Lab7_Assignment_Completed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab7 Vector Analysis II\n",
    "----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HOMEWORK ASSIGNMENT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer the following questions in the jupyter notebook file. (You can also use other IDEs to test your code) **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Review Geocoding (10 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore the geocoding service providers (links in the geocoding page of lecture slides). Compare those resources with the geocoding function parameters in http://geopandas.org/geocoding.html\n",
    "\n",
    "Describe one provide in detail using your own words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#   The geocoding service provider, geocode.farm, and geopandas geocoding function both accept API keys.  They get and display locations; \n",
    "#   geopandas does it through invoking a function, while geocode.farm uses a web address to obtain it.  However, geopandas can \n",
    "#   obtain locations through any service provider, while geocode.farm does not have this versatility.  Geopandas is able to return \n",
    "#   coordinates and a physical address simultaneously as a list while accessing a file, while geocode.farm has a specific format to \n",
    "#   request either coordinates or address, and then display them individually.\n",
    "\n",
    "\n",
    "#   https://geocode.farm/geocoding/free-api-documentation/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.Review Managing Projections (30 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Review the official documentation for \"mannaging projections\" at http://geopandas.org/projections.html. Then choose 3 different projections that you may apply to your study area, locate these projections in http://spatialreference.org/, list the proj4 string, dictionary, and EPSG code representations of these projections in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "WGS84 Latitude/Longitude\n",
    "\"+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs\"\n",
    "{'proj': 'latlong', 'ellps': 'WGS84', 'datum': 'WGS84', 'no_defs': True}\n",
    "epsg:4326"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "UTM Zones (North)\n",
    "\"+proj=utm +zone=15 +ellps=WGS84 +datum=WGS84 +units=m +no_defs\"\n",
    "{'proj': 'utm', 'zone':'15' 'ellps': 'WGS84', 'datum': 'WGS84','units':'m', 'no_defs': True}\n",
    "epsg:2027"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "NAD83 / Texas State Mapping System\n",
    "+proj=lcc +lat_1=27.41666666666667 +lat_2=34.91666666666666 +lat_0=31.16666666666667 +lon_0=-100 +x_0=1000000 +y_0=1000000 +ellps=GRS80 +datum=NAD83 +units=m +no_defs \n",
    "{'proj=lcc', 'lat_1':'27.41666666666667', 'lat_2':'34.91666666666666','lat_0':'31.16666666666667','lon_0':'-100','x_0':'1000000','y_0':'1000000', 'ellps':'GRS80','datum':'NAD83', 'units':'m','no_defs':True} \n",
    "epsg:3081"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  https://proj4.org/operations/projections/index.html\n",
    "#  http://spatialreference.org/ref/sr-org/33/\n",
    "#  http://spatialreference.org/ref/epsg/3081/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Geocoding Addresses and Reproject  (30 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do geocoding in Geopandas using function called geocode(), then reproject the file to a new projection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import necessary modules, \n",
    "# pandas, geopandas and from shapely.geometry import Point\n",
    "# %matplotlib inline\n",
    "\n",
    "import geopandas as gpd\n",
    "from geopandas.tools import geocode\n",
    "from geopandas import GeoDataFrame\n",
    "import pandas as pd\n",
    "from shapely.geometry import Point\n",
    "import fiona\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       7400 San Pedro Ave, San Antonio, TX 78216\n",
       "1    15900 La Cantera Pkwy, San Antonio, TX 78256\n",
       "2         6301 NW Loop 410, San Antonio, TX 78238\n",
       "3        849 E Commerce St, San Antonio, TX 78205\n",
       "4      2310 SW Military Dr, San Antonio, TX 78224\n",
       "Name: address, dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the addresses of shopping centers from shopping_centers.xlsx in data folder\n",
    "# hint: use geopandas.read_csv()\n",
    "\n",
    "#data = gpd.read_csv(\"C:\\Users\\casca\\Downloads\\SimpleAccess\\DataEx\\shopping_centers.xlsx\")\n",
    "\n",
    "shopctrs = pd.read_excel(r\"C:\\Users\\casca\\Downloads\\SimpleAccess\\DataEx\\shopping_centers.xlsx\")\n",
    "shopctrs_addonly = shopctrs['address']\n",
    "#shopctrs\n",
    "\n",
    "shopctrs_addonly\n",
    "#geometry = [Point(xy) for xy in zip(df.x, df.y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import the geocoding tool: geocode in geopandas.tools\n",
    "# Please refer In[72]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Key for our Google Geocoding API\n",
    "# you might need to generate a new key if your key has expired.\n",
    "# https://console.developers.google.com/cloud-resource-manager\n",
    "# key = 'AIzaSyABIdV0iWbpTE-0OlkbJREnPgl-xcKEK78'\n",
    "# key is not needed.  Alternative provided below in geocode function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Geocode the shopping center addresses, using the 'address' column of data\n",
    "\n",
    "#geo = geocode(shopctrs_addonly,api_key=key)\n",
    "\n",
    "geo = geocode(shopctrs_addonly, provider =\"nominatim\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:4326', 'no_defs': True}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the current projection from .crs attribute\n",
    "geo.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>James Avery Jewelry, 7400, San Pedro Avenue, S...</td>\n",
