgDocCuFjIGhs7zvd7/5SQA3v7vG7z66mEyEnvy4NdXIP4NskIcZIUGgV2DsiIiIg2F/urXQvuzPCRsXA2APe8A7vzyqVOnlv2gJxe+vAsbYHVsgtFAnYiISIOiEbtayGMMVvYOrOwdNA3xlUVHRzN06NAKteNqEodlD6mGHoqIiEhtVOOB3fPPP0+HDh0IDg6mZ8+eLF26tMz6OTk53HfffbRr1w6Xy8Wxxx7L66+/fpR6e3QE2W1k/L2FpNfHsGtn8uEfKEXb/lpfJyIi0pDU6FTs+++/zx133MHzzz/PmWeeyUsvvcT5559PQkICsbGxJT4zbNgwdu3axWuvvUbHjh1JSUnB7XaXWLeucths7P/2Wdy7N/vLwsPDK96QgjoREZEGpUYDu5kzZ3LTTTdx8803AzBr1iwWLlzICy+8wPTp04vVX7BgAUuWLOGvv/6iadOmALRv3/5odrnaFWycMDm+82FtNhudO3c+/Po6ERERafBqbCo2NzeX+Ph4Bg4cGFA+cOBAfvrppxKf+eyzz+jVqxczZswgOjqazp07c9ddd5GVlXU0unxUZLkhIcXgjD6RiGNP5YILLmTdunXlWl9njM6DFRERachqbMRuz549eDweoqKiAsqjoqLYuXNnic/89ddf/PDDDwQHB/Pxxx+zZ88ebr31Vvbt21fqOrucnBxycnL81+np6VX3IaqL8dDy4lsBeH/KoAo8lk3B5KurcVdtnBAREWlganzzxKGL+40xpS7493q9WJbF22+/zamnnsoFF1zAzJkzmTNnTqmjdtOnTycyMtL/FRMTU+WfoToYy05IkL2CDxWO2EX3+682ToiIiDQwNRbYNW/eHLvdXmx0LiUlpdgoXoHWrVsTHR1NZGSkv6xbt24YY9i+fXuJz0ycOJG0tDT/17Zt26ruQ1QDYwCbA1oNJn7SYEKd5RtUNcaQ9N21hQUK6kRERBqcGgvsnE4nPXv2ZNGiRQHlixYt4owzzijxmTPPPJMdO3Zw8OBBf9nGjRux2Wy0bdu2xGdcLhcREREBX7VV0RMnKvysJ4uc/ev915Y9uIp6JSIiInVFjU7Fjhs3jldffZXXX3+ddevWMXbsWLZu3cqoUaMA32jbdddd569/9dVX06xZM/71r3+RkJDA999/z913382NN95ISEjdX0+W5fadDwuQ9eYlnHd2by655JJKtaVpWBERkYanRtOdXHnllezdu5cpU6aQnJzM8ccfz5dffkm7du0ASE5OZuvWrf76YWFhLFq0iNtvv51evXrRrFkzhg0bxsMPP1xTH6F6GA9ZW37h530pREdH13RvREREpI6o8bNib731Vm699dYS782ZM6dYWdeuXYtN39Y7xpCV6/G9rMAzAG67hd3VBI3XiYiINDw1vitWSmDZCAv2xdzlCdCMJ4+khf8AIPHYJphxa8HZqBo7KCIiIrWRArvayLLRyFX+wVSz9kNifltGm6QDuJrEKX+diIhIA1WpqdiMjAweffRRvvnmG1JSUvB6vQH3//rrryrpnByeMYa9q5+iRf512/4faOOEiIhIA1WpwO7mm29myZIlXHvttbRu3VqBRFUzBm85F9cZdyb7rV2kdWyCq0lX2jpCq7dvIiIiUmtVKrD73//+xxdffMGZZ55Z1f0RAONhd3q272VAuYG8zCLVctn33jmE5eZxMCyI6IEfKsgWERFpwCoV2DVp0oSmTZtWdV+kCM/BvYEFxsDrg2DbL/4iC2ie/3pbr95YGq0TERFp0Cq1eWLq1KlMmjSJzMzMw1eWcvMf9WpzcM211wMQER7uK8vLDAjqispu1Ii2F3yh0ToREZEGrlIjdk8++SSbN28mKiqK9u3bExQUFHB/xYoVVdK5huTQ48SOP6E7Xbt2ZerUqcUr37UJrwV/fXwKAMcM+wPLZj9KPRUREZHaqlKB3ZAhQ6q4G+I/Tsx4aOpeT06XQaxaMwaXw+6L+nKLrK0LCmH7t1dibPkjdDZlrREREZFKBnaTJ0+u6n4IkLlqLmlfPUzeoFt4LSmKuwd3KXFtnfFkk5OaAKC8dSIiIuJ3REeKxcfHs27dOizLIi4ujpNPPrmq+tUg7f9yEu6U9STPvp2WMcfgePwfxdfWxZwOQYWBnPLWiYiISIFKBXYpKSlcddVVLF68mMaNG2OMIS0tjX79+vHee+/RokWLwzcixZicA/7Xz818DKfDBjSCB9MgN8N3IygU3EU2rSioExERkXyVWpx1++23k56eztq1a9m3bx+pqan88ccfpKenM3r06KruY4PTuk00Q4cO9V0YA3nZvrNfnY0wGLYuuKhmOygiIiK1UqVG7BYsWMDXX39Nt27d/GVxcXE899xzDBw4sMo615AYY8CbC0BGjpvMXDchDhvW7MEQ3gqGvYkxhm0LLibvwBZA6+tEREQkUKVG7Lxeb7EUJwBBQUHFzo2VCrA5AV9gd91rvxaur0v4FHIzMJ4s/6aJoPD2xAz+XOvrRERExK9Sgd25557LmDFj2LFjh78sKSmJsWPHct5551VZ5xqWwgCtZUQwc0f1xnK44IInYODDYHcWyWAMsYPnY1lKcyIiIiKFKjUV++yzz3LppZfSvn17YmJisCyLrVu3csIJJ/DWW29VdR/rPa+BC94pDNosC99InM0BvW4Emx1jDNu/GkJAJREREZEiKhXYxcTEsGLFChYtWsT69esxxhAXF0f//v2run/1nskP6rZs/hE82UD+2F1B/rpzH4AOfQKmYbW2TkREREpyRHnsBgwYwIABA6qqLw1SZh6s2+2FoCaBNwrW1/34NET3AFvhCJ1y14mIiEhJyh3YPfPMM4wYMYLg4GCeeeaZMusq5Un5+M+HtewQeRwLvvyCULsHl8tVWGnTouIPKqgTERGREpQ7sHvqqae45pprCA4O5qmnniq1nmVZCuzKKcsNCSkGjIeuLeC8s04rjNkKEhKLiIiIlFO5A7vExMQSX0vlGWNg7zKsvFQ27ALLurCmuyQiIiJ1WKXyZUyZMoXMzMxi5VlZWUyZMuWIO9VQGAM06YlpdEw5K4uIiIiUzjKm4hGD3W4nOTmZli1bBpTv3buXli1b4vF4qqyDVS09PZ3IyEjS0tKIiIiosX54DZz7JiTuB7xu4kfAz98tICsri5CQEC4a2A+mtQHATExi27dX+nfFHjtsLTZHaI31XURERI6eisQuldoVa4wpcVfm77//TtOmTSvTZINiDFz4bn5QB8RFOWgWCqNGjSIpKYno6Gi2r19ZWN+tVCciIiJyeBUK7Jo0aYJlWViWRefOnQOCO4/Hw8GDBxk1alSVd7K+yXJDwm7AeGni2cwFrSDPc2xhhcw98ERH/+W2Rf/wv1aqExERESlNhQK7WbNmYYzhxhtv5KGHHiIyMtJ/z+l00r59e3r37l3lnay3jJf9uzfy5Fdw01kdCsvduYAv5Ul2WAS5GVvBsnyjdZqCFRERkVJUKLC7/vrrcbvdAPTv35+2bdtWS6fqO/+qRstiaM8YHDaw24qPwnnHrmHb/LPBsggKb0/M4M81WiciIiKlqvAaO4fDwa233sq6deuqoz/1nj8pMYBlZ8qQ7oQGlVI5KNSfjDh28Hwsq1KbmEVERKSBqFSkcNppp7Fy5crDV5Ri/OvrgLgWEJIfWs+dO5ekpCTfRXgruHszOIILH9RInYiIiBxGpXbF3nrrrdx5551s376dnj170qhRo4D73bt3r5LO1Uf+aVivmy3rviZuEsQ/0J9Jkyb564RHRGJCm7F9wUU100kRERGpkyoV2F155ZVA4JmwlmX506DU5jx2NSlgGhbIyiv8Ph04cMD/euqQjpic/UpxIiIiIhVSqcBOR4pVTtFp2G4t7bxyfT8Agh12wsLCCA8Pp40zg6Gu7/F6C4M+pTgRERGR8qhUYNeuXbuq7keD8+EVFo2chalL1q9f70tzsvRJX4G9yI9GQZ2IiIiUQ6UCO4DNmzcza9Ys1q1bh2VZdOvWjTFjxnDsscce/mEpOVZzOKHfRN9rd/GzeEVERETKUqldsQsXLiQuLo5ff/2V7t27c/zxx/PLL79w3HHHsWjRoqruY72U5/Hy2g+JvPZDInkeb013R0REROqBSo3YTZgwgbFjx/Loo48WKx8/fjwDBgyoks7VZ3keL1Pn+zZHDD81hiC7Dbxe2LPBV6FJTA32TkREROqiSo3YrVu3jptuuqlY+Y033khCQsIRd6ohsFsWl57UhrjstfTofgKWZdG//3ncPeQkeP50yM2q6S6KiIhIHVOpwK5FixasWrWqWPmqVato2bLlkfapQXAF2Xn6qpNJXDibDRvWA/DNd4uZ/6fvyLak766tye6JiIhIHVSpqdhbbrmFESNG8Ndff3HGGWdgWRY//PADjz32GHfeeWdV97He8CcnLqIgf53NZqNzp45M7b4NgJz968FmKYediIiIlFulArsHHniA8PBwnnzySSZO9O3ibNOmDQ8++GBA0mIpdGhyYq/XkJlbmKuudevWrFu9Aqa1CXhOOexERESkvCoV2FmWxdixYxk7dqx/xCk8PLxKO1bfFE1O3PSvuXQ/7n627tqLJyO11GdcTeKwHKGl3hcREREpqlJr7AqkpKSwatUqfv/9d3bv3l1Vfar3zrb9QuducdhcjcD4Up2UFBhrtE5EREQqolIjdunp6dx22228++67eL2+wMRut3PllVfy3HPPERkZWaWdrG+mz3iCndsSuXTIpaQGRxMeHs7UqVN9Z+0WraigTkRERCqgUoHdzTffzKpVq/jiiy/o3bs3lmXx008/MWbMGG655RY++OCDqu5nneffOGG8zP1tO067nRUrV/ny1+XzZu72B3auxl21aUJEREQqxDKmpL2aZWvUqBELFy7krLPOCihfunQpgwcPJiMjo8o6WNXS09OJjIwkLS2NiIiIo/KexsAF7/rW2GWueI8Di6fTavg0EqYMItTp8FcyL56FtesPALx3b8LWqMVR6Z+IiIjUXhWJXSo1YtesWbMSp1sjIyNp0qRJZZqs14punMj6+iHsWfs4vYUHW9Gp1rxMf1CX7bLjdGrThIiIiFRMpTZP3H///YwbN47k5GR/2c6dO7n77rt54IEHqqxz9VGkdYDM1BTWvfkAwUH2Eutsj4nQ+joRERGpsEqN2L3wwgts2rSJdu3aERsbC8DWrVtxuVzs3r2bl156yV93xYoVVdPTembv3r013QURERGpZyoV2A0ZMqSKu9FAGMO+jNySbzmC2XnCyeSmb8ZosE5EREQqoVKB3eTJk6u6H/Va4Y5YD3luX3qYQ3esGG8OB3P+BpdDx4iJiIhIpVQqsCsQHx/PunXrsCyLuLg4Tj755KrqV70RcJSYZadpmIvdB6GsQTklJhYREZHKqFRgl5KSwlVXXcXixYtp3LgxxhjS0tLo168f7733Hi1aKE1HgaI7YuNaWqyxlxKweXJpuifT99qbd3Q6JyIiIvVKpXbF3n777aSnp7N27Vr27dtHamoqf/zxB+np6YwePbqq+1hvzBtaxk2Pm2b7smm2Lxs87qPWJxEREak/KjVit2DBAr7++mu6devmL4uLi+O5555j4MCBVda5+sbj9ZKd5yn5ps3O/kgXABG2ktOgiIiIiJSlUoGd1+slKCioWHlQUJD/7FgpLtfjZX+mb5q12HEfdie7oxoBEOFwHd2OiYiISL1QqcDu3HPPZcyYMbz77ru0adMGgKSkJMaOHct5551XpR2sT2yWxWlXjcaTm82o8+L85cYYti+6AstrMDZtmhAREZHKqVRg9+yzz3LppZfSvn17YmJisCyLrVu3csIJJ/DWW29VdR/rtKIn8QYH2fnxlUnF67gzabH6F7JDHGR0OBHLFnwUeygiIiL1RaUCu5iYGFasWMGiRYtYv349xhji4uLo379/VfevTgtIdVKWvCxCst2EZLtp7DyoVCciIiJSKRUO7NxuN8HBwaxatYoBAwYwYMCA6uhXvRCQ6qQFhJT23S4yrGeu/1SBnYiIiFRKhdOdOBwO2rVrh8dTyu5OKdG8oZCd5+H08W/S+545/L4mAfCtr0v67toiNRXUiYiISOVUKo/d/fffz8SJE9m3b19V96fesiz48MO5/DLjen5+/F9ccL4vLYxxZ9Jiza+F9exaXyciIiKVU6k1ds888wybNm2iTZs2tGvXjkaNGgXcX7FiRZV0rr55ZMpD/tcR4eG+F3lZBOf4Rj9N1HFYzkYlPSoiIiJyWJUK7IYMGYJlWRhTLBublMR4+TohhT2paf6iqVOnFq+m9XUiIiJyBCoU2GVmZnL33XfzySefkJeXx3nnncd//vMfmjdvXl39qx+Ml9HvxpOakQtAdHQ0Q4cWnC9WNDhWUCciIiKVV6E1dpMnT2bOnDlceOGFDB8+nK+//pr/+7//q66+1R+WxcmxTQhyHPLtNgbrjUtrpk8iIiJS71RoxO6jjz7itdde46qrrgLgmmuu4cwzz8Tj8WC363zTUll23r7lDDpPc5K0v7DY5GZg7VoLQLbLjjMopGb6JyIiIvVChUbstm3bRp8+ffzXp556Kg6Hgx07dlR5xxoC48n2v959wqlYjtAa7I2IiIjUdRUasfN4PDidzsAGHA7cbneVdqpeMR7Yu4xhLwaupiM3A2w2trf17Y6NHvCBNk6IiIjIEalQYGeM4YYbbsDlcvnLsrOzGTVqVEDKk48++qjqeljXGYOVl8YfSUXKMvfAtDYweBpZoUG+MnulNiiLiIiI+FVoKvb666+nZcuWREZG+r/++c9/0qZNm4Cyinj++efp0KEDwcHB9OzZk6VLl5bruR9//BGHw8FJJ51Uofc7mowBLBumSS9e+Gcvfv75F7b99SfLp1/kq5CXXebzIiIiIhVRoWGi2bNnV+mbv//++9xxxx08//zznHnmmbz00kucf/75JCQkEBsbW+pzaWlpXHfddZx33nns2rWrSvtUVYyBofMAywbBUZzTBQoG5xg5D5NzgK0LL67JLoqIiEg9U6kjxarKzJkzuemmm7j55pvp1q0bs2bNIiYmhhdeeKHM50aOHMnVV19N7969j1JPKy7LDQm7AWPo2sxNSJEQ2hjDtm+vIu/g3wC4msRh2bUjVkRERI5MjQV2ubm5xMfHM3DgwIDygQMH8tNPP5X63OzZs9m8eTOTJ08u1/vk5OSQnp4e8HXUGAN7f6JpZjxT//Ma3bp14+WXX8Z4sshJTQAgKLw9MYM/18YJEREROWI1Ftjt2bMHj8dDVFRUQHlUVBQ7d+4s8Zk///yTCRMm8Pbbb+NwlG8Wefr06QHr/2JiYo647+XnBeNl2V97mDFtCuvXr+eP31die7g1nTbuw/IaYgfPx7JqdOBURERE6okajygOHakyxpQ4euXxeLj66qt56KGH6Ny5c7nbnzhxImlpaf6vbdu2HXGfy82yQ4s+/PbAIBo7PAD85/kXD6mjkToRERGpGjWWY6N58+bY7fZio3MpKSnFRvEADhw4wG+//cbKlSv597//DYDX68UYg8Ph4KuvvuLcc88t9pzL5QpIz1ITQp2F3+bo6DbAwZrrjIiIiNRbNTZi53Q66dmzJ4sWLQooX7RoEWeccUax+hEREaxZs4ZVq1b5v0aNGkWXLl1YtWoVp5122tHqeoV9NG8uSUlJh68oIiIicgRqNCvuuHHjuPbaa+nVqxe9e/fm5ZdfZuvWrYwaNQrwTaMmJSXx5ptvYrPZOP744wOeb9myJcHBwcXKaw3jgX2/cuujd/uLwsPC0IidiIiIVIcaDeyuvPJK9u7dy5QpU0hOTub444/nyy+/pF27dgAkJyezdevWmuxipRnj+w8rdx+ZBwsDuakPPgDrb6u5jomIiEi9ZRljzOGr1R/p6elERkaSlpZGREREtbyHMXDBu5CQ4oXsXRx8qif7UpKJjo5m2+Z1WNPbArCpYxOOuSoBmyO0WvohIiIidV9FYpca3xVbH/mTE1s24mJbExJU5Nv8+mD/S1fjrkpMLCIiIlVGJ89Xs3lD4eJXOxMZGUlUy+ZYu1YBkO2yEz1wnhITi4iISJVRYFedjGHl1lRmvD6Pk2ObYHdnwrQ2AGyPieAYmwZMRUREpOoosKtixkBmXsGFh3++ugyAhCmD0Eo6ERERqU4K7KqQMfCPuRCfnF9gWcQ2DcVmgYWmXEVERKR6KbCrQlnuIkEd0KuNnXlX9Cs8Nczjwnv5y+xaNg6jOE9ERESqmAK7ahJ/CzQL8R0Fe80117Bnzx6aN2/Of+e8zME/JtR090RERKQeUmBXTex4uHFOPACLlyxhR1IS0dHRbP96WA33TEREROorBXbVxGMM323YTcb6H9jjPyfWELR1FUFAXsyJymEnIiIiVUr5NqpJkN3G40O74/p9nr8sPKwRrZMP0jr5IG3PeUM57ERERKRKKbCrQkUPZwuy27iiVwzkZfnLHpo0kcwQB5khDlAOOxEREaliii6qiDEwtHBwjqxcD5m5bv91dHQ0Q68YRlJMBEkxERCkaVgRERGpWlpjV0X858MCcS1g8KzvaJ6ygiT/+jq0cUJERESqlUbsqsG8oWCz4Ou3/uMvCw8Lpc1vP9JhcyrBEV20cUJERESqnAK7amBZsPiufoy/7UYuu+wyAB56YAIOj8HhMUT3+682ToiIiEiV01RsVTOGzFwPzRs5eHjKQ/5ib+YemDHWd6GgTkRERKqBRuyqiDG+/8hc+jCtYzsQ1jSK7DxPTXdLREREGhCN2FWBwh2xhv1L38C9bztuwOVQ3CwiIiJHjyKPKuDfEWvZsLuz/eVaRyciIiJHkwK7KtYs1Pff0dHRhYXGQG5GzXRIREREGgxNxVYlY/B4TbEy8/ogbNt+qZk+iYiISIOhEbuqZDzsOZDje1lQlpeJVSSoyw6LwApuevT7JiIiIvWeArsqdui6OlPkANm/jmmMc/Q6LJ0TKyIiItVAEUZVsjloGeECoCC8M57CzRRBTbthBTWqgY6JiIhIQ6A1dlVsR5GzYQGwB5HS0rejIrr/e9opKyIiItVGI3ZV7JnnXgQgPDzcV2APIq1xMGmNg8HhrMGeiYiISH2nwK4K+JfRGQ9Ldtpp2qYDkx58CLwe2PITIZl5RSqJiIiIVA8Fdkeo8NQJ38Vvua0Jv/Y/XHrZ5eDOxvbWP2i7/QCW4joRERGpZlpjd4T8p04A3VrYGNKjKxbgsNnAa2GadyY3fXON9lFEREQaBgV2VWhM1PfYsnNwuVw4HcdiTAjbjm1JTuqemu6aiIiINAAK7I5Q4fo6w43XX8PO5B1ER0ezfft2jCeLnNQEAFxN4rDsITXXUREREan3tMbuCHgNXPhu/oXxsLvoqRO5mVj/OYXYLWlYXkPb/h8o1YmIiIhUKwV2lWTyg7rE/b7rxn/Nw3Nwb2GF1wdipW3HlevxXSuoExERkWqmwK6Sim6a6NAYcr9+yH8vIqwR7FwDQLbLjlFMJyIiIkeBArsq8MVwOHDggP966oMP+F9vj4nA1fQ4ra8TERGRaqfArgoUnWWNjm7DPy4aGHBf6+tERETkaFBgV9XSk7Ge6OS/dDXuiuUIrcEOiYiISEOhdCeVdOgJYaaEI8Oygh1ED5yn0ToRkXrG6/WSm5tb092QeiIoKAi73V4lbSmwq4SAY8TyFQ3dtpx0Ou6DGzAWHGvToKiISH2Sm5tLYmIiXq+3prsi9Ujjxo1p1arVEQ8GKbCrhKI7YuNaQLDdsHFDAqFPxAKw6eAGjM1SUmIRkXrGGENycjJ2u52YmBhs+se7HCFjDJmZmaSkpADQunXrI2pPgd0RmvsPwxUvLWP137tZc97duGxezM7XAW2aEBGpb9xuN5mZmbRp04bQUK2flqoREuIbBEpJSaFly5ZHNC2rf2ocoWy3h/i/U8nDwf5T78Kcc1fhNlkFdSIi9YrH40s673Q6a7gnUt8U/EMhLy/viNrRiN0RCgmy8+fDg8jesY6w7L8wobE13SUREalmmo2RqlZVv1MK7I6QZVkEeXOYdt0ppOUYIs4dwzXH1XSvREREpCHSVGwVeWWlm6d+zuWll56p6a6IiIgEmD59Oqeccgrh4eG0bNmSIUOGsGHDhoA6xhgefPBB2rRpQ0hICH379mXt2rUBdV5++WX69u1LREQElmWxf//+Yu/Vvn17LMsK+JowYcIRf4ZXXnmFPn360KRJE5o0aUL//v359ddfi9V7/vnn6dChA8HBwfTs2ZOlS5cG3P/oo48YNGgQzZs3x7IsVq1aVayNzZs3c9lll9GiRQsiIiIYNmwYu3btOuLPcDQosDtCuW4v077eyoGg5gAYrxtAO2JFRKTWWLJkCbfddhs///wzixYtwu12M3DgQDIyMvx1ZsyYwcyZM3n22WdZvnw5rVq1YsCAAQFHZmZmZjJ48GDuvffeMt9vypQpJCcn+7/uv//+I/4MixcvZvjw4Xz33XcsW7aM2NhYBg4cSFJSkr/O+++/zx133MF9993HypUr6dOnD+effz5bt27118nIyODMM8/k0UcfLfF9MjIyGDhwIJZl8e233/Ljjz+Sm5vLxRdfXDdS3JgGJi0tzQAmLS2t0m1k5BoTO8v3tftgnmk3fr6xhzUzgIlqYjcb325vPLkHq7DXIiJSG2RlZZmEhASTlZVV0105IikpKQYwS5YsMcYY4/V6TatWrcyjjz7qr5OdnW0iIyPNiy++WOz57777zgAmNTW12L127dqZp556qrq67ud2u014eLh54403/GWnnnqqGTVqVEC9rl27mgkTJhR7PjEx0QBm5cqVAeULFy40NpstIE7Yt2+fAcyiRYuq9kMUUdbvVkViF43YVYYxhJgMQkwGDncWt53Zmkauwm+lq0mcjhETEZFaKy0tDYCmTZsCkJiYyM6dOxk4sPCsc5fLxTnnnMNPP/1U4fYfe+wxmjVrxkknncQjjzxSLad0ZGZmkpeX5/8Mubm5xMfHB3wGgIEDB1boM+Tk5GBZFi6Xy18WHByMzWbjhx9+qJrOVyNtnqgoY3C9OYj1+39h7to8ej+Sw4Ecw8GDhUeKKX+diEjDYIzBeLJq5L0te0il/tYYYxg3bhxnnXUWxx9/PAA7d+4EICoqKqBuVFQUf//9d4XaHzNmDD169KBJkyb8+uuvTJw4kcTERF599dUK97UsEyZMIDo6mv79+wOwZ88ePB5PiZ+h4POVx+mnn06jRo0YP34806ZNwxjD+PHj8Xq9JCcnV+lnqA4K7CoqLxP79l8AmLQ4h/V7AufbG4VYyl8nItJAGE8Wmz+omVQIxw5bW6nZoX//+9+sXr26xNGnQwNFY0yFg8exY8f6X3fv3p0mTZowdOhQ/yjeoaZNm8a0adP81wkJCcTGlp06bMaMGbz77rssXryY4ODgKv0MLVq0YO7cufzf//0fzzzzDDabjeHDh9OjR48qO8+1OimwqyBjCs+F3RVzKU7vL+Tu20Gb1q0JtnZzx9AmNdo/ERGR0tx+++189tlnfP/997Rt29Zf3qpVK8A3clf0SKuUlJRiI2AVdfrppwOwadOmEgO7UaNGMWzYMP91mzZtymzviSeeYNq0aXz99dd0797dX968eXPsdnux0bnKfIaBAweyefNm9uzZg8Ph8J/j2qFDhwq1UxMU2FVQlttQ8O+js+6YzeoE3794Fp71Mrb01TXXMREROeosewjHDlt7+IrV9N7lZYzh9ttv5+OPP2bx4sXFApQOHTrQqlUrFi1axMknnwz41qwtWbKExx577Ij6uXLlSqD0M1CbNm3qXyd3OI8//jgPP/wwCxcupFevXgH3nE4nPXv2ZNGiRVx22WX+8kWLFnHppZdWqu/Nm/syXnz77bekpKRwySWXVKqdo0mBXUUYQ/Cbg/2X71xmx3PFQExeBimf/xsspTkREWlILMuqE5vlbrvtNt555x0+/fRTwsPD/aNakZGRhIT41urdcccdTJs2jU6dOtGpUyemTZtGaGgoV199tb+dnTt3snPnTjZt2gTAmjVrCA8PJzY2lqZNm7Js2TJ+/vln+vXrR2RkJMuXL2fs2LFccsklh51ePZwZM2bwwAMP8M4779C+fXv/ZwgLCyMsLAyAcePGce2119KrVy969+7Nyy+/zNatWxk1apS/nX379rF161Z27NgB4M/n16pVK//I5ezZs+nWrRstWrRg2bJljBkzhrFjx9KlS5cj+gxHRdVu1q39jijdSc5BYyZHGDM5wvwx5UyTkeM1Xq/X/P3lBWbj2+2V5kREpJ6rq+lOgBK/Zs+e7a/j9XrN5MmTTatWrYzL5TJnn322WbNmTUA7kydPLrOd+Ph4c9ppp5nIyEgTHBxsunTpYiZPnmwyMjKO+DO0a9euxPeePHlyQL3nnnvOtGvXzjidTtOjRw9/SpcCs2fPPmw748ePN1FRUSYoKMh06tTJPPnkk8br9R7xZyhLVaU7sYwxhgYkPT2dyMhI0tLSiIiIqNjDuRkwzTf3361xEvG3hRFsZfoXzrqaxBEzeL52xIqI1FPZ2dkkJib6TzYQqSpl/W5VJHbRVGxF2Bzk9byZd/8AN0EMHnAuGxK3EuLM48vJwRyrNCciIiJSgxTYVYTDRe6gJ3ngL9/lpj83krIjCXtYM9w0UpoTERERqVE6eaICjIGh8wqvC+K4IJubYKvqs2qLiIiIVIQCuwrIyjPs3LWHpt49xDU3/nx2kfaDGqwTERGRGqfArgJMbiYr045lZdqxzLs0s6a7IyIiIhJAgV05GQPXfAIvx+fSduYBOnXuTNIO35lxBkv560RERKTGKbArpyw3rNzbiPH2R0g6YEhO3gHGd05soxAbbbUjVkRERGqYdsVWkM0VRpvoaCzA4/XiYh9jhkZqR6yIiIjUOAV25eXO5vmDI6A7nPnqBpwuFzZvNts+OqGmeyYiIiICKLArP6+HC/M+BeCe+bfywe97mTDoWM6r4W6JiIiIFNAau3IyBpIPeNme7uVg6p6a7o6IiEi5TZ8+nVNOOYXw8HBatmzJkCFD2LBhQ0AdYwwPPvggbdq0ISQkhL59+7J27Vr//X379nH77bfTpUsXQkNDiY2NZfTo0aSlpQW0k5qayrXXXktkZCSRkZFce+217N+//4g/wyuvvEKfPn1o0qQJTZo0oX///vz666/F6j3//PP+Y7l69uzJ0qVL/ffy8vIYP348J5xwAo0aNaJNmzZcd9117NixI6CNl19+mb59+xIREYFlWVXS/6NFgV05FOyIPeWVDGKeOsinj4wkYcogbujdtqa7JiIiclhLlizhtttu4+eff2bRokW43W4GDhxIRkaGv86MGTOYOXMmzz77LMuXL6dVq1YMGDCAAwcOALBjxw527NjBE088wZo1a5gzZw4LFizgpptuCnivq6++mlWrVrFgwQIWLFjAqlWruPbaa4/4MyxevJjhw4fz3XffsWzZMmJjYxk4cCBJSUn+Ou+//z533HEH9913HytXrqRPnz6cf/75bN26FYDMzExWrFjBAw88wIoVK/joo4/YuHEjl1xyScB7ZWZmMnjwYO69994j7vdRZxqYtLQ0A5i0tLRyP5ORa0zXpw6Y6HDLACY6uo0xxhhPXobZ+HZ7s/Ht9saTl1FdXRYRkVoiKyvLJCQkmKysrJruyhFJSUkxgFmyZIkxxhiv12tatWplHn30UX+d7OxsExkZaV588cVS2/nggw+M0+k0eXl5xhhjEhISDGB+/vlnf51ly5YZwKxfv75KP4Pb7Tbh4eHmjTfe8JedeuqpZtSoUQH1unbtaiZMmFBqO7/++qsBzN9//13s3nfffWcAk5qaWmX9Lk1Zv1sViV00YlcOH837gJxHWpJ80ACQkp5DZq67hnslIiJSOQXTp02bNgUgMTGRnTt3MnDgQH8dl8vFOeecw08//VRmOxERETgcviX7y5YtIzIyktNOO81f5/TTTycyMrLMdiojMzOTvLw8/2fIzc0lPj4+4DMADBw48LCfwbIsGjduXKX9qyk1HtiVNRd+qI8++ogBAwbQokULIiIi6N27NwsXLqz2Pj784AMk7snC64vrMEH5iYiNqfb3FhGR2ssYyMyrma/K/gkyxjBu3DjOOussjj/+eAB27twJQFRUVEDdqKgo/71D7d27l6lTpzJy5Eh/2c6dO2nZsmWxui1btiy1ncqaMGEC0dHR9O/fH4A9e/bg8Xgq9Bmys7OZMGECV199NREREVXav5pSo7tiC+bCn3/+ec4880xeeuklzj//fBISEoiNjS1W//vvv2fAgAFMmzaNxo0bM3v2bC6++GJ++eUXTj755Grr54GDBwGwWdD+2E6MmzgJl93G9q+GVdt7iohI7Zflhm7P18x7r7sVQoMq/ty///1vVq9ezQ8//FDs3qGJ9o0xJSbfT09P58ILLyQuLo7JkyeX2UZZ7QBMmzaNadOm+a9LiwGKmjFjBu+++y6LFy8mODi4Up8hLy+Pq666Cq/Xy/PP19APsRrUaGA3c+ZMbrrpJm6++WYAZs2axcKFC3nhhReYPn16sfqzZs0KuJ42bRqffvopn3/+ebUGdgVah1msWbWS0EaN8LozyUlNANBxYiIiUifcfvvtfPbZZ3z//fe0bVu4AbBVq1aAb8StdevW/vKUlJRiI2AHDhxg8ODBhIWF8fHHHxMUFBTQzq5du4q97+7du4u1U2DUqFEMG1Y4UNKmTZsyP8MTTzzBtGnT+Prrr+nevbu/vHnz5tjt9mKjcyV9hry8PIYNG0ZiYiLffvttvRmtgxoM7ArmwidMmBBQfri58KK8Xi8HDhzwz6+XJCcnh5ycHP91enp6xTpqDPt2lz18rOPEREQaphCHb+Sspt67vIwx3H777Xz88ccsXryYDh06BNzv0KEDrVq1YtGiRf6BktzcXJYsWcJjjz3mr5eens6gQYNwuVx89tlnxUbLevfuTVpaGr/++iunnnoqAL/88gtpaWmcccYZJfatadOmZf4dL+rxxx/n4YcfZuHChfTq1SvgntPppGfPnixatIjLLrvMX75o0SIuvfRS/3VBUPfnn3/y3Xff0axZs3K9d11RY4FdZebCD/Xkk0+SkZEREOkfavr06Tz00EOV72heJvOGurjo3SycrmDeWZGC02FxZY/mhXUU1ImINEiWVbnp0KPttttu45133uHTTz8lPDzc/3c2MjKSkJAQLMvijjvuYNq0aXTq1IlOnToxbdo0QkNDufrqqwHfSN3AgQPJzMzkrbfeIj093T9Y0qJFC+x2O926dWPw4MHccsstvPTSSwCMGDGCiy66iC5duhzRZ5gxYwYPPPAA77zzDu3bt/d/hrCwMMLCwgAYN24c1157Lb169aJ37968/PLLbN26lVGjRgHgdrsZOnQoK1asYP78+Xg8Hn87TZs2xel0Ar6Ry507d7Jp0yYA1qxZQ3h4OLGxseUOQmtMFe/WLbekpCQDmJ9++img/OGHHzZdunQ57PPvvPOOCQ0NNYsWLSqzXnZ2tklLS/N/bdu2rWLpTnKzzB9PXmJim4ebVtf917QbP9+0Gz/fHDiYplQnIiINTF1NdwKU+DV79mx/Ha/XayZPnmxatWplXC6XOfvss82aNWv89wtSf5T0lZiY6K+3d+9ec80115jw8HATHh5urrnmmipJF9KuXbsS33vy5MkB9Z577jnTrl0743Q6TY8ePfwpXYwxJjExsdTP8N133/nrTZ48+bDfr6pWVelOLGNqZmtnbm4uoaGhzJ07N2DIdMyYMaxatYolS5aU+uz777/Pv/71L+bOncuFF15YofdNT08nMjLSv0W7PDJyIe4FwHgY1GwVdgvGNXkI0tYAcOywtdgcoRXqh4iI1D3Z2dkkJib6szmIVJWyfrcqErvUWLqTonPhRS1atKjUeXiAd999lxtuuIF33nmnwkFdRc2dO5du3bpxxth3fAWWnVlX9eS5q7r5gzptnBAREZHaokZ3xR5uLnzixIkkJSXx5ptvAr6g7rrrruPpp5/m9NNP98+Lh4SEEBkZWeX9mzRpEuvXryciJoHGnSGuhW+xqvEU1tHGCREREaktajRB8ZVXXsmsWbOYMmUKJ510Et9//z1ffvkl7dq1AyA5Odl/vhvASy+9hNvt5rbbbqN169b+rzFjxlRL/wrOx0tf9Ah/p0Yy75KM4vskFNSJiIhILVGjI3YAt956K7feWvJe8Tlz5gRcL168uPo7VILocF/wlp3n4dxpX4MxvNouiGBbXo30R0RERKQkNX6kWG1WsKskxdaKq47ZTHBICLvSc9h1IBeDRupERESkdqnxEbu6wIuN165qTrDD8MXoszCebBzfa7ROREREahcFduVkWWC3WRzXJhKvO4jNVo1kiREREREplaZiyyHCpGH/3zg+/GUzc3/bRp7bc/iHRERERI4yjdiVQ6jJwLXiNe7PPpMsgum+82VFxCIiIlLrKD6pgLM7Nadv56a49ycASk4sIiJSGy1evBjLsti/f39Nd+WoU2BXCmMgw9mS6HCLVmEW3qgTeOnGs3n9uhNx2tyAkhOLiIhUlaMdjG3evJnLLruMFi1aEBERwbBhw9i1a1dAnRUrVjBgwAAaN25Ms2bNGDFiBAcPHvTf37dvHxdffDFhYWH06NGD33//PeD5W2+9lSeffPKofJ4CCuxKkeWGVqOXsn1cOL+NCMO6cUHxZMQK6kREROqcjIwMBg4ciGVZfPvtt/z444/k5uZy8cUX4/V6AdixYwf9+/enY8eO/PLLLyxYsIC1a9dyww03+Nt55JFHOHDgACtWrOCcc87h5ptv9t9btmwZv/76K3fcccdR/WwK7CogM9dd010QERGpMGMMM2bM4JhjjiEkJIQTTzyRefPm+e/179+fwYMHY4wv48P+/fuJjY3lvvvuAwpH07744gtOPPFEgoODOe2001izZk3A+/z000+cffbZhISEEBMTw+jRo8nIyPDfz8nJ4Z577iEmJgaXy0WnTp147bXX2LJlC/369QOgSZMmWJblD6DK6nuBL7/8ks6dOxMSEkK/fv3YsmVLmd+PH3/8kS1btjBnzhxOOOEETjjhBGbPns3y5cv59ttvAZg/fz5BQUE899xzdOnShVNOOYXnnnuODz/8kE2bNgGwbt06rrrqKjp37syIESNISPAt1crLy+P//u//ePHFF7Hb7RX9cR0RBXbl9I8XlxE3aSH9nvqZbG9QTXdHRERqkcxcN5m5bn9gBJDr9pKZ6ybnkEwKBXW93sK6eR5f3ey88tWtqPvvv5/Zs2fzwgsvsHbtWsaOHcs///lPlixZgmVZvPHGG/z6668888wzAIwaNYqoqCgefPDBgHbuvvtunnjiCZYvX07Lli255JJLyMvz5XVds2YNgwYN4vLLL2f16tW8//77/PDDD/z73//2P3/dddfx3nvv8cwzz7Bu3TpefPFFwsLCiImJ4cMPPwRgw4YNJCcn8/TTTx+27wDbtm3j8ssv54ILLmDVqlXcfPPNTJgwoczvR05ODpZl4XK5/GXBwcHYbDZ++OEHfx2n04nNVhgqhYT41tUX1DnxxBP59ttvcbvdLFy4kO7duwPw2GOP0bdvX3r16lWBn1IVMQ1MWlqaAUxaWlqZ9TJyjeny1EFjJkcYMznCxG/cZm6as9wMefZ7s+Gt9mbj2+2NJy/jKPVaRERqg6ysLJOQkGCysrICytuNn2/ajZ9v9hzI9pf955uNpt34+Wb8vN8D6na9/3+m3fj5Zuvewr8hry79y7QbP9+MfndFQN2Tp3xl2o2fbzbsTPeXvfPL3xXq88GDB01wcLD56aefAspvuukmM3z4cP/1Bx98YFwul5k4caIJDQ01GzZs8N/77rvvDGDee+89f9nevXtNSEiIef/9940xxlx77bVmxIgRAe+xdOlSY7PZTFZWltmwYYMBzKJFi0rsZ8F7pKamVqjvEydONN26dTNer9d/f/z48cXaKiolJcVERESYMWPGmIyMDHPw4EFz2223GcD/Gf744w/jcDjMjBkzTE5Ojtm3b5+5/PLLDWCmTZtmjDFm//79Zvjw4SY2NtacffbZZu3atWbjxo2mU6dOZs+ePWbkyJGmQ4cO5oorrjD79+8vsS8FSvvdMqb8sYsxxijdSRl2fnA7V6Rl0jTY4qV7m/B0bBR7vhlC7v6a7pmIiEj5JCQkkJ2dzYABAwLKc3NzOfnkk/3XV1xxBR9//DHTp0/nhRdeoHPnzsXa6t27t/9106ZN6dKlC+vWrQMgPj6eTZs28fbbb/vrGGPwer0kJiayZs0a7HY755xzTpX2fd26dZx++ukBmxmL9rMkLVq0YO7cufzf//0fzzzzDDabjeHDh9OjRw//1Olxxx3HG2+8wbhx45g4cSJ2u53Ro0cTFRXlrxMZGck777wT0Pa5557L448/zttvv81ff/3Fhg0buOWWW5gyZcpR2UihwK4MGQkLmZfmJjrcIjPXTUiwRa5SnYiIyCESpgwCICSocD3ViLOP5cazOmC3BW60i3+gPwDBjsK61/Vux/BTY7Adsinvh/H9itUd2rNthfpWsBngiy++IDo6OuBe0anIzMxM4uPjsdvt/Pnnn+VuvyCg8nq9jBw5ktGjRxerExsb61+XVtV9N6ZyJ0ENHDiQzZs3s2fPHhwOB40bN6ZVq1Z06NDBX+fqq6/m6quvZteuXTRq1AjLspg5c2ZAnaJef/11GjduzKWXXsrll1/OkCFDCAoK4oorrmDSpEmV6mdFKbArp+tnL+f6Ph3paGzYLa9SnYiIiF+os/ifU6fDhrOEpewl1Q2y2wiyl79uRcTFxeFyudi6dWuZo2V33nknNpuN//3vf1xwwQVceOGFnHvuuQF1fv75Z2JjYwFITU1l48aNdO3aFYAePXqwdu1aOnbsWGL7J5xwAl6vlyVLltC/f/9i951OJwAeT+E6w/L0PS4ujk8++aRYP8urefPmAHz77bekpKRwySWXFKsTFRUF+AK34ODgYiOIALt372bq1Kn+9Xcej8e//jAvLy/gc1UnBXbl9EdSGi9+v5WZzfIXrSqoExGROiA8PJy77rqLsWPH4vV6Oeuss0hPT+enn34iLCyM66+/ni+++ILXX3+dZcuW0aNHDyZMmMD111/P6tWradKkib+tKVOm0KxZM6Kiorjvvvto3rw5Q4YMAWD8+PGcfvrp3Hbbbdxyyy00atSIdevWsWjRIv7zn//Qvn17rr/+em688UaeeeYZTjzxRP7++29SUlIYNmwY7dq1w7Is5s+fzwUXXEBISEi5+j5q1CiefPJJxo0bx8iRI4mPj2fOnDmH/b7Mnj2bbt260aJFC5YtW8aYMWMYO3YsXbp08dd59tlnOeOMMwgLC2PRokXcfffdPProozRu3LhYe2PGjOHOO+/0jyyeeeaZ/Pe//2XgwIG8/PLLnHnmmUf0cyy3w67Cq2cqsnnCEdnGACa6SbDJOJhu3LkHzca3tXFCRKShKmuBe23m9XrN008/bbp06WKCgoJMixYtzKBBg8ySJUtMSkqKiYqK8m8IMMaYvLw8c+qpp5phw4YZYwo3Nnz++efmuOOOM06n05xyyilm1apVAe/z66+/mgEDBpiwsDDTqFEj0717d/PII4/472dlZZmxY8ea1q1bG6fTaTp27Ghef/11//0pU6aYVq1aGcuyzPXXX3/Yvhf4/PPPTceOHY3L5TJ9+vQxr7/+epmbJ4zxbbCIiooyQUFBplOnTubJJ58M2IBhjG9DSNOmTY3T6TTdu3c3b775ZoltLViwwJx66qnG4/H4yzIyMswVV1xhwsPDzXnnnWd27dpVal8KvjdVsXnCMqaSk9N1VHp6OpGRkaSlpREREVFqvYxciGzZFk9aEq3bRLMjaTvevAw2zz0egGOHrcXmCD1a3RYRkVogOzubxMREOnToQHBwcE1356hZvHgx/fr1IzU1tcTRKjlyZf1ulTd2AeWxK9EHH8wlqkM3POnJAOzLyMUYw/avh9Vwz0RERERKpzV2JZg0aRIZO9b7r43dSUbWQXJStSNWREREai8FdiUYfOFFbPO2IvPPxXRtbmPYhIm4HIWDm9oRKyIiDUnfvn0rnVZEji4FdiWY9ujjfBLjpaN3A59fBSGtuuD1ZhdWUFAnIiIitZACu1IYy8af9m6YFvhWInr0LxURERGp3RTYlcV4WbJhN047dEi8qaZ7IyIiIlImBXYlMAaCTC63ZT3Ohvc28Zx7CB8e/ychNm2cEBERkdpLgd0hjIGo9l3J2reDl8IyWP/vMH5oORwbvqlYbZwQERGR2kp57A7x9ntzyUjegDfnAAdzfcHcBzf3wGXznfemjRMiIiJSWymwO8TDD03yvw53WdDqBAjS1KuIiEhdsXjxYizLYv/+/TXdlaNOgd0hDhw84H89tZ8L/rWgBnsjIiLSMDTkYKwqaY1dKaLDLYbGBXH1a7+Svv8vHo0NKpyOFREREamFNGJ3GCu3pvJHemu8WNoRKyIidZIxhhkzZnDMMccQEhLCiSeeyLx58/z3+vfvz+DBg/2nS+zfv5/Y2Fjuu+8+oHA07YsvvuDEE08kODiY0047jTVr1gS8z08//cTZZ59NSEgIMTExjB49moyMDP/9nJwc7rnnHmJiYnC5XHTq1InXXnuNLVu20K9fPwCaNGmCZVnccMMNh+17gS+//JLOnTsTEhJCv3792LJly2G/J5Zl8dJLL3HRRRcRGhpKt27dWLZsGZs2baJv3740atSI3r17s3nzZv8zmzdv5tJLLyUqKoqwsDBOOeUUvv76a//99evXExoayjvvvOMv++ijjwgODi72vao2poFJS0szgElLSyvxfpvoaAOY6HDLmMkR5qv4BDP7+UvMureOMZ7cg0e5tyIiUptkZWWZhIQEk5WVFXgj52DFv9x5hc+783xluZnla7eC7r33XtO1a1ezYMECs3nzZjN79mzjcrnM4sWLjTHGbN++3TRp0sTMmjXLGGPMlVdeaXr16mVyc3ONMcZ89913BjDdunUzX331lVm9erW56KKLTPv27f11Vq9ebcLCwsxTTz1lNm7caH788Udz8sknmxtuuMHfj2HDhpmYmBjz0Ucfmc2bN5uvv/7avPfee8btdpsPP/zQAGbDhg0mOTnZ7N+/v1x937p1q3G5XGbMmDFm/fr15q233jJRUVEGMKmpqaV+TwATHR1t3n//fbNhwwYzZMgQ0759e3PuueeaBQsWmISEBHP66aebwYMH+59ZtWqVefHFF83q1avNxo0bzX333WeCg4PN33//7a/z3HPPmcjISLNlyxaTlJRkmjZtap566qnD/oxK/d0yh49dAj7XYWvUMxUN7DwZu83Gt9ubjW+3N568jKPcWxERqU1K/eM7OaLiX398VPj8Hx/5yl6/ILDdxzqU/GwFHDx40AQHB5uffvopoPymm24yw4cP919/8MEHxuVymYkTJ5rQ0FCzYcMG/72CwO69997zl+3du9eEhISY999/3xhjzLXXXmtGjBgR8B5Lly41NpvNZGVlmQ0bNhjALFq0qMR+FrxH0WCsPH2fOHGi6datm/F6vf7748ePL1dgd//99/uvly1bZgDz2muv+cveffddExwcXGobxhgTFxdn/vOf/wSUXXjhhaZPnz7mvPPOMwMGDAjoW2mqKrDTGjsREZF6LCEhgezsbAYMGBBQnpuby8knn+y/vuKKK/j444+ZPn06L7zwAp07dy7WVu/evf2vmzZtSpcuXVi3bh0A8fHxbNq0ibfffttfxxiD1+slMTGRNWvWYLfbOeecc6q07+vWreP0008PyDFbtJ9l6d69u/91VFQUACeccEJAWXZ2Nunp6URERJCRkcFDDz3E/Pnz2bFjB263m6ysLLZu3RrQ7uuvv07nzp2x2Wz88ccfRzX/rQK7Qzz9nxfY+t7ddLb7fki/bkll+8FOHN9oUw33TEREaq17d1T8Gbur8HXXi31tWIcsfb/jyNdleb1eAL744guio6MD7rlchX3IzMwkPj4eu93On3/+We72C4IWr9fLyJEjGT16dLE6sbGxbNpU8b+j5em7MZU/yz0oKMj/uuBzlFRW0I+7776bhQsX8sQTT9CxY0dCQkIYOnQoubm5Ae3+/vvvZGRkYLPZ2LlzJ23atKl0HytKgV0RxsBbGecx47hIOnuDcEedyFVvbgDG8Nnx42q6eyIiUls5Gx3Z83aH76uq2wXi4uJwuVxs3bq1zNGyO++8E5vNxv/+9z8uuOACLrzwQs4999yAOj///DOxsbEApKamsnHjRrp27QpAjx49WLt2LR07diyx/RNOOAGv18uSJUvo379/sftOpxMAj8dTob7HxcXxySefFOtndVi6dCk33HADl112GQAHDx4stlFj37593HDDDdx3333s3LmTa665hhUrVhAScnQ2X2pXbBFZbli5N5QBkb8wpOMOcm74hvahqbRzJWNR+X8RiIiI1JTw8HDuuusuxo4dyxtvvMHmzZtZuXIlzz33HG+88QbgGxF7/fXXefvttxkwYAATJkzg+uuvJzU1NaCtKVOm8M033/DHH39www030Lx5c4YMGQLA+PHjWbZsGbfddhurVq3izz//5LPPPuP2228HoH379lx//fXceOONfPLJJyQmJrJ48WI++OADANq1a4dlWcyfP5/du3dz8ODBcvV91KhRbN68mXHjxrFhwwbeeecd5syZUy3fy44dO/LRRx+xatUqfv/9d66++mr/aF6BUaNGERMTw/3338/MmTMxxnDXXXdVS39KdNhVePVMWQsQM3KNiX3Kazo/lWEO5hjjyT3o3zjx95cXlGvxo4iI1F9lLXCvzbxer3n66adNly5dTFBQkGnRooUZNGiQWbJkiUlJSTFRUVFm2rRp/vp5eXnm1FNPNcOGDTPGFG5s+Pzzz81xxx1nnE6nOeWUU8yqVasC3ufXX381AwYMMGFhYaZRo0ame/fu5pFHHvHfz8rKMmPHjjWtW7c2TqfTdOzY0bz++uv++1OmTDGtWrUylmWZ66+//rB9L/D555+bjh07GpfLZfr06WNef/31cm2e+Pjjj/3XiYmJBjArV670lx26oSMxMdH069fPhISEmJiYGPPss8+ac845x4wZM8YYY8wbb7xhGjVqZDZu3Ohv47fffjNOp9N88cUXZf6MqmrzhJX/4RqM9PR0IiMjSUtLIyIiIuBeRo7hozt643SnE33Tm8SmTiYnNQGAY6/4A1vQkQ+Ji4hI3ZWdnU1iYiIdOnQgODi4prtz1CxevJh+/fqRmppK48aNa7o79VJZv1tlxS6H0hq7fMbANXMz+e3tX0k6YGjz5SUsfty3MNPVJA7LEVrDPRQREREpmwK7fFlu+H1PMPtszYHdWJZFjjeISVtGEhLVm9fcXoKD7DXdTREREZFSKbArwmvZycVZeI3FioNd4WAq3oY1Yy0iIuLXt2/fI0orIkePArsiMlfNxZOW5L92Wm4mxMyh5WmP4bRrA7GIiIjUbgrsikj78gH/6/CwMOxWLuc1+Y1jT2qFTYGdiIiI1HKKVoowOQf8rx96YHwN9kRERESk4hTYlSA63GLoZZfgMRa/H+zI79vT8Xi1tkBERERqNwV2BYyhmdkTcJ1rgrjrrzu47MV4ctye0p8VERERqQW0xq5AXiYOk+d7bQ9i+5LrCbHlEhW0l7atjyFEqU5ERESkltOIXT5jYN1tYaRNCGf5mvXk7F8HwH9P+5C5o87Asqwa7qGIiEjt0L59e2bNmlXu+lu2bMGyLFatWlVtfSpqzpw51XZCxoMPPshJJ51ULW1XBQV25J868QmEuywiXBbh4eG4jY0sr5OOg97BZtO3SUREpMDy5csZMWJElbZZncFYVbrrrrv45ptvarobpVLEgu/UifVFlteFOGD+3j5c8sdMRr7zB9l5Wl8nIiJSoEWLFoSGNqyjNo0xuN1uwsLCaNas2RG1lZeXV0W9Kk6BXQksDE7L903fl5GHy6Fvk4iI1E2ff/45jRs3xuv1ArBq1Sosy+Luu+/21xk5ciTDhw/3X//000+cffbZhISEEBMTw+jRo8nIyPDfP3Qqdv369Zx11lkEBwcTFxfH119/jWVZfPLJJwF9+euvv+jXrx+hoaGceOKJLFu2DIDFixfzr3/9i7S0NCzLwrIsHnzwQQByc3O55557iI6OplGjRpx22mksXrw4oN05c+YQGxtLaGgol112GXv37i3ze1IwNfzee+9xxhlnEBwczHHHHRfQ7uLFi7Esi4ULF9KrVy9cLhdLly4tNhXr9XqZMmUKbdu2xeVycdJJJ7FgwYJi7/XBBx/Qt29fgoODeeutt8rs35FQxFLEzGU5PLg4mwfH9GFg05/57PhxfHDLyVpfJyIiddbZZ5/NgQMHWLlyJQBLliyhefPmLFmyxF9n8eLFnHPOOQCsWbOGQYMGcfnll7N69Wref/99fvjhB/7973+X2L7X62XIkCGEhobyyy+/8PLLL3PfffeVWPe+++7jrrvuYtWqVXTu3Jnhw4fjdrs544wzmDVrFhERESQnJ5OcnMxdd90FwL/+9S9+/PFH3nvvPVavXs0VV1zB4MGD+fPPPwH45ZdfuPHGG7n11ltZtWoV/fr14+GHHy7X9+buu+/mzjvvZOXKlZxxxhlccsklxYLCe+65h+nTp7Nu3Tq6d+9erI2nn36aJ598kieeeILVq1czaNAgLrnkEn//CowfP57Ro0ezbt06Bg0aVK7+VYppYNLS0gxg0tLS/GUZucZ0eeqgiQ63DGCaNw4yX712ukn84gLj9XprsLciIlKbZGVlmYSEBJOVlRVQ/uSTT5ro6OjDfl188cXF2rz44ovL9eyTTz5Z6X736NHDPPHEE8YYY4YMGWIeeeQR43Q6TXp6uklOTjaAWbdunTHGmGuvvdaMGDEi4PmlS5cam83m/9zt2rUzTz31lDHGmP/973/G4XCY5ORkf/1FixYZwHz88cfGGGMSExMNYF599VV/nbVr1wa87+zZs01kZGTA+27atMlYlmWSkpICys877zwzceJEY4wxw4cPN4MHDw64f+WVVxZrq6iC/jz66KP+sry8PNO2bVvz2GOPGWOM+e677wxgPvnkk4BnJ0+ebE488UT/dZs2bcwjjzwSUOeUU04xt956a8B7zZo1q9T+GFP675YxJccupVG6kxLsd4dzy8b7+ePKPhqtExGRw0pPTycpKemw9WJiYoqV7d69u1zPpqenV6pvAH379mXx4sWMGzeOpUuX8vDDD/Phhx/yww8/sH//fqKioujatSsA8fHxbNq0ibffftv/vDEGr9dLYmIi3bp1C2h7w4YNxMTE0KpVK3/ZqaeeWmI/io54tW7dGoCUlBT/ex9qxYoVGGPo3LlzQHlOTo5/ndu6deu47LLLAu737t07YDq0NL179/a/djgc9OrVi3Xr1gXU6dWrV6nPp6ens2PHDs4888yA8jPPPJPff/+93O1UJQV2RXixAR4syxBpPwAK6kREpBwiIiKIjo4+bL0WLVqUWFaeZyMiIirVN/AFdq+99hq///47NpuNuLg4zjnnHJYsWUJqaqp/GhZ8U6sjR45k9OjRxdqJjY0tVmaMKfcgSFBQkP91wTMFa/9K4vV6sdvtxMfHY7cH5pMNCwvzv39VOvSzNGrUqMLPlPQ9KU87VUGBXb4sqxEptlZAEk0d6cw7biKhzktqulsiIlIHjBs3jnHjxlXq2c8++6yKe1NcwTq7WbNmcc4552BZFueccw7Tp08nNTWVMWPG+Ov26NGDtWvX0rFjx3K13bVrV7Zu3cquXbuIiooCfOlQKsrpdOLxBGahOPnkk/F4PKSkpNCnT58Sn4uLi+Pnn38OKDv0ujQ///wzZ599NgBut5v4+PhS1xKWJCIigjZt2vDDDz/42wHf5pPSRi2rmzZPlMLVJA7LHlLT3RARETlikZGRnHTSSbz11lv07dsX8AV7K1asYOPGjf4y8C3yX7ZsGbfddhurVq3izz//5LPPPuP2228vse0BAwZw7LHHcv3117N69Wp+/PFH/+aJiixnat++PQcPHuSbb75hz549ZGZm0rlzZ6655hquu+46PvroIxITE1m+fDmPPfYYX375JQCjR49mwYIFzJgxg40bN/Lss8+WaxoW4LnnnuPjjz9m/fr13HbbbaSmpnLjjTeWu8/g24Dx2GOP8f7777NhwwYmTJjAqlWrAoLlo0mBXb7MlR/gSfOtcTjgCaVt/w+0vk5EROqNfv364fF4/EFckyZNiIuLo0WLFgHr5rp3786SJUv4888/6dOnDyeffDIPPPCAf03coex2O5988gkHDx7klFNO4eabb+b+++8HIDg4uNz9O+OMMxg1ahRXXnklLVq0YMaMGQDMnj2b6667jjvvvJMuXbpwySWX8Msvv/jXK55++um8+uqr/Oc//+Gkk07iq6++8r//4Tz66KM89thjnHjiiSxdupRPP/2U5s2bl7vP4Ass77zzTu68805OOOEEFixYwGeffUanTp0q1E5VsUxVT07Xcunp6URGRpKWluZfr5CZB43bdiEvZSMAkS2asW/HVmyOhpV8UUREypadnU1iYiIdOnSoUNDS0Pz444+cddZZbNq0iWOPPbamu1PMli1b6NChAytXrqw1x4OV9btVUuxSGq2xy2dyDvpfT75S3xYREZHy+vjjjwkLC6NTp05s2rSJMWPGcOaZZ9bKoK6+UwSD76xYZ9uTaRYZxgmR2/jH+T20vk5ERKScDhw4wD333MO2bdto3rw5/fv358knn6zpbjVIDT6w8xq44B0vzS+bCEER3BMzijbnaX2diIhIeV133XVcd911Nd2Ncmvfvn2Vp0mpLRp0YGcMXPgubNuXywXer7CyvTy+7UbOC9LaOhEREal7GnRgl+WGhN0QYvPwQtAsADzj/sRm02ZhERERqXsadGAHgDEkv/oPeqdl0CLU4pN7FNSJiEjZ6us0ntScqvqdathRjDF8eGAQ4Vu/4uftHlYkew7/jIiINFgFx1rl5ubWcE+kvsnMzAQCj12rjAY9YmdyM+nl+cV/7bbs5FgutB9WRERK4nA4CA0NZffu3QQFBWnpjhwxYwyZmZmkpKTQuHHjYmfiVlSDDeyMgWs+gU+KlO3xRuK1KeGkiIiUzLIsWrduTWJiIn///XdNd0fqkcaNG9OqVasjbqfBBnZZbli/J7AsLNhB0BFGyiIiUr85nU46deqk6VipMkFBQUc8UlegxgO7559/nscff5zk5GSOO+44Zs2aRZ8+fUqtv2TJEsaNG8fatWtp06YN99xzD6NGjaqSvoS57DgdGlYXEZGy2Ww2HSkmtVKNRjHvv/8+d9xxB/fddx8rV66kT58+nH/++WzdurXE+omJiVxwwQX06dOHlStXcu+99zJ69Gg+/PDDKuqRkhKLiIhI3VWjgd3MmTO56aabuPnmm+nWrRuzZs0iJiaGF154ocT6L774IrGxscyaNYtu3bpx8803c+ONN/LEE0/8f3t3G9PU+YYB/KpgC8yKE7CVWF7MCo4YYaMz1syVDdaF7IUtW7JkZrCwzCBgwMaYwV50SybOD+qYzoUobsmydSZOcQYMTSbdFmQBlIw4R8RUIJmkqW5SCS/aPv8PhvO3Kc4tOk/P6fVL+HDu85z2OVwCt+et//q9Z7utOCh4CzsREREpl2yN3fT0NHp7e2G320PqdrsdnZ2ds25z6tSpsPHPPPMMenp6cP369X/1/iIwEVbzjk1i4jofeUJERETKJNs1dj6fD4FAAAaDIaRuMBgwOjo66zajo6Ozjr9x4wZ8Ph8WL14cts3U1BSmpqak5atXrwK4+YHFgUmBsSmB/x+jExgbG8MNreyXHhIREREBAMbGxgD8s7OKsncwGk3odW1CiLDancbPVp/R0NCADz74IKy+zJwFAEi8pRa4dgWLU5L+ybSJiIiI7iu/34/ExMS/HSNbY5ecnIyYmJiwo3NerzfsqNwMo9E46/jY2FgkJc3ekNXV1cHhcEjLwWAQV65cwdy5c5GWloaRkRHMnz//LveG5DA2NgaTycQMFY45qgNzVD5mGLmEEPD7/UhNTb3jWNkaO61Wi/z8fLhcLrz00ktS3eVyoaSkZNZtrFYrvv/++5Bae3s7LBbLbT+CQ6fTQafThdQWLFggHdacP38+/wErHDNUB+aoDsxR+ZhhZLrTkboZst4V63A4sH//fjQ3N+PcuXPYuHEjhoeHpefS1dXVobS0VBpfUVGBoaEhOBwOnDt3Ds3NzThw4AA2bdok1y4QERERRQxZr7F79dVXcfnyZXz44Ye4dOkSli9fjtbWVqSnpwMALl26FPJMu8zMTLS2tmLjxo3Yu3cvUlNT0djYiJdfflmuXSAiIiKKGLLfPFFZWYnKyspZ133xxRdhNZvNhtOnT9/1++p0OmzZsiXsNC0pBzNUB+aoDsxR+ZihOmgEn8hLREREpAr8YFQiIiIilWBjR0RERKQSbOyIiIiIVCIqG7vPPvsMmZmZiIuLQ35+Pn766Se5p0R/48cff8Tzzz+P1NRUaDQaHD16NGS9EAJbt25Famoq4uPjUVBQgLNnz8ozWZpVQ0MDHnvsMej1eixatAgvvvgiBgYGQsYwx8i3b98+rFixQnrOmdVqRVtbm7SeGSpPQ0MDNBoNamtrpRpzVLaoa+y+/fZb1NbW4p133sGZM2ewZs0aFBcXhzxWhSLL+Pg4cnNzsWfPnlnX79ixAzt37sSePXvQ3d0No9GIp59+Gn6//z7PlG7H7XajqqoKXV1dcLlcuHHjBux2O8bHx6UxzDHyLVmyBNu3b0dPTw96enrw1FNPoaSkRPqjzwyVpbu7G01NTVixYkVInTkqnIgyK1euFBUVFSG1ZcuWibffflumGdG/AUAcOXJEWg4Gg8JoNIrt27dLtcnJSZGYmCg+//xzGWZI/4TX6xUAhNvtFkIwRyV78MEHxf79+5mhwvj9fmE2m4XL5RI2m03U1NQIIfizqAZRdcRuenoavb29sNvtIXW73Y7Ozk6ZZkV3w+PxYHR0NCRTnU4Hm83GTCPY1atXAQALFy4EwByVKBAIwOl0Ynx8HFarlRkqTFVVFZ599lkUFRWF1Jmj8sn+gOL7yefzIRAIwGAwhNQNBgNGR0dlmhXdjZncZst0aGhIjinRHQgh4HA48Pjjj2P58uUAmKOS9Pf3w2q1YnJyEvPmzcORI0eQk5Mj/dFnhpHP6XTi9OnT6O7uDlvHn0Xli6rGboZGowlZFkKE1UhZmKlyVFdX49dff8XPP/8cto45Rr7s7Gz09fXhr7/+wuHDh1FWVga32y2tZ4aRbWRkBDU1NWhvb0dcXNxtxzFH5YqqU7HJycmIiYkJOzrn9XrD/ndCymA0GgGAmSrEhg0bcOzYMZw8eRJLliyR6sxRObRaLR566CFYLBY0NDQgNzcXn3zyCTNUiN7eXni9XuTn5yM2NhaxsbFwu91obGxEbGyslBVzVK6oauy0Wi3y8/PhcrlC6i6XC6tXr5ZpVnQ3MjMzYTQaQzKdnp6G2+1mphFECIHq6mp89913+OGHH5CZmRmynjkqlxACU1NTzFAhCgsL0d/fj76+PunLYrFg7dq16Ovrw9KlS5mjwkXdqViHw4HXX38dFosFVqsVTU1NGB4eRkVFhdxTo9u4du0aBgcHpWWPx4O+vj4sXLgQaWlpqK2txbZt22A2m2E2m7Ft2zYkJCTgtddek3HWdKuqqip8/fXXaGlpgV6vl44GJCYmIj4+XnqOFnOMbPX19SguLobJZILf74fT6URHRwdOnDjBDBVCr9dL17bOeOCBB5CUlCTVmaPCyXdDrnz27t0r0tPThVarFY8++qj0yAWKTCdPnhQAwr7KysqEEDdvz9+yZYswGo1Cp9OJJ554QvT398s7aQoxW34AxMGDB6UxzDHylZeXS787U1JSRGFhoWhvb5fWM0NluvVxJ0IwR6XTCCGETD0lEREREd1DUXWNHREREZGasbEjIiIiUgk2dkREREQqwcaOiIiISCXY2BERERGpBBs7IiIiIpVgY0dERESkEmzsiIiIiFSCjR0RERGRSrCxIyL6D01MTCAhIQG///673FMhoijAxo6I6D/kcrlgMpmwbNkyuadCRFGAjR0RRbWCggJUV1ejuroaCxYsQFJSEt59913MfIz21NQUNm/eDJPJBJ1OB7PZjAMHDgAA/vzzT6xduxYpKSmIj4+H2WzGwYMHQ16/paUFL7zwAgBg69atyMvLQ3NzM9LS0jBv3jysX78egUAAO3bsgNFoxKJFi/DRRx/d328CEalGrNwTICKS25dffok333wTv/zyC3p6erBu3Tqkp6fjrbfeQmlpKU6dOoXGxkbk5ubC4/HA5/MBAN577z389ttvaGtrQ3JyMgYHBzExMSG9bjAYxPHjx3H48GGpduHCBbS1teHEiRO4cOECXnnlFXg8HmRlZcHtdqOzsxPl5eUoLCzEqlWr7vv3goiUjY0dEUU9k8mEXbt2QaPRIDs7G/39/di1axdsNhsOHToEl8uFoqIiAMDSpUul7YaHh/HII4/AYrEAADIyMkJet6urC8FgEKtXr5ZqwWAQzc3N0Ov1yMnJwZNPPomBgQG0trZizpw5yM7Oxscff4yOjg42dkT0r/FULBFFvVWrVkGj0UjLVqsV58+fx5kzZxATEwObzTbrduvXr4fT6UReXh42b96Mzs7OkPUtLS147rnnMGfO/3/VZmRkQK/XS8sGgwE5OTkhYwwGA7xe773aPSKKImzsiIhuIy4u7m/XFxcXY2hoCLW1tfjjjz9QWFiITZs2SeuPHTuGkpKSkG3mzp0bsqzRaGatBYPBu5w9EUUjNnZEFPW6urrCls1mM3JzcxEMBuF2u2+7bUpKCt544w189dVX2L17N5qamgAA58+fx8WLF2G32//TuRMR3YqNHRFFvZGRETgcDgwMDOCbb77Bp59+ipqaGmRkZKCsrAzl5eU4evQoPB4POjo6cOjQIQDA+++/j5aWFgwODuLs2bM4fvw4Hn74YQA3T8MWFRUhISFBzl0joijDmyeIKOqVlpZiYmICK1euRExMDDZs2IB169YBAPbt24f6+npUVlbi8uXLSEtLQ319PQBAq9Wirq4OFy9eRHx8PNasWQOn0wngZmNXVlYm2z4RUXTSiJmHNRERRaGCggLk5eVh9+7d9+w1fT4fFi9ejJGRERiNxnv2ukREd8JTsURE99iVK1ewc+dONnVEdN/xVCwR0T2WlZWFrKwsuadBRFGIp2KJiIiIVIKnYom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",
       "application/papermill.record/text/plain": "<Figure size 640x480 with 1 Axes>"
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@@ -524,8 +530,8 @@
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     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_0b524 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_0b524 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_0b524 tr {\n  font-size: 10px;\n}\n#T_0b524 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_0b524 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_0b524 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_0b524\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_0b524_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_0b524_level0_row0\" class=\"row_heading level0 row0\" >glass</th>\n      <td id=\"T_0b524_row0_col0\" class=\"data row0 col0\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_0b524_level0_row1\" class=\"row_heading level0 row1\" >metal</th>\n      <td id=\"T_0b524_row1_col0\" class=\"data row1 col0\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_0b524_level0_row2\" class=\"row_heading level0 row2\" >paper</th>\n      <td id=\"T_0b524_row2_col0\" class=\"data row2 col0\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_0b524_level0_row3\" class=\"row_heading level0 row3\" >plastic</th>\n      <td id=\"T_0b524_row3_col0\" class=\"data row3 col0\" >83%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb21c40>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_020a9 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_020a9 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_020a9 tr {\n  font-size: 10px;\n}\n#T_020a9 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_020a9 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_020a9 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_020a9\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_020a9_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_020a9_level0_row0\" class=\"row_heading level0 row0\" >glass</th>\n      <td id=\"T_020a9_row0_col0\" class=\"data row0 col0\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_020a9_level0_row1\" class=\"row_heading level0 row1\" >metal</th>\n      <td id=\"T_020a9_row1_col0\" class=\"data row1 col0\" >4%</td>\n    </tr>\n    <tr>\n      <th id=\"T_020a9_level0_row2\" class=\"row_heading level0 row2\" >paper</th>\n      <td id=\"T_020a9_row2_col0\" class=\"data row2 col0\" >3%</td>\n    </tr>\n    <tr>\n      <th id=\"T_020a9_level0_row3\" class=\"row_heading level0 row3\" >plastic</th>\n      <td id=\"T_020a9_row3_col0\" class=\"data row3 col0\" >83%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e62423a0>"
      },
      "metadata": {
       "scrapbook": {
@@ -537,7 +543,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 4.24\n* HDI 95%: 0.3 - 13.0\n* 90% Range: 0.5 - 13.0",
+      "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 3.97\n* HDI 95%: 0.3 - 10.7\n* 90% Range: 0.3 - 10.7",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -550,7 +556,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 10.81\n* HDI 95%: 0.14 - 48.54\n* 90% Range: 0.55 - 51.72",
+      "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 8.0\n* HDI 95%: 0.14 - 58.18\n* 90% Range: 0.64 - 54.9",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -563,7 +569,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 5.53\n* HDI 95%: 0.4 - 18.7\n* 90% Range: 0.7 - 17.94",
+      "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 4.6\n* HDI 95%: 0.1 - 15.6\n* 90% Range: 0.5 - 14.84",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -589,8 +595,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_0ece9 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_0ece9 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_0ece9 tr {\n  font-size: 10px;\n}\n#T_0ece9 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_0ece9 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_0ece9 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_0ece9 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_0ece9 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_0ece9\">\n  <thead>\n    <tr>\n      <th id=\"T_0ece9_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_0ece9_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_0ece9_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_0ece9_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_0ece9_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_0ece9_row0_col0\" class=\"data row0 col0\" >Fragmented plastics</td>\n      <td id=\"T_0ece9_row0_col1\" class=\"data row0 col1\" >6'735</td>\n      <td id=\"T_0ece9_row0_col2\" class=\"data row0 col2\" >0,94</td>\n      <td id=\"T_0ece9_row0_col3\" class=\"data row0 col3\" >0,16</td>\n      <td id=\"T_0ece9_row0_col4\" class=\"data row0 col4\" >0,93</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_0ece9_row1_col1\" class=\"data row1 col1\" >6'081</td>\n      <td id=\"T_0ece9_row1_col2\" class=\"data row1 col2\" >0,66</td>\n      <td id=\"T_0ece9_row1_col3\" class=\"data row1 col3\" >0,14</td>\n      <td id=\"T_0ece9_row1_col4\" class=\"data row1 col4\" >0,91</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row2_col0\" class=\"data row2 col0\" >Expanded polystyrene</td>\n      <td id=\"T_0ece9_row2_col1\" class=\"data row2 col1\" >4'302</td>\n      <td id=\"T_0ece9_row2_col2\" class=\"data row2 col2\" >0,63</td>\n      <td id=\"T_0ece9_row2_col3\" class=\"data row2 col3\" >0,10</td>\n      <td id=\"T_0ece9_row2_col4\" class=\"data row2 col4\" >0,75</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row3_col0\" class=\"data row3 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_0ece9_row3_col1\" class=\"data row3 col1\" >2'789</td>\n      <td id=\"T_0ece9_row3_col2\" class=\"data row3 col2\" >0,34</td>\n      <td id=\"T_0ece9_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_0ece9_row3_col4\" class=\"data row3 col4\" >0,91</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row4_col0\" class=\"data row4 col0\" >Industrial sheeting</td>\n      <td id=\"T_0ece9_row4_col1\" class=\"data row4 col1\" >1'882</td>\n      <td id=\"T_0ece9_row4_col2\" class=\"data row4 col2\" >0,26</td>\n      <td id=\"T_0ece9_row4_col3\" class=\"data row4 col3\" >0,04</td>\n      <td id=\"T_0ece9_row4_col4\" class=\"data row4 col4\" >0,74</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row5_col0\" class=\"data row5 col0\" >Industrial pellets (nurdles)</td>\n      <td id=\"T_0ece9_row5_col1\" class=\"data row5 col1\" >1'705</td>\n      <td id=\"T_0ece9_row5_col2\" class=\"data row5 col2\" >0,21</td>\n      <td id=\"T_0ece9_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_0ece9_row5_col4\" class=\"data row5 col4\" >0,40</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row6_col0\" class=\"data row6 col0\" >plastic caps, lid rings: G21, G22, G23, G24</td>\n      <td id=\"T_0ece9_row6_col1\" class=\"data row6 col1\" >1'677</td>\n      <td id=\"T_0ece9_row6_col2\" class=\"data row6 col2\" >0,21</td>\n      <td id=\"T_0ece9_row6_col3\" class=\"data row6 col3\" >0,04</td>\n      <td id=\"T_0ece9_row6_col4\" class=\"data row6 col4\" >0,78</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row7_col0\" class=\"data row7 col0\" >Foam packaging/insulation/polyurethane</td>\n      <td id=\"T_0ece9_row7_col1\" class=\"data row7 col1\" >1'422</td>\n      <td id=\"T_0ece9_row7_col2\" class=\"data row7 col2\" >0,10</td>\n      <td id=\"T_0ece9_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_0ece9_row7_col4\" class=\"data row7 col4\" >0,98</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row8_col0\" class=\"data row8 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_0ece9_row8_col1\" class=\"data row8 col1\" >1'400</td>\n      <td id=\"T_0ece9_row8_col2\" class=\"data row8 col2\" >0,20</td>\n      <td id=\"T_0ece9_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_0ece9_row8_col4\" class=\"data row8 col4\" >0,69</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row9_col0\" class=\"data row9 col0\" >Cotton bud/swab sticks</td>\n      <td id=\"T_0ece9_row9_col1\" class=\"data row9 col1\" >1'379</td>\n      <td id=\"T_0ece9_row9_col2\" class=\"data row9 col2\" >0,18</td>\n      <td id=\"T_0ece9_row9_col3\" class=\"data row9 col3\" >0,03</td>\n      <td id=\"T_0ece9_row9_col4\" class=\"data row9 col4\" >0,64</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row10_col0\" class=\"data row10 col0\" >Plastic construction waste</td>\n      <td id=\"T_0ece9_row10_col1\" class=\"data row10 col1\" >804</td>\n      <td id=\"T_0ece9_row10_col2\" class=\"data row10 col2\" >0,10</td>\n      <td id=\"T_0ece9_row10_col3\" class=\"data row10 col3\" >0,02</td>\n      <td id=\"T_0ece9_row10_col4\" class=\"data row10 col4\" >0,59</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row11_col0\" class=\"data row11 col0\" >Tobacco; plastic packaging, containers</td>\n      <td id=\"T_0ece9_row11_col1\" class=\"data row11 col1\" >522</td>\n      <td id=\"T_0ece9_row11_col2\" class=\"data row11 col2\" >0,07</td>\n      <td id=\"T_0ece9_row11_col3\" class=\"data row11 col3\" >0,01</td>\n      <td id=\"T_0ece9_row11_col4\" class=\"data row11 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row12_col0\" class=\"data row12 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_0ece9_row12_col1\" class=\"data row12 col1\" >488</td>\n      <td id=\"T_0ece9_row12_col2\" class=\"data row12 col2\" >0,05</td>\n      <td id=\"T_0ece9_row12_col3\" class=\"data row12 col3\" >0,01</td>\n      <td id=\"T_0ece9_row12_col4\" class=\"data row12 col4\" >0,60</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row13_col0\" class=\"data row13 col0\" >Straws and stirrers</td>\n      <td id=\"T_0ece9_row13_col1\" class=\"data row13 col1\" >374</td>\n      <td id=\"T_0ece9_row13_col2\" class=\"data row13 col2\" >0,05</td>\n      <td id=\"T_0ece9_row13_col3\" class=\"data row13 col3\" >0,01</td>\n      <td id=\"T_0ece9_row13_col4\" class=\"data row13 col4\" >0,54</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row14_col0\" class=\"data row14 col0\" >Foil wrappers, aluminum foil</td>\n      <td id=\"T_0ece9_row14_col1\" class=\"data row14 col1\" >371</td>\n      <td id=\"T_0ece9_row14_col2\" class=\"data row14 col2\" >0,04</td>\n      <td id=\"T_0ece9_row14_col3\" class=\"data row14 col3\" >0,01</td>\n      <td id=\"T_0ece9_row14_col4\" class=\"data row14 col4\" >0,53</td>\n    </tr>\n    <tr>\n      <td id=\"T_0ece9_row15_col0\" class=\"data row15 col0\" >Lollypop sticks</td>\n      <td id=\"T_0ece9_row15_col1\" class=\"data row15 col1\" >354</td>\n      <td id=\"T_0ece9_row15_col2\" class=\"data row15 col2\" >0,04</td>\n      <td id=\"T_0ece9_row15_col3\" class=\"data row15 col3\" >0,01</td>\n      <td id=\"T_0ece9_row15_col4\" class=\"data row15 col4\" >0,52</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a534ea60>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_c1178 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_c1178 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_c1178 tr {\n  font-size: 10px;\n}\n#T_c1178 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_c1178 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_c1178 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_c1178 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_c1178 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_c1178\">\n  <thead>\n    <tr>\n      <th id=\"T_c1178_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_c1178_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_c1178_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_c1178_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_c1178_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c1178_row0_col0\" class=\"data row0 col0\" >Fragmented plastics</td>\n      <td id=\"T_c1178_row0_col1\" class=\"data row0 col1\" >6'735</td>\n      <td id=\"T_c1178_row0_col2\" class=\"data row0 col2\" >0,94</td>\n      <td id=\"T_c1178_row0_col3\" class=\"data row0 col3\" >0,16</td>\n      <td id=\"T_c1178_row0_col4\" class=\"data row0 col4\" >0,93</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_c1178_row1_col1\" class=\"data row1 col1\" >6'081</td>\n      <td id=\"T_c1178_row1_col2\" class=\"data row1 col2\" >0,66</td>\n      <td id=\"T_c1178_row1_col3\" class=\"data row1 col3\" >0,14</td>\n      <td id=\"T_c1178_row1_col4\" class=\"data row1 col4\" >0,91</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row2_col0\" class=\"data row2 col0\" >Expanded polystyrene</td>\n      <td id=\"T_c1178_row2_col1\" class=\"data row2 col1\" >4'302</td>\n      <td id=\"T_c1178_row2_col2\" class=\"data row2 col2\" >0,63</td>\n      <td id=\"T_c1178_row2_col3\" class=\"data row2 col3\" >0,10</td>\n      <td id=\"T_c1178_row2_col4\" class=\"data row2 col4\" >0,75</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row3_col0\" class=\"data row3 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_c1178_row3_col1\" class=\"data row3 col1\" >2'789</td>\n      <td id=\"T_c1178_row3_col2\" class=\"data row3 col2\" >0,34</td>\n      <td id=\"T_c1178_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_c1178_row3_col4\" class=\"data row3 col4\" >0,91</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row4_col0\" class=\"data row4 col0\" >Industrial sheeting</td>\n      <td id=\"T_c1178_row4_col1\" class=\"data row4 col1\" >1'882</td>\n      <td id=\"T_c1178_row4_col2\" class=\"data row4 col2\" >0,26</td>\n      <td id=\"T_c1178_row4_col3\" class=\"data row4 col3\" >0,04</td>\n      <td id=\"T_c1178_row4_col4\" class=\"data row4 col4\" >0,74</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row5_col0\" class=\"data row5 col0\" >Industrial pellets (nurdles)</td>\n      <td id=\"T_c1178_row5_col1\" class=\"data row5 col1\" >1'705</td>\n      <td id=\"T_c1178_row5_col2\" class=\"data row5 col2\" >0,21</td>\n      <td id=\"T_c1178_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_c1178_row5_col4\" class=\"data row5 col4\" >0,40</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row6_col0\" class=\"data row6 col0\" >plastic caps, lid rings: G21, G22, G23, G24</td>\n      <td id=\"T_c1178_row6_col1\" class=\"data row6 col1\" >1'677</td>\n      <td id=\"T_c1178_row6_col2\" class=\"data row6 col2\" >0,21</td>\n      <td id=\"T_c1178_row6_col3\" class=\"data row6 col3\" >0,04</td>\n      <td id=\"T_c1178_row6_col4\" class=\"data row6 col4\" >0,78</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row7_col0\" class=\"data row7 col0\" >Foam packaging/insulation/polyurethane</td>\n      <td id=\"T_c1178_row7_col1\" class=\"data row7 col1\" >1'422</td>\n      <td id=\"T_c1178_row7_col2\" class=\"data row7 col2\" >0,10</td>\n      <td id=\"T_c1178_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_c1178_row7_col4\" class=\"data row7 col4\" >0,98</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row8_col0\" class=\"data row8 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_c1178_row8_col1\" class=\"data row8 col1\" >1'400</td>\n      <td id=\"T_c1178_row8_col2\" class=\"data row8 col2\" >0,20</td>\n      <td id=\"T_c1178_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_c1178_row8_col4\" class=\"data row8 col4\" >0,69</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row9_col0\" class=\"data row9 col0\" >Cotton bud/swab sticks</td>\n      <td id=\"T_c1178_row9_col1\" class=\"data row9 col1\" >1'379</td>\n      <td id=\"T_c1178_row9_col2\" class=\"data row9 col2\" >0,18</td>\n      <td id=\"T_c1178_row9_col3\" class=\"data row9 col3\" >0,03</td>\n      <td id=\"T_c1178_row9_col4\" class=\"data row9 col4\" >0,64</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row10_col0\" class=\"data row10 col0\" >Plastic construction waste</td>\n      <td id=\"T_c1178_row10_col1\" class=\"data row10 col1\" >804</td>\n      <td id=\"T_c1178_row10_col2\" class=\"data row10 col2\" >0,10</td>\n      <td id=\"T_c1178_row10_col3\" class=\"data row10 col3\" >0,02</td>\n      <td id=\"T_c1178_row10_col4\" class=\"data row10 col4\" >0,59</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row11_col0\" class=\"data row11 col0\" >Tobacco; plastic packaging, containers</td>\n      <td id=\"T_c1178_row11_col1\" class=\"data row11 col1\" >522</td>\n      <td id=\"T_c1178_row11_col2\" class=\"data row11 col2\" >0,07</td>\n      <td id=\"T_c1178_row11_col3\" class=\"data row11 col3\" >0,01</td>\n      <td id=\"T_c1178_row11_col4\" class=\"data row11 col4\" >0,56</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row12_col0\" class=\"data row12 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_c1178_row12_col1\" class=\"data row12 col1\" >488</td>\n      <td id=\"T_c1178_row12_col2\" class=\"data row12 col2\" >0,05</td>\n      <td id=\"T_c1178_row12_col3\" class=\"data row12 col3\" >0,01</td>\n      <td id=\"T_c1178_row12_col4\" class=\"data row12 col4\" >0,60</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row13_col0\" class=\"data row13 col0\" >Straws and stirrers</td>\n      <td id=\"T_c1178_row13_col1\" class=\"data row13 col1\" >374</td>\n      <td id=\"T_c1178_row13_col2\" class=\"data row13 col2\" >0,05</td>\n      <td id=\"T_c1178_row13_col3\" class=\"data row13 col3\" >0,01</td>\n      <td id=\"T_c1178_row13_col4\" class=\"data row13 col4\" >0,54</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row14_col0\" class=\"data row14 col0\" >Foil wrappers, aluminum foil</td>\n      <td id=\"T_c1178_row14_col1\" class=\"data row14 col1\" >371</td>\n      <td id=\"T_c1178_row14_col2\" class=\"data row14 col2\" >0,04</td>\n      <td id=\"T_c1178_row14_col3\" class=\"data row14 col3\" >0,01</td>\n      <td id=\"T_c1178_row14_col4\" class=\"data row14 col4\" >0,53</td>\n    </tr>\n    <tr>\n      <td id=\"T_c1178_row15_col0\" class=\"data row15 col0\" >Lollypop sticks</td>\n      <td id=\"T_c1178_row15_col1\" class=\"data row15 col1\" >354</td>\n      <td id=\"T_c1178_row15_col2\" class=\"data row15 col2\" >0,04</td>\n      <td id=\"T_c1178_row15_col3\" class=\"data row15 col3\" >0,01</td>\n      <td id=\"T_c1178_row15_col4\" class=\"data row15 col4\" >0,52</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e39df4f0>"
      },
      "metadata": {
       "scrapbook": {
@@ -785,6 +791,9 @@
    "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -808,7 +817,7 @@
     },
     {
      "data": {
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",
       "application/papermill.record/text/plain": "<Figure size 640x480 with 1 Axes>"
      },
      "metadata": {
@@ -834,8 +843,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_a28fd tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_a28fd tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_a28fd tr {\n  font-size: 10px;\n}\n#T_a28fd th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_a28fd td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_a28fd caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_a28fd\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a28fd_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a28fd_level0_row0\" class=\"row_heading level0 row0\" >cloth</th>\n      <td id=\"T_a28fd_row0_col0\" class=\"data row0 col0\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row1\" class=\"row_heading level0 row1\" >glass</th>\n      <td id=\"T_a28fd_row1_col0\" class=\"data row1 col0\" >7%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row2\" class=\"row_heading level0 row2\" >metal</th>\n      <td id=\"T_a28fd_row2_col0\" class=\"data row2 col0\" >9%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row3\" class=\"row_heading level0 row3\" >paper</th>\n      <td id=\"T_a28fd_row3_col0\" class=\"data row3 col0\" >7%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row4\" class=\"row_heading level0 row4\" >plastic</th>\n      <td id=\"T_a28fd_row4_col0\" class=\"data row4 col0\" >65%</td>\n    </tr>\n    <tr>\n      <th id=\"T_a28fd_level0_row5\" class=\"row_heading level0 row5\" >wood</th>\n      <td id=\"T_a28fd_row5_col0\" class=\"data row5 col0\" >2%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a53f4bb0>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_39019 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_39019 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_39019 tr {\n  font-size: 10px;\n}\n#T_39019 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_39019 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_39019 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_39019\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_39019_level0_col0\" class=\"col_heading level0 col0\" >% of total</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >material</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_39019_level0_row0\" class=\"row_heading level0 row0\" >cloth</th>\n      <td id=\"T_39019_row0_col0\" class=\"data row0 col0\" >5%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row1\" class=\"row_heading level0 row1\" >glass</th>\n      <td id=\"T_39019_row1_col0\" class=\"data row1 col0\" >7%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row2\" class=\"row_heading level0 row2\" >metal</th>\n      <td id=\"T_39019_row2_col0\" class=\"data row2 col0\" >9%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row3\" class=\"row_heading level0 row3\" >paper</th>\n      <td id=\"T_39019_row3_col0\" class=\"data row3 col0\" >7%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row4\" class=\"row_heading level0 row4\" >plastic</th>\n      <td id=\"T_39019_row4_col0\" class=\"data row4 col0\" >65%</td>\n    </tr>\n    <tr>\n      <th id=\"T_39019_level0_row5\" class=\"row_heading level0 row5\" >wood</th>\n      <td id=\"T_39019_row5_col0\" class=\"data row5 col0\" >2%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e6257160>"
      },
      "metadata": {
       "scrapbook": {
@@ -847,7 +856,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 1.24\n* HDI 95%: 0.1 - 5.0\n* 90% Range: 0.1 - 4.62",
+      "application/papermill.record/text/markdown": "__Given the weighted prior__\n* Average: 1.45\n* HDI 95%: 0.1 - 5.5\n* 90% Range: 0.1 - 4.64",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -860,7 +869,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 1.76\n* HDI 95%: 0.02 - 10.96\n* 90% Range: 0.1 - 4.93",
+      "application/papermill.record/text/markdown": "__Given the observed max__\n* Average: 1.45\n* HDI 95%: 0.02 - 4.55\n* 90% Range: 0.1 - 6.08",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -873,7 +882,7 @@
     },
     {
      "data": {
-      "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 1.16\n* HDI 95%: 0.1 - 3.6\n* 90% Range: 0.1 - 3.41",
+      "application/papermill.record/text/markdown": "__Given the 99th percentile__\n* Average: 1.14\n* HDI 95%: 0.1 - 3.3\n* 90% Range: 0.1 - 3.11",
       "application/papermill.record/text/plain": "<IPython.core.display.Markdown object>"
      },
      "metadata": {
@@ -899,8 +908,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_0a3c6 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_0a3c6 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_0a3c6 tr {\n  font-size: 10px;\n}\n#T_0a3c6 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_0a3c6 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_0a3c6 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_0a3c6 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_0a3c6 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_0a3c6\">\n  <thead>\n    <tr>\n      <th id=\"T_0a3c6_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_0a3c6_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_0a3c6_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_0a3c6_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_0a3c6_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_0a3c6_row0_col0\" class=\"data row0 col0\" >Diapers - wipes</td>\n      <td id=\"T_0a3c6_row0_col1\" class=\"data row0 col1\" >299</td>\n      <td id=\"T_0a3c6_row0_col2\" class=\"data row0 col2\" >0,21</td>\n      <td id=\"T_0a3c6_row0_col3\" class=\"data row0 col3\" >0,16</td>\n      <td id=\"T_0a3c6_row0_col4\" class=\"data row0 col4\" >0,30</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_0a3c6_row1_col1\" class=\"data row1 col1\" >225</td>\n      <td id=\"T_0a3c6_row1_col2\" class=\"data row1 col2\" >0,21</td>\n      <td id=\"T_0a3c6_row1_col3\" class=\"data row1 col3\" >0,12</td>\n      <td id=\"T_0a3c6_row1_col4\" class=\"data row1 col4\" >0,64</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row2_col0\" class=\"data row2 col0\" >Industrial sheeting</td>\n      <td id=\"T_0a3c6_row2_col1\" class=\"data row2 col1\" >149</td>\n      <td id=\"T_0a3c6_row2_col2\" class=\"data row2 col2\" >0,09</td>\n      <td id=\"T_0a3c6_row2_col3\" class=\"data row2 col3\" >0,08</td>\n      <td id=\"T_0a3c6_row2_col4\" class=\"data row2 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row3_col0\" class=\"data row3 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_0a3c6_row3_col1\" class=\"data row3 col1\" >102</td>\n      <td id=\"T_0a3c6_row3_col2\" class=\"data row3 col2\" >0,11</td>\n      <td id=\"T_0a3c6_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_0a3c6_row3_col4\" class=\"data row3 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row4_col0\" class=\"data row4 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_0a3c6_row4_col1\" class=\"data row4 col1\" >101</td>\n      <td id=\"T_0a3c6_row4_col2\" class=\"data row4 col2\" >0,11</td>\n      <td id=\"T_0a3c6_row4_col3\" class=\"data row4 col3\" >0,05</td>\n      <td id=\"T_0a3c6_row4_col4\" class=\"data row4 col4\" >0,67</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row5_col0\" class=\"data row5 col0\" >Packaging films nonfood or unknown</td>\n      <td id=\"T_0a3c6_row5_col1\" class=\"data row5 col1\" >68</td>\n      <td id=\"T_0a3c6_row5_col2\" class=\"data row5 col2\" >0,09</td>\n      <td id=\"T_0a3c6_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_0a3c6_row5_col4\" class=\"data row5 col4\" >0,27</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row6_col0\" class=\"data row6 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_0a3c6_row6_col1\" class=\"data row6 col1\" >63</td>\n      <td id=\"T_0a3c6_row6_col2\" class=\"data row6 col2\" >0,08</td>\n      <td id=\"T_0a3c6_row6_col3\" class=\"data row6 col3\" >0,03</td>\n      <td id=\"T_0a3c6_row6_col4\" class=\"data row6 col4\" >0,30</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row7_col0\" class=\"data row7 col0\" >Fragmented plastics</td>\n      <td id=\"T_0a3c6_row7_col1\" class=\"data row7 col1\" >61</td>\n      <td id=\"T_0a3c6_row7_col2\" class=\"data row7 col2\" >0,05</td>\n      <td id=\"T_0a3c6_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_0a3c6_row7_col4\" class=\"data row7 col4\" >0,52</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row8_col0\" class=\"data row8 col0\" >Bags; plastic shopping/carrier/grocery and pieces</td>\n      <td id=\"T_0a3c6_row8_col1\" class=\"data row8 col1\" >55</td>\n      <td id=\"T_0a3c6_row8_col2\" class=\"data row8 col2\" >0,05</td>\n      <td id=\"T_0a3c6_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_0a3c6_row8_col4\" class=\"data row8 col4\" >0,24</td>\n    </tr>\n    <tr>\n      <td id=\"T_0a3c6_row9_col0\" class=\"data row9 col0\" >Construction material; bricks, pipes, cement</td>\n      <td id=\"T_0a3c6_row9_col1\" class=\"data row9 col1\" >42</td>\n      <td id=\"T_0a3c6_row9_col2\" class=\"data row9 col2\" >0,02</td>\n      <td id=\"T_0a3c6_row9_col3\" class=\"data row9 col3\" >0,02</td>\n      <td id=\"T_0a3c6_row9_col4\" class=\"data row9 col4\" >0,12</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a3fd0c10>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_22ca4 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_22ca4 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_22ca4 tr {\n  font-size: 10px;\n}\n#T_22ca4 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_22ca4 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_22ca4 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_22ca4 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_22ca4 td:nth-child(1) {\n  text-align: left;\n}\n</style>\n<table id=\"T_22ca4\">\n  <thead>\n    <tr>\n      <th id=\"T_22ca4_level0_col0\" class=\"col_heading level0 col0\" >Object</th>\n      <th id=\"T_22ca4_level0_col1\" class=\"col_heading level0 col1\" >Quantity</th>\n      <th id=\"T_22ca4_level0_col2\" class=\"col_heading level0 col2\" >pcs/m</th>\n      <th id=\"T_22ca4_level0_col3\" class=\"col_heading level0 col3\" >% of total</th>\n      <th id=\"T_22ca4_level0_col4\" class=\"col_heading level0 col4\" >Fail rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_22ca4_row0_col0\" class=\"data row0 col0\" >Diapers - wipes</td>\n      <td id=\"T_22ca4_row0_col1\" class=\"data row0 col1\" >299</td>\n      <td id=\"T_22ca4_row0_col2\" class=\"data row0 col2\" >0,21</td>\n      <td id=\"T_22ca4_row0_col3\" class=\"data row0 col3\" >0,16</td>\n      <td id=\"T_22ca4_row0_col4\" class=\"data row0 col4\" >0,30</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row1_col0\" class=\"data row1 col0\" >Cigarette filters</td>\n      <td id=\"T_22ca4_row1_col1\" class=\"data row1 col1\" >225</td>\n      <td id=\"T_22ca4_row1_col2\" class=\"data row1 col2\" >0,21</td>\n      <td id=\"T_22ca4_row1_col3\" class=\"data row1 col3\" >0,12</td>\n      <td id=\"T_22ca4_row1_col4\" class=\"data row1 col4\" >0,64</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row2_col0\" class=\"data row2 col0\" >Industrial sheeting</td>\n      <td id=\"T_22ca4_row2_col1\" class=\"data row2 col1\" >149</td>\n      <td id=\"T_22ca4_row2_col2\" class=\"data row2 col2\" >0,09</td>\n      <td id=\"T_22ca4_row2_col3\" class=\"data row2 col3\" >0,08</td>\n      <td id=\"T_22ca4_row2_col4\" class=\"data row2 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row3_col0\" class=\"data row3 col0\" >Glass drink bottles, pieces</td>\n      <td id=\"T_22ca4_row3_col1\" class=\"data row3 col1\" >102</td>\n      <td id=\"T_22ca4_row3_col2\" class=\"data row3 col2\" >0,11</td>\n      <td id=\"T_22ca4_row3_col3\" class=\"data row3 col3\" >0,06</td>\n      <td id=\"T_22ca4_row3_col4\" class=\"data row3 col4\" >0,36</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row4_col0\" class=\"data row4 col0\" >Food wrappers; candy, snacks</td>\n      <td id=\"T_22ca4_row4_col1\" class=\"data row4 col1\" >101</td>\n      <td id=\"T_22ca4_row4_col2\" class=\"data row4 col2\" >0,11</td>\n      <td id=\"T_22ca4_row4_col3\" class=\"data row4 col3\" >0,05</td>\n      <td id=\"T_22ca4_row4_col4\" class=\"data row4 col4\" >0,67</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row5_col0\" class=\"data row5 col0\" >Packaging films nonfood or unknown</td>\n      <td id=\"T_22ca4_row5_col1\" class=\"data row5 col1\" >68</td>\n      <td id=\"T_22ca4_row5_col2\" class=\"data row5 col2\" >0,09</td>\n      <td id=\"T_22ca4_row5_col3\" class=\"data row5 col3\" >0,04</td>\n      <td id=\"T_22ca4_row5_col4\" class=\"data row5 col4\" >0,27</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row6_col0\" class=\"data row6 col0\" >Metal bottle caps, lids & pull tabs from cans</td>\n      <td id=\"T_22ca4_row6_col1\" class=\"data row6 col1\" >63</td>\n      <td id=\"T_22ca4_row6_col2\" class=\"data row6 col2\" >0,08</td>\n      <td id=\"T_22ca4_row6_col3\" class=\"data row6 col3\" >0,03</td>\n      <td id=\"T_22ca4_row6_col4\" class=\"data row6 col4\" >0,30</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row7_col0\" class=\"data row7 col0\" >Fragmented plastics</td>\n      <td id=\"T_22ca4_row7_col1\" class=\"data row7 col1\" >61</td>\n      <td id=\"T_22ca4_row7_col2\" class=\"data row7 col2\" >0,05</td>\n      <td id=\"T_22ca4_row7_col3\" class=\"data row7 col3\" >0,03</td>\n      <td id=\"T_22ca4_row7_col4\" class=\"data row7 col4\" >0,52</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row8_col0\" class=\"data row8 col0\" >Bags; plastic shopping/carrier/grocery and pieces</td>\n      <td id=\"T_22ca4_row8_col1\" class=\"data row8 col1\" >55</td>\n      <td id=\"T_22ca4_row8_col2\" class=\"data row8 col2\" >0,05</td>\n      <td id=\"T_22ca4_row8_col3\" class=\"data row8 col3\" >0,03</td>\n      <td id=\"T_22ca4_row8_col4\" class=\"data row8 col4\" >0,24</td>\n    </tr>\n    <tr>\n      <td id=\"T_22ca4_row9_col0\" class=\"data row9 col0\" >Construction material; bricks, pipes, cement</td>\n      <td id=\"T_22ca4_row9_col1\" class=\"data row9 col1\" >42</td>\n      <td id=\"T_22ca4_row9_col2\" class=\"data row9 col2\" >0,02</td>\n      <td id=\"T_22ca4_row9_col3\" class=\"data row9 col3\" >0,02</td>\n      <td id=\"T_22ca4_row9_col4\" class=\"data row9 col4\" >0,12</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e2ed2ac0>"
      },
      "metadata": {
       "scrapbook": {
@@ -1094,6 +1103,9 @@
    "id": "12c52a87-8340-419f-bfd9-75ca85260a97",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1670,6 +1682,9 @@
    "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1680,8 +1695,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_1c1b9 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_1c1b9 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_1c1b9 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_1c1b9_row0_col0, #T_1c1b9_row1_col0, #T_1c1b9_row2_col1, #T_1c1b9_row3_col2, #T_1c1b9_row4_col1, #T_1c1b9_row5_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_1c1b9\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_1c1b9_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_1c1b9_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_1c1b9_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_1c1b9_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_1c1b9_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_1c1b9_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_1c1b9_row0_col0\" class=\"data row0 col0\" >5.03</td>\n      <td id=\"T_1c1b9_row0_col1\" class=\"data row0 col1\" >0.09</td>\n      <td id=\"T_1c1b9_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_1c1b9_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_1c1b9_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_1c1b9_row1_col0\" class=\"data row1 col0\" >5.04</td>\n      <td id=\"T_1c1b9_row1_col1\" class=\"data row1 col1\" >0.56</td>\n      <td id=\"T_1c1b9_row1_col2\" class=\"data row1 col2\" >5.01</td>\n      <td id=\"T_1c1b9_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_1c1b9_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_1c1b9_row2_col0\" class=\"data row2 col0\" >2.62</td>\n      <td id=\"T_1c1b9_row2_col1\" class=\"data row2 col1\" >7.46</td>\n      <td id=\"T_1c1b9_row2_col2\" class=\"data row2 col2\" >3.80</td>\n      <td id=\"T_1c1b9_row2_col3\" class=\"data row2 col3\" >2.92</td>\n      <td id=\"T_1c1b9_row2_col4\" class=\"data row2 col4\" >6.27</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_1c1b9_row3_col0\" class=\"data row3 col0\" >4.86</td>\n      <td id=\"T_1c1b9_row3_col1\" class=\"data row3 col1\" >2.73</td>\n      <td id=\"T_1c1b9_row3_col2\" class=\"data row3 col2\" >13.13</td>\n      <td id=\"T_1c1b9_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_1c1b9_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_1c1b9_row4_col0\" class=\"data row4 col0\" >5.45</td>\n      <td id=\"T_1c1b9_row4_col1\" class=\"data row4 col1\" >6.22</td>\n      <td id=\"T_1c1b9_row4_col2\" class=\"data row4 col2\" >2.95</td>\n      <td id=\"T_1c1b9_row4_col3\" class=\"data row4 col3\" >1.84</td>\n      <td id=\"T_1c1b9_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_1c1b9_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_1c1b9_row5_col0\" class=\"data row5 col0\" >4.26</td>\n      <td id=\"T_1c1b9_row5_col1\" class=\"data row5 col1\" >14.60</td>\n      <td id=\"T_1c1b9_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_1c1b9_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_1c1b9_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a53f4340>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_87bf0 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_87bf0 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_87bf0 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_87bf0_row0_col0, #T_87bf0_row1_col0, #T_87bf0_row2_col1, #T_87bf0_row3_col2, #T_87bf0_row4_col1, #T_87bf0_row5_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_87bf0\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_87bf0_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_87bf0_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_87bf0_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_87bf0_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_87bf0_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_87bf0_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_87bf0_row0_col0\" class=\"data row0 col0\" >5.03</td>\n      <td id=\"T_87bf0_row0_col1\" class=\"data row0 col1\" >0.09</td>\n      <td id=\"T_87bf0_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_87bf0_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_87bf0_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_87bf0_row1_col0\" class=\"data row1 col0\" >5.04</td>\n      <td id=\"T_87bf0_row1_col1\" class=\"data row1 col1\" >0.56</td>\n      <td id=\"T_87bf0_row1_col2\" class=\"data row1 col2\" >5.01</td>\n      <td id=\"T_87bf0_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_87bf0_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_87bf0_row2_col0\" class=\"data row2 col0\" >2.62</td>\n      <td id=\"T_87bf0_row2_col1\" class=\"data row2 col1\" >7.46</td>\n      <td id=\"T_87bf0_row2_col2\" class=\"data row2 col2\" >3.80</td>\n      <td id=\"T_87bf0_row2_col3\" class=\"data row2 col3\" >2.92</td>\n      <td id=\"T_87bf0_row2_col4\" class=\"data row2 col4\" >6.27</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_87bf0_row3_col0\" class=\"data row3 col0\" >4.86</td>\n      <td id=\"T_87bf0_row3_col1\" class=\"data row3 col1\" >2.73</td>\n      <td id=\"T_87bf0_row3_col2\" class=\"data row3 col2\" >13.13</td>\n      <td id=\"T_87bf0_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_87bf0_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_87bf0_row4_col0\" class=\"data row4 col0\" >5.45</td>\n      <td id=\"T_87bf0_row4_col1\" class=\"data row4 col1\" >6.22</td>\n      <td id=\"T_87bf0_row4_col2\" class=\"data row4 col2\" >2.95</td>\n      <td id=\"T_87bf0_row4_col3\" class=\"data row4 col3\" >1.84</td>\n      <td id=\"T_87bf0_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_87bf0_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_87bf0_row5_col0\" class=\"data row5 col0\" >4.26</td>\n      <td id=\"T_87bf0_row5_col1\" class=\"data row5 col1\" >14.60</td>\n      <td id=\"T_87bf0_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_87bf0_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_87bf0_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e657d26e310>"
      },
      "metadata": {
       "scrapbook": {
@@ -1693,8 +1708,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_fc5d9 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_fc5d9 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_fc5d9 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_fc5d9_row0_col0, #T_fc5d9_row1_col0, #T_fc5d9_row2_col4, #T_fc5d9_row3_col0, #T_fc5d9_row4_col0, #T_fc5d9_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_fc5d9\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_fc5d9_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_fc5d9_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_fc5d9_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_fc5d9_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_fc5d9_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_fc5d9_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_fc5d9_row0_col0\" class=\"data row0 col0\" >100%</td>\n      <td id=\"T_fc5d9_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_fc5d9_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_fc5d9_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_fc5d9_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_fc5d9_row1_col0\" class=\"data row1 col0\" >98%</td>\n      <td id=\"T_fc5d9_row1_col1\" class=\"data row1 col1\" >1%</td>\n      <td id=\"T_fc5d9_row1_col2\" class=\"data row1 col2\" >1%</td>\n      <td id=\"T_fc5d9_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_fc5d9_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_fc5d9_row2_col0\" class=\"data row2 col0\" >14%</td>\n      <td id=\"T_fc5d9_row2_col1\" class=\"data row2 col1\" >18%</td>\n      <td id=\"T_fc5d9_row2_col2\" class=\"data row2 col2\" >10%</td>\n      <td id=\"T_fc5d9_row2_col3\" class=\"data row2 col3\" >22%</td>\n      <td id=\"T_fc5d9_row2_col4\" class=\"data row2 col4\" >36%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_fc5d9_row3_col0\" class=\"data row3 col0\" >68%</td>\n      <td id=\"T_fc5d9_row3_col1\" class=\"data row3 col1\" >24%</td>\n      <td id=\"T_fc5d9_row3_col2\" class=\"data row3 col2\" >8%</td>\n      <td id=\"T_fc5d9_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_fc5d9_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_fc5d9_row4_col0\" class=\"data row4 col0\" >57%</td>\n      <td id=\"T_fc5d9_row4_col1\" class=\"data row4 col1\" >22%</td>\n      <td id=\"T_fc5d9_row4_col2\" class=\"data row4 col2\" >16%</td>\n      <td id=\"T_fc5d9_row4_col3\" class=\"data row4 col3\" >6%</td>\n      <td id=\"T_fc5d9_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_fc5d9_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_fc5d9_row5_col0\" class=\"data row5 col0\" >93%</td>\n      <td id=\"T_fc5d9_row5_col1\" class=\"data row5 col1\" >7%</td>\n      <td id=\"T_fc5d9_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_fc5d9_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_fc5d9_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb76b80>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_685fe caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_685fe caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_685fe td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_685fe_row0_col0, #T_685fe_row1_col0, #T_685fe_row2_col4, #T_685fe_row3_col0, #T_685fe_row4_col0, #T_685fe_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_685fe\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_685fe_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_685fe_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_685fe_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_685fe_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_685fe_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_685fe_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_685fe_row0_col0\" class=\"data row0 col0\" >100%</td>\n      <td id=\"T_685fe_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_685fe_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_685fe_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_685fe_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_685fe_row1_col0\" class=\"data row1 col0\" >98%</td>\n      <td id=\"T_685fe_row1_col1\" class=\"data row1 col1\" >1%</td>\n      <td id=\"T_685fe_row1_col2\" class=\"data row1 col2\" >1%</td>\n      <td id=\"T_685fe_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_685fe_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_685fe_row2_col0\" class=\"data row2 col0\" >14%</td>\n      <td id=\"T_685fe_row2_col1\" class=\"data row2 col1\" >18%</td>\n      <td id=\"T_685fe_row2_col2\" class=\"data row2 col2\" >10%</td>\n      <td id=\"T_685fe_row2_col3\" class=\"data row2 col3\" >22%</td>\n      <td id=\"T_685fe_row2_col4\" class=\"data row2 col4\" >36%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_685fe_row3_col0\" class=\"data row3 col0\" >68%</td>\n      <td id=\"T_685fe_row3_col1\" class=\"data row3 col1\" >24%</td>\n      <td id=\"T_685fe_row3_col2\" class=\"data row3 col2\" >8%</td>\n      <td id=\"T_685fe_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_685fe_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_685fe_row4_col0\" class=\"data row4 col0\" >57%</td>\n      <td id=\"T_685fe_row4_col1\" class=\"data row4 col1\" >22%</td>\n      <td id=\"T_685fe_row4_col2\" class=\"data row4 col2\" >16%</td>\n      <td id=\"T_685fe_row4_col3\" class=\"data row4 col3\" >6%</td>\n      <td id=\"T_685fe_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_685fe_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_685fe_row5_col0\" class=\"data row5 col0\" >93%</td>\n      <td id=\"T_685fe_row5_col1\" class=\"data row5 col1\" >7%</td>\n      <td id=\"T_685fe_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_685fe_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_685fe_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e39db3a0>"
      },
      "metadata": {
       "scrapbook": {
@@ -1724,6 +1739,9 @@
    "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1734,8 +1752,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_34a15 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_34a15 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_34a15 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_34a15_row0_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_34a15\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_34a15_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_34a15_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_34a15_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_34a15_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_34a15_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_34a15_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_34a15_row0_col0\" class=\"data row0 col0\" >31%</td>\n      <td id=\"T_34a15_row0_col1\" class=\"data row0 col1\" >46%</td>\n      <td id=\"T_34a15_row0_col2\" class=\"data row0 col2\" >21%</td>\n      <td id=\"T_34a15_row0_col3\" class=\"data row0 col3\" >2%</td>\n      <td id=\"T_34a15_row0_col4\" class=\"data row0 col4\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a473ebe8520>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_30111 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_30111 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_30111 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_30111_row0_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_30111\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_30111_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_30111_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_30111_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_30111_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_30111_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_30111_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_30111_row0_col0\" class=\"data row0 col0\" >31%</td>\n      <td id=\"T_30111_row0_col1\" class=\"data row0 col1\" >46%</td>\n      <td id=\"T_30111_row0_col2\" class=\"data row0 col2\" >21%</td>\n      <td id=\"T_30111_row0_col3\" class=\"data row0 col3\" >2%</td>\n      <td id=\"T_30111_row0_col4\" class=\"data row0 col4\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e25d1e50>"
      },
      "metadata": {
       "scrapbook": {
@@ -1747,8 +1765,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_a1596 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_a1596 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_a1596 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_a1596_row0_col4 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_a1596\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a1596_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_a1596_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_a1596_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_a1596_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_a1596_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a1596_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_a1596_row0_col0\" class=\"data row0 col0\" >5.26</td>\n      <td id=\"T_a1596_row0_col1\" class=\"data row0 col1\" >4.33</td>\n      <td id=\"T_a1596_row0_col2\" class=\"data row0 col2\" >5.66</td>\n      <td id=\"T_a1596_row0_col3\" class=\"data row0 col3\" >4.12</td>\n      <td id=\"T_a1596_row0_col4\" class=\"data row0 col4\" >14.56</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a5360130>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_6a0e0 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_6a0e0 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_6a0e0 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_6a0e0_row0_col4 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_6a0e0\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_6a0e0_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_6a0e0_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_6a0e0_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_6a0e0_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_6a0e0_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_6a0e0_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_6a0e0_row0_col0\" class=\"data row0 col0\" >5.26</td>\n      <td id=\"T_6a0e0_row0_col1\" class=\"data row0 col1\" >4.33</td>\n      <td id=\"T_6a0e0_row0_col2\" class=\"data row0 col2\" >5.66</td>\n      <td id=\"T_6a0e0_row0_col3\" class=\"data row0 col3\" >4.12</td>\n      <td id=\"T_6a0e0_row0_col4\" class=\"data row0 col4\" >14.56</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e4240a90>"
      },
      "metadata": {
       "scrapbook": {
@@ -1782,6 +1800,9 @@
    "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1792,8 +1813,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_13be6 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_13be6 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_13be6 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_13be6_row0_col0, #T_13be6_row1_col0, #T_13be6_row2_col1, #T_13be6_row3_col2, #T_13be6_row4_col1, #T_13be6_row5_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_13be6\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_13be6_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_13be6_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_13be6_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_13be6_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_13be6_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_13be6_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_13be6_row0_col0\" class=\"data row0 col0\" >5.50</td>\n      <td id=\"T_13be6_row0_col1\" class=\"data row0 col1\" >0.00</td>\n      <td id=\"T_13be6_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_13be6_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_13be6_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_13be6_row1_col0\" class=\"data row1 col0\" >5.51</td>\n      <td id=\"T_13be6_row1_col1\" class=\"data row1 col1\" >0.00</td>\n      <td id=\"T_13be6_row1_col2\" class=\"data row1 col2\" >5.01</td>\n      <td id=\"T_13be6_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_13be6_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_13be6_row2_col0\" class=\"data row2 col0\" >2.94</td>\n      <td id=\"T_13be6_row2_col1\" class=\"data row2 col1\" >7.83</td>\n      <td id=\"T_13be6_row2_col2\" class=\"data row2 col2\" >5.12</td>\n      <td id=\"T_13be6_row2_col3\" class=\"data row2 col3\" >3.33</td>\n      <td id=\"T_13be6_row2_col4\" class=\"data row2 col4\" >6.39</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_13be6_row3_col0\" class=\"data row3 col0\" >5.20</td>\n      <td id=\"T_13be6_row3_col1\" class=\"data row3 col1\" >3.13</td>\n      <td id=\"T_13be6_row3_col2\" class=\"data row3 col2\" >14.92</td>\n      <td id=\"T_13be6_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_13be6_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_13be6_row4_col0\" class=\"data row4 col0\" >5.88</td>\n      <td id=\"T_13be6_row4_col1\" class=\"data row4 col1\" >7.42</td>\n      <td id=\"T_13be6_row4_col2\" class=\"data row4 col2\" >3.00</td>\n      <td id=\"T_13be6_row4_col3\" class=\"data row4 col3\" >1.91</td>\n      <td id=\"T_13be6_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_13be6_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_13be6_row5_col0\" class=\"data row5 col0\" >4.64</td>\n      <td id=\"T_13be6_row5_col1\" class=\"data row5 col1\" >16.31</td>\n      <td id=\"T_13be6_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_13be6_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_13be6_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a536dd60>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_b413e caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_b413e caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_b413e td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_b413e_row0_col0, #T_b413e_row1_col0, #T_b413e_row2_col1, #T_b413e_row3_col2, #T_b413e_row4_col1, #T_b413e_row5_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_b413e\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_b413e_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_b413e_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_b413e_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_b413e_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_b413e_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_b413e_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_b413e_row0_col0\" class=\"data row0 col0\" >5.50</td>\n      <td id=\"T_b413e_row0_col1\" class=\"data row0 col1\" >0.00</td>\n      <td id=\"T_b413e_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_b413e_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_b413e_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_b413e_row1_col0\" class=\"data row1 col0\" >5.51</td>\n      <td id=\"T_b413e_row1_col1\" class=\"data row1 col1\" >0.00</td>\n      <td id=\"T_b413e_row1_col2\" class=\"data row1 col2\" >5.01</td>\n      <td id=\"T_b413e_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_b413e_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_b413e_row2_col0\" class=\"data row2 col0\" >2.94</td>\n      <td id=\"T_b413e_row2_col1\" class=\"data row2 col1\" >7.83</td>\n      <td id=\"T_b413e_row2_col2\" class=\"data row2 col2\" >5.12</td>\n      <td id=\"T_b413e_row2_col3\" class=\"data row2 col3\" >3.33</td>\n      <td id=\"T_b413e_row2_col4\" class=\"data row2 col4\" >6.39</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_b413e_row3_col0\" class=\"data row3 col0\" >5.20</td>\n      <td id=\"T_b413e_row3_col1\" class=\"data row3 col1\" >3.13</td>\n      <td id=\"T_b413e_row3_col2\" class=\"data row3 col2\" >14.92</td>\n      <td id=\"T_b413e_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_b413e_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_b413e_row4_col0\" class=\"data row4 col0\" >5.88</td>\n      <td id=\"T_b413e_row4_col1\" class=\"data row4 col1\" >7.42</td>\n      <td id=\"T_b413e_row4_col2\" class=\"data row4 col2\" >3.00</td>\n      <td id=\"T_b413e_row4_col3\" class=\"data row4 col3\" >1.91</td>\n      <td id=\"T_b413e_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_b413e_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_b413e_row5_col0\" class=\"data row5 col0\" >4.64</td>\n      <td id=\"T_b413e_row5_col1\" class=\"data row5 col1\" >16.31</td>\n      <td id=\"T_b413e_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_b413e_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_b413e_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e65562c37f0>"
      },
      "metadata": {
       "scrapbook": {
@@ -1805,8 +1826,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_81787 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_81787 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_81787 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_81787_row0_col0, #T_81787_row1_col0, #T_81787_row2_col4, #T_81787_row3_col0, #T_81787_row4_col0, #T_81787_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_81787\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_81787_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_81787_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_81787_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_81787_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_81787_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_81787_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_81787_row0_col0\" class=\"data row0 col0\" >100%</td>\n      <td id=\"T_81787_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_81787_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_81787_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_81787_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_81787_row1_col0\" class=\"data row1 col0\" >99%</td>\n      <td id=\"T_81787_row1_col1\" class=\"data row1 col1\" >0%</td>\n      <td id=\"T_81787_row1_col2\" class=\"data row1 col2\" >1%</td>\n      <td id=\"T_81787_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_81787_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_81787_row2_col0\" class=\"data row2 col0\" >12%</td>\n      <td id=\"T_81787_row2_col1\" class=\"data row2 col1\" >20%</td>\n      <td id=\"T_81787_row2_col2\" class=\"data row2 col2\" >8%</td>\n      <td id=\"T_81787_row2_col3\" class=\"data row2 col3\" >21%</td>\n      <td id=\"T_81787_row2_col4\" class=\"data row2 col4\" >39%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_81787_row3_col0\" class=\"data row3 col0\" >70%</td>\n      <td id=\"T_81787_row3_col1\" class=\"data row3 col1\" >22%</td>\n      <td id=\"T_81787_row3_col2\" class=\"data row3 col2\" >8%</td>\n      <td id=\"T_81787_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_81787_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_81787_row4_col0\" class=\"data row4 col0\" >58%</td>\n      <td id=\"T_81787_row4_col1\" class=\"data row4 col1\" >20%</td>\n      <td id=\"T_81787_row4_col2\" class=\"data row4 col2\" >16%</td>\n      <td id=\"T_81787_row4_col3\" class=\"data row4 col3\" >6%</td>\n      <td id=\"T_81787_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_81787_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_81787_row5_col0\" class=\"data row5 col0\" >93%</td>\n      <td id=\"T_81787_row5_col1\" class=\"data row5 col1\" >7%</td>\n      <td id=\"T_81787_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_81787_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_81787_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a7f1a220>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_1d812 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_1d812 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_1d812 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_1d812_row0_col0, #T_1d812_row1_col0, #T_1d812_row2_col4, #T_1d812_row3_col0, #T_1d812_row4_col0, #T_1d812_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_1d812\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_1d812_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_1d812_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_1d812_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_1d812_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_1d812_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_1d812_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_1d812_row0_col0\" class=\"data row0 col0\" >100%</td>\n      <td id=\"T_1d812_row0_col1\" class=\"data row0 col1\" >0%</td>\n      <td id=\"T_1d812_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_1d812_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_1d812_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_1d812_row1_col0\" class=\"data row1 col0\" >99%</td>\n      <td id=\"T_1d812_row1_col1\" class=\"data row1 col1\" >0%</td>\n      <td id=\"T_1d812_row1_col2\" class=\"data row1 col2\" >1%</td>\n      <td id=\"T_1d812_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_1d812_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_1d812_row2_col0\" class=\"data row2 col0\" >12%</td>\n      <td id=\"T_1d812_row2_col1\" class=\"data row2 col1\" >20%</td>\n      <td id=\"T_1d812_row2_col2\" class=\"data row2 col2\" >8%</td>\n      <td id=\"T_1d812_row2_col3\" class=\"data row2 col3\" >21%</td>\n      <td id=\"T_1d812_row2_col4\" class=\"data row2 col4\" >39%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_1d812_row3_col0\" class=\"data row3 col0\" >70%</td>\n      <td id=\"T_1d812_row3_col1\" class=\"data row3 col1\" >22%</td>\n      <td id=\"T_1d812_row3_col2\" class=\"data row3 col2\" >8%</td>\n      <td id=\"T_1d812_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_1d812_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_1d812_row4_col0\" class=\"data row4 col0\" >58%</td>\n      <td id=\"T_1d812_row4_col1\" class=\"data row4 col1\" >20%</td>\n      <td id=\"T_1d812_row4_col2\" class=\"data row4 col2\" >16%</td>\n      <td id=\"T_1d812_row4_col3\" class=\"data row4 col3\" >6%</td>\n      <td id=\"T_1d812_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_1d812_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_1d812_row5_col0\" class=\"data row5 col0\" >93%</td>\n      <td id=\"T_1d812_row5_col1\" class=\"data row5 col1\" >7%</td>\n      <td id=\"T_1d812_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_1d812_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_1d812_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e6242c10>"
      },
      "metadata": {
       "scrapbook": {
@@ -1837,6 +1858,9 @@
    "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1847,8 +1871,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_edd53 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_edd53 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_edd53 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_edd53_row0_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_edd53\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_edd53_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_edd53_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_edd53_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_edd53_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_edd53_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_edd53_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_edd53_row0_col0\" class=\"data row0 col0\" >35%</td>\n      <td id=\"T_edd53_row0_col1\" class=\"data row0 col1\" >48%</td>\n      <td id=\"T_edd53_row0_col2\" class=\"data row0 col2\" >16%</td>\n      <td id=\"T_edd53_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_edd53_row0_col4\" class=\"data row0 col4\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a52ce190>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_9a253 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_9a253 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_9a253 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_9a253_row0_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_9a253\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_9a253_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_9a253_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_9a253_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_9a253_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_9a253_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_9a253_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_9a253_row0_col0\" class=\"data row0 col0\" >35%</td>\n      <td id=\"T_9a253_row0_col1\" class=\"data row0 col1\" >48%</td>\n      <td id=\"T_9a253_row0_col2\" class=\"data row0 col2\" >16%</td>\n      <td id=\"T_9a253_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_9a253_row0_col4\" class=\"data row0 col4\" >1%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e6257910>"
      },
      "metadata": {
       "scrapbook": {
@@ -1860,8 +1884,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_43d42 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_43d42 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_43d42 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_43d42_row0_col4 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_43d42\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_43d42_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_43d42_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_43d42_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_43d42_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_43d42_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_43d42_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_43d42_row0_col0\" class=\"data row0 col0\" >5.33</td>\n      <td id=\"T_43d42_row0_col1\" class=\"data row0 col1\" >4.60</td>\n      <td id=\"T_43d42_row0_col2\" class=\"data row0 col2\" >7.68</td>\n      <td id=\"T_43d42_row0_col3\" class=\"data row0 col3\" >0</td>\n      <td id=\"T_43d42_row0_col4\" class=\"data row0 col4\" >21.80</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a49fa100>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_a1639 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_a1639 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_a1639 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_a1639_row0_col4 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_a1639\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a1639_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_a1639_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_a1639_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_a1639_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_a1639_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a1639_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_a1639_row0_col0\" class=\"data row0 col0\" >5.33</td>\n      <td id=\"T_a1639_row0_col1\" class=\"data row0 col1\" >4.60</td>\n      <td id=\"T_a1639_row0_col2\" class=\"data row0 col2\" >7.68</td>\n      <td id=\"T_a1639_row0_col3\" class=\"data row0 col3\" >0</td>\n      <td id=\"T_a1639_row0_col4\" class=\"data row0 col4\" >21.80</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e4266460>"
      },
      "metadata": {
       "scrapbook": {
@@ -1896,6 +1920,9 @@
    "id": "82f55461-c497-483a-8c38-fbd509809afb",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1906,8 +1933,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_28094 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_28094 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_28094 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_28094_row0_col0, #T_28094_row1_col0, #T_28094_row2_col4, #T_28094_row3_col2, #T_28094_row4_col2, #T_28094_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_28094\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_28094_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_28094_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_28094_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_28094_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_28094_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_28094_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_28094_row0_col0\" class=\"data row0 col0\" >1.62</td>\n      <td id=\"T_28094_row0_col1\" class=\"data row0 col1\" >0.09</td>\n      <td id=\"T_28094_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_28094_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_28094_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_28094_row1_col0\" class=\"data row1 col0\" >1.64</td>\n      <td id=\"T_28094_row1_col1\" class=\"data row1 col1\" >0.56</td>\n      <td id=\"T_28094_row1_col2\" class=\"data row1 col2\" >0.00</td>\n      <td id=\"T_28094_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_28094_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_28094_row2_col0\" class=\"data row2 col0\" >1.50</td>\n      <td id=\"T_28094_row2_col1\" class=\"data row2 col1\" >1.93</td>\n      <td id=\"T_28094_row2_col2\" class=\"data row2 col2\" >1.17</td>\n      <td id=\"T_28094_row2_col3\" class=\"data row2 col3\" >0.43</td>\n      <td id=\"T_28094_row2_col4\" class=\"data row2 col4\" >4.06</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_28094_row3_col0\" class=\"data row3 col0\" >1.82</td>\n      <td id=\"T_28094_row3_col1\" class=\"data row3 col1\" >1.01</td>\n      <td id=\"T_28094_row3_col2\" class=\"data row3 col2\" >2.38</td>\n      <td id=\"T_28094_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_28094_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_28094_row4_col0\" class=\"data row4 col0\" >1.64</td>\n      <td id=\"T_28094_row4_col1\" class=\"data row4 col1\" >1.19</td>\n      <td id=\"T_28094_row4_col2\" class=\"data row4 col2\" >2.46</td>\n      <td id=\"T_28094_row4_col3\" class=\"data row4 col3\" >1.50</td>\n      <td id=\"T_28094_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_28094_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_28094_row5_col0\" class=\"data row5 col0\" >1.67</td>\n      <td id=\"T_28094_row5_col1\" class=\"data row5 col1\" >0.07</td>\n      <td id=\"T_28094_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_28094_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_28094_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a7f0beb0>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_f4453 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_f4453 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_f4453 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_f4453_row0_col0, #T_f4453_row1_col0, #T_f4453_row2_col4, #T_f4453_row3_col2, #T_f4453_row4_col2, #T_f4453_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_f4453\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_f4453_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_f4453_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_f4453_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_f4453_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_f4453_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_f4453_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_f4453_row0_col0\" class=\"data row0 col0\" >1.62</td>\n      <td id=\"T_f4453_row0_col1\" class=\"data row0 col1\" >0.09</td>\n      <td id=\"T_f4453_row0_col2\" class=\"data row0 col2\" >0.00</td>\n      <td id=\"T_f4453_row0_col3\" class=\"data row0 col3\" >0.00</td>\n      <td id=\"T_f4453_row0_col4\" class=\"data row0 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_f4453_row1_col0\" class=\"data row1 col0\" >1.64</td>\n      <td id=\"T_f4453_row1_col1\" class=\"data row1 col1\" >0.56</td>\n      <td id=\"T_f4453_row1_col2\" class=\"data row1 col2\" >0.00</td>\n      <td id=\"T_f4453_row1_col3\" class=\"data row1 col3\" >0.00</td>\n      <td id=\"T_f4453_row1_col4\" class=\"data row1 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_f4453_row2_col0\" class=\"data row2 col0\" >1.50</td>\n      <td id=\"T_f4453_row2_col1\" class=\"data row2 col1\" >1.93</td>\n      <td id=\"T_f4453_row2_col2\" class=\"data row2 col2\" >1.17</td>\n      <td id=\"T_f4453_row2_col3\" class=\"data row2 col3\" >0.43</td>\n      <td id=\"T_f4453_row2_col4\" class=\"data row2 col4\" >4.06</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_f4453_row3_col0\" class=\"data row3 col0\" >1.82</td>\n      <td id=\"T_f4453_row3_col1\" class=\"data row3 col1\" >1.01</td>\n      <td id=\"T_f4453_row3_col2\" class=\"data row3 col2\" >2.38</td>\n      <td id=\"T_f4453_row3_col3\" class=\"data row3 col3\" >0.00</td>\n      <td id=\"T_f4453_row3_col4\" class=\"data row3 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_f4453_row4_col0\" class=\"data row4 col0\" >1.64</td>\n      <td id=\"T_f4453_row4_col1\" class=\"data row4 col1\" >1.19</td>\n      <td id=\"T_f4453_row4_col2\" class=\"data row4 col2\" >2.46</td>\n      <td id=\"T_f4453_row4_col3\" class=\"data row4 col3\" >1.50</td>\n      <td id=\"T_f4453_row4_col4\" class=\"data row4 col4\" >0.00</td>\n    </tr>\n    <tr>\n      <th id=\"T_f4453_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_f4453_row5_col0\" class=\"data row5 col0\" >1.67</td>\n      <td id=\"T_f4453_row5_col1\" class=\"data row5 col1\" >0.07</td>\n      <td id=\"T_f4453_row5_col2\" class=\"data row5 col2\" >0.00</td>\n      <td id=\"T_f4453_row5_col3\" class=\"data row5 col3\" >0.00</td>\n      <td id=\"T_f4453_row5_col4\" class=\"data row5 col4\" >0.00</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e2f69820>"
      },
      "metadata": {
       "scrapbook": {
@@ -1919,8 +1946,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_2c804 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_2c804 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_2c804 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_2c804_row0_col0, #T_2c804_row1_col0, #T_2c804_row2_col2, #T_2c804_row3_col0, #T_2c804_row4_col0, #T_2c804_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_2c804\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_2c804_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_2c804_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_2c804_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_2c804_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_2c804_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_2c804_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_2c804_row0_col0\" class=\"data row0 col0\" >97%</td>\n      <td id=\"T_2c804_row0_col1\" class=\"data row0 col1\" >3%</td>\n      <td id=\"T_2c804_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_2c804_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_2c804_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_2c804_row1_col0\" class=\"data row1 col0\" >94%</td>\n      <td id=\"T_2c804_row1_col1\" class=\"data row1 col1\" >6%</td>\n      <td id=\"T_2c804_row1_col2\" class=\"data row1 col2\" >0%</td>\n      <td id=\"T_2c804_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_2c804_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_2c804_row2_col0\" class=\"data row2 col0\" >24%</td>\n      <td id=\"T_2c804_row2_col1\" class=\"data row2 col1\" >9%</td>\n      <td id=\"T_2c804_row2_col2\" class=\"data row2 col2\" >27%</td>\n      <td id=\"T_2c804_row2_col3\" class=\"data row2 col3\" >24%</td>\n      <td id=\"T_2c804_row2_col4\" class=\"data row2 col4\" >15%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_2c804_row3_col0\" class=\"data row3 col0\" >55%</td>\n      <td id=\"T_2c804_row3_col1\" class=\"data row3 col1\" >36%</td>\n      <td id=\"T_2c804_row3_col2\" class=\"data row3 col2\" >9%</td>\n      <td id=\"T_2c804_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_2c804_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_2c804_row4_col0\" class=\"data row4 col0\" >45%</td>\n      <td id=\"T_2c804_row4_col1\" class=\"data row4 col1\" >33%</td>\n      <td id=\"T_2c804_row4_col2\" class=\"data row4 col2\" >12%</td>\n      <td id=\"T_2c804_row4_col3\" class=\"data row4 col3\" >9%</td>\n      <td id=\"T_2c804_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_2c804_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_2c804_row5_col0\" class=\"data row5 col0\" >94%</td>\n      <td id=\"T_2c804_row5_col1\" class=\"data row5 col1\" >6%</td>\n      <td id=\"T_2c804_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_2c804_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_2c804_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a473eb76f10>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_b4d9b caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_b4d9b caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_b4d9b td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_b4d9b_row0_col0, #T_b4d9b_row1_col0, #T_b4d9b_row2_col2, #T_b4d9b_row3_col0, #T_b4d9b_row4_col0, #T_b4d9b_row5_col0 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_b4d9b\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_b4d9b_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_b4d9b_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_b4d9b_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_b4d9b_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_b4d9b_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_b4d9b_level0_row0\" class=\"row_heading level0 row0\" >Orchards</th>\n      <td id=\"T_b4d9b_row0_col0\" class=\"data row0 col0\" >97%</td>\n      <td id=\"T_b4d9b_row0_col1\" class=\"data row0 col1\" >3%</td>\n      <td id=\"T_b4d9b_row0_col2\" class=\"data row0 col2\" >0%</td>\n      <td id=\"T_b4d9b_row0_col3\" class=\"data row0 col3\" >0%</td>\n      <td id=\"T_b4d9b_row0_col4\" class=\"data row0 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row1\" class=\"row_heading level0 row1\" >Vineyards</th>\n      <td id=\"T_b4d9b_row1_col0\" class=\"data row1 col0\" >94%</td>\n      <td id=\"T_b4d9b_row1_col1\" class=\"data row1 col1\" >6%</td>\n      <td id=\"T_b4d9b_row1_col2\" class=\"data row1 col2\" >0%</td>\n      <td id=\"T_b4d9b_row1_col3\" class=\"data row1 col3\" >0%</td>\n      <td id=\"T_b4d9b_row1_col4\" class=\"data row1 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row2\" class=\"row_heading level0 row2\" >Buildings</th>\n      <td id=\"T_b4d9b_row2_col0\" class=\"data row2 col0\" >24%</td>\n      <td id=\"T_b4d9b_row2_col1\" class=\"data row2 col1\" >9%</td>\n      <td id=\"T_b4d9b_row2_col2\" class=\"data row2 col2\" >27%</td>\n      <td id=\"T_b4d9b_row2_col3\" class=\"data row2 col3\" >24%</td>\n      <td id=\"T_b4d9b_row2_col4\" class=\"data row2 col4\" >15%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row3\" class=\"row_heading level0 row3\" >Forest</th>\n      <td id=\"T_b4d9b_row3_col0\" class=\"data row3 col0\" >55%</td>\n      <td id=\"T_b4d9b_row3_col1\" class=\"data row3 col1\" >36%</td>\n      <td id=\"T_b4d9b_row3_col2\" class=\"data row3 col2\" >9%</td>\n      <td id=\"T_b4d9b_row3_col3\" class=\"data row3 col3\" >0%</td>\n      <td id=\"T_b4d9b_row3_col4\" class=\"data row3 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row4\" class=\"row_heading level0 row4\" >Undefined</th>\n      <td id=\"T_b4d9b_row4_col0\" class=\"data row4 col0\" >45%</td>\n      <td id=\"T_b4d9b_row4_col1\" class=\"data row4 col1\" >33%</td>\n      <td id=\"T_b4d9b_row4_col2\" class=\"data row4 col2\" >12%</td>\n      <td id=\"T_b4d9b_row4_col3\" class=\"data row4 col3\" >9%</td>\n      <td id=\"T_b4d9b_row4_col4\" class=\"data row4 col4\" >0%</td>\n    </tr>\n    <tr>\n      <th id=\"T_b4d9b_level0_row5\" class=\"row_heading level0 row5\" >Public Services</th>\n      <td id=\"T_b4d9b_row5_col0\" class=\"data row5 col0\" >94%</td>\n      <td id=\"T_b4d9b_row5_col1\" class=\"data row5 col1\" >6%</td>\n      <td id=\"T_b4d9b_row5_col2\" class=\"data row5 col2\" >0%</td>\n      <td id=\"T_b4d9b_row5_col3\" class=\"data row5 col3\" >0%</td>\n      <td id=\"T_b4d9b_row5_col4\" class=\"data row5 col4\" >0%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e2f6e4f0>"
      },
      "metadata": {
       "scrapbook": {
@@ -1952,6 +1979,9 @@
    "id": "9b396025-1fa6-4661-9116-593fa1ed741d",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1962,8 +1992,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_0b35c caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_0b35c caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_0b35c td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_0b35c_row0_col2 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_0b35c\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_0b35c_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_0b35c_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_0b35c_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_0b35c_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_0b35c_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_0b35c_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_0b35c_row0_col0\" class=\"data row0 col0\" >3%</td>\n      <td id=\"T_0b35c_row0_col1\" class=\"data row0 col1\" >30%</td>\n      <td id=\"T_0b35c_row0_col2\" class=\"data row0 col2\" >52%</td>\n      <td id=\"T_0b35c_row0_col3\" class=\"data row0 col3\" >12%</td>\n      <td id=\"T_0b35c_row0_col4\" class=\"data row0 col4\" >3%</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a48a7d00>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_1e644 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_1e644 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_1e644 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_1e644_row0_col2 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_1e644\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_1e644_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_1e644_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_1e644_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_1e644_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_1e644_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_1e644_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_1e644_row0_col0\" class=\"data row0 col0\" >3%</td>\n      <td id=\"T_1e644_row0_col1\" class=\"data row0 col1\" >30%</td>\n      <td id=\"T_1e644_row0_col2\" class=\"data row0 col2\" >52%</td>\n      <td id=\"T_1e644_row0_col3\" class=\"data row0 col3\" >12%</td>\n      <td id=\"T_1e644_row0_col4\" class=\"data row0 col4\" >3%</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e626cfa0>"
      },
      "metadata": {
       "scrapbook": {
@@ -1975,8 +2005,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_af839 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_af839 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_af839 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_af839_row0_col3 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_af839\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_af839_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_af839_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_af839_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_af839_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_af839_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_af839_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_af839_row0_col0\" class=\"data row0 col0\" >0.22</td>\n      <td id=\"T_af839_row0_col1\" class=\"data row0 col1\" >1.36</td>\n      <td id=\"T_af839_row0_col2\" class=\"data row0 col2\" >1.27</td>\n      <td id=\"T_af839_row0_col3\" class=\"data row0 col3\" >4.12</td>\n      <td id=\"T_af839_row0_col4\" class=\"data row0 col4\" >0.08</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a5326490>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_dbbe5 caption {\n  caption-side: top;\n  font-size: 1em;\n  text-align: left;\n  margin-bottom: 10px;\n}\n#T_dbbe5 caption {\n  caption-side: top;\n  font-size: .9em;\n  text-align: left;\n  font-style: italic;\n  color: #000;\n}\n#T_dbbe5 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n  padding: 4px;\n}\n#T_dbbe5_row0_col3 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_dbbe5\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_dbbe5_level0_col0\" class=\"col_heading level0 col0\" >0 - 20%</th>\n      <th id=\"T_dbbe5_level0_col1\" class=\"col_heading level0 col1\" >20 - 40%</th>\n      <th id=\"T_dbbe5_level0_col2\" class=\"col_heading level0 col2\" >40 - 60%</th>\n      <th id=\"T_dbbe5_level0_col3\" class=\"col_heading level0 col3\" >60 - 80%</th>\n      <th id=\"T_dbbe5_level0_col4\" class=\"col_heading level0 col4\" >80 - 100%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_dbbe5_level0_row0\" class=\"row_heading level0 row0\" >streets</th>\n      <td id=\"T_dbbe5_row0_col0\" class=\"data row0 col0\" >0.22</td>\n      <td id=\"T_dbbe5_row0_col1\" class=\"data row0 col1\" >1.36</td>\n      <td id=\"T_dbbe5_row0_col2\" class=\"data row0 col2\" >1.27</td>\n      <td id=\"T_dbbe5_row0_col3\" class=\"data row0 col3\" >4.12</td>\n      <td id=\"T_dbbe5_row0_col4\" class=\"data row0 col4\" >0.08</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e251b430>"
      },
      "metadata": {
       "scrapbook": {
@@ -2340,8 +2370,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_bb25a tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_bb25a tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_bb25a tr {\n  font-size: 10px;\n}\n#T_bb25a th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_bb25a td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_bb25a caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n#T_bb25a_row1_col1, #T_bb25a_row2_col1, #T_bb25a_row3_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_bb25a\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_bb25a_level0_col0\" class=\"col_heading level0 col0\" >quantity</th>\n      <th id=\"T_bb25a_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n      <th id=\"T_bb25a_level0_col2\" class=\"col_heading level0 col2\" >samples</th>\n      <th id=\"T_bb25a_level0_col3\" class=\"col_heading level0 col3\" >orchards</th>\n      <th id=\"T_bb25a_level0_col4\" class=\"col_heading level0 col4\" >vineyards</th>\n      <th id=\"T_bb25a_level0_col5\" class=\"col_heading level0 col5\" >buildings</th>\n      <th id=\"T_bb25a_level0_col6\" class=\"col_heading level0 col6\" >forest</th>\n      <th id=\"T_bb25a_level0_col7\" class=\"col_heading level0 col7\" >undefined</th>\n      <th id=\"T_bb25a_level0_col8\" class=\"col_heading level0 col8\" >public services</th>\n      <th id=\"T_bb25a_level0_col9\" class=\"col_heading level0 col9\" >streets</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >canton</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n      <th class=\"blank col3\" >&nbsp;</th>\n      <th class=\"blank col4\" >&nbsp;</th>\n      <th class=\"blank col5\" >&nbsp;</th>\n      <th class=\"blank col6\" >&nbsp;</th>\n      <th class=\"blank col7\" >&nbsp;</th>\n      <th class=\"blank col8\" >&nbsp;</th>\n      <th class=\"blank col9\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_bb25a_level0_row0\" class=\"row_heading level0 row0\" >Bern</th>\n      <td id=\"T_bb25a_row0_col0\" class=\"data row0 col0\" >9889</td>\n      <td id=\"T_bb25a_row0_col1\" class=\"data row0 col1\" >2.81</td>\n      <td id=\"T_bb25a_row0_col2\" class=\"data row0 col2\" >98</td>\n      <td id=\"T_bb25a_row0_col3\" class=\"data row0 col3\" >1</td>\n      <td id=\"T_bb25a_row0_col4\" class=\"data row0 col4\" >1</td>\n      <td id=\"T_bb25a_row0_col5\" class=\"data row0 col5\" >2</td>\n      <td id=\"T_bb25a_row0_col6\" class=\"data row0 col6\" >2</td>\n      <td id=\"T_bb25a_row0_col7\" class=\"data row0 col7\" >3</td>\n      <td id=\"T_bb25a_row0_col8\" class=\"data row0 col8\" >1</td>\n      <td id=\"T_bb25a_row0_col9\" class=\"data row0 col9\" >2</td>\n    </tr>\n    <tr>\n      <th id=\"T_bb25a_level0_row1\" class=\"row_heading level0 row1\" >Genève</th>\n      <td id=\"T_bb25a_row1_col0\" class=\"data row1 col0\" >9559</td>\n      <td id=\"T_bb25a_row1_col1\" class=\"data row1 col1\" >5.88</td>\n      <td id=\"T_bb25a_row1_col2\" class=\"data row1 col2\" >33</td>\n      <td id=\"T_bb25a_row1_col3\" class=\"data row1 col3\" >1</td>\n      <td id=\"T_bb25a_row1_col4\" class=\"data row1 col4\" >1</td>\n      <td id=\"T_bb25a_row1_col5\" class=\"data row1 col5\" >5</td>\n      <td id=\"T_bb25a_row1_col6\" class=\"data row1 col6\" >1</td>\n      <td id=\"T_bb25a_row1_col7\" class=\"data row1 col7\" >1</td>\n      <td id=\"T_bb25a_row1_col8\" class=\"data row1 col8\" >1</td>\n      <td id=\"T_bb25a_row1_col9\" class=\"data row1 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_bb25a_level0_row2\" class=\"row_heading level0 row2\" >Valais</th>\n      <td id=\"T_bb25a_row2_col0\" class=\"data row2 col0\" >8902</td>\n      <td id=\"T_bb25a_row2_col1\" class=\"data row2 col1\" >23.70</td>\n      <td id=\"T_bb25a_row2_col2\" class=\"data row2 col2\" >13</td>\n      <td id=\"T_bb25a_row2_col3\" class=\"data row2 col3\" >1</td>\n      <td id=\"T_bb25a_row2_col4\" class=\"data row2 col4\" >1</td>\n      <td id=\"T_bb25a_row2_col5\" class=\"data row2 col5\" >2</td>\n      <td id=\"T_bb25a_row2_col6\" class=\"data row2 col6\" >3</td>\n      <td id=\"T_bb25a_row2_col7\" class=\"data row2 col7\" >2</td>\n      <td id=\"T_bb25a_row2_col8\" class=\"data row2 col8\" >1</td>\n      <td id=\"T_bb25a_row2_col9\" class=\"data row2 col9\" >1</td>\n    </tr>\n    <tr>\n      <th id=\"T_bb25a_level0_row3\" class=\"row_heading level0 row3\" >Vaud</th>\n      <td id=\"T_bb25a_row3_col0\" class=\"data row3 col0\" >62468</td>\n      <td id=\"T_bb25a_row3_col1\" class=\"data row3 col1\" >7.78</td>\n      <td id=\"T_bb25a_row3_col2\" class=\"data row3 col2\" >226</td>\n      <td id=\"T_bb25a_row3_col3\" class=\"data row3 col3\" >1</td>\n      <td id=\"T_bb25a_row3_col4\" class=\"data row3 col4\" >1</td>\n      <td id=\"T_bb25a_row3_col5\" class=\"data row3 col5\" >4</td>\n      <td id=\"T_bb25a_row3_col6\" class=\"data row3 col6\" >1</td>\n      <td id=\"T_bb25a_row3_col7\" class=\"data row3 col7\" >1</td>\n      <td id=\"T_bb25a_row3_col8\" class=\"data row3 col8\" >1</td>\n      <td id=\"T_bb25a_row3_col9\" class=\"data row3 col9\" >2</td>\n    </tr>\n    <tr>\n      <th id=\"T_bb25a_level0_row4\" class=\"row_heading level0 row4\" >Zürich</th>\n      <td id=\"T_bb25a_row4_col0\" class=\"data row4 col0\" >27228</td>\n      <td id=\"T_bb25a_row4_col1\" class=\"data row4 col1\" >3.99</td>\n      <td id=\"T_bb25a_row4_col2\" class=\"data row4 col2\" >171</td>\n      <td id=\"T_bb25a_row4_col3\" class=\"data row4 col3\" >1</td>\n      <td id=\"T_bb25a_row4_col4\" class=\"data row4 col4\" >1</td>\n      <td id=\"T_bb25a_row4_col5\" class=\"data row4 col5\" >4</td>\n      <td id=\"T_bb25a_row4_col6\" class=\"data row4 col6\" >1</td>\n      <td id=\"T_bb25a_row4_col7\" class=\"data row4 col7\" >2</td>\n      <td id=\"T_bb25a_row4_col8\" class=\"data row4 col8\" >1</td>\n      <td id=\"T_bb25a_row4_col9\" class=\"data row4 col9\" >2</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a46a7b903d0>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_190e1 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_190e1 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_190e1 tr {\n  font-size: 10px;\n}\n#T_190e1 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_190e1 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_190e1 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n#T_190e1_row1_col1, #T_190e1_row2_col1, #T_190e1_row3_col1 {\n  background-color: #FAE8E8;\n}\n</style>\n<table id=\"T_190e1\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_190e1_level0_col0\" class=\"col_heading level0 col0\" >quantity</th>\n      <th id=\"T_190e1_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n      <th id=\"T_190e1_level0_col2\" class=\"col_heading level0 col2\" >samples</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >canton</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_190e1_level0_row0\" class=\"row_heading level0 row0\" >Bern</th>\n      <td id=\"T_190e1_row0_col0\" class=\"data row0 col0\" >9889</td>\n      <td id=\"T_190e1_row0_col1\" class=\"data row0 col1\" >2.81</td>\n      <td id=\"T_190e1_row0_col2\" class=\"data row0 col2\" >98</td>\n    </tr>\n    <tr>\n      <th id=\"T_190e1_level0_row1\" class=\"row_heading level0 row1\" >Genève</th>\n      <td id=\"T_190e1_row1_col0\" class=\"data row1 col0\" >9559</td>\n      <td id=\"T_190e1_row1_col1\" class=\"data row1 col1\" >5.88</td>\n      <td id=\"T_190e1_row1_col2\" class=\"data row1 col2\" >33</td>\n    </tr>\n    <tr>\n      <th id=\"T_190e1_level0_row2\" class=\"row_heading level0 row2\" >Valais</th>\n      <td id=\"T_190e1_row2_col0\" class=\"data row2 col0\" >8902</td>\n      <td id=\"T_190e1_row2_col1\" class=\"data row2 col1\" >23.70</td>\n      <td id=\"T_190e1_row2_col2\" class=\"data row2 col2\" >13</td>\n    </tr>\n    <tr>\n      <th id=\"T_190e1_level0_row3\" class=\"row_heading level0 row3\" >Vaud</th>\n      <td id=\"T_190e1_row3_col0\" class=\"data row3 col0\" >62468</td>\n      <td id=\"T_190e1_row3_col1\" class=\"data row3 col1\" >7.78</td>\n      <td id=\"T_190e1_row3_col2\" class=\"data row3 col2\" >226</td>\n    </tr>\n    <tr>\n      <th id=\"T_190e1_level0_row4\" class=\"row_heading level0 row4\" >Zürich</th>\n      <td id=\"T_190e1_row4_col0\" class=\"data row4 col0\" >27228</td>\n      <td id=\"T_190e1_row4_col1\" class=\"data row4 col1\" >3.99</td>\n      <td id=\"T_190e1_row4_col2\" class=\"data row4 col2\" >171</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e60b4a30>"
      },
      "metadata": {
       "scrapbook": {
@@ -2359,19 +2389,18 @@
     "dxlc = dxl[['location', 'canton', 'feature_type']].drop_duplicates('location')\n",
     "dxlc.set_index(['location'], inplace=True, drop=True)\n",
     "dxf['canton'] = dxf.location.apply(lambda x : dxlc.loc[x, 'canton'])\n",
-    "sumlu = {x:'sum' for x in session_config.feature_variables}\n",
-    "dxf = dxf.groupby(['sample_id', 'canton', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n",
+    "# sumlu = {x:'sum' for x in session_config.feature_variables}\n",
+    "dxf = dxf.groupby(['sample_id', 'canton'], as_index=False).agg(session_config.unit_agg)\n",
     "\n",
-    "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n",
+    "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique'})\n",
     "\n",
-    "for alabel in session_config.feature_variables:\n",
-    "    dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
+    "# for alabel in session_config.feature_variables:\n",
+    "#     dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
     "    \n",
-    "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
-    "dxfc = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
-    "dxfc.rename(columns={'sample_id':'samples'}, inplace=True)\n",
-    "dxfc.drop('check', axis=1, inplace=True)\n",
-    "dxfc = dxfc.style.set_table_styles(userdisplay.table_css_styles)\n",
+    "# dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
+    "# dxfc = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
+    "dxf.rename(columns={'sample_id':'samples'}, inplace=True)\n",
+    "dxfc = dxf.style.set_table_styles(userdisplay.table_css_styles)\n",
     "dxfc = dxfc.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n",
     "dxfc = dxfc.format(userdisplay.format_kwargs, precision=2)\n",
     "\n",
@@ -2394,8 +2423,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_795cb tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_795cb tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_795cb tr {\n  font-size: 10px;\n}\n#T_795cb th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_795cb td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_795cb caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_795cb\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_795cb_level0_col0\" class=\"col_heading level0 col0\" >quantity</th>\n      <th id=\"T_795cb_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n      <th id=\"T_795cb_level0_col2\" class=\"col_heading level0 col2\" >samples</th>\n      <th id=\"T_795cb_level0_col3\" class=\"col_heading level0 col3\" >orchards</th>\n      <th id=\"T_795cb_level0_col4\" class=\"col_heading level0 col4\" >vineyards</th>\n      <th id=\"T_795cb_level0_col5\" class=\"col_heading level0 col5\" >buildings</th>\n      <th id=\"T_795cb_level0_col6\" class=\"col_heading level0 col6\" >forest</th>\n      <th id=\"T_795cb_level0_col7\" class=\"col_heading level0 col7\" >undefined</th>\n      <th id=\"T_795cb_level0_col8\" class=\"col_heading level0 col8\" >public services</th>\n      <th id=\"T_795cb_level0_col9\" class=\"col_heading level0 col9\" >streets</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >canton</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n      <th class=\"blank col3\" >&nbsp;</th>\n      <th class=\"blank col4\" >&nbsp;</th>\n      <th class=\"blank col5\" >&nbsp;</th>\n      <th class=\"blank col6\" >&nbsp;</th>\n      <th class=\"blank col7\" >&nbsp;</th>\n      <th class=\"blank col8\" >&nbsp;</th>\n      <th class=\"blank col9\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_795cb_level0_row0\" class=\"row_heading level0 row0\" >Bern</th>\n      <td id=\"T_795cb_row0_col0\" class=\"data row0 col0\" >3701</td>\n      <td id=\"T_795cb_row0_col1\" class=\"data row0 col1\" >1.21</td>\n      <td id=\"T_795cb_row0_col2\" class=\"data row0 col2\" >97</td>\n      <td id=\"T_795cb_row0_col3\" class=\"data row0 col3\" >1</td>\n      <td id=\"T_795cb_row0_col4\" class=\"data row0 col4\" >1</td>\n      <td id=\"T_795cb_row0_col5\" class=\"data row0 col5\" >3</td>\n      <td id=\"T_795cb_row0_col6\" class=\"data row0 col6\" >1</td>\n      <td id=\"T_795cb_row0_col7\" class=\"data row0 col7\" >2</td>\n      <td id=\"T_795cb_row0_col8\" class=\"data row0 col8\" >1</td>\n      <td id=\"T_795cb_row0_col9\" class=\"data row0 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_795cb_level0_row1\" class=\"row_heading level0 row1\" >Genève</th>\n      <td id=\"T_795cb_row1_col0\" class=\"data row1 col0\" >1616</td>\n      <td id=\"T_795cb_row1_col1\" class=\"data row1 col1\" >4.38</td>\n      <td id=\"T_795cb_row1_col2\" class=\"data row1 col2\" >9</td>\n      <td id=\"T_795cb_row1_col3\" class=\"data row1 col3\" >1</td>\n      <td id=\"T_795cb_row1_col4\" class=\"data row1 col4\" >1</td>\n      <td id=\"T_795cb_row1_col5\" class=\"data row1 col5\" >4</td>\n      <td id=\"T_795cb_row1_col6\" class=\"data row1 col6\" >1</td>\n      <td id=\"T_795cb_row1_col7\" class=\"data row1 col7\" >1</td>\n      <td id=\"T_795cb_row1_col8\" class=\"data row1 col8\" >1</td>\n      <td id=\"T_795cb_row1_col9\" class=\"data row1 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_795cb_level0_row2\" class=\"row_heading level0 row2\" >Valais</th>\n      <td id=\"T_795cb_row2_col0\" class=\"data row2 col0\" >78</td>\n      <td id=\"T_795cb_row2_col1\" class=\"data row2 col1\" >0.40</td>\n      <td id=\"T_795cb_row2_col2\" class=\"data row2 col2\" >4</td>\n      <td id=\"T_795cb_row2_col3\" class=\"data row2 col3\" >1</td>\n      <td id=\"T_795cb_row2_col4\" class=\"data row2 col4\" >1</td>\n      <td id=\"T_795cb_row2_col5\" class=\"data row2 col5\" >2</td>\n      <td id=\"T_795cb_row2_col6\" class=\"data row2 col6\" >2</td>\n      <td id=\"T_795cb_row2_col7\" class=\"data row2 col7\" >2</td>\n      <td id=\"T_795cb_row2_col8\" class=\"data row2 col8\" >1</td>\n      <td id=\"T_795cb_row2_col9\" class=\"data row2 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_795cb_level0_row3\" class=\"row_heading level0 row3\" >Vaud</th>\n      <td id=\"T_795cb_row3_col0\" class=\"data row3 col0\" >738</td>\n      <td id=\"T_795cb_row3_col1\" class=\"data row3 col1\" >2.64</td>\n      <td id=\"T_795cb_row3_col2\" class=\"data row3 col2\" >5</td>\n      <td id=\"T_795cb_row3_col3\" class=\"data row3 col3\" >1</td>\n      <td id=\"T_795cb_row3_col4\" class=\"data row3 col4\" >1</td>\n      <td id=\"T_795cb_row3_col5\" class=\"data row3 col5\" >3</td>\n      <td id=\"T_795cb_row3_col6\" class=\"data row3 col6\" >2</td>\n      <td id=\"T_795cb_row3_col7\" class=\"data row3 col7\" >2</td>\n      <td id=\"T_795cb_row3_col8\" class=\"data row3 col8\" >1</td>\n      <td id=\"T_795cb_row3_col9\" class=\"data row3 col9\" >3</td>\n    </tr>\n    <tr>\n      <th id=\"T_795cb_level0_row4\" class=\"row_heading level0 row4\" >Zürich</th>\n      <td id=\"T_795cb_row4_col0\" class=\"data row4 col0\" >6557</td>\n      <td id=\"T_795cb_row4_col1\" class=\"data row4 col1\" >1.15</td>\n      <td id=\"T_795cb_row4_col2\" class=\"data row4 col2\" >175</td>\n      <td id=\"T_795cb_row4_col3\" class=\"data row4 col3\" >1</td>\n      <td id=\"T_795cb_row4_col4\" class=\"data row4 col4\" >1</td>\n      <td id=\"T_795cb_row4_col5\" class=\"data row4 col5\" >4</td>\n      <td id=\"T_795cb_row4_col6\" class=\"data row4 col6\" >1</td>\n      <td id=\"T_795cb_row4_col7\" class=\"data row4 col7\" >2</td>\n      <td id=\"T_795cb_row4_col8\" class=\"data row4 col8\" >1</td>\n      <td id=\"T_795cb_row4_col9\" class=\"data row4 col9\" >3</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a4699f34340>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_1cfe3 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_1cfe3 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_1cfe3 tr {\n  font-size: 10px;\n}\n#T_1cfe3 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_1cfe3 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_1cfe3 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_1cfe3\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_1cfe3_level0_col0\" class=\"col_heading level0 col0\" >quantity</th>\n      <th id=\"T_1cfe3_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n      <th id=\"T_1cfe3_level0_col2\" class=\"col_heading level0 col2\" >samples</th>\n    </tr>\n    <tr>\n      <th class=\"index_name level0\" >canton</th>\n      <th class=\"blank col0\" >&nbsp;</th>\n      <th class=\"blank col1\" >&nbsp;</th>\n      <th class=\"blank col2\" >&nbsp;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_1cfe3_level0_row0\" class=\"row_heading level0 row0\" >Bern</th>\n      <td id=\"T_1cfe3_row0_col0\" class=\"data row0 col0\" >3701</td>\n      <td id=\"T_1cfe3_row0_col1\" class=\"data row0 col1\" >1.21</td>\n      <td id=\"T_1cfe3_row0_col2\" class=\"data row0 col2\" >97</td>\n    </tr>\n    <tr>\n      <th id=\"T_1cfe3_level0_row1\" class=\"row_heading level0 row1\" >Genève</th>\n      <td id=\"T_1cfe3_row1_col0\" class=\"data row1 col0\" >1616</td>\n      <td id=\"T_1cfe3_row1_col1\" class=\"data row1 col1\" >4.38</td>\n      <td id=\"T_1cfe3_row1_col2\" class=\"data row1 col2\" >9</td>\n    </tr>\n    <tr>\n      <th id=\"T_1cfe3_level0_row2\" class=\"row_heading level0 row2\" >Valais</th>\n      <td id=\"T_1cfe3_row2_col0\" class=\"data row2 col0\" >78</td>\n      <td id=\"T_1cfe3_row2_col1\" class=\"data row2 col1\" >0.40</td>\n      <td id=\"T_1cfe3_row2_col2\" class=\"data row2 col2\" >4</td>\n    </tr>\n    <tr>\n      <th id=\"T_1cfe3_level0_row3\" class=\"row_heading level0 row3\" >Vaud</th>\n      <td id=\"T_1cfe3_row3_col0\" class=\"data row3 col0\" >738</td>\n      <td id=\"T_1cfe3_row3_col1\" class=\"data row3 col1\" >2.64</td>\n      <td id=\"T_1cfe3_row3_col2\" class=\"data row3 col2\" >5</td>\n    </tr>\n    <tr>\n      <th id=\"T_1cfe3_level0_row4\" class=\"row_heading level0 row4\" >Zürich</th>\n      <td id=\"T_1cfe3_row4_col0\" class=\"data row4 col0\" >6557</td>\n      <td id=\"T_1cfe3_row4_col1\" class=\"data row4 col1\" >1.15</td>\n      <td id=\"T_1cfe3_row4_col2\" class=\"data row4 col2\" >175</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e2f1fa60>"
      },
      "metadata": {
       "scrapbook": {
@@ -2413,19 +2442,19 @@
     "dxlc = dxl[['location', 'canton', 'feature_type']].drop_duplicates('location')\n",
     "dxlc.set_index(['location'], inplace=True, drop=True)\n",
     "dxf['canton'] = dxf.location.apply(lambda x : dxlc.loc[x, 'canton'])\n",
-    "sumlu = {x:'sum' for x in session_config.feature_variables}\n",
-    "dxf = dxf.groupby(['sample_id', 'canton', *session_config.feature_variables], as_index=False).agg(session_config.unit_agg)\n",
+    "# sumlu = {x:'sum' for x in session_config.feature_variables}\n",
+    "dxf = dxf.groupby(['sample_id', 'canton'], as_index=False).agg(session_config.unit_agg)\n",
     "\n",
-    "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique', **sumlu})\n",
+    "dxf = dxf.groupby(['canton']).agg({'quantity':'sum', 'pcs/m':'mean', 'sample_id':'nunique'})\n",
     "\n",
-    "for alabel in session_config.feature_variables:\n",
-    "    dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
+    "# for alabel in session_config.feature_variables:\n",
+    "#     dxf[alabel] = dxf[alabel]/dxf.sample_id\n",
     "    \n",
-    "dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
-    "dxfcr = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
-    "dxfcr.rename(columns={'sample_id':'samples'}, inplace=True)\n",
-    "dxfcr.drop('check', axis=1, inplace=True)\n",
-    "dxfcr = dxfcr.style.set_table_styles(userdisplay.table_css_styles)\n",
+    "# dxf['check'] = dxf[session_config.feature_variables[:-1]].sum(axis=1)\n",
+    "# dxfcr = geospatial.categorize_features(dxf, feature_columns=session_config.feature_variables)\n",
+    "dxf.rename(columns={'sample_id':'samples'}, inplace=True)\n",
+    "# dxfcr.drop('check', axis=1, inplace=True)\n",
+    "dxfcr = dxf.style.set_table_styles(userdisplay.table_css_styles)\n",
     "dxfcr = dxfcr.apply(highlight_max, arg=lake_results['this_report'].sampling_results_summary['average'], subset=pd.IndexSlice[:, ['pcs/m']])\n",
     "dxfcr = dxfcr.format(userdisplay.format_kwargs, precision=2)\n",
     "# glue('all-data-municipal-results', i , display=False)\n",
@@ -2448,8 +2477,8 @@
    "outputs": [
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_ecb27 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_ecb27 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_ecb27 tr {\n  font-size: 10px;\n}\n#T_ecb27 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_ecb27 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_ecb27 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_ecb27\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_ecb27_level0_col0\" class=\"col_heading level0 col0\" >samples</th>\n      <th id=\"T_ecb27_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_ecb27_level0_row0\" class=\"row_heading level0 row0\" >Bielersee</th>\n      <td id=\"T_ecb27_row0_col0\" class=\"data row0 col0\" >50</td>\n      <td id=\"T_ecb27_row0_col1\" class=\"data row0 col1\" >4.08</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row1\" class=\"row_heading level0 row1\" >Brienzersee</th>\n      <td id=\"T_ecb27_row1_col0\" class=\"data row1 col0\" >5</td>\n      <td id=\"T_ecb27_row1_col1\" class=\"data row1 col1\" >3.96</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row2\" class=\"row_heading level0 row2\" >Greifensee</th>\n      <td id=\"T_ecb27_row2_col0\" class=\"data row2 col0\" >34</td>\n      <td id=\"T_ecb27_row2_col1\" class=\"data row2 col1\" >4.71</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row3\" class=\"row_heading level0 row3\" >Katzensee</th>\n      <td id=\"T_ecb27_row3_col0\" class=\"data row3 col0\" >12</td>\n      <td id=\"T_ecb27_row3_col1\" class=\"data row3 col1\" >1.77</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row4\" class=\"row_heading level0 row4\" >Lac-leman</th>\n      <td id=\"T_ecb27_row4_col0\" class=\"data row4 col0\" >249</td>\n      <td id=\"T_ecb27_row4_col1\" class=\"data row4 col1\" >8.91</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row5\" class=\"row_heading level0 row5\" >Neuenburgersee</th>\n      <td id=\"T_ecb27_row5_col0\" class=\"data row5 col0\" >23</td>\n      <td id=\"T_ecb27_row5_col1\" class=\"data row5 col1\" >1.78</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row6\" class=\"row_heading level0 row6\" >Thunersee</th>\n      <td id=\"T_ecb27_row6_col0\" class=\"data row6 col0\" >43</td>\n      <td id=\"T_ecb27_row6_col1\" class=\"data row6 col1\" >1.21</td>\n    </tr>\n    <tr>\n      <th id=\"T_ecb27_level0_row7\" class=\"row_heading level0 row7\" >Zurichsee</th>\n      <td id=\"T_ecb27_row7_col0\" class=\"data row7 col0\" >125</td>\n      <td id=\"T_ecb27_row7_col1\" class=\"data row7 col1\" >4.01</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a4699f138b0>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_a9b8d tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_a9b8d tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_a9b8d tr {\n  font-size: 10px;\n}\n#T_a9b8d th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_a9b8d td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_a9b8d caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_a9b8d\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_a9b8d_level0_col0\" class=\"col_heading level0 col0\" >samples</th>\n      <th id=\"T_a9b8d_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_a9b8d_level0_row0\" class=\"row_heading level0 row0\" >Bielersee</th>\n      <td id=\"T_a9b8d_row0_col0\" class=\"data row0 col0\" >50</td>\n      <td id=\"T_a9b8d_row0_col1\" class=\"data row0 col1\" >4.08</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row1\" class=\"row_heading level0 row1\" >Brienzersee</th>\n      <td id=\"T_a9b8d_row1_col0\" class=\"data row1 col0\" >5</td>\n      <td id=\"T_a9b8d_row1_col1\" class=\"data row1 col1\" >3.96</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row2\" class=\"row_heading level0 row2\" >Greifensee</th>\n      <td id=\"T_a9b8d_row2_col0\" class=\"data row2 col0\" >34</td>\n      <td id=\"T_a9b8d_row2_col1\" class=\"data row2 col1\" >4.71</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row3\" class=\"row_heading level0 row3\" >Katzensee</th>\n      <td id=\"T_a9b8d_row3_col0\" class=\"data row3 col0\" >12</td>\n      <td id=\"T_a9b8d_row3_col1\" class=\"data row3 col1\" >1.77</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row4\" class=\"row_heading level0 row4\" >Lac-leman</th>\n      <td id=\"T_a9b8d_row4_col0\" class=\"data row4 col0\" >249</td>\n      <td id=\"T_a9b8d_row4_col1\" class=\"data row4 col1\" >8.91</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row5\" class=\"row_heading level0 row5\" >Neuenburgersee</th>\n      <td id=\"T_a9b8d_row5_col0\" class=\"data row5 col0\" >23</td>\n      <td id=\"T_a9b8d_row5_col1\" class=\"data row5 col1\" >1.78</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row6\" class=\"row_heading level0 row6\" >Thunersee</th>\n      <td id=\"T_a9b8d_row6_col0\" class=\"data row6 col0\" >43</td>\n      <td id=\"T_a9b8d_row6_col1\" class=\"data row6 col1\" >1.21</td>\n    </tr>\n    <tr>\n      <th id=\"T_a9b8d_level0_row7\" class=\"row_heading level0 row7\" >Zurichsee</th>\n      <td id=\"T_a9b8d_row7_col0\" class=\"data row7 col0\" >125</td>\n      <td id=\"T_a9b8d_row7_col1\" class=\"data row7 col1\" >4.01</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e626c4c0>"
      },
      "metadata": {
       "scrapbook": {
@@ -2461,8 +2490,8 @@
     },
     {
      "data": {
-      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_e63cc tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_e63cc tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_e63cc tr {\n  font-size: 10px;\n}\n#T_e63cc th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_e63cc td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_e63cc caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_e63cc\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_e63cc_level0_col0\" class=\"col_heading level0 col0\" >samples</th>\n      <th id=\"T_e63cc_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_e63cc_level0_row0\" class=\"row_heading level0 row0\" >Aare</th>\n      <td id=\"T_e63cc_row0_col0\" class=\"data row0 col0\" >61</td>\n      <td id=\"T_e63cc_row0_col1\" class=\"data row0 col1\" >1.27</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row1\" class=\"row_heading level0 row1\" >Aarenidau-buren-kanal</th>\n      <td id=\"T_e63cc_row1_col0\" class=\"data row1 col0\" >3</td>\n      <td id=\"T_e63cc_row1_col1\" class=\"data row1 col1\" >1.26</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row2\" class=\"row_heading level0 row2\" >Arve</th>\n      <td id=\"T_e63cc_row2_col0\" class=\"data row2 col0\" >8</td>\n      <td id=\"T_e63cc_row2_col1\" class=\"data row2 col1\" >2.92</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row3\" class=\"row_heading level0 row3\" >Chriesbach</th>\n      <td id=\"T_e63cc_row3_col0\" class=\"data row3 col0\" >12</td>\n      <td id=\"T_e63cc_row3_col1\" class=\"data row3 col1\" >0.92</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row4\" class=\"row_heading level0 row4\" >Dorfbach</th>\n      <td id=\"T_e63cc_row4_col0\" class=\"data row4 col0\" >1</td>\n      <td id=\"T_e63cc_row4_col1\" class=\"data row4 col1\" >0.11</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row5\" class=\"row_heading level0 row5\" >Emme</th>\n      <td id=\"T_e63cc_row5_col0\" class=\"data row5 col0\" >9</td>\n      <td id=\"T_e63cc_row5_col1\" class=\"data row5 col1\" >0.60</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row6\" class=\"row_heading level0 row6\" >Glatt</th>\n      <td id=\"T_e63cc_row6_col0\" class=\"data row6 col0\" >7</td>\n      <td id=\"T_e63cc_row6_col1\" class=\"data row6 col1\" >1.67</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row7\" class=\"row_heading level0 row7\" >Grandelbach</th>\n      <td id=\"T_e63cc_row7_col0\" class=\"data row7 col0\" >4</td>\n      <td id=\"T_e63cc_row7_col1\" class=\"data row7 col1\" >1.33</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row8\" class=\"row_heading level0 row8\" >Jona</th>\n      <td id=\"T_e63cc_row8_col0\" class=\"data row8 col0\" >12</td>\n      <td id=\"T_e63cc_row8_col1\" class=\"data row8 col1\" >2.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row9\" class=\"row_heading level0 row9\" >La-thiele</th>\n      <td id=\"T_e63cc_row9_col0\" class=\"data row9 col0\" >1</td>\n      <td id=\"T_e63cc_row9_col1\" class=\"data row9 col1\" >0.46</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row10\" class=\"row_heading level0 row10\" >Langeten</th>\n      <td id=\"T_e63cc_row10_col0\" class=\"data row10 col0\" >11</td>\n      <td id=\"T_e63cc_row10_col1\" class=\"data row10 col1\" >1.94</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row11\" class=\"row_heading level0 row11\" >Limmat</th>\n      <td id=\"T_e63cc_row11_col0\" class=\"data row11 col0\" >61</td>\n      <td id=\"T_e63cc_row11_col1\" class=\"data row11 col1\" >1.13</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row12\" class=\"row_heading level0 row12\" >Ognonnaz</th>\n      <td id=\"T_e63cc_row12_col0\" class=\"data row12 col0\" >1</td>\n      <td id=\"T_e63cc_row12_col1\" class=\"data row12 col1\" >2.85</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row13\" class=\"row_heading level0 row13\" >Rhein</th>\n      <td id=\"T_e63cc_row13_col0\" class=\"data row13 col0\" >10</td>\n      <td id=\"T_e63cc_row13_col1\" class=\"data row13 col1\" >1.52</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row14\" class=\"row_heading level0 row14\" >Rhone</th>\n      <td id=\"T_e63cc_row14_col0\" class=\"data row14 col0\" >8</td>\n      <td id=\"T_e63cc_row14_col1\" class=\"data row14 col1\" >3.43</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row15\" class=\"row_heading level0 row15\" >Schuss</th>\n      <td id=\"T_e63cc_row15_col0\" class=\"data row15 col0\" >2</td>\n      <td id=\"T_e63cc_row15_col1\" class=\"data row15 col1\" >1.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row16\" class=\"row_heading level0 row16\" >Sihl</th>\n      <td id=\"T_e63cc_row16_col0\" class=\"data row16 col0\" >51</td>\n      <td id=\"T_e63cc_row16_col1\" class=\"data row16 col1\" >1.08</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row17\" class=\"row_heading level0 row17\" >Toss</th>\n      <td id=\"T_e63cc_row17_col0\" class=\"data row17 col0\" >17</td>\n      <td id=\"T_e63cc_row17_col1\" class=\"data row17 col1\" >0.59</td>\n    </tr>\n    <tr>\n      <th id=\"T_e63cc_level0_row18\" class=\"row_heading level0 row18\" >Zulg</th>\n      <td id=\"T_e63cc_row18_col0\" class=\"data row18 col0\" >11</td>\n      <td id=\"T_e63cc_row18_col1\" class=\"data row18 col1\" >0.63</td>\n    </tr>\n  </tbody>\n</table>\n",
-      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7a4699ebd310>"
+      "application/papermill.record/text/html": "<style type=\"text/css\">\n#T_90020 tr:nth-child(even) {\n  background-color: rgba(139, 69, 19, 0.08);\n}\n#T_90020 tr:nth-child(odd) {\n  background: #FFF;\n}\n#T_90020 tr {\n  font-size: 10px;\n}\n#T_90020 th {\n  background-color: #FFF;\n  font-size: 12px;\n  text-align: left;\n  width: auto;\n  word-break: keep-all;\n}\n#T_90020 td {\n  padding: 4px;\n  font-size: 12px;\n  text-align: center;\n}\n#T_90020 caption {\n  caption-side: bottom;\n  font-size: 1em;\n  text-align: left;\n}\n</style>\n<table id=\"T_90020\">\n  <thead>\n    <tr>\n      <th class=\"blank level0\" >&nbsp;</th>\n      <th id=\"T_90020_level0_col0\" class=\"col_heading level0 col0\" >samples</th>\n      <th id=\"T_90020_level0_col1\" class=\"col_heading level0 col1\" >pcs/m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th id=\"T_90020_level0_row0\" class=\"row_heading level0 row0\" >Aare</th>\n      <td id=\"T_90020_row0_col0\" class=\"data row0 col0\" >61</td>\n      <td id=\"T_90020_row0_col1\" class=\"data row0 col1\" >1.27</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row1\" class=\"row_heading level0 row1\" >Aarenidau-buren-kanal</th>\n      <td id=\"T_90020_row1_col0\" class=\"data row1 col0\" >3</td>\n      <td id=\"T_90020_row1_col1\" class=\"data row1 col1\" >1.26</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row2\" class=\"row_heading level0 row2\" >Arve</th>\n      <td id=\"T_90020_row2_col0\" class=\"data row2 col0\" >8</td>\n      <td id=\"T_90020_row2_col1\" class=\"data row2 col1\" >2.92</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row3\" class=\"row_heading level0 row3\" >Chriesbach</th>\n      <td id=\"T_90020_row3_col0\" class=\"data row3 col0\" >12</td>\n      <td id=\"T_90020_row3_col1\" class=\"data row3 col1\" >0.92</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row4\" class=\"row_heading level0 row4\" >Dorfbach</th>\n      <td id=\"T_90020_row4_col0\" class=\"data row4 col0\" >1</td>\n      <td id=\"T_90020_row4_col1\" class=\"data row4 col1\" >0.11</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row5\" class=\"row_heading level0 row5\" >Emme</th>\n      <td id=\"T_90020_row5_col0\" class=\"data row5 col0\" >9</td>\n      <td id=\"T_90020_row5_col1\" class=\"data row5 col1\" >0.60</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row6\" class=\"row_heading level0 row6\" >Glatt</th>\n      <td id=\"T_90020_row6_col0\" class=\"data row6 col0\" >7</td>\n      <td id=\"T_90020_row6_col1\" class=\"data row6 col1\" >1.67</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row7\" class=\"row_heading level0 row7\" >Grandelbach</th>\n      <td id=\"T_90020_row7_col0\" class=\"data row7 col0\" >4</td>\n      <td id=\"T_90020_row7_col1\" class=\"data row7 col1\" >1.33</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row8\" class=\"row_heading level0 row8\" >Jona</th>\n      <td id=\"T_90020_row8_col0\" class=\"data row8 col0\" >12</td>\n      <td id=\"T_90020_row8_col1\" class=\"data row8 col1\" >2.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row9\" class=\"row_heading level0 row9\" >La-thiele</th>\n      <td id=\"T_90020_row9_col0\" class=\"data row9 col0\" >1</td>\n      <td id=\"T_90020_row9_col1\" class=\"data row9 col1\" >0.46</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row10\" class=\"row_heading level0 row10\" >Langeten</th>\n      <td id=\"T_90020_row10_col0\" class=\"data row10 col0\" >11</td>\n      <td id=\"T_90020_row10_col1\" class=\"data row10 col1\" >1.94</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row11\" class=\"row_heading level0 row11\" >Limmat</th>\n      <td id=\"T_90020_row11_col0\" class=\"data row11 col0\" >61</td>\n      <td id=\"T_90020_row11_col1\" class=\"data row11 col1\" >1.13</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row12\" class=\"row_heading level0 row12\" >Ognonnaz</th>\n      <td id=\"T_90020_row12_col0\" class=\"data row12 col0\" >1</td>\n      <td id=\"T_90020_row12_col1\" class=\"data row12 col1\" >2.85</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row13\" class=\"row_heading level0 row13\" >Rhein</th>\n      <td id=\"T_90020_row13_col0\" class=\"data row13 col0\" >10</td>\n      <td id=\"T_90020_row13_col1\" class=\"data row13 col1\" >1.52</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row14\" class=\"row_heading level0 row14\" >Rhone</th>\n      <td id=\"T_90020_row14_col0\" class=\"data row14 col0\" >8</td>\n      <td id=\"T_90020_row14_col1\" class=\"data row14 col1\" >3.43</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row15\" class=\"row_heading level0 row15\" >Schuss</th>\n      <td id=\"T_90020_row15_col0\" class=\"data row15 col0\" >2</td>\n      <td id=\"T_90020_row15_col1\" class=\"data row15 col1\" >1.04</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row16\" class=\"row_heading level0 row16\" >Sihl</th>\n      <td id=\"T_90020_row16_col0\" class=\"data row16 col0\" >51</td>\n      <td id=\"T_90020_row16_col1\" class=\"data row16 col1\" >1.08</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row17\" class=\"row_heading level0 row17\" >Toss</th>\n      <td id=\"T_90020_row17_col0\" class=\"data row17 col0\" >17</td>\n      <td id=\"T_90020_row17_col1\" class=\"data row17 col1\" >0.59</td>\n    </tr>\n    <tr>\n      <th id=\"T_90020_level0_row18\" class=\"row_heading level0 row18\" >Zulg</th>\n      <td id=\"T_90020_row18_col0\" class=\"data row18 col0\" >11</td>\n      <td id=\"T_90020_row18_col1\" class=\"data row18 col1\" >0.63</td>\n    </tr>\n  </tbody>\n</table>\n",
+      "application/papermill.record/text/plain": "<pandas.io.formats.style.Styler at 0x7e64e624d0a0>"
      },
      "metadata": {
       "scrapbook": {
@@ -2521,18 +2550,22 @@
     ":::\n",
     "::::\n",
     "\n",
-    "## Municipal Results - all data\n",
+    "## Cantonal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each canton.\n",
+    "The average pieces per meter for each canton.\n",
     "\n",
-    "::::::::::{tab-set}\n",
+    "::::::::::{grid}\n",
     "\n",
-    ":::::::::{tab-item} Lakes\n",
+    ":::::::::{grid-item-card}\n",
+    "Lakes\n",
+    "^^^\n",
     "```{glue} lake-municipal-results\n",
     "```\n",
     ":::::::::\n",
     "\n",
-    ":::::::::{tab-item} Rivers\n",
+    ":::::::::{grid-item-card}\n",
+    "Rivers\n",
+    "^^^\n",
     "```{glue} river-municipal-results\n",
     "``` \n",
     ":::::::::\n",
diff --git a/docs/_sources/geneve.ipynb b/docs/_sources/geneve.ipynb
index 708ecda..ca8c4c4 100644
--- a/docs/_sources/geneve.ipynb
+++ b/docs/_sources/geneve.ipynb
@@ -6,6 +6,9 @@
    "id": "5bf92247-ef77-4aae-b62f-9388cba6765e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -113,6 +116,9 @@
    "id": "fb4d8635-eb6f-402a-9cd6-8be9c76cce30",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -178,6 +184,9 @@
    "id": "d504b355-6ec4-4d6d-b9d6-3459215fcb8b",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -473,6 +482,9 @@
    "id": "c016aff6-dc3f-49a1-be4d-48b6448c9d22",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -781,6 +793,9 @@
    "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1089,6 +1104,9 @@
    "id": "12c52a87-8340-419f-bfd9-75ca85260a97",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1675,6 +1693,9 @@
    "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1729,6 +1750,9 @@
    "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1787,6 +1811,9 @@
    "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1842,6 +1869,9 @@
    "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1901,6 +1931,9 @@
    "id": "82f55461-c497-483a-8c38-fbd509809afb",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1957,6 +1990,9 @@
    "id": "9b396025-1fa6-4661-9116-593fa1ed741d",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -2335,6 +2371,9 @@
    "id": "b0bda4c0-1b4a-4011-9ad1-fb3a52aac885",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -2389,6 +2428,9 @@
    "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -2443,6 +2485,9 @@
    "id": "2d5b8904-044b-4aed-916c-5e36018f4087",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -2528,7 +2573,7 @@
     "\n",
     "## Municipal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
     "\n",
     "::::::::::{tab-set}\n",
     "\n",
diff --git a/docs/_sources/valais.ipynb b/docs/_sources/valais.ipynb
index bd9e763..d5c5176 100644
--- a/docs/_sources/valais.ipynb
+++ b/docs/_sources/valais.ipynb
@@ -6,6 +6,9 @@
    "id": "5bf92247-ef77-4aae-b62f-9388cba6765e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -112,6 +115,9 @@
    "id": "fb4d8635-eb6f-402a-9cd6-8be9c76cce30",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -786,6 +792,9 @@
    "id": "671460d2-c096-49b6-ab55-7cc9a003da78",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1164,6 +1173,9 @@
    "id": "7eed90fa-3a84-41d1-8700-6fda0d356f7f",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1180,6 +1192,9 @@
    "id": "12c52a87-8340-419f-bfd9-75ca85260a97",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1761,6 +1776,9 @@
    "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1815,6 +1833,9 @@
    "id": "e2bf0a4b-fc49-420b-bfc1-3e21e0a11cb9",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1873,6 +1894,9 @@
    "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1928,6 +1952,9 @@
    "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1987,6 +2014,9 @@
    "id": "82f55461-c497-483a-8c38-fbd509809afb",
    "metadata": {
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@@ -2043,6 +2073,9 @@
    "id": "9b396025-1fa6-4661-9116-593fa1ed741d",
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@@ -2421,6 +2454,9 @@
    "id": "b0bda4c0-1b4a-4011-9ad1-fb3a52aac885",
    "metadata": {
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+    "jupyter": {
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@@ -2475,6 +2511,9 @@
    "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e",
    "metadata": {
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@@ -2529,6 +2568,9 @@
    "id": "2d5b8904-044b-4aed-916c-5e36018f4087",
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@@ -2614,7 +2656,7 @@
     "\n",
     "## Municipal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
     "\n",
     "::::::::::{tab-set}\n",
     "\n",
diff --git a/docs/_sources/vaud.ipynb b/docs/_sources/vaud.ipynb
index a997b8c..5c4761e 100644
--- a/docs/_sources/vaud.ipynb
+++ b/docs/_sources/vaud.ipynb
@@ -6,6 +6,9 @@
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@@ -113,6 +116,9 @@
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@@ -178,6 +184,9 @@
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@@ -473,6 +482,9 @@
    "id": "c016aff6-dc3f-49a1-be4d-48b6448c9d22",
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@@ -781,6 +793,9 @@
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@@ -1089,6 +1104,9 @@
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    "metadata": {
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@@ -1675,6 +1693,9 @@
    "id": "fa022a3b-f3bd-41c8-aa11-816ff202eda3",
    "metadata": {
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@@ -1729,6 +1750,9 @@
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@@ -1787,6 +1811,9 @@
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@@ -1842,6 +1869,9 @@
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@@ -1901,6 +1931,9 @@
    "id": "82f55461-c497-483a-8c38-fbd509809afb",
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@@ -1957,6 +1990,9 @@
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@@ -2335,6 +2371,9 @@
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@@ -2389,6 +2428,9 @@
    "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e",
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@@ -2443,6 +2485,9 @@
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    "metadata": {
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+    },
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@@ -2528,7 +2573,7 @@
     "\n",
     "## Municipal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
     "\n",
     "::::::::::{tab-set}\n",
     "\n",
diff --git a/docs/_sources/zurich.ipynb b/docs/_sources/zurich.ipynb
index faee719..b9c2001 100644
--- a/docs/_sources/zurich.ipynb
+++ b/docs/_sources/zurich.ipynb
@@ -2528,7 +2528,7 @@
     "\n",
     "## Municipal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
     "\n",
     "::::::::::{tab-set}\n",
     "\n",
diff --git a/docs/bern.html b/docs/bern.html
index 1b2914f..2b6aca6 100644
--- a/docs/bern.html
+++ b/docs/bern.html
@@ -2640,7 +2640,7 @@ <h2>Lakes and rivers sampled - all data<a class="headerlink" href="#lakes-and-ri
 </div>
 <div class="section" id="municipal-results-all-data">
 <h2>Municipal Results - all data<a class="headerlink" href="#municipal-results-all-data" title="Permalink to this heading">#</a></h2>
-<p>The average pieces per meter of the most common objects for each city.</p>
+<p>The average pieces per meter and the combined land use classification for each city.</p>
 <div class="sd-tab-set docutils">
 <input checked="checked" id="sd-tab-item-15" name="sd-tab-set-5" type="radio">
 </input><label class="sd-tab-label" for="sd-tab-item-15">
diff --git a/docs/combined.html b/docs/combined.html
index 71d3566..194710d 100644
--- a/docs/combined.html
+++ b/docs/combined.html
@@ -319,7 +319,7 @@ <h2> Contents </h2>
 </li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#forecast">Forecast</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#lakes-and-rivers-sampled-all-data">Lakes and rivers sampled - all data</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#municipal-results-all-data">Municipal Results - all data</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#cantonal-results-all-data">Cantonal Results - all data</a></li>
 </ul>
             </nav>
         </div>
@@ -425,38 +425,38 @@ <h2>Vital statistics<a class="headerlink" href="#vital-statistics" title="Permal
 </ul>
 <p><strong>Material composition</strong></p>
 <div class="output text_html"><style type="text/css">
-#T_d466c tr:nth-child(even) {
+#T_92ad7 tr:nth-child(even) {
   background-color: rgba(139, 69, 19, 0.08);
 }
-#T_d466c tr:nth-child(odd) {
+#T_92ad7 tr:nth-child(odd) {
   background: #FFF;
 }
-#T_d466c tr {
+#T_92ad7 tr {
   font-size: 10px;
 }
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+#T_92ad7 th {
   background-color: #FFF;
   font-size: 12px;
   text-align: left;
   width: auto;
   word-break: keep-all;
 }
-#T_d466c td {
+#T_92ad7 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
 }
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+#T_92ad7 caption {
   caption-side: bottom;
   font-size: 1em;
   text-align: left;
 }
 </style>
-<table id="T_d466c">
+<table id="T_92ad7">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_d466c_level0_col0" class="col_heading level0 col0" >% of total</th>
+      <th id="T_92ad7_level0_col0" class="col_heading level0 col0" >% of total</th>
     </tr>
     <tr>
       <th class="index_name level0" >material</th>
@@ -465,20 +465,20 @@ <h2>Vital statistics<a class="headerlink" href="#vital-statistics" title="Permal
   </thead>
   <tbody>
     <tr>
-      <th id="T_d466c_level0_row0" class="row_heading level0 row0" >glass</th>
-      <td id="T_d466c_row0_col0" class="data row0 col0" >6%</td>
+      <th id="T_92ad7_level0_row0" class="row_heading level0 row0" >glass</th>
+      <td id="T_92ad7_row0_col0" class="data row0 col0" >6%</td>
     </tr>
     <tr>
-      <th id="T_d466c_level0_row1" class="row_heading level0 row1" >metal</th>
-      <td id="T_d466c_row1_col0" class="data row1 col0" >4%</td>
+      <th id="T_92ad7_level0_row1" class="row_heading level0 row1" >metal</th>
+      <td id="T_92ad7_row1_col0" class="data row1 col0" >4%</td>
     </tr>
     <tr>
-      <th id="T_d466c_level0_row2" class="row_heading level0 row2" >paper</th>
-      <td id="T_d466c_row2_col0" class="data row2 col0" >3%</td>
+      <th id="T_92ad7_level0_row2" class="row_heading level0 row2" >paper</th>
+      <td id="T_92ad7_row2_col0" class="data row2 col0" >3%</td>
     </tr>
     <tr>
-      <th id="T_d466c_level0_row3" class="row_heading level0 row3" >plastic</th>
-      <td id="T_d466c_row3_col0" class="data row3 col0" >81%</td>
+      <th id="T_92ad7_level0_row3" class="row_heading level0 row3" >plastic</th>
+      <td id="T_92ad7_row3_col0" class="data row3 col0" >81%</td>
     </tr>
   </tbody>
 </table>
@@ -559,38 +559,38 @@ <h2>Vital statistics<a class="headerlink" href="#vital-statistics" title="Permal
 </ul>
 <p><strong>Material composition</strong></p>
 <div class="output text_html"><style type="text/css">
-#T_0b524 tr:nth-child(even) {
+#T_020a9 tr:nth-child(even) {
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-#T_0b524 tr:nth-child(odd) {
+#T_020a9 tr:nth-child(odd) {
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 }
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 }
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 }
-#T_0b524 td {
+#T_020a9 td {
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   text-align: center;
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+#T_020a9 caption {
   caption-side: bottom;
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 }
 </style>
-<table id="T_0b524">
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   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_0b524_level0_col0" class="col_heading level0 col0" >% of total</th>
+      <th id="T_020a9_level0_col0" class="col_heading level0 col0" >% of total</th>
     </tr>
     <tr>
       <th class="index_name level0" >material</th>
@@ -599,20 +599,20 @@ <h2>Vital statistics<a class="headerlink" href="#vital-statistics" title="Permal
   </thead>
   <tbody>
     <tr>
-      <th id="T_0b524_level0_row0" class="row_heading level0 row0" >glass</th>
-      <td id="T_0b524_row0_col0" class="data row0 col0" >5%</td>
+      <th id="T_020a9_level0_row0" class="row_heading level0 row0" >glass</th>
+      <td id="T_020a9_row0_col0" class="data row0 col0" >5%</td>
     </tr>
     <tr>
-      <th id="T_0b524_level0_row1" class="row_heading level0 row1" >metal</th>
-      <td id="T_0b524_row1_col0" class="data row1 col0" >4%</td>
+      <th id="T_020a9_level0_row1" class="row_heading level0 row1" >metal</th>
+      <td id="T_020a9_row1_col0" class="data row1 col0" >4%</td>
     </tr>
     <tr>
-      <th id="T_0b524_level0_row2" class="row_heading level0 row2" >paper</th>
-      <td id="T_0b524_row2_col0" class="data row2 col0" >3%</td>
+      <th id="T_020a9_level0_row2" class="row_heading level0 row2" >paper</th>
+      <td id="T_020a9_row2_col0" class="data row2 col0" >3%</td>
     </tr>
     <tr>
-      <th id="T_0b524_level0_row3" class="row_heading level0 row3" >plastic</th>
-      <td id="T_0b524_row3_col0" class="data row3 col0" >83%</td>
+      <th id="T_020a9_level0_row3" class="row_heading level0 row3" >plastic</th>
+      <td id="T_020a9_row3_col0" class="data row3 col0" >83%</td>
     </tr>
   </tbody>
 </table>
@@ -693,38 +693,38 @@ <h2>Vital statistics<a class="headerlink" href="#vital-statistics" title="Permal
 </ul>
 <p><strong>Material composition</strong></p>
 <div class="output text_html"><style type="text/css">
-#T_a28fd tr:nth-child(even) {
+#T_39019 tr:nth-child(even) {
   background-color: rgba(139, 69, 19, 0.08);
 }
-#T_a28fd tr:nth-child(odd) {
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+#T_39019 tr {
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 }
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   font-size: 12px;
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   width: auto;
   word-break: keep-all;
 }
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 }
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   caption-side: bottom;
   font-size: 1em;
   text-align: left;
 }
 </style>
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   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_a28fd_level0_col0" class="col_heading level0 col0" >% of total</th>
+      <th id="T_39019_level0_col0" class="col_heading level0 col0" >% of total</th>
     </tr>
     <tr>
       <th class="index_name level0" >material</th>
@@ -733,28 +733,28 @@ <h2>Vital statistics<a class="headerlink" href="#vital-statistics" title="Permal
   </thead>
   <tbody>
     <tr>
-      <th id="T_a28fd_level0_row0" class="row_heading level0 row0" >cloth</th>
-      <td id="T_a28fd_row0_col0" class="data row0 col0" >5%</td>
+      <th id="T_39019_level0_row0" class="row_heading level0 row0" >cloth</th>
+      <td id="T_39019_row0_col0" class="data row0 col0" >5%</td>
     </tr>
     <tr>
-      <th id="T_a28fd_level0_row1" class="row_heading level0 row1" >glass</th>
-      <td id="T_a28fd_row1_col0" class="data row1 col0" >7%</td>
+      <th id="T_39019_level0_row1" class="row_heading level0 row1" >glass</th>
+      <td id="T_39019_row1_col0" class="data row1 col0" >7%</td>
     </tr>
     <tr>
-      <th id="T_a28fd_level0_row2" class="row_heading level0 row2" >metal</th>
-      <td id="T_a28fd_row2_col0" class="data row2 col0" >9%</td>
+      <th id="T_39019_level0_row2" class="row_heading level0 row2" >metal</th>
+      <td id="T_39019_row2_col0" class="data row2 col0" >9%</td>
     </tr>
     <tr>
-      <th id="T_a28fd_level0_row3" class="row_heading level0 row3" >paper</th>
-      <td id="T_a28fd_row3_col0" class="data row3 col0" >7%</td>
+      <th id="T_39019_level0_row3" class="row_heading level0 row3" >paper</th>
+      <td id="T_39019_row3_col0" class="data row3 col0" >7%</td>
     </tr>
     <tr>
-      <th id="T_a28fd_level0_row4" class="row_heading level0 row4" >plastic</th>
-      <td id="T_a28fd_row4_col0" class="data row4 col0" >65%</td>
+      <th id="T_39019_level0_row4" class="row_heading level0 row4" >plastic</th>
+      <td id="T_39019_row4_col0" class="data row4 col0" >65%</td>
     </tr>
     <tr>
-      <th id="T_a28fd_level0_row5" class="row_heading level0 row5" >wood</th>
-      <td id="T_a28fd_row5_col0" class="data row5 col0" >2%</td>
+      <th id="T_39019_level0_row5" class="row_heading level0 row5" >wood</th>
+      <td id="T_39019_row5_col0" class="data row5 col0" >2%</td>
     </tr>
   </tbody>
 </table>
@@ -922,159 +922,159 @@ <h2>Most common objects 2020 - 2021<a class="headerlink" href="#most-common-obje
 <div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-none docutils">
 <div class="sd-card-body docutils">
 <div class="output text_html"><style type="text/css">
-#T_b04cc tr:nth-child(even) {
+#T_3adc8 tr:nth-child(even) {
   background-color: rgba(139, 69, 19, 0.08);
 }
-#T_b04cc tr:nth-child(odd) {
+#T_3adc8 tr:nth-child(odd) {
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 }
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+#T_3adc8 tr {
   font-size: 10px;
 }
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   background-color: #FFF;
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   width: auto;
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 }
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+#T_3adc8 td {
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 }
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   font-style: italic;
   color: #000;
 }
-#T_b04cc td:nth-child(1) {
+#T_3adc8 td:nth-child(1) {
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-      <th id="T_b04cc_level0_col0" class="col_heading level0 col0" >Object</th>
-      <th id="T_b04cc_level0_col1" class="col_heading level0 col1" >Quantity</th>
-      <th id="T_b04cc_level0_col2" class="col_heading level0 col2" >pcs/m</th>
-      <th id="T_b04cc_level0_col3" class="col_heading level0 col3" >% of total</th>
-      <th id="T_b04cc_level0_col4" class="col_heading level0 col4" >Fail rate</th>
+      <th id="T_3adc8_level0_col0" class="col_heading level0 col0" >Object</th>
+      <th id="T_3adc8_level0_col1" class="col_heading level0 col1" >Quantity</th>
+      <th id="T_3adc8_level0_col2" class="col_heading level0 col2" >pcs/m</th>
+      <th id="T_3adc8_level0_col3" class="col_heading level0 col3" >% of total</th>
+      <th id="T_3adc8_level0_col4" class="col_heading level0 col4" >Fail rate</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <td id="T_b04cc_row0_col0" class="data row0 col0" >Fragmented plastics</td>
-      <td id="T_b04cc_row0_col1" class="data row0 col1" >6'796</td>
-      <td id="T_b04cc_row0_col2" class="data row0 col2" >0,83</td>
-      <td id="T_b04cc_row0_col3" class="data row0 col3" >0,15</td>
-      <td id="T_b04cc_row0_col4" class="data row0 col4" >0,88</td>
+      <td id="T_3adc8_row0_col0" class="data row0 col0" >Fragmented plastics</td>
+      <td id="T_3adc8_row0_col1" class="data row0 col1" >6'796</td>
+      <td id="T_3adc8_row0_col2" class="data row0 col2" >0,83</td>
+      <td id="T_3adc8_row0_col3" class="data row0 col3" >0,15</td>
+      <td id="T_3adc8_row0_col4" class="data row0 col4" >0,88</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row1_col0" class="data row1 col0" >Cigarette filters</td>
-      <td id="T_b04cc_row1_col1" class="data row1 col1" >6'306</td>
-      <td id="T_b04cc_row1_col2" class="data row1 col2" >0,60</td>
-      <td id="T_b04cc_row1_col3" class="data row1 col3" >0,14</td>
-      <td id="T_b04cc_row1_col4" class="data row1 col4" >0,87</td>
+      <td id="T_3adc8_row1_col0" class="data row1 col0" >Cigarette filters</td>
+      <td id="T_3adc8_row1_col1" class="data row1 col1" >6'306</td>
+      <td id="T_3adc8_row1_col2" class="data row1 col2" >0,60</td>
+      <td id="T_3adc8_row1_col3" class="data row1 col3" >0,14</td>
+      <td id="T_3adc8_row1_col4" class="data row1 col4" >0,87</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row2_col0" class="data row2 col0" >Expanded polystyrene</td>
-      <td id="T_b04cc_row2_col1" class="data row2 col1" >4'319</td>
-      <td id="T_b04cc_row2_col2" class="data row2 col2" >0,55</td>
-      <td id="T_b04cc_row2_col3" class="data row2 col3" >0,10</td>
-      <td id="T_b04cc_row2_col4" class="data row2 col4" >0,67</td>
+      <td id="T_3adc8_row2_col0" class="data row2 col0" >Expanded polystyrene</td>
+      <td id="T_3adc8_row2_col1" class="data row2 col1" >4'319</td>
+      <td id="T_3adc8_row2_col2" class="data row2 col2" >0,55</td>
+      <td id="T_3adc8_row2_col3" class="data row2 col3" >0,10</td>
+      <td id="T_3adc8_row2_col4" class="data row2 col4" >0,67</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row3_col0" class="data row3 col0" >Food wrappers; candy, snacks</td>
-      <td id="T_b04cc_row3_col1" class="data row3 col1" >2'890</td>
-      <td id="T_b04cc_row3_col2" class="data row3 col2" >0,31</td>
-      <td id="T_b04cc_row3_col3" class="data row3 col3" >0,06</td>
-      <td id="T_b04cc_row3_col4" class="data row3 col4" >0,88</td>
+      <td id="T_3adc8_row3_col0" class="data row3 col0" >Food wrappers; candy, snacks</td>
+      <td id="T_3adc8_row3_col1" class="data row3 col1" >2'890</td>
+      <td id="T_3adc8_row3_col2" class="data row3 col2" >0,31</td>
+      <td id="T_3adc8_row3_col3" class="data row3 col3" >0,06</td>
+      <td id="T_3adc8_row3_col4" class="data row3 col4" >0,88</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row4_col0" class="data row4 col0" >Industrial sheeting</td>
-      <td id="T_b04cc_row4_col1" class="data row4 col1" >2'031</td>
-      <td id="T_b04cc_row4_col2" class="data row4 col2" >0,24</td>
-      <td id="T_b04cc_row4_col3" class="data row4 col3" >0,05</td>
-      <td id="T_b04cc_row4_col4" class="data row4 col4" >0,69</td>
+      <td id="T_3adc8_row4_col0" class="data row4 col0" >Industrial sheeting</td>
+      <td id="T_3adc8_row4_col1" class="data row4 col1" >2'031</td>
+      <td id="T_3adc8_row4_col2" class="data row4 col2" >0,24</td>
+      <td id="T_3adc8_row4_col3" class="data row4 col3" >0,05</td>
+      <td id="T_3adc8_row4_col4" class="data row4 col4" >0,69</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row5_col0" class="data row5 col0" >Industrial pellets (nurdles)</td>
-      <td id="T_b04cc_row5_col1" class="data row5 col1" >1'715</td>
-      <td id="T_b04cc_row5_col2" class="data row5 col2" >0,18</td>
-      <td id="T_b04cc_row5_col3" class="data row5 col3" >0,04</td>
-      <td id="T_b04cc_row5_col4" class="data row5 col4" >0,36</td>
+      <td id="T_3adc8_row5_col0" class="data row5 col0" >Industrial pellets (nurdles)</td>
+      <td id="T_3adc8_row5_col1" class="data row5 col1" >1'715</td>
+      <td id="T_3adc8_row5_col2" class="data row5 col2" >0,18</td>
+      <td id="T_3adc8_row5_col3" class="data row5 col3" >0,04</td>
+      <td id="T_3adc8_row5_col4" class="data row5 col4" >0,36</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row6_col0" class="data row6 col0" >plastic caps, lid rings: G21, G22, G23, G24</td>
-      <td id="T_b04cc_row6_col1" class="data row6 col1" >1'704</td>
-      <td id="T_b04cc_row6_col2" class="data row6 col2" >0,19</td>
-      <td id="T_b04cc_row6_col3" class="data row6 col3" >0,04</td>
-      <td id="T_b04cc_row6_col4" class="data row6 col4" >0,71</td>
+      <td id="T_3adc8_row6_col0" class="data row6 col0" >plastic caps, lid rings: G21, G22, G23, G24</td>
+      <td id="T_3adc8_row6_col1" class="data row6 col1" >1'704</td>
+      <td id="T_3adc8_row6_col2" class="data row6 col2" >0,19</td>
+      <td id="T_3adc8_row6_col3" class="data row6 col3" >0,04</td>
+      <td id="T_3adc8_row6_col4" class="data row6 col4" >0,71</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row7_col0" class="data row7 col0" >Glass drink bottles, pieces</td>
-      <td id="T_b04cc_row7_col1" class="data row7 col1" >1'502</td>
-      <td id="T_b04cc_row7_col2" class="data row7 col2" >0,19</td>
-      <td id="T_b04cc_row7_col3" class="data row7 col3" >0,03</td>
-      <td id="T_b04cc_row7_col4" class="data row7 col4" >0,65</td>
+      <td id="T_3adc8_row7_col0" class="data row7 col0" >Glass drink bottles, pieces</td>
+      <td id="T_3adc8_row7_col1" class="data row7 col1" >1'502</td>
+      <td id="T_3adc8_row7_col2" class="data row7 col2" >0,19</td>
+      <td id="T_3adc8_row7_col3" class="data row7 col3" >0,03</td>
+      <td id="T_3adc8_row7_col4" class="data row7 col4" >0,65</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row8_col0" class="data row8 col0" >Foam packaging/insulation/polyurethane</td>
-      <td id="T_b04cc_row8_col1" class="data row8 col1" >1'435</td>
-      <td id="T_b04cc_row8_col2" class="data row8 col2" >0,09</td>
-      <td id="T_b04cc_row8_col3" class="data row8 col3" >0,03</td>
-      <td id="T_b04cc_row8_col4" class="data row8 col4" >0,87</td>
+      <td id="T_3adc8_row8_col0" class="data row8 col0" >Foam packaging/insulation/polyurethane</td>
+      <td id="T_3adc8_row8_col1" class="data row8 col1" >1'435</td>
+      <td id="T_3adc8_row8_col2" class="data row8 col2" >0,09</td>
+      <td id="T_3adc8_row8_col3" class="data row8 col3" >0,03</td>
+      <td id="T_3adc8_row8_col4" class="data row8 col4" >0,87</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row9_col0" class="data row9 col0" >Cotton bud/swab sticks</td>
-      <td id="T_b04cc_row9_col1" class="data row9 col1" >1'385</td>
-      <td id="T_b04cc_row9_col2" class="data row9 col2" >0,16</td>
-      <td id="T_b04cc_row9_col3" class="data row9 col3" >0,03</td>
-      <td id="T_b04cc_row9_col4" class="data row9 col4" >0,56</td>
+      <td id="T_3adc8_row9_col0" class="data row9 col0" >Cotton bud/swab sticks</td>
+      <td id="T_3adc8_row9_col1" class="data row9 col1" >1'385</td>
+      <td id="T_3adc8_row9_col2" class="data row9 col2" >0,16</td>
+      <td id="T_3adc8_row9_col3" class="data row9 col3" >0,03</td>
+      <td id="T_3adc8_row9_col4" class="data row9 col4" >0,56</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row10_col0" class="data row10 col0" >Plastic construction waste</td>
-      <td id="T_b04cc_row10_col1" class="data row10 col1" >836</td>
-      <td id="T_b04cc_row10_col2" class="data row10 col2" >0,09</td>
-      <td id="T_b04cc_row10_col3" class="data row10 col3" >0,02</td>
-      <td id="T_b04cc_row10_col4" class="data row10 col4" >0,53</td>
+      <td id="T_3adc8_row10_col0" class="data row10 col0" >Plastic construction waste</td>
+      <td id="T_3adc8_row10_col1" class="data row10 col1" >836</td>
+      <td id="T_3adc8_row10_col2" class="data row10 col2" >0,09</td>
+      <td id="T_3adc8_row10_col3" class="data row10 col3" >0,02</td>
+      <td id="T_3adc8_row10_col4" class="data row10 col4" >0,53</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row11_col0" class="data row11 col0" >Metal bottle caps, lids & pull tabs from cans</td>
-      <td id="T_b04cc_row11_col1" class="data row11 col1" >551</td>
-      <td id="T_b04cc_row11_col2" class="data row11 col2" >0,05</td>
-      <td id="T_b04cc_row11_col3" class="data row11 col3" >0,01</td>
-      <td id="T_b04cc_row11_col4" class="data row11 col4" >0,56</td>
+      <td id="T_3adc8_row11_col0" class="data row11 col0" >Metal bottle caps, lids & pull tabs from cans</td>
+      <td id="T_3adc8_row11_col1" class="data row11 col1" >551</td>
+      <td id="T_3adc8_row11_col2" class="data row11 col2" >0,05</td>
+      <td id="T_3adc8_row11_col3" class="data row11 col3" >0,01</td>
+      <td id="T_3adc8_row11_col4" class="data row11 col4" >0,56</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row12_col0" class="data row12 col0" >Tobacco; plastic packaging, containers</td>
-      <td id="T_b04cc_row12_col1" class="data row12 col1" >529</td>
-      <td id="T_b04cc_row12_col2" class="data row12 col2" >0,06</td>
-      <td id="T_b04cc_row12_col3" class="data row12 col3" >0,01</td>
-      <td id="T_b04cc_row12_col4" class="data row12 col4" >0,50</td>
+      <td id="T_3adc8_row12_col0" class="data row12 col0" >Tobacco; plastic packaging, containers</td>
+      <td id="T_3adc8_row12_col1" class="data row12 col1" >529</td>
+      <td id="T_3adc8_row12_col2" class="data row12 col2" >0,06</td>
+      <td id="T_3adc8_row12_col3" class="data row12 col3" >0,01</td>
+      <td id="T_3adc8_row12_col4" class="data row12 col4" >0,50</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row13_col0" class="data row13 col0" >Foil wrappers, aluminum foil</td>
-      <td id="T_b04cc_row13_col1" class="data row13 col1" >386</td>
-      <td id="T_b04cc_row13_col2" class="data row13 col2" >0,04</td>
-      <td id="T_b04cc_row13_col3" class="data row13 col3" >0,01</td>
-      <td id="T_b04cc_row13_col4" class="data row13 col4" >0,50</td>
+      <td id="T_3adc8_row13_col0" class="data row13 col0" >Foil wrappers, aluminum foil</td>
+      <td id="T_3adc8_row13_col1" class="data row13 col1" >386</td>
+      <td id="T_3adc8_row13_col2" class="data row13 col2" >0,04</td>
+      <td id="T_3adc8_row13_col3" class="data row13 col3" >0,01</td>
+      <td id="T_3adc8_row13_col4" class="data row13 col4" >0,50</td>
     </tr>
     <tr>
-      <td id="T_b04cc_row14_col0" class="data row14 col0" >Straws and stirrers</td>
-      <td id="T_b04cc_row14_col1" class="data row14 col1" >382</td>
-      <td id="T_b04cc_row14_col2" class="data row14 col2" >0,04</td>
-      <td id="T_b04cc_row14_col3" class="data row14 col3" >0,01</td>
-      <td id="T_b04cc_row14_col4" class="data row14 col4" >0,50</td>
+      <td id="T_3adc8_row14_col0" class="data row14 col0" >Straws and stirrers</td>
+      <td id="T_3adc8_row14_col1" class="data row14 col1" >382</td>
+      <td id="T_3adc8_row14_col2" class="data row14 col2" >0,04</td>
+      <td id="T_3adc8_row14_col3" class="data row14 col3" >0,01</td>
+      <td id="T_3adc8_row14_col4" class="data row14 col4" >0,50</td>
     </tr>
   </tbody>
 </table>
@@ -1097,166 +1097,166 @@ <h2>Most common objects 2020 - 2021<a class="headerlink" href="#most-common-obje
 <div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-none docutils">
 <div class="sd-card-body docutils">
 <div class="output text_html"><style type="text/css">
-#T_0ece9 tr:nth-child(even) {
+#T_c1178 tr:nth-child(even) {
   background-color: rgba(139, 69, 19, 0.08);
 }
-#T_0ece9 tr:nth-child(odd) {
+#T_c1178 tr:nth-child(odd) {
   background: #FFF;
 }
-#T_0ece9 tr {
+#T_c1178 tr {
   font-size: 10px;
 }
-#T_0ece9 th {
+#T_c1178 th {
   background-color: #FFF;
   font-size: 12px;
   text-align: left;
   width: auto;
   word-break: keep-all;
 }
-#T_0ece9 td {
+#T_c1178 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
 }
-#T_0ece9 caption {
+#T_c1178 caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_0ece9 caption {
+#T_c1178 caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_0ece9 td:nth-child(1) {
+#T_c1178 td:nth-child(1) {
   text-align: left;
 }
 </style>
-<table id="T_0ece9">
+<table id="T_c1178">
   <thead>
     <tr>
-      <th id="T_0ece9_level0_col0" class="col_heading level0 col0" >Object</th>
-      <th id="T_0ece9_level0_col1" class="col_heading level0 col1" >Quantity</th>
-      <th id="T_0ece9_level0_col2" class="col_heading level0 col2" >pcs/m</th>
-      <th id="T_0ece9_level0_col3" class="col_heading level0 col3" >% of total</th>
-      <th id="T_0ece9_level0_col4" class="col_heading level0 col4" >Fail rate</th>
+      <th id="T_c1178_level0_col0" class="col_heading level0 col0" >Object</th>
+      <th id="T_c1178_level0_col1" class="col_heading level0 col1" >Quantity</th>
+      <th id="T_c1178_level0_col2" class="col_heading level0 col2" >pcs/m</th>
+      <th id="T_c1178_level0_col3" class="col_heading level0 col3" >% of total</th>
+      <th id="T_c1178_level0_col4" class="col_heading level0 col4" >Fail rate</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <td id="T_0ece9_row0_col0" class="data row0 col0" >Fragmented plastics</td>
-      <td id="T_0ece9_row0_col1" class="data row0 col1" >6'735</td>
-      <td id="T_0ece9_row0_col2" class="data row0 col2" >0,94</td>
-      <td id="T_0ece9_row0_col3" class="data row0 col3" >0,16</td>
-      <td id="T_0ece9_row0_col4" class="data row0 col4" >0,93</td>
+      <td id="T_c1178_row0_col0" class="data row0 col0" >Fragmented plastics</td>
+      <td id="T_c1178_row0_col1" class="data row0 col1" >6'735</td>
+      <td id="T_c1178_row0_col2" class="data row0 col2" >0,94</td>
+      <td id="T_c1178_row0_col3" class="data row0 col3" >0,16</td>
+      <td id="T_c1178_row0_col4" class="data row0 col4" >0,93</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row1_col0" class="data row1 col0" >Cigarette filters</td>
-      <td id="T_0ece9_row1_col1" class="data row1 col1" >6'081</td>
-      <td id="T_0ece9_row1_col2" class="data row1 col2" >0,66</td>
-      <td id="T_0ece9_row1_col3" class="data row1 col3" >0,14</td>
-      <td id="T_0ece9_row1_col4" class="data row1 col4" >0,91</td>
+      <td id="T_c1178_row1_col0" class="data row1 col0" >Cigarette filters</td>
+      <td id="T_c1178_row1_col1" class="data row1 col1" >6'081</td>
+      <td id="T_c1178_row1_col2" class="data row1 col2" >0,66</td>
+      <td id="T_c1178_row1_col3" class="data row1 col3" >0,14</td>
+      <td id="T_c1178_row1_col4" class="data row1 col4" >0,91</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row2_col0" class="data row2 col0" >Expanded polystyrene</td>
-      <td id="T_0ece9_row2_col1" class="data row2 col1" >4'302</td>
-      <td id="T_0ece9_row2_col2" class="data row2 col2" >0,63</td>
-      <td id="T_0ece9_row2_col3" class="data row2 col3" >0,10</td>
-      <td id="T_0ece9_row2_col4" class="data row2 col4" >0,75</td>
+      <td id="T_c1178_row2_col0" class="data row2 col0" >Expanded polystyrene</td>
+      <td id="T_c1178_row2_col1" class="data row2 col1" >4'302</td>
+      <td id="T_c1178_row2_col2" class="data row2 col2" >0,63</td>
+      <td id="T_c1178_row2_col3" class="data row2 col3" >0,10</td>
+      <td id="T_c1178_row2_col4" class="data row2 col4" >0,75</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row3_col0" class="data row3 col0" >Food wrappers; candy, snacks</td>
-      <td id="T_0ece9_row3_col1" class="data row3 col1" >2'789</td>
-      <td id="T_0ece9_row3_col2" class="data row3 col2" >0,34</td>
-      <td id="T_0ece9_row3_col3" class="data row3 col3" >0,06</td>
-      <td id="T_0ece9_row3_col4" class="data row3 col4" >0,91</td>
+      <td id="T_c1178_row3_col0" class="data row3 col0" >Food wrappers; candy, snacks</td>
+      <td id="T_c1178_row3_col1" class="data row3 col1" >2'789</td>
+      <td id="T_c1178_row3_col2" class="data row3 col2" >0,34</td>
+      <td id="T_c1178_row3_col3" class="data row3 col3" >0,06</td>
+      <td id="T_c1178_row3_col4" class="data row3 col4" >0,91</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row4_col0" class="data row4 col0" >Industrial sheeting</td>
-      <td id="T_0ece9_row4_col1" class="data row4 col1" >1'882</td>
-      <td id="T_0ece9_row4_col2" class="data row4 col2" >0,26</td>
-      <td id="T_0ece9_row4_col3" class="data row4 col3" >0,04</td>
-      <td id="T_0ece9_row4_col4" class="data row4 col4" >0,74</td>
+      <td id="T_c1178_row4_col0" class="data row4 col0" >Industrial sheeting</td>
+      <td id="T_c1178_row4_col1" class="data row4 col1" >1'882</td>
+      <td id="T_c1178_row4_col2" class="data row4 col2" >0,26</td>
+      <td id="T_c1178_row4_col3" class="data row4 col3" >0,04</td>
+      <td id="T_c1178_row4_col4" class="data row4 col4" >0,74</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row5_col0" class="data row5 col0" >Industrial pellets (nurdles)</td>
-      <td id="T_0ece9_row5_col1" class="data row5 col1" >1'705</td>
-      <td id="T_0ece9_row5_col2" class="data row5 col2" >0,21</td>
-      <td id="T_0ece9_row5_col3" class="data row5 col3" >0,04</td>
-      <td id="T_0ece9_row5_col4" class="data row5 col4" >0,40</td>
+      <td id="T_c1178_row5_col0" class="data row5 col0" >Industrial pellets (nurdles)</td>
+      <td id="T_c1178_row5_col1" class="data row5 col1" >1'705</td>
+      <td id="T_c1178_row5_col2" class="data row5 col2" >0,21</td>
+      <td id="T_c1178_row5_col3" class="data row5 col3" >0,04</td>
+      <td id="T_c1178_row5_col4" class="data row5 col4" >0,40</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row6_col0" class="data row6 col0" >plastic caps, lid rings: G21, G22, G23, G24</td>
-      <td id="T_0ece9_row6_col1" class="data row6 col1" >1'677</td>
-      <td id="T_0ece9_row6_col2" class="data row6 col2" >0,21</td>
-      <td id="T_0ece9_row6_col3" class="data row6 col3" >0,04</td>
-      <td id="T_0ece9_row6_col4" class="data row6 col4" >0,78</td>
+      <td id="T_c1178_row6_col0" class="data row6 col0" >plastic caps, lid rings: G21, G22, G23, G24</td>
+      <td id="T_c1178_row6_col1" class="data row6 col1" >1'677</td>
+      <td id="T_c1178_row6_col2" class="data row6 col2" >0,21</td>
+      <td id="T_c1178_row6_col3" class="data row6 col3" >0,04</td>
+      <td id="T_c1178_row6_col4" class="data row6 col4" >0,78</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row7_col0" class="data row7 col0" >Foam packaging/insulation/polyurethane</td>
-      <td id="T_0ece9_row7_col1" class="data row7 col1" >1'422</td>
-      <td id="T_0ece9_row7_col2" class="data row7 col2" >0,10</td>
-      <td id="T_0ece9_row7_col3" class="data row7 col3" >0,03</td>
-      <td id="T_0ece9_row7_col4" class="data row7 col4" >0,98</td>
+      <td id="T_c1178_row7_col0" class="data row7 col0" >Foam packaging/insulation/polyurethane</td>
+      <td id="T_c1178_row7_col1" class="data row7 col1" >1'422</td>
+      <td id="T_c1178_row7_col2" class="data row7 col2" >0,10</td>
+      <td id="T_c1178_row7_col3" class="data row7 col3" >0,03</td>
+      <td id="T_c1178_row7_col4" class="data row7 col4" >0,98</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row8_col0" class="data row8 col0" >Glass drink bottles, pieces</td>
-      <td id="T_0ece9_row8_col1" class="data row8 col1" >1'400</td>
-      <td id="T_0ece9_row8_col2" class="data row8 col2" >0,20</td>
-      <td id="T_0ece9_row8_col3" class="data row8 col3" >0,03</td>
-      <td id="T_0ece9_row8_col4" class="data row8 col4" >0,69</td>
+      <td id="T_c1178_row8_col0" class="data row8 col0" >Glass drink bottles, pieces</td>
+      <td id="T_c1178_row8_col1" class="data row8 col1" >1'400</td>
+      <td id="T_c1178_row8_col2" class="data row8 col2" >0,20</td>
+      <td id="T_c1178_row8_col3" class="data row8 col3" >0,03</td>
+      <td id="T_c1178_row8_col4" class="data row8 col4" >0,69</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row9_col0" class="data row9 col0" >Cotton bud/swab sticks</td>
-      <td id="T_0ece9_row9_col1" class="data row9 col1" >1'379</td>
-      <td id="T_0ece9_row9_col2" class="data row9 col2" >0,18</td>
-      <td id="T_0ece9_row9_col3" class="data row9 col3" >0,03</td>
-      <td id="T_0ece9_row9_col4" class="data row9 col4" >0,64</td>
+      <td id="T_c1178_row9_col0" class="data row9 col0" >Cotton bud/swab sticks</td>
+      <td id="T_c1178_row9_col1" class="data row9 col1" >1'379</td>
+      <td id="T_c1178_row9_col2" class="data row9 col2" >0,18</td>
+      <td id="T_c1178_row9_col3" class="data row9 col3" >0,03</td>
+      <td id="T_c1178_row9_col4" class="data row9 col4" >0,64</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row10_col0" class="data row10 col0" >Plastic construction waste</td>
-      <td id="T_0ece9_row10_col1" class="data row10 col1" >804</td>
-      <td id="T_0ece9_row10_col2" class="data row10 col2" >0,10</td>
-      <td id="T_0ece9_row10_col3" class="data row10 col3" >0,02</td>
-      <td id="T_0ece9_row10_col4" class="data row10 col4" >0,59</td>
+      <td id="T_c1178_row10_col0" class="data row10 col0" >Plastic construction waste</td>
+      <td id="T_c1178_row10_col1" class="data row10 col1" >804</td>
+      <td id="T_c1178_row10_col2" class="data row10 col2" >0,10</td>
+      <td id="T_c1178_row10_col3" class="data row10 col3" >0,02</td>
+      <td id="T_c1178_row10_col4" class="data row10 col4" >0,59</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row11_col0" class="data row11 col0" >Tobacco; plastic packaging, containers</td>
-      <td id="T_0ece9_row11_col1" class="data row11 col1" >522</td>
-      <td id="T_0ece9_row11_col2" class="data row11 col2" >0,07</td>
-      <td id="T_0ece9_row11_col3" class="data row11 col3" >0,01</td>
-      <td id="T_0ece9_row11_col4" class="data row11 col4" >0,56</td>
+      <td id="T_c1178_row11_col0" class="data row11 col0" >Tobacco; plastic packaging, containers</td>
+      <td id="T_c1178_row11_col1" class="data row11 col1" >522</td>
+      <td id="T_c1178_row11_col2" class="data row11 col2" >0,07</td>
+      <td id="T_c1178_row11_col3" class="data row11 col3" >0,01</td>
+      <td id="T_c1178_row11_col4" class="data row11 col4" >0,56</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row12_col0" class="data row12 col0" >Metal bottle caps, lids & pull tabs from cans</td>
-      <td id="T_0ece9_row12_col1" class="data row12 col1" >488</td>
-      <td id="T_0ece9_row12_col2" class="data row12 col2" >0,05</td>
-      <td id="T_0ece9_row12_col3" class="data row12 col3" >0,01</td>
-      <td id="T_0ece9_row12_col4" class="data row12 col4" >0,60</td>
+      <td id="T_c1178_row12_col0" class="data row12 col0" >Metal bottle caps, lids & pull tabs from cans</td>
+      <td id="T_c1178_row12_col1" class="data row12 col1" >488</td>
+      <td id="T_c1178_row12_col2" class="data row12 col2" >0,05</td>
+      <td id="T_c1178_row12_col3" class="data row12 col3" >0,01</td>
+      <td id="T_c1178_row12_col4" class="data row12 col4" >0,60</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row13_col0" class="data row13 col0" >Straws and stirrers</td>
-      <td id="T_0ece9_row13_col1" class="data row13 col1" >374</td>
-      <td id="T_0ece9_row13_col2" class="data row13 col2" >0,05</td>
-      <td id="T_0ece9_row13_col3" class="data row13 col3" >0,01</td>
-      <td id="T_0ece9_row13_col4" class="data row13 col4" >0,54</td>
+      <td id="T_c1178_row13_col0" class="data row13 col0" >Straws and stirrers</td>
+      <td id="T_c1178_row13_col1" class="data row13 col1" >374</td>
+      <td id="T_c1178_row13_col2" class="data row13 col2" >0,05</td>
+      <td id="T_c1178_row13_col3" class="data row13 col3" >0,01</td>
+      <td id="T_c1178_row13_col4" class="data row13 col4" >0,54</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row14_col0" class="data row14 col0" >Foil wrappers, aluminum foil</td>
-      <td id="T_0ece9_row14_col1" class="data row14 col1" >371</td>
-      <td id="T_0ece9_row14_col2" class="data row14 col2" >0,04</td>
-      <td id="T_0ece9_row14_col3" class="data row14 col3" >0,01</td>
-      <td id="T_0ece9_row14_col4" class="data row14 col4" >0,53</td>
+      <td id="T_c1178_row14_col0" class="data row14 col0" >Foil wrappers, aluminum foil</td>
+      <td id="T_c1178_row14_col1" class="data row14 col1" >371</td>
+      <td id="T_c1178_row14_col2" class="data row14 col2" >0,04</td>
+      <td id="T_c1178_row14_col3" class="data row14 col3" >0,01</td>
+      <td id="T_c1178_row14_col4" class="data row14 col4" >0,53</td>
     </tr>
     <tr>
-      <td id="T_0ece9_row15_col0" class="data row15 col0" >Lollypop sticks</td>
-      <td id="T_0ece9_row15_col1" class="data row15 col1" >354</td>
-      <td id="T_0ece9_row15_col2" class="data row15 col2" >0,04</td>
-      <td id="T_0ece9_row15_col3" class="data row15 col3" >0,01</td>
-      <td id="T_0ece9_row15_col4" class="data row15 col4" >0,52</td>
+      <td id="T_c1178_row15_col0" class="data row15 col0" >Lollypop sticks</td>
+      <td id="T_c1178_row15_col1" class="data row15 col1" >354</td>
+      <td id="T_c1178_row15_col2" class="data row15 col2" >0,04</td>
+      <td id="T_c1178_row15_col3" class="data row15 col3" >0,01</td>
+      <td id="T_c1178_row15_col4" class="data row15 col4" >0,52</td>
     </tr>
   </tbody>
 </table>
@@ -1279,124 +1279,124 @@ <h2>Most common objects 2020 - 2021<a class="headerlink" href="#most-common-obje
 <div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-none docutils">
 <div class="sd-card-body docutils">
 <div class="output text_html"><style type="text/css">
-#T_0a3c6 tr:nth-child(even) {
+#T_22ca4 tr:nth-child(even) {
   background-color: rgba(139, 69, 19, 0.08);
 }
-#T_0a3c6 tr:nth-child(odd) {
+#T_22ca4 tr:nth-child(odd) {
   background: #FFF;
 }
-#T_0a3c6 tr {
+#T_22ca4 tr {
   font-size: 10px;
 }
-#T_0a3c6 th {
+#T_22ca4 th {
   background-color: #FFF;
   font-size: 12px;
   text-align: left;
   width: auto;
   word-break: keep-all;
 }
-#T_0a3c6 td {
+#T_22ca4 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
 }
-#T_0a3c6 caption {
+#T_22ca4 caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_0a3c6 caption {
+#T_22ca4 caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_0a3c6 td:nth-child(1) {
+#T_22ca4 td:nth-child(1) {
   text-align: left;
 }
 </style>
-<table id="T_0a3c6">
+<table id="T_22ca4">
   <thead>
     <tr>
-      <th id="T_0a3c6_level0_col0" class="col_heading level0 col0" >Object</th>
-      <th id="T_0a3c6_level0_col1" class="col_heading level0 col1" >Quantity</th>
-      <th id="T_0a3c6_level0_col2" class="col_heading level0 col2" >pcs/m</th>
-      <th id="T_0a3c6_level0_col3" class="col_heading level0 col3" >% of total</th>
-      <th id="T_0a3c6_level0_col4" class="col_heading level0 col4" >Fail rate</th>
+      <th id="T_22ca4_level0_col0" class="col_heading level0 col0" >Object</th>
+      <th id="T_22ca4_level0_col1" class="col_heading level0 col1" >Quantity</th>
+      <th id="T_22ca4_level0_col2" class="col_heading level0 col2" >pcs/m</th>
+      <th id="T_22ca4_level0_col3" class="col_heading level0 col3" >% of total</th>
+      <th id="T_22ca4_level0_col4" class="col_heading level0 col4" >Fail rate</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <td id="T_0a3c6_row0_col0" class="data row0 col0" >Diapers - wipes</td>
-      <td id="T_0a3c6_row0_col1" class="data row0 col1" >299</td>
-      <td id="T_0a3c6_row0_col2" class="data row0 col2" >0,21</td>
-      <td id="T_0a3c6_row0_col3" class="data row0 col3" >0,16</td>
-      <td id="T_0a3c6_row0_col4" class="data row0 col4" >0,30</td>
+      <td id="T_22ca4_row0_col0" class="data row0 col0" >Diapers - wipes</td>
+      <td id="T_22ca4_row0_col1" class="data row0 col1" >299</td>
+      <td id="T_22ca4_row0_col2" class="data row0 col2" >0,21</td>
+      <td id="T_22ca4_row0_col3" class="data row0 col3" >0,16</td>
+      <td id="T_22ca4_row0_col4" class="data row0 col4" >0,30</td>
     </tr>
     <tr>
-      <td id="T_0a3c6_row1_col0" class="data row1 col0" >Cigarette filters</td>
-      <td id="T_0a3c6_row1_col1" class="data row1 col1" >225</td>
-      <td id="T_0a3c6_row1_col2" class="data row1 col2" >0,21</td>
-      <td id="T_0a3c6_row1_col3" class="data row1 col3" >0,12</td>
-      <td id="T_0a3c6_row1_col4" class="data row1 col4" >0,64</td>
+      <td id="T_22ca4_row1_col0" class="data row1 col0" >Cigarette filters</td>
+      <td id="T_22ca4_row1_col1" class="data row1 col1" >225</td>
+      <td id="T_22ca4_row1_col2" class="data row1 col2" >0,21</td>
+      <td id="T_22ca4_row1_col3" class="data row1 col3" >0,12</td>
+      <td id="T_22ca4_row1_col4" class="data row1 col4" >0,64</td>
     </tr>
     <tr>
-      <td id="T_0a3c6_row2_col0" class="data row2 col0" >Industrial sheeting</td>
-      <td id="T_0a3c6_row2_col1" class="data row2 col1" >149</td>
-      <td id="T_0a3c6_row2_col2" class="data row2 col2" >0,09</td>
-      <td id="T_0a3c6_row2_col3" class="data row2 col3" >0,08</td>
-      <td id="T_0a3c6_row2_col4" class="data row2 col4" >0,36</td>
+      <td id="T_22ca4_row2_col0" class="data row2 col0" >Industrial sheeting</td>
+      <td id="T_22ca4_row2_col1" class="data row2 col1" >149</td>
+      <td id="T_22ca4_row2_col2" class="data row2 col2" >0,09</td>
+      <td id="T_22ca4_row2_col3" class="data row2 col3" >0,08</td>
+      <td id="T_22ca4_row2_col4" class="data row2 col4" >0,36</td>
     </tr>
     <tr>
-      <td id="T_0a3c6_row3_col0" class="data row3 col0" >Glass drink bottles, pieces</td>
-      <td id="T_0a3c6_row3_col1" class="data row3 col1" >102</td>
-      <td id="T_0a3c6_row3_col2" class="data row3 col2" >0,11</td>
-      <td id="T_0a3c6_row3_col3" class="data row3 col3" >0,06</td>
-      <td id="T_0a3c6_row3_col4" class="data row3 col4" >0,36</td>
+      <td id="T_22ca4_row3_col0" class="data row3 col0" >Glass drink bottles, pieces</td>
+      <td id="T_22ca4_row3_col1" class="data row3 col1" >102</td>
+      <td id="T_22ca4_row3_col2" class="data row3 col2" >0,11</td>
+      <td id="T_22ca4_row3_col3" class="data row3 col3" >0,06</td>
+      <td id="T_22ca4_row3_col4" class="data row3 col4" >0,36</td>
     </tr>
     <tr>
-      <td id="T_0a3c6_row4_col0" class="data row4 col0" >Food wrappers; candy, snacks</td>
-      <td id="T_0a3c6_row4_col1" class="data row4 col1" >101</td>
-      <td id="T_0a3c6_row4_col2" class="data row4 col2" >0,11</td>
-      <td id="T_0a3c6_row4_col3" class="data row4 col3" >0,05</td>
-      <td id="T_0a3c6_row4_col4" class="data row4 col4" >0,67</td>
+      <td id="T_22ca4_row4_col0" class="data row4 col0" >Food wrappers; candy, snacks</td>
+      <td id="T_22ca4_row4_col1" class="data row4 col1" >101</td>
+      <td id="T_22ca4_row4_col2" class="data row4 col2" >0,11</td>
+      <td id="T_22ca4_row4_col3" class="data row4 col3" >0,05</td>
+      <td id="T_22ca4_row4_col4" class="data row4 col4" >0,67</td>
     </tr>
     <tr>
-      <td id="T_0a3c6_row5_col0" class="data row5 col0" >Packaging films nonfood or unknown</td>
-      <td id="T_0a3c6_row5_col1" class="data row5 col1" >68</td>
-      <td id="T_0a3c6_row5_col2" class="data row5 col2" >0,09</td>
-      <td id="T_0a3c6_row5_col3" class="data row5 col3" >0,04</td>
-      <td id="T_0a3c6_row5_col4" class="data row5 col4" >0,27</td>
+      <td id="T_22ca4_row5_col0" class="data row5 col0" >Packaging films nonfood or unknown</td>
+      <td id="T_22ca4_row5_col1" class="data row5 col1" >68</td>
+      <td id="T_22ca4_row5_col2" class="data row5 col2" >0,09</td>
+      <td id="T_22ca4_row5_col3" class="data row5 col3" >0,04</td>
+      <td id="T_22ca4_row5_col4" class="data row5 col4" >0,27</td>
     </tr>
     <tr>
-      <td id="T_0a3c6_row6_col0" class="data row6 col0" >Metal bottle caps, lids & pull tabs from cans</td>
-      <td id="T_0a3c6_row6_col1" class="data row6 col1" >63</td>
-      <td id="T_0a3c6_row6_col2" class="data row6 col2" >0,08</td>
-      <td id="T_0a3c6_row6_col3" class="data row6 col3" >0,03</td>
-      <td id="T_0a3c6_row6_col4" class="data row6 col4" >0,30</td>
+      <td id="T_22ca4_row6_col0" class="data row6 col0" >Metal bottle caps, lids & pull tabs from cans</td>
+      <td id="T_22ca4_row6_col1" class="data row6 col1" >63</td>
+      <td id="T_22ca4_row6_col2" class="data row6 col2" >0,08</td>
+      <td id="T_22ca4_row6_col3" class="data row6 col3" >0,03</td>
+      <td id="T_22ca4_row6_col4" class="data row6 col4" >0,30</td>
     </tr>
     <tr>
-      <td id="T_0a3c6_row7_col0" class="data row7 col0" >Fragmented plastics</td>
-      <td id="T_0a3c6_row7_col1" class="data row7 col1" >61</td>
-      <td id="T_0a3c6_row7_col2" class="data row7 col2" >0,05</td>
-      <td id="T_0a3c6_row7_col3" class="data row7 col3" >0,03</td>
-      <td id="T_0a3c6_row7_col4" class="data row7 col4" >0,52</td>
+      <td id="T_22ca4_row7_col0" class="data row7 col0" >Fragmented plastics</td>
+      <td id="T_22ca4_row7_col1" class="data row7 col1" >61</td>
+      <td id="T_22ca4_row7_col2" class="data row7 col2" >0,05</td>
+      <td id="T_22ca4_row7_col3" class="data row7 col3" >0,03</td>
+      <td id="T_22ca4_row7_col4" class="data row7 col4" >0,52</td>
     </tr>
     <tr>
-      <td id="T_0a3c6_row8_col0" class="data row8 col0" >Bags; plastic shopping/carrier/grocery and pieces</td>
-      <td id="T_0a3c6_row8_col1" class="data row8 col1" >55</td>
-      <td id="T_0a3c6_row8_col2" class="data row8 col2" >0,05</td>
-      <td id="T_0a3c6_row8_col3" class="data row8 col3" >0,03</td>
-      <td id="T_0a3c6_row8_col4" class="data row8 col4" >0,24</td>
+      <td id="T_22ca4_row8_col0" class="data row8 col0" >Bags; plastic shopping/carrier/grocery and pieces</td>
+      <td id="T_22ca4_row8_col1" class="data row8 col1" >55</td>
+      <td id="T_22ca4_row8_col2" class="data row8 col2" >0,05</td>
+      <td id="T_22ca4_row8_col3" class="data row8 col3" >0,03</td>
+      <td id="T_22ca4_row8_col4" class="data row8 col4" >0,24</td>
     </tr>
     <tr>
-      <td id="T_0a3c6_row9_col0" class="data row9 col0" >Construction material; bricks, pipes, cement</td>
-      <td id="T_0a3c6_row9_col1" class="data row9 col1" >42</td>
-      <td id="T_0a3c6_row9_col2" class="data row9 col2" >0,02</td>
-      <td id="T_0a3c6_row9_col3" class="data row9 col3" >0,02</td>
-      <td id="T_0a3c6_row9_col4" class="data row9 col4" >0,12</td>
+      <td id="T_22ca4_row9_col0" class="data row9 col0" >Construction material; bricks, pipes, cement</td>
+      <td id="T_22ca4_row9_col1" class="data row9 col1" >42</td>
+      <td id="T_22ca4_row9_col2" class="data row9 col2" >0,02</td>
+      <td id="T_22ca4_row9_col3" class="data row9 col3" >0,02</td>
+      <td id="T_22ca4_row9_col4" class="data row9 col4" >0,12</td>
     </tr>
   </tbody>
 </table>
@@ -1464,88 +1464,88 @@ <h3>Rate per feature 2020 - 2021<a class="headerlink" href="#rate-per-feature-20
 All lakes and rivers</label><div class="sd-tab-content docutils">
 <p><strong>Land use</strong></p>
 <div class="output text_html"><style type="text/css">
-#T_1c1b9 caption {
+#T_87bf0 caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_1c1b9 caption {
+#T_87bf0 caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_1c1b9 td {
+#T_87bf0 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
   padding: 4px;
 }
-#T_1c1b9_row0_col0, #T_1c1b9_row1_col0, #T_1c1b9_row2_col1, #T_1c1b9_row3_col2, #T_1c1b9_row4_col1, #T_1c1b9_row5_col1 {
+#T_87bf0_row0_col0, #T_87bf0_row1_col0, #T_87bf0_row2_col1, #T_87bf0_row3_col2, #T_87bf0_row4_col1, #T_87bf0_row5_col1 {
   background-color: #FAE8E8;
 }
 </style>
-<table id="T_1c1b9">
+<table id="T_87bf0">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_1c1b9_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
-      <th id="T_1c1b9_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
-      <th id="T_1c1b9_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
-      <th id="T_1c1b9_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
-      <th id="T_1c1b9_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
+      <th id="T_87bf0_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
+      <th id="T_87bf0_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
+      <th id="T_87bf0_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
+      <th id="T_87bf0_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
+      <th id="T_87bf0_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_1c1b9_level0_row0" class="row_heading level0 row0" >Orchards</th>
-      <td id="T_1c1b9_row0_col0" class="data row0 col0" >5.03</td>
-      <td id="T_1c1b9_row0_col1" class="data row0 col1" >0.09</td>
-      <td id="T_1c1b9_row0_col2" class="data row0 col2" >0.00</td>
-      <td id="T_1c1b9_row0_col3" class="data row0 col3" >0.00</td>
-      <td id="T_1c1b9_row0_col4" class="data row0 col4" >0.00</td>
+      <th id="T_87bf0_level0_row0" class="row_heading level0 row0" >Orchards</th>
+      <td id="T_87bf0_row0_col0" class="data row0 col0" >5.03</td>
+      <td id="T_87bf0_row0_col1" class="data row0 col1" >0.09</td>
+      <td id="T_87bf0_row0_col2" class="data row0 col2" >0.00</td>
+      <td id="T_87bf0_row0_col3" class="data row0 col3" >0.00</td>
+      <td id="T_87bf0_row0_col4" class="data row0 col4" >0.00</td>
     </tr>
     <tr>
-      <th id="T_1c1b9_level0_row1" class="row_heading level0 row1" >Vineyards</th>
-      <td id="T_1c1b9_row1_col0" class="data row1 col0" >5.04</td>
-      <td id="T_1c1b9_row1_col1" class="data row1 col1" >0.56</td>
-      <td id="T_1c1b9_row1_col2" class="data row1 col2" >5.01</td>
-      <td id="T_1c1b9_row1_col3" class="data row1 col3" >0.00</td>
-      <td id="T_1c1b9_row1_col4" class="data row1 col4" >0.00</td>
+      <th id="T_87bf0_level0_row1" class="row_heading level0 row1" >Vineyards</th>
+      <td id="T_87bf0_row1_col0" class="data row1 col0" >5.04</td>
+      <td id="T_87bf0_row1_col1" class="data row1 col1" >0.56</td>
+      <td id="T_87bf0_row1_col2" class="data row1 col2" >5.01</td>
+      <td id="T_87bf0_row1_col3" class="data row1 col3" >0.00</td>
+      <td id="T_87bf0_row1_col4" class="data row1 col4" >0.00</td>
     </tr>
     <tr>
-      <th id="T_1c1b9_level0_row2" class="row_heading level0 row2" >Buildings</th>
-      <td id="T_1c1b9_row2_col0" class="data row2 col0" >2.62</td>
-      <td id="T_1c1b9_row2_col1" class="data row2 col1" >7.46</td>
-      <td id="T_1c1b9_row2_col2" class="data row2 col2" >3.80</td>
-      <td id="T_1c1b9_row2_col3" class="data row2 col3" >2.92</td>
-      <td id="T_1c1b9_row2_col4" class="data row2 col4" >6.27</td>
+      <th id="T_87bf0_level0_row2" class="row_heading level0 row2" >Buildings</th>
+      <td id="T_87bf0_row2_col0" class="data row2 col0" >2.62</td>
+      <td id="T_87bf0_row2_col1" class="data row2 col1" >7.46</td>
+      <td id="T_87bf0_row2_col2" class="data row2 col2" >3.80</td>
+      <td id="T_87bf0_row2_col3" class="data row2 col3" >2.92</td>
+      <td id="T_87bf0_row2_col4" class="data row2 col4" >6.27</td>
     </tr>
     <tr>
-      <th id="T_1c1b9_level0_row3" class="row_heading level0 row3" >Forest</th>
-      <td id="T_1c1b9_row3_col0" class="data row3 col0" >4.86</td>
-      <td id="T_1c1b9_row3_col1" class="data row3 col1" >2.73</td>
-      <td id="T_1c1b9_row3_col2" class="data row3 col2" >13.13</td>
-      <td id="T_1c1b9_row3_col3" class="data row3 col3" >0.00</td>
-      <td id="T_1c1b9_row3_col4" class="data row3 col4" >0.00</td>
+      <th id="T_87bf0_level0_row3" class="row_heading level0 row3" >Forest</th>
+      <td id="T_87bf0_row3_col0" class="data row3 col0" >4.86</td>
+      <td id="T_87bf0_row3_col1" class="data row3 col1" >2.73</td>
+      <td id="T_87bf0_row3_col2" class="data row3 col2" >13.13</td>
+      <td id="T_87bf0_row3_col3" class="data row3 col3" >0.00</td>
+      <td id="T_87bf0_row3_col4" class="data row3 col4" >0.00</td>
     </tr>
     <tr>
-      <th id="T_1c1b9_level0_row4" class="row_heading level0 row4" >Undefined</th>
-      <td id="T_1c1b9_row4_col0" class="data row4 col0" >5.45</td>
-      <td id="T_1c1b9_row4_col1" class="data row4 col1" >6.22</td>
-      <td id="T_1c1b9_row4_col2" class="data row4 col2" >2.95</td>
-      <td id="T_1c1b9_row4_col3" class="data row4 col3" >1.84</td>
-      <td id="T_1c1b9_row4_col4" class="data row4 col4" >0.00</td>
+      <th id="T_87bf0_level0_row4" class="row_heading level0 row4" >Undefined</th>
+      <td id="T_87bf0_row4_col0" class="data row4 col0" >5.45</td>
+      <td id="T_87bf0_row4_col1" class="data row4 col1" >6.22</td>
+      <td id="T_87bf0_row4_col2" class="data row4 col2" >2.95</td>
+      <td id="T_87bf0_row4_col3" class="data row4 col3" >1.84</td>
+      <td id="T_87bf0_row4_col4" class="data row4 col4" >0.00</td>
     </tr>
     <tr>
-      <th id="T_1c1b9_level0_row5" class="row_heading level0 row5" >Public Services</th>
-      <td id="T_1c1b9_row5_col0" class="data row5 col0" >4.26</td>
-      <td id="T_1c1b9_row5_col1" class="data row5 col1" >14.60</td>
-      <td id="T_1c1b9_row5_col2" class="data row5 col2" >0.00</td>
-      <td id="T_1c1b9_row5_col3" class="data row5 col3" >0.00</td>
-      <td id="T_1c1b9_row5_col4" class="data row5 col4" >0.00</td>
+      <th id="T_87bf0_level0_row5" class="row_heading level0 row5" >Public Services</th>
+      <td id="T_87bf0_row5_col0" class="data row5 col0" >4.26</td>
+      <td id="T_87bf0_row5_col1" class="data row5 col1" >14.60</td>
+      <td id="T_87bf0_row5_col2" class="data row5 col2" >0.00</td>
+      <td id="T_87bf0_row5_col3" class="data row5 col3" >0.00</td>
+      <td id="T_87bf0_row5_col4" class="data row5 col4" >0.00</td>
     </tr>
   </tbody>
 </table>
@@ -1553,48 +1553,48 @@ <h3>Rate per feature 2020 - 2021<a class="headerlink" href="#rate-per-feature-20
 <p>The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area <span class="math notranslate nohighlight">\(\pi r²\)</span> and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.
 <br></br></p>
 <div class="output text_html"><style type="text/css">
-#T_a1596 caption {
+#T_6a0e0 caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_a1596 caption {
+#T_6a0e0 caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_a1596 td {
+#T_6a0e0 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
   padding: 4px;
 }
-#T_a1596_row0_col4 {
+#T_6a0e0_row0_col4 {
   background-color: #FAE8E8;
 }
 </style>
-<table id="T_a1596">
+<table id="T_6a0e0">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_a1596_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
-      <th id="T_a1596_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
-      <th id="T_a1596_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
-      <th id="T_a1596_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
-      <th id="T_a1596_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
+      <th id="T_6a0e0_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
+      <th id="T_6a0e0_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
+      <th id="T_6a0e0_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
+      <th id="T_6a0e0_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
+      <th id="T_6a0e0_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_a1596_level0_row0" class="row_heading level0 row0" >streets</th>
-      <td id="T_a1596_row0_col0" class="data row0 col0" >5.26</td>
-      <td id="T_a1596_row0_col1" class="data row0 col1" >4.33</td>
-      <td id="T_a1596_row0_col2" class="data row0 col2" >5.66</td>
-      <td id="T_a1596_row0_col3" class="data row0 col3" >4.12</td>
-      <td id="T_a1596_row0_col4" class="data row0 col4" >14.56</td>
+      <th id="T_6a0e0_level0_row0" class="row_heading level0 row0" >streets</th>
+      <td id="T_6a0e0_row0_col0" class="data row0 col0" >5.26</td>
+      <td id="T_6a0e0_row0_col1" class="data row0 col1" >4.33</td>
+      <td id="T_6a0e0_row0_col2" class="data row0 col2" >5.66</td>
+      <td id="T_6a0e0_row0_col3" class="data row0 col3" >4.12</td>
+      <td id="T_6a0e0_row0_col4" class="data row0 col4" >14.56</td>
     </tr>
   </tbody>
 </table>
@@ -1604,88 +1604,88 @@ <h3>Rate per feature 2020 - 2021<a class="headerlink" href="#rate-per-feature-20
 Lakes</label><div class="sd-tab-content docutils">
 <p><strong>Land use</strong></p>
 <div class="output text_html"><style type="text/css">
-#T_13be6 caption {
+#T_b413e caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_13be6 caption {
+#T_b413e caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_13be6 td {
+#T_b413e td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
   padding: 4px;
 }
-#T_13be6_row0_col0, #T_13be6_row1_col0, #T_13be6_row2_col1, #T_13be6_row3_col2, #T_13be6_row4_col1, #T_13be6_row5_col1 {
+#T_b413e_row0_col0, #T_b413e_row1_col0, #T_b413e_row2_col1, #T_b413e_row3_col2, #T_b413e_row4_col1, #T_b413e_row5_col1 {
   background-color: #FAE8E8;
 }
 </style>
-<table id="T_13be6">
+<table id="T_b413e">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_13be6_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
-      <th id="T_13be6_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
-      <th id="T_13be6_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
-      <th id="T_13be6_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
-      <th id="T_13be6_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
+      <th id="T_b413e_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
+      <th id="T_b413e_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
+      <th id="T_b413e_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
+      <th id="T_b413e_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
+      <th id="T_b413e_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_13be6_level0_row0" class="row_heading level0 row0" >Orchards</th>
-      <td id="T_13be6_row0_col0" class="data row0 col0" >5.50</td>
-      <td id="T_13be6_row0_col1" class="data row0 col1" >0.00</td>
-      <td id="T_13be6_row0_col2" class="data row0 col2" >0.00</td>
-      <td id="T_13be6_row0_col3" class="data row0 col3" >0.00</td>
-      <td id="T_13be6_row0_col4" class="data row0 col4" >0.00</td>
+      <th id="T_b413e_level0_row0" class="row_heading level0 row0" >Orchards</th>
+      <td id="T_b413e_row0_col0" class="data row0 col0" >5.50</td>
+      <td id="T_b413e_row0_col1" class="data row0 col1" >0.00</td>
+      <td id="T_b413e_row0_col2" class="data row0 col2" >0.00</td>
+      <td id="T_b413e_row0_col3" class="data row0 col3" >0.00</td>
+      <td id="T_b413e_row0_col4" class="data row0 col4" >0.00</td>
     </tr>
     <tr>
-      <th id="T_13be6_level0_row1" class="row_heading level0 row1" >Vineyards</th>
-      <td id="T_13be6_row1_col0" class="data row1 col0" >5.51</td>
-      <td id="T_13be6_row1_col1" class="data row1 col1" >0.00</td>
-      <td id="T_13be6_row1_col2" class="data row1 col2" >5.01</td>
-      <td id="T_13be6_row1_col3" class="data row1 col3" >0.00</td>
-      <td id="T_13be6_row1_col4" class="data row1 col4" >0.00</td>
+      <th id="T_b413e_level0_row1" class="row_heading level0 row1" >Vineyards</th>
+      <td id="T_b413e_row1_col0" class="data row1 col0" >5.51</td>
+      <td id="T_b413e_row1_col1" class="data row1 col1" >0.00</td>
+      <td id="T_b413e_row1_col2" class="data row1 col2" >5.01</td>
+      <td id="T_b413e_row1_col3" class="data row1 col3" >0.00</td>
+      <td id="T_b413e_row1_col4" class="data row1 col4" >0.00</td>
     </tr>
     <tr>
-      <th id="T_13be6_level0_row2" class="row_heading level0 row2" >Buildings</th>
-      <td id="T_13be6_row2_col0" class="data row2 col0" >2.94</td>
-      <td id="T_13be6_row2_col1" class="data row2 col1" >7.83</td>
-      <td id="T_13be6_row2_col2" class="data row2 col2" >5.12</td>
-      <td id="T_13be6_row2_col3" class="data row2 col3" >3.33</td>
-      <td id="T_13be6_row2_col4" class="data row2 col4" >6.39</td>
+      <th id="T_b413e_level0_row2" class="row_heading level0 row2" >Buildings</th>
+      <td id="T_b413e_row2_col0" class="data row2 col0" >2.94</td>
+      <td id="T_b413e_row2_col1" class="data row2 col1" >7.83</td>
+      <td id="T_b413e_row2_col2" class="data row2 col2" >5.12</td>
+      <td id="T_b413e_row2_col3" class="data row2 col3" >3.33</td>
+      <td id="T_b413e_row2_col4" class="data row2 col4" >6.39</td>
     </tr>
     <tr>
-      <th id="T_13be6_level0_row3" class="row_heading level0 row3" >Forest</th>
-      <td id="T_13be6_row3_col0" class="data row3 col0" >5.20</td>
-      <td id="T_13be6_row3_col1" class="data row3 col1" >3.13</td>
-      <td id="T_13be6_row3_col2" class="data row3 col2" >14.92</td>
-      <td id="T_13be6_row3_col3" class="data row3 col3" >0.00</td>
-      <td id="T_13be6_row3_col4" class="data row3 col4" >0.00</td>
+      <th id="T_b413e_level0_row3" class="row_heading level0 row3" >Forest</th>
+      <td id="T_b413e_row3_col0" class="data row3 col0" >5.20</td>
+      <td id="T_b413e_row3_col1" class="data row3 col1" >3.13</td>
+      <td id="T_b413e_row3_col2" class="data row3 col2" >14.92</td>
+      <td id="T_b413e_row3_col3" class="data row3 col3" >0.00</td>
+      <td id="T_b413e_row3_col4" class="data row3 col4" >0.00</td>
     </tr>
     <tr>
-      <th id="T_13be6_level0_row4" class="row_heading level0 row4" >Undefined</th>
-      <td id="T_13be6_row4_col0" class="data row4 col0" >5.88</td>
-      <td id="T_13be6_row4_col1" class="data row4 col1" >7.42</td>
-      <td id="T_13be6_row4_col2" class="data row4 col2" >3.00</td>
-      <td id="T_13be6_row4_col3" class="data row4 col3" >1.91</td>
-      <td id="T_13be6_row4_col4" class="data row4 col4" >0.00</td>
+      <th id="T_b413e_level0_row4" class="row_heading level0 row4" >Undefined</th>
+      <td id="T_b413e_row4_col0" class="data row4 col0" >5.88</td>
+      <td id="T_b413e_row4_col1" class="data row4 col1" >7.42</td>
+      <td id="T_b413e_row4_col2" class="data row4 col2" >3.00</td>
+      <td id="T_b413e_row4_col3" class="data row4 col3" >1.91</td>
+      <td id="T_b413e_row4_col4" class="data row4 col4" >0.00</td>
     </tr>
     <tr>
-      <th id="T_13be6_level0_row5" class="row_heading level0 row5" >Public Services</th>
-      <td id="T_13be6_row5_col0" class="data row5 col0" >4.64</td>
-      <td id="T_13be6_row5_col1" class="data row5 col1" >16.31</td>
-      <td id="T_13be6_row5_col2" class="data row5 col2" >0.00</td>
-      <td id="T_13be6_row5_col3" class="data row5 col3" >0.00</td>
-      <td id="T_13be6_row5_col4" class="data row5 col4" >0.00</td>
+      <th id="T_b413e_level0_row5" class="row_heading level0 row5" >Public Services</th>
+      <td id="T_b413e_row5_col0" class="data row5 col0" >4.64</td>
+      <td id="T_b413e_row5_col1" class="data row5 col1" >16.31</td>
+      <td id="T_b413e_row5_col2" class="data row5 col2" >0.00</td>
+      <td id="T_b413e_row5_col3" class="data row5 col3" >0.00</td>
+      <td id="T_b413e_row5_col4" class="data row5 col4" >0.00</td>
     </tr>
   </tbody>
 </table>
@@ -1693,48 +1693,48 @@ <h3>Rate per feature 2020 - 2021<a class="headerlink" href="#rate-per-feature-20
 <p>The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area <span class="math notranslate nohighlight">\(\pi r²\)</span> and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.
 <br></br></p>
 <div class="output text_html"><style type="text/css">
-#T_43d42 caption {
+#T_a1639 caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_43d42 caption {
+#T_a1639 caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_43d42 td {
+#T_a1639 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
   padding: 4px;
 }
-#T_43d42_row0_col4 {
+#T_a1639_row0_col4 {
   background-color: #FAE8E8;
 }
 </style>
-<table id="T_43d42">
+<table id="T_a1639">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_43d42_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
-      <th id="T_43d42_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
-      <th id="T_43d42_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
-      <th id="T_43d42_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
-      <th id="T_43d42_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
+      <th id="T_a1639_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
+      <th id="T_a1639_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
+      <th id="T_a1639_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
+      <th id="T_a1639_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
+      <th id="T_a1639_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_43d42_level0_row0" class="row_heading level0 row0" >streets</th>
-      <td id="T_43d42_row0_col0" class="data row0 col0" >5.33</td>
-      <td id="T_43d42_row0_col1" class="data row0 col1" >4.60</td>
-      <td id="T_43d42_row0_col2" class="data row0 col2" >7.68</td>
-      <td id="T_43d42_row0_col3" class="data row0 col3" >0</td>
-      <td id="T_43d42_row0_col4" class="data row0 col4" >21.80</td>
+      <th id="T_a1639_level0_row0" class="row_heading level0 row0" >streets</th>
+      <td id="T_a1639_row0_col0" class="data row0 col0" >5.33</td>
+      <td id="T_a1639_row0_col1" class="data row0 col1" >4.60</td>
+      <td id="T_a1639_row0_col2" class="data row0 col2" >7.68</td>
+      <td id="T_a1639_row0_col3" class="data row0 col3" >0</td>
+      <td id="T_a1639_row0_col4" class="data row0 col4" >21.80</td>
     </tr>
   </tbody>
 </table>
@@ -1744,88 +1744,88 @@ <h3>Rate per feature 2020 - 2021<a class="headerlink" href="#rate-per-feature-20
 Rivers</label><div class="sd-tab-content docutils">
 <p><strong>Land use</strong></p>
 <div class="output text_html"><style type="text/css">
-#T_28094 caption {
+#T_f4453 caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_28094 caption {
+#T_f4453 caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_28094 td {
+#T_f4453 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
   padding: 4px;
 }
-#T_28094_row0_col0, #T_28094_row1_col0, #T_28094_row2_col4, #T_28094_row3_col2, #T_28094_row4_col2, #T_28094_row5_col0 {
+#T_f4453_row0_col0, #T_f4453_row1_col0, #T_f4453_row2_col4, #T_f4453_row3_col2, #T_f4453_row4_col2, #T_f4453_row5_col0 {
   background-color: #FAE8E8;
 }
 </style>
-<table id="T_28094">
+<table id="T_f4453">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_28094_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
-      <th id="T_28094_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
-      <th id="T_28094_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
-      <th id="T_28094_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
-      <th id="T_28094_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
+      <th id="T_f4453_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
+      <th id="T_f4453_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
+      <th id="T_f4453_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
+      <th id="T_f4453_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
+      <th id="T_f4453_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_28094_level0_row0" class="row_heading level0 row0" >Orchards</th>
-      <td id="T_28094_row0_col0" class="data row0 col0" >1.62</td>
-      <td id="T_28094_row0_col1" class="data row0 col1" >0.09</td>
-      <td id="T_28094_row0_col2" class="data row0 col2" >0.00</td>
-      <td id="T_28094_row0_col3" class="data row0 col3" >0.00</td>
-      <td id="T_28094_row0_col4" class="data row0 col4" >0.00</td>
+      <th id="T_f4453_level0_row0" class="row_heading level0 row0" >Orchards</th>
+      <td id="T_f4453_row0_col0" class="data row0 col0" >1.62</td>
+      <td id="T_f4453_row0_col1" class="data row0 col1" >0.09</td>
+      <td id="T_f4453_row0_col2" class="data row0 col2" >0.00</td>
+      <td id="T_f4453_row0_col3" class="data row0 col3" >0.00</td>
+      <td id="T_f4453_row0_col4" class="data row0 col4" >0.00</td>
     </tr>
     <tr>
-      <th id="T_28094_level0_row1" class="row_heading level0 row1" >Vineyards</th>
-      <td id="T_28094_row1_col0" class="data row1 col0" >1.64</td>
-      <td id="T_28094_row1_col1" class="data row1 col1" >0.56</td>
-      <td id="T_28094_row1_col2" class="data row1 col2" >0.00</td>
-      <td id="T_28094_row1_col3" class="data row1 col3" >0.00</td>
-      <td id="T_28094_row1_col4" class="data row1 col4" >0.00</td>
+      <th id="T_f4453_level0_row1" class="row_heading level0 row1" >Vineyards</th>
+      <td id="T_f4453_row1_col0" class="data row1 col0" >1.64</td>
+      <td id="T_f4453_row1_col1" class="data row1 col1" >0.56</td>
+      <td id="T_f4453_row1_col2" class="data row1 col2" >0.00</td>
+      <td id="T_f4453_row1_col3" class="data row1 col3" >0.00</td>
+      <td id="T_f4453_row1_col4" class="data row1 col4" >0.00</td>
     </tr>
     <tr>
-      <th id="T_28094_level0_row2" class="row_heading level0 row2" >Buildings</th>
-      <td id="T_28094_row2_col0" class="data row2 col0" >1.50</td>
-      <td id="T_28094_row2_col1" class="data row2 col1" >1.93</td>
-      <td id="T_28094_row2_col2" class="data row2 col2" >1.17</td>
-      <td id="T_28094_row2_col3" class="data row2 col3" >0.43</td>
-      <td id="T_28094_row2_col4" class="data row2 col4" >4.06</td>
+      <th id="T_f4453_level0_row2" class="row_heading level0 row2" >Buildings</th>
+      <td id="T_f4453_row2_col0" class="data row2 col0" >1.50</td>
+      <td id="T_f4453_row2_col1" class="data row2 col1" >1.93</td>
+      <td id="T_f4453_row2_col2" class="data row2 col2" >1.17</td>
+      <td id="T_f4453_row2_col3" class="data row2 col3" >0.43</td>
+      <td id="T_f4453_row2_col4" class="data row2 col4" >4.06</td>
     </tr>
     <tr>
-      <th id="T_28094_level0_row3" class="row_heading level0 row3" >Forest</th>
-      <td id="T_28094_row3_col0" class="data row3 col0" >1.82</td>
-      <td id="T_28094_row3_col1" class="data row3 col1" >1.01</td>
-      <td id="T_28094_row3_col2" class="data row3 col2" >2.38</td>
-      <td id="T_28094_row3_col3" class="data row3 col3" >0.00</td>
-      <td id="T_28094_row3_col4" class="data row3 col4" >0.00</td>
+      <th id="T_f4453_level0_row3" class="row_heading level0 row3" >Forest</th>
+      <td id="T_f4453_row3_col0" class="data row3 col0" >1.82</td>
+      <td id="T_f4453_row3_col1" class="data row3 col1" >1.01</td>
+      <td id="T_f4453_row3_col2" class="data row3 col2" >2.38</td>
+      <td id="T_f4453_row3_col3" class="data row3 col3" >0.00</td>
+      <td id="T_f4453_row3_col4" class="data row3 col4" >0.00</td>
     </tr>
     <tr>
-      <th id="T_28094_level0_row4" class="row_heading level0 row4" >Undefined</th>
-      <td id="T_28094_row4_col0" class="data row4 col0" >1.64</td>
-      <td id="T_28094_row4_col1" class="data row4 col1" >1.19</td>
-      <td id="T_28094_row4_col2" class="data row4 col2" >2.46</td>
-      <td id="T_28094_row4_col3" class="data row4 col3" >1.50</td>
-      <td id="T_28094_row4_col4" class="data row4 col4" >0.00</td>
+      <th id="T_f4453_level0_row4" class="row_heading level0 row4" >Undefined</th>
+      <td id="T_f4453_row4_col0" class="data row4 col0" >1.64</td>
+      <td id="T_f4453_row4_col1" class="data row4 col1" >1.19</td>
+      <td id="T_f4453_row4_col2" class="data row4 col2" >2.46</td>
+      <td id="T_f4453_row4_col3" class="data row4 col3" >1.50</td>
+      <td id="T_f4453_row4_col4" class="data row4 col4" >0.00</td>
     </tr>
     <tr>
-      <th id="T_28094_level0_row5" class="row_heading level0 row5" >Public Services</th>
-      <td id="T_28094_row5_col0" class="data row5 col0" >1.67</td>
-      <td id="T_28094_row5_col1" class="data row5 col1" >0.07</td>
-      <td id="T_28094_row5_col2" class="data row5 col2" >0.00</td>
-      <td id="T_28094_row5_col3" class="data row5 col3" >0.00</td>
-      <td id="T_28094_row5_col4" class="data row5 col4" >0.00</td>
+      <th id="T_f4453_level0_row5" class="row_heading level0 row5" >Public Services</th>
+      <td id="T_f4453_row5_col0" class="data row5 col0" >1.67</td>
+      <td id="T_f4453_row5_col1" class="data row5 col1" >0.07</td>
+      <td id="T_f4453_row5_col2" class="data row5 col2" >0.00</td>
+      <td id="T_f4453_row5_col3" class="data row5 col3" >0.00</td>
+      <td id="T_f4453_row5_col4" class="data row5 col4" >0.00</td>
     </tr>
   </tbody>
 </table>
@@ -1833,48 +1833,48 @@ <h3>Rate per feature 2020 - 2021<a class="headerlink" href="#rate-per-feature-20
 <p>The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area <span class="math notranslate nohighlight">\(\pi r²\)</span> and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.
 <br></br></p>
 <div class="output text_html"><style type="text/css">
-#T_af839 caption {
+#T_dbbe5 caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_af839 caption {
+#T_dbbe5 caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_af839 td {
+#T_dbbe5 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
   padding: 4px;
 }
-#T_af839_row0_col3 {
+#T_dbbe5_row0_col3 {
   background-color: #FAE8E8;
 }
 </style>
-<table id="T_af839">
+<table id="T_dbbe5">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_af839_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
-      <th id="T_af839_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
-      <th id="T_af839_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
-      <th id="T_af839_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
-      <th id="T_af839_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
+      <th id="T_dbbe5_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
+      <th id="T_dbbe5_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
+      <th id="T_dbbe5_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
+      <th id="T_dbbe5_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
+      <th id="T_dbbe5_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_af839_level0_row0" class="row_heading level0 row0" >streets</th>
-      <td id="T_af839_row0_col0" class="data row0 col0" >0.22</td>
-      <td id="T_af839_row0_col1" class="data row0 col1" >1.36</td>
-      <td id="T_af839_row0_col2" class="data row0 col2" >1.27</td>
-      <td id="T_af839_row0_col3" class="data row0 col3" >4.12</td>
-      <td id="T_af839_row0_col4" class="data row0 col4" >0.08</td>
+      <th id="T_dbbe5_level0_row0" class="row_heading level0 row0" >streets</th>
+      <td id="T_dbbe5_row0_col0" class="data row0 col0" >0.22</td>
+      <td id="T_dbbe5_row0_col1" class="data row0 col1" >1.36</td>
+      <td id="T_dbbe5_row0_col2" class="data row0 col2" >1.27</td>
+      <td id="T_dbbe5_row0_col3" class="data row0 col3" >4.12</td>
+      <td id="T_dbbe5_row0_col4" class="data row0 col4" >0.08</td>
     </tr>
   </tbody>
 </table>
@@ -1889,88 +1889,88 @@ <h3>Sampling profile 2020 - 2021<a class="headerlink" href="#sampling-profile-20
 All lakes and rivers</label><div class="sd-tab-content docutils">
 <p><strong>Land use</strong></p>
 <div class="output text_html"><style type="text/css">
-#T_fc5d9 caption {
+#T_685fe caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_fc5d9 caption {
+#T_685fe caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_fc5d9 td {
+#T_685fe td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
   padding: 4px;
 }
-#T_fc5d9_row0_col0, #T_fc5d9_row1_col0, #T_fc5d9_row2_col4, #T_fc5d9_row3_col0, #T_fc5d9_row4_col0, #T_fc5d9_row5_col0 {
+#T_685fe_row0_col0, #T_685fe_row1_col0, #T_685fe_row2_col4, #T_685fe_row3_col0, #T_685fe_row4_col0, #T_685fe_row5_col0 {
   background-color: #FAE8E8;
 }
 </style>
-<table id="T_fc5d9">
+<table id="T_685fe">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_fc5d9_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
-      <th id="T_fc5d9_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
-      <th id="T_fc5d9_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
-      <th id="T_fc5d9_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
-      <th id="T_fc5d9_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
+      <th id="T_685fe_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
+      <th id="T_685fe_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
+      <th id="T_685fe_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
+      <th id="T_685fe_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
+      <th id="T_685fe_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_fc5d9_level0_row0" class="row_heading level0 row0" >Orchards</th>
-      <td id="T_fc5d9_row0_col0" class="data row0 col0" >100%</td>
-      <td id="T_fc5d9_row0_col1" class="data row0 col1" >0%</td>
-      <td id="T_fc5d9_row0_col2" class="data row0 col2" >0%</td>
-      <td id="T_fc5d9_row0_col3" class="data row0 col3" >0%</td>
-      <td id="T_fc5d9_row0_col4" class="data row0 col4" >0%</td>
+      <th id="T_685fe_level0_row0" class="row_heading level0 row0" >Orchards</th>
+      <td id="T_685fe_row0_col0" class="data row0 col0" >100%</td>
+      <td id="T_685fe_row0_col1" class="data row0 col1" >0%</td>
+      <td id="T_685fe_row0_col2" class="data row0 col2" >0%</td>
+      <td id="T_685fe_row0_col3" class="data row0 col3" >0%</td>
+      <td id="T_685fe_row0_col4" class="data row0 col4" >0%</td>
     </tr>
     <tr>
-      <th id="T_fc5d9_level0_row1" class="row_heading level0 row1" >Vineyards</th>
-      <td id="T_fc5d9_row1_col0" class="data row1 col0" >98%</td>
-      <td id="T_fc5d9_row1_col1" class="data row1 col1" >1%</td>
-      <td id="T_fc5d9_row1_col2" class="data row1 col2" >1%</td>
-      <td id="T_fc5d9_row1_col3" class="data row1 col3" >0%</td>
-      <td id="T_fc5d9_row1_col4" class="data row1 col4" >0%</td>
+      <th id="T_685fe_level0_row1" class="row_heading level0 row1" >Vineyards</th>
+      <td id="T_685fe_row1_col0" class="data row1 col0" >98%</td>
+      <td id="T_685fe_row1_col1" class="data row1 col1" >1%</td>
+      <td id="T_685fe_row1_col2" class="data row1 col2" >1%</td>
+      <td id="T_685fe_row1_col3" class="data row1 col3" >0%</td>
+      <td id="T_685fe_row1_col4" class="data row1 col4" >0%</td>
     </tr>
     <tr>
-      <th id="T_fc5d9_level0_row2" class="row_heading level0 row2" >Buildings</th>
-      <td id="T_fc5d9_row2_col0" class="data row2 col0" >14%</td>
-      <td id="T_fc5d9_row2_col1" class="data row2 col1" >18%</td>
-      <td id="T_fc5d9_row2_col2" class="data row2 col2" >10%</td>
-      <td id="T_fc5d9_row2_col3" class="data row2 col3" >22%</td>
-      <td id="T_fc5d9_row2_col4" class="data row2 col4" >36%</td>
+      <th id="T_685fe_level0_row2" class="row_heading level0 row2" >Buildings</th>
+      <td id="T_685fe_row2_col0" class="data row2 col0" >14%</td>
+      <td id="T_685fe_row2_col1" class="data row2 col1" >18%</td>
+      <td id="T_685fe_row2_col2" class="data row2 col2" >10%</td>
+      <td id="T_685fe_row2_col3" class="data row2 col3" >22%</td>
+      <td id="T_685fe_row2_col4" class="data row2 col4" >36%</td>
     </tr>
     <tr>
-      <th id="T_fc5d9_level0_row3" class="row_heading level0 row3" >Forest</th>
-      <td id="T_fc5d9_row3_col0" class="data row3 col0" >68%</td>
-      <td id="T_fc5d9_row3_col1" class="data row3 col1" >24%</td>
-      <td id="T_fc5d9_row3_col2" class="data row3 col2" >8%</td>
-      <td id="T_fc5d9_row3_col3" class="data row3 col3" >0%</td>
-      <td id="T_fc5d9_row3_col4" class="data row3 col4" >0%</td>
+      <th id="T_685fe_level0_row3" class="row_heading level0 row3" >Forest</th>
+      <td id="T_685fe_row3_col0" class="data row3 col0" >68%</td>
+      <td id="T_685fe_row3_col1" class="data row3 col1" >24%</td>
+      <td id="T_685fe_row3_col2" class="data row3 col2" >8%</td>
+      <td id="T_685fe_row3_col3" class="data row3 col3" >0%</td>
+      <td id="T_685fe_row3_col4" class="data row3 col4" >0%</td>
     </tr>
     <tr>
-      <th id="T_fc5d9_level0_row4" class="row_heading level0 row4" >Undefined</th>
-      <td id="T_fc5d9_row4_col0" class="data row4 col0" >57%</td>
-      <td id="T_fc5d9_row4_col1" class="data row4 col1" >22%</td>
-      <td id="T_fc5d9_row4_col2" class="data row4 col2" >16%</td>
-      <td id="T_fc5d9_row4_col3" class="data row4 col3" >6%</td>
-      <td id="T_fc5d9_row4_col4" class="data row4 col4" >0%</td>
+      <th id="T_685fe_level0_row4" class="row_heading level0 row4" >Undefined</th>
+      <td id="T_685fe_row4_col0" class="data row4 col0" >57%</td>
+      <td id="T_685fe_row4_col1" class="data row4 col1" >22%</td>
+      <td id="T_685fe_row4_col2" class="data row4 col2" >16%</td>
+      <td id="T_685fe_row4_col3" class="data row4 col3" >6%</td>
+      <td id="T_685fe_row4_col4" class="data row4 col4" >0%</td>
     </tr>
     <tr>
-      <th id="T_fc5d9_level0_row5" class="row_heading level0 row5" >Public Services</th>
-      <td id="T_fc5d9_row5_col0" class="data row5 col0" >93%</td>
-      <td id="T_fc5d9_row5_col1" class="data row5 col1" >7%</td>
-      <td id="T_fc5d9_row5_col2" class="data row5 col2" >0%</td>
-      <td id="T_fc5d9_row5_col3" class="data row5 col3" >0%</td>
-      <td id="T_fc5d9_row5_col4" class="data row5 col4" >0%</td>
+      <th id="T_685fe_level0_row5" class="row_heading level0 row5" >Public Services</th>
+      <td id="T_685fe_row5_col0" class="data row5 col0" >93%</td>
+      <td id="T_685fe_row5_col1" class="data row5 col1" >7%</td>
+      <td id="T_685fe_row5_col2" class="data row5 col2" >0%</td>
+      <td id="T_685fe_row5_col3" class="data row5 col3" >0%</td>
+      <td id="T_685fe_row5_col4" class="data row5 col4" >0%</td>
     </tr>
   </tbody>
 </table>
@@ -1978,48 +1978,48 @@ <h3>Sampling profile 2020 - 2021<a class="headerlink" href="#sampling-profile-20
 <p>The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area <span class="math notranslate nohighlight">\(\pi r²\)</span> and the survey location in the middle. The lengths for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.
 <br></br></p>
 <div class="output text_html"><style type="text/css">
-#T_34a15 caption {
+#T_30111 caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_34a15 caption {
+#T_30111 caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_34a15 td {
+#T_30111 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
   padding: 4px;
 }
-#T_34a15_row0_col1 {
+#T_30111_row0_col1 {
   background-color: #FAE8E8;
 }
 </style>
-<table id="T_34a15">
+<table id="T_30111">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_34a15_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
-      <th id="T_34a15_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
-      <th id="T_34a15_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
-      <th id="T_34a15_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
-      <th id="T_34a15_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
+      <th id="T_30111_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
+      <th id="T_30111_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
+      <th id="T_30111_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
+      <th id="T_30111_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
+      <th id="T_30111_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_34a15_level0_row0" class="row_heading level0 row0" >streets</th>
-      <td id="T_34a15_row0_col0" class="data row0 col0" >31%</td>
-      <td id="T_34a15_row0_col1" class="data row0 col1" >46%</td>
-      <td id="T_34a15_row0_col2" class="data row0 col2" >21%</td>
-      <td id="T_34a15_row0_col3" class="data row0 col3" >2%</td>
-      <td id="T_34a15_row0_col4" class="data row0 col4" >1%</td>
+      <th id="T_30111_level0_row0" class="row_heading level0 row0" >streets</th>
+      <td id="T_30111_row0_col0" class="data row0 col0" >31%</td>
+      <td id="T_30111_row0_col1" class="data row0 col1" >46%</td>
+      <td id="T_30111_row0_col2" class="data row0 col2" >21%</td>
+      <td id="T_30111_row0_col3" class="data row0 col3" >2%</td>
+      <td id="T_30111_row0_col4" class="data row0 col4" >1%</td>
     </tr>
   </tbody>
 </table>
@@ -2029,88 +2029,88 @@ <h3>Sampling profile 2020 - 2021<a class="headerlink" href="#sampling-profile-20
 Lakes</label><div class="sd-tab-content docutils">
 <p><strong>Land use</strong></p>
 <div class="output text_html"><style type="text/css">
-#T_81787 caption {
+#T_1d812 caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_81787 caption {
+#T_1d812 caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_81787 td {
+#T_1d812 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
   padding: 4px;
 }
-#T_81787_row0_col0, #T_81787_row1_col0, #T_81787_row2_col4, #T_81787_row3_col0, #T_81787_row4_col0, #T_81787_row5_col0 {
+#T_1d812_row0_col0, #T_1d812_row1_col0, #T_1d812_row2_col4, #T_1d812_row3_col0, #T_1d812_row4_col0, #T_1d812_row5_col0 {
   background-color: #FAE8E8;
 }
 </style>
-<table id="T_81787">
+<table id="T_1d812">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_81787_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
-      <th id="T_81787_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
-      <th id="T_81787_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
-      <th id="T_81787_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
-      <th id="T_81787_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
+      <th id="T_1d812_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
+      <th id="T_1d812_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
+      <th id="T_1d812_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
+      <th id="T_1d812_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
+      <th id="T_1d812_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_81787_level0_row0" class="row_heading level0 row0" >Orchards</th>
-      <td id="T_81787_row0_col0" class="data row0 col0" >100%</td>
-      <td id="T_81787_row0_col1" class="data row0 col1" >0%</td>
-      <td id="T_81787_row0_col2" class="data row0 col2" >0%</td>
-      <td id="T_81787_row0_col3" class="data row0 col3" >0%</td>
-      <td id="T_81787_row0_col4" class="data row0 col4" >0%</td>
+      <th id="T_1d812_level0_row0" class="row_heading level0 row0" >Orchards</th>
+      <td id="T_1d812_row0_col0" class="data row0 col0" >100%</td>
+      <td id="T_1d812_row0_col1" class="data row0 col1" >0%</td>
+      <td id="T_1d812_row0_col2" class="data row0 col2" >0%</td>
+      <td id="T_1d812_row0_col3" class="data row0 col3" >0%</td>
+      <td id="T_1d812_row0_col4" class="data row0 col4" >0%</td>
     </tr>
     <tr>
-      <th id="T_81787_level0_row1" class="row_heading level0 row1" >Vineyards</th>
-      <td id="T_81787_row1_col0" class="data row1 col0" >99%</td>
-      <td id="T_81787_row1_col1" class="data row1 col1" >0%</td>
-      <td id="T_81787_row1_col2" class="data row1 col2" >1%</td>
-      <td id="T_81787_row1_col3" class="data row1 col3" >0%</td>
-      <td id="T_81787_row1_col4" class="data row1 col4" >0%</td>
+      <th id="T_1d812_level0_row1" class="row_heading level0 row1" >Vineyards</th>
+      <td id="T_1d812_row1_col0" class="data row1 col0" >99%</td>
+      <td id="T_1d812_row1_col1" class="data row1 col1" >0%</td>
+      <td id="T_1d812_row1_col2" class="data row1 col2" >1%</td>
+      <td id="T_1d812_row1_col3" class="data row1 col3" >0%</td>
+      <td id="T_1d812_row1_col4" class="data row1 col4" >0%</td>
     </tr>
     <tr>
-      <th id="T_81787_level0_row2" class="row_heading level0 row2" >Buildings</th>
-      <td id="T_81787_row2_col0" class="data row2 col0" >12%</td>
-      <td id="T_81787_row2_col1" class="data row2 col1" >20%</td>
-      <td id="T_81787_row2_col2" class="data row2 col2" >8%</td>
-      <td id="T_81787_row2_col3" class="data row2 col3" >21%</td>
-      <td id="T_81787_row2_col4" class="data row2 col4" >39%</td>
+      <th id="T_1d812_level0_row2" class="row_heading level0 row2" >Buildings</th>
+      <td id="T_1d812_row2_col0" class="data row2 col0" >12%</td>
+      <td id="T_1d812_row2_col1" class="data row2 col1" >20%</td>
+      <td id="T_1d812_row2_col2" class="data row2 col2" >8%</td>
+      <td id="T_1d812_row2_col3" class="data row2 col3" >21%</td>
+      <td id="T_1d812_row2_col4" class="data row2 col4" >39%</td>
     </tr>
     <tr>
-      <th id="T_81787_level0_row3" class="row_heading level0 row3" >Forest</th>
-      <td id="T_81787_row3_col0" class="data row3 col0" >70%</td>
-      <td id="T_81787_row3_col1" class="data row3 col1" >22%</td>
-      <td id="T_81787_row3_col2" class="data row3 col2" >8%</td>
-      <td id="T_81787_row3_col3" class="data row3 col3" >0%</td>
-      <td id="T_81787_row3_col4" class="data row3 col4" >0%</td>
+      <th id="T_1d812_level0_row3" class="row_heading level0 row3" >Forest</th>
+      <td id="T_1d812_row3_col0" class="data row3 col0" >70%</td>
+      <td id="T_1d812_row3_col1" class="data row3 col1" >22%</td>
+      <td id="T_1d812_row3_col2" class="data row3 col2" >8%</td>
+      <td id="T_1d812_row3_col3" class="data row3 col3" >0%</td>
+      <td id="T_1d812_row3_col4" class="data row3 col4" >0%</td>
     </tr>
     <tr>
-      <th id="T_81787_level0_row4" class="row_heading level0 row4" >Undefined</th>
-      <td id="T_81787_row4_col0" class="data row4 col0" >58%</td>
-      <td id="T_81787_row4_col1" class="data row4 col1" >20%</td>
-      <td id="T_81787_row4_col2" class="data row4 col2" >16%</td>
-      <td id="T_81787_row4_col3" class="data row4 col3" >6%</td>
-      <td id="T_81787_row4_col4" class="data row4 col4" >0%</td>
+      <th id="T_1d812_level0_row4" class="row_heading level0 row4" >Undefined</th>
+      <td id="T_1d812_row4_col0" class="data row4 col0" >58%</td>
+      <td id="T_1d812_row4_col1" class="data row4 col1" >20%</td>
+      <td id="T_1d812_row4_col2" class="data row4 col2" >16%</td>
+      <td id="T_1d812_row4_col3" class="data row4 col3" >6%</td>
+      <td id="T_1d812_row4_col4" class="data row4 col4" >0%</td>
     </tr>
     <tr>
-      <th id="T_81787_level0_row5" class="row_heading level0 row5" >Public Services</th>
-      <td id="T_81787_row5_col0" class="data row5 col0" >93%</td>
-      <td id="T_81787_row5_col1" class="data row5 col1" >7%</td>
-      <td id="T_81787_row5_col2" class="data row5 col2" >0%</td>
-      <td id="T_81787_row5_col3" class="data row5 col3" >0%</td>
-      <td id="T_81787_row5_col4" class="data row5 col4" >0%</td>
+      <th id="T_1d812_level0_row5" class="row_heading level0 row5" >Public Services</th>
+      <td id="T_1d812_row5_col0" class="data row5 col0" >93%</td>
+      <td id="T_1d812_row5_col1" class="data row5 col1" >7%</td>
+      <td id="T_1d812_row5_col2" class="data row5 col2" >0%</td>
+      <td id="T_1d812_row5_col3" class="data row5 col3" >0%</td>
+      <td id="T_1d812_row5_col4" class="data row5 col4" >0%</td>
     </tr>
   </tbody>
 </table>
@@ -2118,48 +2118,48 @@ <h3>Sampling profile 2020 - 2021<a class="headerlink" href="#sampling-profile-20
 <p>The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area <span class="math notranslate nohighlight">\(\pi r²\)</span> and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.
 <br></br></p>
 <div class="output text_html"><style type="text/css">
-#T_edd53 caption {
+#T_9a253 caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_edd53 caption {
+#T_9a253 caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_edd53 td {
+#T_9a253 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
   padding: 4px;
 }
-#T_edd53_row0_col1 {
+#T_9a253_row0_col1 {
   background-color: #FAE8E8;
 }
 </style>
-<table id="T_edd53">
+<table id="T_9a253">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_edd53_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
-      <th id="T_edd53_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
-      <th id="T_edd53_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
-      <th id="T_edd53_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
-      <th id="T_edd53_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
+      <th id="T_9a253_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
+      <th id="T_9a253_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
+      <th id="T_9a253_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
+      <th id="T_9a253_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
+      <th id="T_9a253_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_edd53_level0_row0" class="row_heading level0 row0" >streets</th>
-      <td id="T_edd53_row0_col0" class="data row0 col0" >35%</td>
-      <td id="T_edd53_row0_col1" class="data row0 col1" >48%</td>
-      <td id="T_edd53_row0_col2" class="data row0 col2" >16%</td>
-      <td id="T_edd53_row0_col3" class="data row0 col3" >0%</td>
-      <td id="T_edd53_row0_col4" class="data row0 col4" >1%</td>
+      <th id="T_9a253_level0_row0" class="row_heading level0 row0" >streets</th>
+      <td id="T_9a253_row0_col0" class="data row0 col0" >35%</td>
+      <td id="T_9a253_row0_col1" class="data row0 col1" >48%</td>
+      <td id="T_9a253_row0_col2" class="data row0 col2" >16%</td>
+      <td id="T_9a253_row0_col3" class="data row0 col3" >0%</td>
+      <td id="T_9a253_row0_col4" class="data row0 col4" >1%</td>
     </tr>
   </tbody>
 </table>
@@ -2169,88 +2169,88 @@ <h3>Sampling profile 2020 - 2021<a class="headerlink" href="#sampling-profile-20
 Rivers</label><div class="sd-tab-content docutils">
 <p><strong>Land use</strong></p>
 <div class="output text_html"><style type="text/css">
-#T_2c804 caption {
+#T_b4d9b caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_2c804 caption {
+#T_b4d9b caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_2c804 td {
+#T_b4d9b td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
   padding: 4px;
 }
-#T_2c804_row0_col0, #T_2c804_row1_col0, #T_2c804_row2_col2, #T_2c804_row3_col0, #T_2c804_row4_col0, #T_2c804_row5_col0 {
+#T_b4d9b_row0_col0, #T_b4d9b_row1_col0, #T_b4d9b_row2_col2, #T_b4d9b_row3_col0, #T_b4d9b_row4_col0, #T_b4d9b_row5_col0 {
   background-color: #FAE8E8;
 }
 </style>
-<table id="T_2c804">
+<table id="T_b4d9b">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_2c804_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
-      <th id="T_2c804_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
-      <th id="T_2c804_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
-      <th id="T_2c804_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
-      <th id="T_2c804_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
+      <th id="T_b4d9b_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
+      <th id="T_b4d9b_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
+      <th id="T_b4d9b_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
+      <th id="T_b4d9b_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
+      <th id="T_b4d9b_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_2c804_level0_row0" class="row_heading level0 row0" >Orchards</th>
-      <td id="T_2c804_row0_col0" class="data row0 col0" >97%</td>
-      <td id="T_2c804_row0_col1" class="data row0 col1" >3%</td>
-      <td id="T_2c804_row0_col2" class="data row0 col2" >0%</td>
-      <td id="T_2c804_row0_col3" class="data row0 col3" >0%</td>
-      <td id="T_2c804_row0_col4" class="data row0 col4" >0%</td>
+      <th id="T_b4d9b_level0_row0" class="row_heading level0 row0" >Orchards</th>
+      <td id="T_b4d9b_row0_col0" class="data row0 col0" >97%</td>
+      <td id="T_b4d9b_row0_col1" class="data row0 col1" >3%</td>
+      <td id="T_b4d9b_row0_col2" class="data row0 col2" >0%</td>
+      <td id="T_b4d9b_row0_col3" class="data row0 col3" >0%</td>
+      <td id="T_b4d9b_row0_col4" class="data row0 col4" >0%</td>
     </tr>
     <tr>
-      <th id="T_2c804_level0_row1" class="row_heading level0 row1" >Vineyards</th>
-      <td id="T_2c804_row1_col0" class="data row1 col0" >94%</td>
-      <td id="T_2c804_row1_col1" class="data row1 col1" >6%</td>
-      <td id="T_2c804_row1_col2" class="data row1 col2" >0%</td>
-      <td id="T_2c804_row1_col3" class="data row1 col3" >0%</td>
-      <td id="T_2c804_row1_col4" class="data row1 col4" >0%</td>
+      <th id="T_b4d9b_level0_row1" class="row_heading level0 row1" >Vineyards</th>
+      <td id="T_b4d9b_row1_col0" class="data row1 col0" >94%</td>
+      <td id="T_b4d9b_row1_col1" class="data row1 col1" >6%</td>
+      <td id="T_b4d9b_row1_col2" class="data row1 col2" >0%</td>
+      <td id="T_b4d9b_row1_col3" class="data row1 col3" >0%</td>
+      <td id="T_b4d9b_row1_col4" class="data row1 col4" >0%</td>
     </tr>
     <tr>
-      <th id="T_2c804_level0_row2" class="row_heading level0 row2" >Buildings</th>
-      <td id="T_2c804_row2_col0" class="data row2 col0" >24%</td>
-      <td id="T_2c804_row2_col1" class="data row2 col1" >9%</td>
-      <td id="T_2c804_row2_col2" class="data row2 col2" >27%</td>
-      <td id="T_2c804_row2_col3" class="data row2 col3" >24%</td>
-      <td id="T_2c804_row2_col4" class="data row2 col4" >15%</td>
+      <th id="T_b4d9b_level0_row2" class="row_heading level0 row2" >Buildings</th>
+      <td id="T_b4d9b_row2_col0" class="data row2 col0" >24%</td>
+      <td id="T_b4d9b_row2_col1" class="data row2 col1" >9%</td>
+      <td id="T_b4d9b_row2_col2" class="data row2 col2" >27%</td>
+      <td id="T_b4d9b_row2_col3" class="data row2 col3" >24%</td>
+      <td id="T_b4d9b_row2_col4" class="data row2 col4" >15%</td>
     </tr>
     <tr>
-      <th id="T_2c804_level0_row3" class="row_heading level0 row3" >Forest</th>
-      <td id="T_2c804_row3_col0" class="data row3 col0" >55%</td>
-      <td id="T_2c804_row3_col1" class="data row3 col1" >36%</td>
-      <td id="T_2c804_row3_col2" class="data row3 col2" >9%</td>
-      <td id="T_2c804_row3_col3" class="data row3 col3" >0%</td>
-      <td id="T_2c804_row3_col4" class="data row3 col4" >0%</td>
+      <th id="T_b4d9b_level0_row3" class="row_heading level0 row3" >Forest</th>
+      <td id="T_b4d9b_row3_col0" class="data row3 col0" >55%</td>
+      <td id="T_b4d9b_row3_col1" class="data row3 col1" >36%</td>
+      <td id="T_b4d9b_row3_col2" class="data row3 col2" >9%</td>
+      <td id="T_b4d9b_row3_col3" class="data row3 col3" >0%</td>
+      <td id="T_b4d9b_row3_col4" class="data row3 col4" >0%</td>
     </tr>
     <tr>
-      <th id="T_2c804_level0_row4" class="row_heading level0 row4" >Undefined</th>
-      <td id="T_2c804_row4_col0" class="data row4 col0" >45%</td>
-      <td id="T_2c804_row4_col1" class="data row4 col1" >33%</td>
-      <td id="T_2c804_row4_col2" class="data row4 col2" >12%</td>
-      <td id="T_2c804_row4_col3" class="data row4 col3" >9%</td>
-      <td id="T_2c804_row4_col4" class="data row4 col4" >0%</td>
+      <th id="T_b4d9b_level0_row4" class="row_heading level0 row4" >Undefined</th>
+      <td id="T_b4d9b_row4_col0" class="data row4 col0" >45%</td>
+      <td id="T_b4d9b_row4_col1" class="data row4 col1" >33%</td>
+      <td id="T_b4d9b_row4_col2" class="data row4 col2" >12%</td>
+      <td id="T_b4d9b_row4_col3" class="data row4 col3" >9%</td>
+      <td id="T_b4d9b_row4_col4" class="data row4 col4" >0%</td>
     </tr>
     <tr>
-      <th id="T_2c804_level0_row5" class="row_heading level0 row5" >Public Services</th>
-      <td id="T_2c804_row5_col0" class="data row5 col0" >94%</td>
-      <td id="T_2c804_row5_col1" class="data row5 col1" >6%</td>
-      <td id="T_2c804_row5_col2" class="data row5 col2" >0%</td>
-      <td id="T_2c804_row5_col3" class="data row5 col3" >0%</td>
-      <td id="T_2c804_row5_col4" class="data row5 col4" >0%</td>
+      <th id="T_b4d9b_level0_row5" class="row_heading level0 row5" >Public Services</th>
+      <td id="T_b4d9b_row5_col0" class="data row5 col0" >94%</td>
+      <td id="T_b4d9b_row5_col1" class="data row5 col1" >6%</td>
+      <td id="T_b4d9b_row5_col2" class="data row5 col2" >0%</td>
+      <td id="T_b4d9b_row5_col3" class="data row5 col3" >0%</td>
+      <td id="T_b4d9b_row5_col4" class="data row5 col4" >0%</td>
     </tr>
   </tbody>
 </table>
@@ -2258,48 +2258,48 @@ <h3>Sampling profile 2020 - 2021<a class="headerlink" href="#sampling-profile-20
 <p>The streets are measured as the length of the road network in the cirlce with r= 1 500 m and area <span class="math notranslate nohighlight">\(\pi r²\)</span> and the survey location in the middle. The lenghts for each location are normalized from 0 - 1. Thus in the table below, the locations that have the shortest road net work will be in category 1, the those with a more dense network will be higher.
 <b></b></p>
 <div class="output text_html"><style type="text/css">
-#T_0b35c caption {
+#T_1e644 caption {
   caption-side: top;
   font-size: 1em;
   text-align: left;
   margin-bottom: 10px;
 }
-#T_0b35c caption {
+#T_1e644 caption {
   caption-side: top;
   font-size: .9em;
   text-align: left;
   font-style: italic;
   color: #000;
 }
-#T_0b35c td {
+#T_1e644 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
   padding: 4px;
 }
-#T_0b35c_row0_col2 {
+#T_1e644_row0_col2 {
   background-color: #FAE8E8;
 }
 </style>
-<table id="T_0b35c">
+<table id="T_1e644">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_0b35c_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
-      <th id="T_0b35c_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
-      <th id="T_0b35c_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
-      <th id="T_0b35c_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
-      <th id="T_0b35c_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
+      <th id="T_1e644_level0_col0" class="col_heading level0 col0" >0 - 20%</th>
+      <th id="T_1e644_level0_col1" class="col_heading level0 col1" >20 - 40%</th>
+      <th id="T_1e644_level0_col2" class="col_heading level0 col2" >40 - 60%</th>
+      <th id="T_1e644_level0_col3" class="col_heading level0 col3" >60 - 80%</th>
+      <th id="T_1e644_level0_col4" class="col_heading level0 col4" >80 - 100%</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_0b35c_level0_row0" class="row_heading level0 row0" >streets</th>
-      <td id="T_0b35c_row0_col0" class="data row0 col0" >3%</td>
-      <td id="T_0b35c_row0_col1" class="data row0 col1" >30%</td>
-      <td id="T_0b35c_row0_col2" class="data row0 col2" >52%</td>
-      <td id="T_0b35c_row0_col3" class="data row0 col3" >12%</td>
-      <td id="T_0b35c_row0_col4" class="data row0 col4" >3%</td>
+      <th id="T_1e644_level0_row0" class="row_heading level0 row0" >streets</th>
+      <td id="T_1e644_row0_col0" class="data row0 col0" >3%</td>
+      <td id="T_1e644_row0_col1" class="data row0 col1" >30%</td>
+      <td id="T_1e644_row0_col2" class="data row0 col2" >52%</td>
+      <td id="T_1e644_row0_col3" class="data row0 col3" >12%</td>
+      <td id="T_1e644_row0_col4" class="data row0 col4" >3%</td>
     </tr>
   </tbody>
 </table>
@@ -2362,21 +2362,21 @@ <h2>Forecast<a class="headerlink" href="#forecast" title="Permalink to this head
 <div class="sd-card-body docutils">
 <p class="sd-card-text"><strong>Given the 99th percentile</strong></p>
 <ul class="simple">
-<li><p class="sd-card-text">Average: 3.63</p></li>
-<li><p class="sd-card-text">HDI 95%: 0.1 - 10.4</p></li>
-<li><p class="sd-card-text">90% Range: 0.2 - 9.83</p></li>
+<li><p class="sd-card-text">Average: 3.84</p></li>
+<li><p class="sd-card-text">HDI 95%: 0.1 - 13.7</p></li>
+<li><p class="sd-card-text">90% Range: 0.2 - 13.03</p></li>
 </ul>
 <p class="sd-card-text"><strong>Given the weighted prior</strong></p>
 <ul class="simple">
-<li><p class="sd-card-text">Average: 3.33</p></li>
-<li><p class="sd-card-text">HDI 95%: 0.1 - 8.1</p></li>
-<li><p class="sd-card-text">90% Range: 0.4 - 7.05</p></li>
+<li><p class="sd-card-text">Average: 5.2</p></li>
+<li><p class="sd-card-text">HDI 95%: 0.1 - 18.7</p></li>
+<li><p class="sd-card-text">90% Range: 0.2 - 16.51</p></li>
 </ul>
 <p class="sd-card-text"><strong>Given the observed max</strong></p>
 <ul class="simple">
-<li><p class="sd-card-text">Average: 6.38</p></li>
-<li><p class="sd-card-text">HDI 95%: 0.02 - 31.4</p></li>
-<li><p class="sd-card-text">90% Range: 0.14 - 38.0</p></li>
+<li><p class="sd-card-text">Average: 7.55</p></li>
+<li><p class="sd-card-text">HDI 95%: 0.02 - 42.66</p></li>
+<li><p class="sd-card-text">90% Range: 0.16 - 34.52</p></li>
 </ul>
 </div>
 </div>
@@ -2384,7 +2384,7 @@ <h2>Forecast<a class="headerlink" href="#forecast" title="Permalink to this head
 <div class="sd-col sd-d-flex-row sd-col-12 sd-col-xs-12 sd-col-sm-7 sd-col-md-7 sd-col-lg-7 docutils">
 <div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-none docutils">
 <div class="sd-card-body docutils">
-<img alt="_images/8fcb3ebcae81d5e2f80a17c87ada3647d1a874805847c220c5e58cc6e36f9968.png" src="_images/8fcb3ebcae81d5e2f80a17c87ada3647d1a874805847c220c5e58cc6e36f9968.png" />
+<img alt="_images/87a0ec4fc6b99ffea8fbc20ff0dc7df517845056f254e77f92b220cd932edd96.png" src="_images/87a0ec4fc6b99ffea8fbc20ff0dc7df517845056f254e77f92b220cd932edd96.png" />
 </div>
 <div class="sd-card-footer docutils">
 <p class="sd-card-text">Cumulative distribution of observed, sampling history and forecasts using to different priors.</p>
@@ -2407,21 +2407,21 @@ <h2>Forecast<a class="headerlink" href="#forecast" title="Permalink to this head
 <div class="sd-card-body docutils">
 <p class="sd-card-text"><strong>Given the 99th percentile</strong></p>
 <ul class="simple">
-<li><p class="sd-card-text">Average: 5.53</p></li>
-<li><p class="sd-card-text">HDI 95%: 0.4 - 18.7</p></li>
-<li><p class="sd-card-text">90% Range: 0.7 - 17.94</p></li>
+<li><p class="sd-card-text">Average: 4.6</p></li>
+<li><p class="sd-card-text">HDI 95%: 0.1 - 15.6</p></li>
+<li><p class="sd-card-text">90% Range: 0.5 - 14.84</p></li>
 </ul>
 <p class="sd-card-text"><strong>Given the weighted prior</strong></p>
 <ul class="simple">
-<li><p class="sd-card-text">Average: 4.24</p></li>
-<li><p class="sd-card-text">HDI 95%: 0.3 - 13.0</p></li>
-<li><p class="sd-card-text">90% Range: 0.5 - 13.0</p></li>
+<li><p class="sd-card-text">Average: 3.97</p></li>
+<li><p class="sd-card-text">HDI 95%: 0.3 - 10.7</p></li>
+<li><p class="sd-card-text">90% Range: 0.3 - 10.7</p></li>
 </ul>
 <p class="sd-card-text"><strong>Given the observed max</strong></p>
 <ul class="simple">
-<li><p class="sd-card-text">Average: 10.81</p></li>
-<li><p class="sd-card-text">HDI 95%: 0.14 - 48.54</p></li>
-<li><p class="sd-card-text">90% Range: 0.55 - 51.72</p></li>
+<li><p class="sd-card-text">Average: 8.0</p></li>
+<li><p class="sd-card-text">HDI 95%: 0.14 - 58.18</p></li>
+<li><p class="sd-card-text">90% Range: 0.64 - 54.9</p></li>
 </ul>
 </div>
 </div>
@@ -2429,7 +2429,7 @@ <h2>Forecast<a class="headerlink" href="#forecast" title="Permalink to this head
 <div class="sd-col sd-d-flex-row sd-col-12 sd-col-xs-12 sd-col-sm-7 sd-col-md-7 sd-col-lg-7 docutils">
 <div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-none docutils">
 <div class="sd-card-body docutils">
-<img alt="_images/0a9ec2d2feba1c6d1a60449ef39dcd379748b26ade040ebe37bbf600e0d5de7b.png" src="_images/0a9ec2d2feba1c6d1a60449ef39dcd379748b26ade040ebe37bbf600e0d5de7b.png" />
+<img alt="_images/d0ad3252da2fb520f54bd04cbfcaed0f3b5d93a0bc136a375a20fe05efbfd62f.png" src="_images/d0ad3252da2fb520f54bd04cbfcaed0f3b5d93a0bc136a375a20fe05efbfd62f.png" />
 </div>
 <div class="sd-card-footer docutils">
 <p class="sd-card-text">Cumulative distribution of observed, sampling history and forecasts using to different priors.</p>
@@ -2452,21 +2452,21 @@ <h2>Forecast<a class="headerlink" href="#forecast" title="Permalink to this head
 <div class="sd-card-body docutils">
 <p class="sd-card-text"><strong>Given the 99th percentile</strong></p>
 <ul class="simple">
-<li><p class="sd-card-text">Average: 1.16</p></li>
-<li><p class="sd-card-text">HDI 95%: 0.1 - 3.6</p></li>
-<li><p class="sd-card-text">90% Range: 0.1 - 3.41</p></li>
+<li><p class="sd-card-text">Average: 1.14</p></li>
+<li><p class="sd-card-text">HDI 95%: 0.1 - 3.3</p></li>
+<li><p class="sd-card-text">90% Range: 0.1 - 3.11</p></li>
 </ul>
 <p class="sd-card-text"><strong>Given the weighted prior</strong></p>
 <ul class="simple">
-<li><p class="sd-card-text">Average: 1.24</p></li>
-<li><p class="sd-card-text">HDI 95%: 0.1 - 5.0</p></li>
-<li><p class="sd-card-text">90% Range: 0.1 - 4.62</p></li>
+<li><p class="sd-card-text">Average: 1.45</p></li>
+<li><p class="sd-card-text">HDI 95%: 0.1 - 5.5</p></li>
+<li><p class="sd-card-text">90% Range: 0.1 - 4.64</p></li>
 </ul>
 <p class="sd-card-text"><strong>Given the observed max</strong></p>
 <ul class="simple">
-<li><p class="sd-card-text">Average: 1.76</p></li>
-<li><p class="sd-card-text">HDI 95%: 0.02 - 10.96</p></li>
-<li><p class="sd-card-text">90% Range: 0.1 - 4.93</p></li>
+<li><p class="sd-card-text">Average: 1.45</p></li>
+<li><p class="sd-card-text">HDI 95%: 0.02 - 4.55</p></li>
+<li><p class="sd-card-text">90% Range: 0.1 - 6.08</p></li>
 </ul>
 </div>
 </div>
@@ -2474,7 +2474,7 @@ <h2>Forecast<a class="headerlink" href="#forecast" title="Permalink to this head
 <div class="sd-col sd-d-flex-row sd-col-12 sd-col-xs-12 sd-col-sm-7 sd-col-md-7 sd-col-lg-7 docutils">
 <div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-none docutils">
 <div class="sd-card-body docutils">
-<img alt="_images/9db72909d1384140b5842115fdc75d83937e53dd7b994fc785eeb41ba7988491.png" src="_images/9db72909d1384140b5842115fdc75d83937e53dd7b994fc785eeb41ba7988491.png" />
+<img alt="_images/bbb0295b24461e6c0d06645b175f985f93efbe6ecd1a4e36bf93e16672cd971b.png" src="_images/bbb0295b24461e6c0d06645b175f985f93efbe6ecd1a4e36bf93e16672cd971b.png" />
 </div>
 <div class="sd-card-footer docutils">
 <p class="sd-card-text">Cumulative distribution of observed, sampling history and forecasts using to different priors.</p>
@@ -2536,81 +2536,81 @@ <h2>Lakes and rivers sampled - all data<a class="headerlink" href="#lakes-and-ri
 <div class="sd-col sd-d-flex-column docutils">
 <p><strong>Lakes sampled</strong></p>
 <div class="output text_html"><style type="text/css">
-#T_ecb27 tr:nth-child(even) {
+#T_a9b8d tr:nth-child(even) {
   background-color: rgba(139, 69, 19, 0.08);
 }
-#T_ecb27 tr:nth-child(odd) {
+#T_a9b8d tr:nth-child(odd) {
   background: #FFF;
 }
-#T_ecb27 tr {
+#T_a9b8d tr {
   font-size: 10px;
 }
-#T_ecb27 th {
+#T_a9b8d th {
   background-color: #FFF;
   font-size: 12px;
   text-align: left;
   width: auto;
   word-break: keep-all;
 }
-#T_ecb27 td {
+#T_a9b8d td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
 }
-#T_ecb27 caption {
+#T_a9b8d caption {
   caption-side: bottom;
   font-size: 1em;
   text-align: left;
 }
 </style>
-<table id="T_ecb27">
+<table id="T_a9b8d">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_ecb27_level0_col0" class="col_heading level0 col0" >samples</th>
-      <th id="T_ecb27_level0_col1" class="col_heading level0 col1" >pcs/m</th>
+      <th id="T_a9b8d_level0_col0" class="col_heading level0 col0" >samples</th>
+      <th id="T_a9b8d_level0_col1" class="col_heading level0 col1" >pcs/m</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_ecb27_level0_row0" class="row_heading level0 row0" >Bielersee</th>
-      <td id="T_ecb27_row0_col0" class="data row0 col0" >50</td>
-      <td id="T_ecb27_row0_col1" class="data row0 col1" >4.08</td>
+      <th id="T_a9b8d_level0_row0" class="row_heading level0 row0" >Bielersee</th>
+      <td id="T_a9b8d_row0_col0" class="data row0 col0" >50</td>
+      <td id="T_a9b8d_row0_col1" class="data row0 col1" >4.08</td>
     </tr>
     <tr>
-      <th id="T_ecb27_level0_row1" class="row_heading level0 row1" >Brienzersee</th>
-      <td id="T_ecb27_row1_col0" class="data row1 col0" >5</td>
-      <td id="T_ecb27_row1_col1" class="data row1 col1" >3.96</td>
+      <th id="T_a9b8d_level0_row1" class="row_heading level0 row1" >Brienzersee</th>
+      <td id="T_a9b8d_row1_col0" class="data row1 col0" >5</td>
+      <td id="T_a9b8d_row1_col1" class="data row1 col1" >3.96</td>
     </tr>
     <tr>
-      <th id="T_ecb27_level0_row2" class="row_heading level0 row2" >Greifensee</th>
-      <td id="T_ecb27_row2_col0" class="data row2 col0" >34</td>
-      <td id="T_ecb27_row2_col1" class="data row2 col1" >4.71</td>
+      <th id="T_a9b8d_level0_row2" class="row_heading level0 row2" >Greifensee</th>
+      <td id="T_a9b8d_row2_col0" class="data row2 col0" >34</td>
+      <td id="T_a9b8d_row2_col1" class="data row2 col1" >4.71</td>
     </tr>
     <tr>
-      <th id="T_ecb27_level0_row3" class="row_heading level0 row3" >Katzensee</th>
-      <td id="T_ecb27_row3_col0" class="data row3 col0" >12</td>
-      <td id="T_ecb27_row3_col1" class="data row3 col1" >1.77</td>
+      <th id="T_a9b8d_level0_row3" class="row_heading level0 row3" >Katzensee</th>
+      <td id="T_a9b8d_row3_col0" class="data row3 col0" >12</td>
+      <td id="T_a9b8d_row3_col1" class="data row3 col1" >1.77</td>
     </tr>
     <tr>
-      <th id="T_ecb27_level0_row4" class="row_heading level0 row4" >Lac-leman</th>
-      <td id="T_ecb27_row4_col0" class="data row4 col0" >249</td>
-      <td id="T_ecb27_row4_col1" class="data row4 col1" >8.91</td>
+      <th id="T_a9b8d_level0_row4" class="row_heading level0 row4" >Lac-leman</th>
+      <td id="T_a9b8d_row4_col0" class="data row4 col0" >249</td>
+      <td id="T_a9b8d_row4_col1" class="data row4 col1" >8.91</td>
     </tr>
     <tr>
-      <th id="T_ecb27_level0_row5" class="row_heading level0 row5" >Neuenburgersee</th>
-      <td id="T_ecb27_row5_col0" class="data row5 col0" >23</td>
-      <td id="T_ecb27_row5_col1" class="data row5 col1" >1.78</td>
+      <th id="T_a9b8d_level0_row5" class="row_heading level0 row5" >Neuenburgersee</th>
+      <td id="T_a9b8d_row5_col0" class="data row5 col0" >23</td>
+      <td id="T_a9b8d_row5_col1" class="data row5 col1" >1.78</td>
     </tr>
     <tr>
-      <th id="T_ecb27_level0_row6" class="row_heading level0 row6" >Thunersee</th>
-      <td id="T_ecb27_row6_col0" class="data row6 col0" >43</td>
-      <td id="T_ecb27_row6_col1" class="data row6 col1" >1.21</td>
+      <th id="T_a9b8d_level0_row6" class="row_heading level0 row6" >Thunersee</th>
+      <td id="T_a9b8d_row6_col0" class="data row6 col0" >43</td>
+      <td id="T_a9b8d_row6_col1" class="data row6 col1" >1.21</td>
     </tr>
     <tr>
-      <th id="T_ecb27_level0_row7" class="row_heading level0 row7" >Zurichsee</th>
-      <td id="T_ecb27_row7_col0" class="data row7 col0" >125</td>
-      <td id="T_ecb27_row7_col1" class="data row7 col1" >4.01</td>
+      <th id="T_a9b8d_level0_row7" class="row_heading level0 row7" >Zurichsee</th>
+      <td id="T_a9b8d_row7_col0" class="data row7 col0" >125</td>
+      <td id="T_a9b8d_row7_col1" class="data row7 col1" >4.01</td>
     </tr>
   </tbody>
 </table>
@@ -2618,136 +2618,136 @@ <h2>Lakes and rivers sampled - all data<a class="headerlink" href="#lakes-and-ri
 <div class="sd-col sd-d-flex-column docutils">
 <p><strong>Rivers sampled</strong></p>
 <div class="output text_html"><style type="text/css">
-#T_e63cc tr:nth-child(even) {
+#T_90020 tr:nth-child(even) {
   background-color: rgba(139, 69, 19, 0.08);
 }
-#T_e63cc tr:nth-child(odd) {
+#T_90020 tr:nth-child(odd) {
   background: #FFF;
 }
-#T_e63cc tr {
+#T_90020 tr {
   font-size: 10px;
 }
-#T_e63cc th {
+#T_90020 th {
   background-color: #FFF;
   font-size: 12px;
   text-align: left;
   width: auto;
   word-break: keep-all;
 }
-#T_e63cc td {
+#T_90020 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
 }
-#T_e63cc caption {
+#T_90020 caption {
   caption-side: bottom;
   font-size: 1em;
   text-align: left;
 }
 </style>
-<table id="T_e63cc">
+<table id="T_90020">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_e63cc_level0_col0" class="col_heading level0 col0" >samples</th>
-      <th id="T_e63cc_level0_col1" class="col_heading level0 col1" >pcs/m</th>
+      <th id="T_90020_level0_col0" class="col_heading level0 col0" >samples</th>
+      <th id="T_90020_level0_col1" class="col_heading level0 col1" >pcs/m</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_e63cc_level0_row0" class="row_heading level0 row0" >Aare</th>
-      <td id="T_e63cc_row0_col0" class="data row0 col0" >61</td>
-      <td id="T_e63cc_row0_col1" class="data row0 col1" >1.27</td>
+      <th id="T_90020_level0_row0" class="row_heading level0 row0" >Aare</th>
+      <td id="T_90020_row0_col0" class="data row0 col0" >61</td>
+      <td id="T_90020_row0_col1" class="data row0 col1" >1.27</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row1" class="row_heading level0 row1" >Aarenidau-buren-kanal</th>
-      <td id="T_e63cc_row1_col0" class="data row1 col0" >3</td>
-      <td id="T_e63cc_row1_col1" class="data row1 col1" >1.26</td>
+      <th id="T_90020_level0_row1" class="row_heading level0 row1" >Aarenidau-buren-kanal</th>
+      <td id="T_90020_row1_col0" class="data row1 col0" >3</td>
+      <td id="T_90020_row1_col1" class="data row1 col1" >1.26</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row2" class="row_heading level0 row2" >Arve</th>
-      <td id="T_e63cc_row2_col0" class="data row2 col0" >8</td>
-      <td id="T_e63cc_row2_col1" class="data row2 col1" >2.92</td>
+      <th id="T_90020_level0_row2" class="row_heading level0 row2" >Arve</th>
+      <td id="T_90020_row2_col0" class="data row2 col0" >8</td>
+      <td id="T_90020_row2_col1" class="data row2 col1" >2.92</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row3" class="row_heading level0 row3" >Chriesbach</th>
-      <td id="T_e63cc_row3_col0" class="data row3 col0" >12</td>
-      <td id="T_e63cc_row3_col1" class="data row3 col1" >0.92</td>
+      <th id="T_90020_level0_row3" class="row_heading level0 row3" >Chriesbach</th>
+      <td id="T_90020_row3_col0" class="data row3 col0" >12</td>
+      <td id="T_90020_row3_col1" class="data row3 col1" >0.92</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row4" class="row_heading level0 row4" >Dorfbach</th>
-      <td id="T_e63cc_row4_col0" class="data row4 col0" >1</td>
-      <td id="T_e63cc_row4_col1" class="data row4 col1" >0.11</td>
+      <th id="T_90020_level0_row4" class="row_heading level0 row4" >Dorfbach</th>
+      <td id="T_90020_row4_col0" class="data row4 col0" >1</td>
+      <td id="T_90020_row4_col1" class="data row4 col1" >0.11</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row5" class="row_heading level0 row5" >Emme</th>
-      <td id="T_e63cc_row5_col0" class="data row5 col0" >9</td>
-      <td id="T_e63cc_row5_col1" class="data row5 col1" >0.60</td>
+      <th id="T_90020_level0_row5" class="row_heading level0 row5" >Emme</th>
+      <td id="T_90020_row5_col0" class="data row5 col0" >9</td>
+      <td id="T_90020_row5_col1" class="data row5 col1" >0.60</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row6" class="row_heading level0 row6" >Glatt</th>
-      <td id="T_e63cc_row6_col0" class="data row6 col0" >7</td>
-      <td id="T_e63cc_row6_col1" class="data row6 col1" >1.67</td>
+      <th id="T_90020_level0_row6" class="row_heading level0 row6" >Glatt</th>
+      <td id="T_90020_row6_col0" class="data row6 col0" >7</td>
+      <td id="T_90020_row6_col1" class="data row6 col1" >1.67</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row7" class="row_heading level0 row7" >Grandelbach</th>
-      <td id="T_e63cc_row7_col0" class="data row7 col0" >4</td>
-      <td id="T_e63cc_row7_col1" class="data row7 col1" >1.33</td>
+      <th id="T_90020_level0_row7" class="row_heading level0 row7" >Grandelbach</th>
+      <td id="T_90020_row7_col0" class="data row7 col0" >4</td>
+      <td id="T_90020_row7_col1" class="data row7 col1" >1.33</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row8" class="row_heading level0 row8" >Jona</th>
-      <td id="T_e63cc_row8_col0" class="data row8 col0" >12</td>
-      <td id="T_e63cc_row8_col1" class="data row8 col1" >2.04</td>
+      <th id="T_90020_level0_row8" class="row_heading level0 row8" >Jona</th>
+      <td id="T_90020_row8_col0" class="data row8 col0" >12</td>
+      <td id="T_90020_row8_col1" class="data row8 col1" >2.04</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row9" class="row_heading level0 row9" >La-thiele</th>
-      <td id="T_e63cc_row9_col0" class="data row9 col0" >1</td>
-      <td id="T_e63cc_row9_col1" class="data row9 col1" >0.46</td>
+      <th id="T_90020_level0_row9" class="row_heading level0 row9" >La-thiele</th>
+      <td id="T_90020_row9_col0" class="data row9 col0" >1</td>
+      <td id="T_90020_row9_col1" class="data row9 col1" >0.46</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row10" class="row_heading level0 row10" >Langeten</th>
-      <td id="T_e63cc_row10_col0" class="data row10 col0" >11</td>
-      <td id="T_e63cc_row10_col1" class="data row10 col1" >1.94</td>
+      <th id="T_90020_level0_row10" class="row_heading level0 row10" >Langeten</th>
+      <td id="T_90020_row10_col0" class="data row10 col0" >11</td>
+      <td id="T_90020_row10_col1" class="data row10 col1" >1.94</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row11" class="row_heading level0 row11" >Limmat</th>
-      <td id="T_e63cc_row11_col0" class="data row11 col0" >61</td>
-      <td id="T_e63cc_row11_col1" class="data row11 col1" >1.13</td>
+      <th id="T_90020_level0_row11" class="row_heading level0 row11" >Limmat</th>
+      <td id="T_90020_row11_col0" class="data row11 col0" >61</td>
+      <td id="T_90020_row11_col1" class="data row11 col1" >1.13</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row12" class="row_heading level0 row12" >Ognonnaz</th>
-      <td id="T_e63cc_row12_col0" class="data row12 col0" >1</td>
-      <td id="T_e63cc_row12_col1" class="data row12 col1" >2.85</td>
+      <th id="T_90020_level0_row12" class="row_heading level0 row12" >Ognonnaz</th>
+      <td id="T_90020_row12_col0" class="data row12 col0" >1</td>
+      <td id="T_90020_row12_col1" class="data row12 col1" >2.85</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row13" class="row_heading level0 row13" >Rhein</th>
-      <td id="T_e63cc_row13_col0" class="data row13 col0" >10</td>
-      <td id="T_e63cc_row13_col1" class="data row13 col1" >1.52</td>
+      <th id="T_90020_level0_row13" class="row_heading level0 row13" >Rhein</th>
+      <td id="T_90020_row13_col0" class="data row13 col0" >10</td>
+      <td id="T_90020_row13_col1" class="data row13 col1" >1.52</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row14" class="row_heading level0 row14" >Rhone</th>
-      <td id="T_e63cc_row14_col0" class="data row14 col0" >8</td>
-      <td id="T_e63cc_row14_col1" class="data row14 col1" >3.43</td>
+      <th id="T_90020_level0_row14" class="row_heading level0 row14" >Rhone</th>
+      <td id="T_90020_row14_col0" class="data row14 col0" >8</td>
+      <td id="T_90020_row14_col1" class="data row14 col1" >3.43</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row15" class="row_heading level0 row15" >Schuss</th>
-      <td id="T_e63cc_row15_col0" class="data row15 col0" >2</td>
-      <td id="T_e63cc_row15_col1" class="data row15 col1" >1.04</td>
+      <th id="T_90020_level0_row15" class="row_heading level0 row15" >Schuss</th>
+      <td id="T_90020_row15_col0" class="data row15 col0" >2</td>
+      <td id="T_90020_row15_col1" class="data row15 col1" >1.04</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row16" class="row_heading level0 row16" >Sihl</th>
-      <td id="T_e63cc_row16_col0" class="data row16 col0" >51</td>
-      <td id="T_e63cc_row16_col1" class="data row16 col1" >1.08</td>
+      <th id="T_90020_level0_row16" class="row_heading level0 row16" >Sihl</th>
+      <td id="T_90020_row16_col0" class="data row16 col0" >51</td>
+      <td id="T_90020_row16_col1" class="data row16 col1" >1.08</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row17" class="row_heading level0 row17" >Toss</th>
-      <td id="T_e63cc_row17_col0" class="data row17 col0" >17</td>
-      <td id="T_e63cc_row17_col1" class="data row17 col1" >0.59</td>
+      <th id="T_90020_level0_row17" class="row_heading level0 row17" >Toss</th>
+      <td id="T_90020_row17_col0" class="data row17 col0" >17</td>
+      <td id="T_90020_row17_col1" class="data row17 col1" >0.59</td>
     </tr>
     <tr>
-      <th id="T_e63cc_level0_row18" class="row_heading level0 row18" >Zulg</th>
-      <td id="T_e63cc_row18_col0" class="data row18 col0" >11</td>
-      <td id="T_e63cc_row18_col1" class="data row18 col1" >0.63</td>
+      <th id="T_90020_level0_row18" class="row_heading level0 row18" >Zulg</th>
+      <td id="T_90020_row18_col0" class="data row18 col0" >11</td>
+      <td id="T_90020_row18_col1" class="data row18 col1" >0.63</td>
     </tr>
   </tbody>
 </table>
@@ -2755,273 +2755,187 @@ <h2>Lakes and rivers sampled - all data<a class="headerlink" href="#lakes-and-ri
 </div>
 </div>
 </div>
-<div class="section" id="municipal-results-all-data">
-<h2>Municipal Results - all data<a class="headerlink" href="#municipal-results-all-data" title="Permalink to this heading">#</a></h2>
-<p>The average pieces per meter of the most common objects for each canton.</p>
-<div class="sd-tab-set docutils">
-<input checked="checked" id="sd-tab-item-15" name="sd-tab-set-5" type="radio">
-</input><label class="sd-tab-label" for="sd-tab-item-15">
-Lakes</label><div class="sd-tab-content docutils">
+<div class="section" id="cantonal-results-all-data">
+<h2>Cantonal Results - all data<a class="headerlink" href="#cantonal-results-all-data" title="Permalink to this heading">#</a></h2>
+<p>The average pieces per meter for each canton.</p>
+<div class="sd-container-fluid sd-sphinx-override sd-mb-4 docutils">
+<div class="sd-row docutils">
+<div class="sd-col sd-d-flex-row docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm docutils">
+<div class="sd-card-header docutils">
+<p class="sd-card-text">Lakes</p>
+</div>
+<div class="sd-card-body docutils">
 <div class="output text_html"><style type="text/css">
-#T_bb25a tr:nth-child(even) {
+#T_190e1 tr:nth-child(even) {
   background-color: rgba(139, 69, 19, 0.08);
 }
-#T_bb25a tr:nth-child(odd) {
+#T_190e1 tr:nth-child(odd) {
   background: #FFF;
 }
-#T_bb25a tr {
+#T_190e1 tr {
   font-size: 10px;
 }
-#T_bb25a th {
+#T_190e1 th {
   background-color: #FFF;
   font-size: 12px;
   text-align: left;
   width: auto;
   word-break: keep-all;
 }
-#T_bb25a td {
+#T_190e1 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
 }
-#T_bb25a caption {
+#T_190e1 caption {
   caption-side: bottom;
   font-size: 1em;
   text-align: left;
 }
-#T_bb25a_row1_col1, #T_bb25a_row2_col1, #T_bb25a_row3_col1 {
+#T_190e1_row1_col1, #T_190e1_row2_col1, #T_190e1_row3_col1 {
   background-color: #FAE8E8;
 }
 </style>
-<table id="T_bb25a">
+<table id="T_190e1">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_bb25a_level0_col0" class="col_heading level0 col0" >quantity</th>
-      <th id="T_bb25a_level0_col1" class="col_heading level0 col1" >pcs/m</th>
-      <th id="T_bb25a_level0_col2" class="col_heading level0 col2" >samples</th>
-      <th id="T_bb25a_level0_col3" class="col_heading level0 col3" >orchards</th>
-      <th id="T_bb25a_level0_col4" class="col_heading level0 col4" >vineyards</th>
-      <th id="T_bb25a_level0_col5" class="col_heading level0 col5" >buildings</th>
-      <th id="T_bb25a_level0_col6" class="col_heading level0 col6" >forest</th>
-      <th id="T_bb25a_level0_col7" class="col_heading level0 col7" >undefined</th>
-      <th id="T_bb25a_level0_col8" class="col_heading level0 col8" >public services</th>
-      <th id="T_bb25a_level0_col9" class="col_heading level0 col9" >streets</th>
+      <th id="T_190e1_level0_col0" class="col_heading level0 col0" >quantity</th>
+      <th id="T_190e1_level0_col1" class="col_heading level0 col1" >pcs/m</th>
+      <th id="T_190e1_level0_col2" class="col_heading level0 col2" >samples</th>
     </tr>
     <tr>
       <th class="index_name level0" >canton</th>
       <th class="blank col0" >&nbsp;</th>
       <th class="blank col1" >&nbsp;</th>
       <th class="blank col2" >&nbsp;</th>
-      <th class="blank col3" >&nbsp;</th>
-      <th class="blank col4" >&nbsp;</th>
-      <th class="blank col5" >&nbsp;</th>
-      <th class="blank col6" >&nbsp;</th>
-      <th class="blank col7" >&nbsp;</th>
-      <th class="blank col8" >&nbsp;</th>
-      <th class="blank col9" >&nbsp;</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_bb25a_level0_row0" class="row_heading level0 row0" >Bern</th>
-      <td id="T_bb25a_row0_col0" class="data row0 col0" >9889</td>
-      <td id="T_bb25a_row0_col1" class="data row0 col1" >2.81</td>
-      <td id="T_bb25a_row0_col2" class="data row0 col2" >98</td>
-      <td id="T_bb25a_row0_col3" class="data row0 col3" >1</td>
-      <td id="T_bb25a_row0_col4" class="data row0 col4" >1</td>
-      <td id="T_bb25a_row0_col5" class="data row0 col5" >2</td>
-      <td id="T_bb25a_row0_col6" class="data row0 col6" >2</td>
-      <td id="T_bb25a_row0_col7" class="data row0 col7" >3</td>
-      <td id="T_bb25a_row0_col8" class="data row0 col8" >1</td>
-      <td id="T_bb25a_row0_col9" class="data row0 col9" >2</td>
-    </tr>
-    <tr>
-      <th id="T_bb25a_level0_row1" class="row_heading level0 row1" >Genève</th>
-      <td id="T_bb25a_row1_col0" class="data row1 col0" >9559</td>
-      <td id="T_bb25a_row1_col1" class="data row1 col1" >5.88</td>
-      <td id="T_bb25a_row1_col2" class="data row1 col2" >33</td>
-      <td id="T_bb25a_row1_col3" class="data row1 col3" >1</td>
-      <td id="T_bb25a_row1_col4" class="data row1 col4" >1</td>
-      <td id="T_bb25a_row1_col5" class="data row1 col5" >5</td>
-      <td id="T_bb25a_row1_col6" class="data row1 col6" >1</td>
-      <td id="T_bb25a_row1_col7" class="data row1 col7" >1</td>
-      <td id="T_bb25a_row1_col8" class="data row1 col8" >1</td>
-      <td id="T_bb25a_row1_col9" class="data row1 col9" >3</td>
-    </tr>
-    <tr>
-      <th id="T_bb25a_level0_row2" class="row_heading level0 row2" >Valais</th>
-      <td id="T_bb25a_row2_col0" class="data row2 col0" >8902</td>
-      <td id="T_bb25a_row2_col1" class="data row2 col1" >23.70</td>
-      <td id="T_bb25a_row2_col2" class="data row2 col2" >13</td>
-      <td id="T_bb25a_row2_col3" class="data row2 col3" >1</td>
-      <td id="T_bb25a_row2_col4" class="data row2 col4" >1</td>
-      <td id="T_bb25a_row2_col5" class="data row2 col5" >2</td>
-      <td id="T_bb25a_row2_col6" class="data row2 col6" >3</td>
-      <td id="T_bb25a_row2_col7" class="data row2 col7" >2</td>
-      <td id="T_bb25a_row2_col8" class="data row2 col8" >1</td>
-      <td id="T_bb25a_row2_col9" class="data row2 col9" >1</td>
-    </tr>
-    <tr>
-      <th id="T_bb25a_level0_row3" class="row_heading level0 row3" >Vaud</th>
-      <td id="T_bb25a_row3_col0" class="data row3 col0" >62468</td>
-      <td id="T_bb25a_row3_col1" class="data row3 col1" >7.78</td>
-      <td id="T_bb25a_row3_col2" class="data row3 col2" >226</td>
-      <td id="T_bb25a_row3_col3" class="data row3 col3" >1</td>
-      <td id="T_bb25a_row3_col4" class="data row3 col4" >1</td>
-      <td id="T_bb25a_row3_col5" class="data row3 col5" >4</td>
-      <td id="T_bb25a_row3_col6" class="data row3 col6" >1</td>
-      <td id="T_bb25a_row3_col7" class="data row3 col7" >1</td>
-      <td id="T_bb25a_row3_col8" class="data row3 col8" >1</td>
-      <td id="T_bb25a_row3_col9" class="data row3 col9" >2</td>
-    </tr>
-    <tr>
-      <th id="T_bb25a_level0_row4" class="row_heading level0 row4" >Zürich</th>
-      <td id="T_bb25a_row4_col0" class="data row4 col0" >27228</td>
-      <td id="T_bb25a_row4_col1" class="data row4 col1" >3.99</td>
-      <td id="T_bb25a_row4_col2" class="data row4 col2" >171</td>
-      <td id="T_bb25a_row4_col3" class="data row4 col3" >1</td>
-      <td id="T_bb25a_row4_col4" class="data row4 col4" >1</td>
-      <td id="T_bb25a_row4_col5" class="data row4 col5" >4</td>
-      <td id="T_bb25a_row4_col6" class="data row4 col6" >1</td>
-      <td id="T_bb25a_row4_col7" class="data row4 col7" >2</td>
-      <td id="T_bb25a_row4_col8" class="data row4 col8" >1</td>
-      <td id="T_bb25a_row4_col9" class="data row4 col9" >2</td>
+      <th id="T_190e1_level0_row0" class="row_heading level0 row0" >Bern</th>
+      <td id="T_190e1_row0_col0" class="data row0 col0" >9889</td>
+      <td id="T_190e1_row0_col1" class="data row0 col1" >2.81</td>
+      <td id="T_190e1_row0_col2" class="data row0 col2" >98</td>
+    </tr>
+    <tr>
+      <th id="T_190e1_level0_row1" class="row_heading level0 row1" >Genève</th>
+      <td id="T_190e1_row1_col0" class="data row1 col0" >9559</td>
+      <td id="T_190e1_row1_col1" class="data row1 col1" >5.88</td>
+      <td id="T_190e1_row1_col2" class="data row1 col2" >33</td>
+    </tr>
+    <tr>
+      <th id="T_190e1_level0_row2" class="row_heading level0 row2" >Valais</th>
+      <td id="T_190e1_row2_col0" class="data row2 col0" >8902</td>
+      <td id="T_190e1_row2_col1" class="data row2 col1" >23.70</td>
+      <td id="T_190e1_row2_col2" class="data row2 col2" >13</td>
+    </tr>
+    <tr>
+      <th id="T_190e1_level0_row3" class="row_heading level0 row3" >Vaud</th>
+      <td id="T_190e1_row3_col0" class="data row3 col0" >62468</td>
+      <td id="T_190e1_row3_col1" class="data row3 col1" >7.78</td>
+      <td id="T_190e1_row3_col2" class="data row3 col2" >226</td>
+    </tr>
+    <tr>
+      <th id="T_190e1_level0_row4" class="row_heading level0 row4" >Zürich</th>
+      <td id="T_190e1_row4_col0" class="data row4 col0" >27228</td>
+      <td id="T_190e1_row4_col1" class="data row4 col1" >3.99</td>
+      <td id="T_190e1_row4_col2" class="data row4 col2" >171</td>
     </tr>
   </tbody>
 </table>
 </div></div>
-<input id="sd-tab-item-16" name="sd-tab-set-5" type="radio">
-</input><label class="sd-tab-label" for="sd-tab-item-16">
-Rivers</label><div class="sd-tab-content docutils">
+</div>
+</div>
+<div class="sd-col sd-d-flex-row docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm docutils">
+<div class="sd-card-header docutils">
+<p class="sd-card-text">Rivers</p>
+</div>
+<div class="sd-card-body docutils">
 <div class="output text_html"><style type="text/css">
-#T_795cb tr:nth-child(even) {
+#T_1cfe3 tr:nth-child(even) {
   background-color: rgba(139, 69, 19, 0.08);
 }
-#T_795cb tr:nth-child(odd) {
+#T_1cfe3 tr:nth-child(odd) {
   background: #FFF;
 }
-#T_795cb tr {
+#T_1cfe3 tr {
   font-size: 10px;
 }
-#T_795cb th {
+#T_1cfe3 th {
   background-color: #FFF;
   font-size: 12px;
   text-align: left;
   width: auto;
   word-break: keep-all;
 }
-#T_795cb td {
+#T_1cfe3 td {
   padding: 4px;
   font-size: 12px;
   text-align: center;
 }
-#T_795cb caption {
+#T_1cfe3 caption {
   caption-side: bottom;
   font-size: 1em;
   text-align: left;
 }
 </style>
-<table id="T_795cb">
+<table id="T_1cfe3">
   <thead>
     <tr>
       <th class="blank level0" >&nbsp;</th>
-      <th id="T_795cb_level0_col0" class="col_heading level0 col0" >quantity</th>
-      <th id="T_795cb_level0_col1" class="col_heading level0 col1" >pcs/m</th>
-      <th id="T_795cb_level0_col2" class="col_heading level0 col2" >samples</th>
-      <th id="T_795cb_level0_col3" class="col_heading level0 col3" >orchards</th>
-      <th id="T_795cb_level0_col4" class="col_heading level0 col4" >vineyards</th>
-      <th id="T_795cb_level0_col5" class="col_heading level0 col5" >buildings</th>
-      <th id="T_795cb_level0_col6" class="col_heading level0 col6" >forest</th>
-      <th id="T_795cb_level0_col7" class="col_heading level0 col7" >undefined</th>
-      <th id="T_795cb_level0_col8" class="col_heading level0 col8" >public services</th>
-      <th id="T_795cb_level0_col9" class="col_heading level0 col9" >streets</th>
+      <th id="T_1cfe3_level0_col0" class="col_heading level0 col0" >quantity</th>
+      <th id="T_1cfe3_level0_col1" class="col_heading level0 col1" >pcs/m</th>
+      <th id="T_1cfe3_level0_col2" class="col_heading level0 col2" >samples</th>
     </tr>
     <tr>
       <th class="index_name level0" >canton</th>
       <th class="blank col0" >&nbsp;</th>
       <th class="blank col1" >&nbsp;</th>
       <th class="blank col2" >&nbsp;</th>
-      <th class="blank col3" >&nbsp;</th>
-      <th class="blank col4" >&nbsp;</th>
-      <th class="blank col5" >&nbsp;</th>
-      <th class="blank col6" >&nbsp;</th>
-      <th class="blank col7" >&nbsp;</th>
-      <th class="blank col8" >&nbsp;</th>
-      <th class="blank col9" >&nbsp;</th>
     </tr>
   </thead>
   <tbody>
     <tr>
-      <th id="T_795cb_level0_row0" class="row_heading level0 row0" >Bern</th>
-      <td id="T_795cb_row0_col0" class="data row0 col0" >3701</td>
-      <td id="T_795cb_row0_col1" class="data row0 col1" >1.21</td>
-      <td id="T_795cb_row0_col2" class="data row0 col2" >97</td>
-      <td id="T_795cb_row0_col3" class="data row0 col3" >1</td>
-      <td id="T_795cb_row0_col4" class="data row0 col4" >1</td>
-      <td id="T_795cb_row0_col5" class="data row0 col5" >3</td>
-      <td id="T_795cb_row0_col6" class="data row0 col6" >1</td>
-      <td id="T_795cb_row0_col7" class="data row0 col7" >2</td>
-      <td id="T_795cb_row0_col8" class="data row0 col8" >1</td>
-      <td id="T_795cb_row0_col9" class="data row0 col9" >3</td>
-    </tr>
-    <tr>
-      <th id="T_795cb_level0_row1" class="row_heading level0 row1" >Genève</th>
-      <td id="T_795cb_row1_col0" class="data row1 col0" >1616</td>
-      <td id="T_795cb_row1_col1" class="data row1 col1" >4.38</td>
-      <td id="T_795cb_row1_col2" class="data row1 col2" >9</td>
-      <td id="T_795cb_row1_col3" class="data row1 col3" >1</td>
-      <td id="T_795cb_row1_col4" class="data row1 col4" >1</td>
-      <td id="T_795cb_row1_col5" class="data row1 col5" >4</td>
-      <td id="T_795cb_row1_col6" class="data row1 col6" >1</td>
-      <td id="T_795cb_row1_col7" class="data row1 col7" >1</td>
-      <td id="T_795cb_row1_col8" class="data row1 col8" >1</td>
-      <td id="T_795cb_row1_col9" class="data row1 col9" >3</td>
-    </tr>
-    <tr>
-      <th id="T_795cb_level0_row2" class="row_heading level0 row2" >Valais</th>
-      <td id="T_795cb_row2_col0" class="data row2 col0" >78</td>
-      <td id="T_795cb_row2_col1" class="data row2 col1" >0.40</td>
-      <td id="T_795cb_row2_col2" class="data row2 col2" >4</td>
-      <td id="T_795cb_row2_col3" class="data row2 col3" >1</td>
-      <td id="T_795cb_row2_col4" class="data row2 col4" >1</td>
-      <td id="T_795cb_row2_col5" class="data row2 col5" >2</td>
-      <td id="T_795cb_row2_col6" class="data row2 col6" >2</td>
-      <td id="T_795cb_row2_col7" class="data row2 col7" >2</td>
-      <td id="T_795cb_row2_col8" class="data row2 col8" >1</td>
-      <td id="T_795cb_row2_col9" class="data row2 col9" >3</td>
-    </tr>
-    <tr>
-      <th id="T_795cb_level0_row3" class="row_heading level0 row3" >Vaud</th>
-      <td id="T_795cb_row3_col0" class="data row3 col0" >738</td>
-      <td id="T_795cb_row3_col1" class="data row3 col1" >2.64</td>
-      <td id="T_795cb_row3_col2" class="data row3 col2" >5</td>
-      <td id="T_795cb_row3_col3" class="data row3 col3" >1</td>
-      <td id="T_795cb_row3_col4" class="data row3 col4" >1</td>
-      <td id="T_795cb_row3_col5" class="data row3 col5" >3</td>
-      <td id="T_795cb_row3_col6" class="data row3 col6" >2</td>
-      <td id="T_795cb_row3_col7" class="data row3 col7" >2</td>
-      <td id="T_795cb_row3_col8" class="data row3 col8" >1</td>
-      <td id="T_795cb_row3_col9" class="data row3 col9" >3</td>
-    </tr>
-    <tr>
-      <th id="T_795cb_level0_row4" class="row_heading level0 row4" >Zürich</th>
-      <td id="T_795cb_row4_col0" class="data row4 col0" >6557</td>
-      <td id="T_795cb_row4_col1" class="data row4 col1" >1.15</td>
-      <td id="T_795cb_row4_col2" class="data row4 col2" >175</td>
-      <td id="T_795cb_row4_col3" class="data row4 col3" >1</td>
-      <td id="T_795cb_row4_col4" class="data row4 col4" >1</td>
-      <td id="T_795cb_row4_col5" class="data row4 col5" >4</td>
-      <td id="T_795cb_row4_col6" class="data row4 col6" >1</td>
-      <td id="T_795cb_row4_col7" class="data row4 col7" >2</td>
-      <td id="T_795cb_row4_col8" class="data row4 col8" >1</td>
-      <td id="T_795cb_row4_col9" class="data row4 col9" >3</td>
+      <th id="T_1cfe3_level0_row0" class="row_heading level0 row0" >Bern</th>
+      <td id="T_1cfe3_row0_col0" class="data row0 col0" >3701</td>
+      <td id="T_1cfe3_row0_col1" class="data row0 col1" >1.21</td>
+      <td id="T_1cfe3_row0_col2" class="data row0 col2" >97</td>
+    </tr>
+    <tr>
+      <th id="T_1cfe3_level0_row1" class="row_heading level0 row1" >Genève</th>
+      <td id="T_1cfe3_row1_col0" class="data row1 col0" >1616</td>
+      <td id="T_1cfe3_row1_col1" class="data row1 col1" >4.38</td>
+      <td id="T_1cfe3_row1_col2" class="data row1 col2" >9</td>
+    </tr>
+    <tr>
+      <th id="T_1cfe3_level0_row2" class="row_heading level0 row2" >Valais</th>
+      <td id="T_1cfe3_row2_col0" class="data row2 col0" >78</td>
+      <td id="T_1cfe3_row2_col1" class="data row2 col1" >0.40</td>
+      <td id="T_1cfe3_row2_col2" class="data row2 col2" >4</td>
+    </tr>
+    <tr>
+      <th id="T_1cfe3_level0_row3" class="row_heading level0 row3" >Vaud</th>
+      <td id="T_1cfe3_row3_col0" class="data row3 col0" >738</td>
+      <td id="T_1cfe3_row3_col1" class="data row3 col1" >2.64</td>
+      <td id="T_1cfe3_row3_col2" class="data row3 col2" >5</td>
+    </tr>
+    <tr>
+      <th id="T_1cfe3_level0_row4" class="row_heading level0 row4" >Zürich</th>
+      <td id="T_1cfe3_row4_col0" class="data row4 col0" >6557</td>
+      <td id="T_1cfe3_row4_col1" class="data row4 col1" >1.15</td>
+      <td id="T_1cfe3_row4_col2" class="data row4 col2" >175</td>
     </tr>
   </tbody>
 </table>
 </div></div>
 </div>
 </div>
+</div>
+</div>
+</div>
 </div>
 
     <script type="text/x-thebe-config">
@@ -3100,7 +3014,7 @@ <h2>Municipal Results - all data<a class="headerlink" href="#municipal-results-a
 </li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#forecast">Forecast</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#lakes-and-rivers-sampled-all-data">Lakes and rivers sampled - all data</a></li>
-<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#municipal-results-all-data">Municipal Results - all data</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#cantonal-results-all-data">Cantonal Results - all data</a></li>
 </ul>
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diff --git a/docs/geneve.html b/docs/geneve.html
index 1cb859e..ccef3bc 100644
--- a/docs/geneve.html
+++ b/docs/geneve.html
@@ -2782,7 +2782,7 @@ <h2>Lakes and rivers sampled - all data<a class="headerlink" href="#lakes-and-ri
 </div>
 <div class="section" id="municipal-results-all-data">
 <h2>Municipal Results - all data<a class="headerlink" href="#municipal-results-all-data" title="Permalink to this heading">#</a></h2>
-<p>The average pieces per meter of the most common objects for each city.</p>
+<p>The average pieces per meter and the combined land use classification for each city.</p>
 <div class="sd-tab-set docutils">
 <input checked="checked" id="sd-tab-item-15" name="sd-tab-set-5" type="radio">
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index bf7ef7a..2c9ae8c 100644
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diff --git a/docs/valais.html b/docs/valais.html
index 904758f..2dd1c64 100644
--- a/docs/valais.html
+++ b/docs/valais.html
@@ -2838,7 +2838,7 @@ <h2>Lakes and rivers sampled - all data<a class="headerlink" href="#lakes-and-ri
 </div>
 <div class="section" id="municipal-results-all-data">
 <h2>Municipal Results - all data<a class="headerlink" href="#municipal-results-all-data" title="Permalink to this heading">#</a></h2>
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index 37e74b9..0f1a4f4 100644
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@@ -2528,7 +2573,7 @@
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     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
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index bd9e763..d5c5176 100644
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     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
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     "slideshow": {
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@@ -1787,6 +1811,9 @@
    "id": "ef975bf2-e403-4ca2-b9b3-dd80a14c3a86",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -1842,6 +1869,9 @@
    "id": "2fc10d30-4cc2-456a-aa5e-65365b829ef3",
    "metadata": {
     "editable": true,
+    "jupyter": {
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@@ -1901,6 +1931,9 @@
    "id": "82f55461-c497-483a-8c38-fbd509809afb",
    "metadata": {
     "editable": true,
+    "jupyter": {
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@@ -1957,6 +1990,9 @@
    "id": "9b396025-1fa6-4661-9116-593fa1ed741d",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -2335,6 +2371,9 @@
    "id": "b0bda4c0-1b4a-4011-9ad1-fb3a52aac885",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -2389,6 +2428,9 @@
    "id": "abc83f2f-6eb7-49c5-8617-4e4df3b5cc8e",
    "metadata": {
     "editable": true,
+    "jupyter": {
+     "source_hidden": true
+    },
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@@ -2443,6 +2485,9 @@
    "id": "2d5b8904-044b-4aed-916c-5e36018f4087",
    "metadata": {
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+    "jupyter": {
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@@ -2528,7 +2573,7 @@
     "\n",
     "## Municipal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
     "\n",
     "::::::::::{tab-set}\n",
     "\n",
diff --git a/zurich.ipynb b/zurich.ipynb
index faee719..b9c2001 100644
--- a/zurich.ipynb
+++ b/zurich.ipynb
@@ -2528,7 +2528,7 @@
     "\n",
     "## Municipal Results - all data\n",
     "\n",
-    "The average pieces per meter of the most common objects for each city.\n",
+    "The average pieces per meter and the combined land use classification for each city.\n",
     "\n",
     "::::::::::{tab-set}\n",
     "\n",