       "      <td>POINT (-98.4965251 29.5184339)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Banamex USA, 15900, La Cantera Parkway, San An...</td>\n",
       "      <td>POINT (-98.6182580494653 29.5930121)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>James Avery Jewelry, 6301, Northwest Loop 410,...</td>\n",
       "      <td>POINT (-98.62098279999999 29.4691627)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chipotle, 849, East Commerce Street, La Villit...</td>\n",
       "      <td>POINT (-98.4871096 29.4237317)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>James Avery Jewelry, 2310, Southwest Military ...</td>\n",
       "      <td>POINT (-98.53079870000001 29.3545032)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             address  \\\n",
       "0  James Avery Jewelry, 7400, San Pedro Avenue, S...   \n",
       "1  Banamex USA, 15900, La Cantera Parkway, San An...   \n",
       "2  James Avery Jewelry, 6301, Northwest Loop 410,...   \n",
       "3  Chipotle, 849, East Commerce Street, La Villit...   \n",
       "4  James Avery Jewelry, 2310, Southwest Military ...   \n",
       "\n",
       "                                geometry  \n",
       "0         POINT (-98.4965251 29.5184339)  \n",
       "1   POINT (-98.6182580494653 29.5930121)  \n",
       "2  POINT (-98.62098279999999 29.4691627)  \n",
       "3         POINT (-98.4871096 29.4237317)  \n",
       "4  POINT (-98.53079870000001 29.3545032)  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let’s also check the data.\n",
    "geo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Okey, the coordinate values are lat-lon values.\n",
    "# let's make a copy of the geo\n",
    "\n",
    "geo_proj = geo.copy()\n",
    "\n",
    "# reproject the data into projection \"NAD 1983 StatePlane Texas Central FIPS 4203 Feet\"\n",
    "# http://spatialreference.org/ref/esri/102740/\n",
    "# hint: use .to_crs() function with parameter {'init':'esri:102740'}\n",
    "\n",
    "geo_proj = geo_proj.to_crs({'init':'esri:102740'})\n",
    "\n",
    "#geo_proj = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>James Avery Jewelry, 7400, San Pedro Avenue, S...</td>\n",
       "      <td>POINT (2128619.98943185 13736386.34092815)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Banamex USA, 15900, La Cantera Parkway, San An...</td>\n",
       "      <td>POINT (2089816.935232 13763357.57343532)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>James Avery Jewelry, 6301, Northwest Loop 410,...</td>\n",
       "      <td>POINT (2089096.962473878 13718321.48804088)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chipotle, 849, East Commerce Street, La Villit...</td>\n",
       "      <td>POINT (2131765.392905668 13701964.95648583)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>James Avery Jewelry, 2310, Southwest Military ...</td>\n",
       "      <td>POINT (2117959.163884356 13676734.91816202)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             address  \\\n",
       "0  James Avery Jewelry, 7400, San Pedro Avenue, S...   \n",
       "1  Banamex USA, 15900, La Cantera Parkway, San An...   \n",
       "2  James Avery Jewelry, 6301, Northwest Loop 410,...   \n",
       "3  Chipotle, 849, East Commerce Street, La Villit...   \n",
       "4  James Avery Jewelry, 2310, Southwest Military ...   \n",
       "\n",
       "                                      geometry  \n",
       "0   POINT (2128619.98943185 13736386.34092815)  \n",
       "1     POINT (2089816.935232 13763357.57343532)  \n",
       "2  POINT (2089096.962473878 13718321.48804088)  \n",
       "3  POINT (2131765.392905668 13701964.95648583)  \n",
       "4  POINT (2117959.163884356 13676734.91816202)  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the current projection of geo_proj from .crs attribute\n",
    "geo_proj.crs\n",
    "# Let’s check the values in our geometry column of geo_proj.\n",
    "geo_proj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17cc3df0>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAALEAAAD8CAYAAAA470V3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17b48610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKAAAAEDCAYAAACyMA/ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17ca5c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the geo and geo_proj seperately using .plot()\n",
    "geo.plot()\n",
    "geo_proj.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Data Recalssification (30 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Recalssify Austin Census tract based on the area into 5 equal interval groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import necessary modules, \n",
    "# geopandas \n",
    "# %matplotlib inline\n",
    "import geopandas as gpd\n",
    "import pysal as ps\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GIST_ID</th>\n",
       "      <th>FIPSSTCO</th>\n",
       "      <th>TRT2000</th>\n",
       "      <th>STFID</th>\n",
       "      <th>TRACTID</th>\n",
       "      <th>EditedBy</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>48491</td>\n",
       "      <td>020301</td>\n",
       "      <td>48491020301</td>\n",
       "      <td>203.01</td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((3045090.606274828 10229355.30075569,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>48491</td>\n",
       "      <td>020303</td>\n",
       "      <td>48491020303</td>\n",
       "      <td>203.03</td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((3078201.24957263 10174754.79228604, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>48491</td>\n",
       "      <td>020305</td>\n",
       "      <td>48491020305</td>\n",
       "      <td>203.05</td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((3075502.688383114 10174268.29249388,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>48491</td>\n",
       "      <td>020306</td>\n",
       "      <td>48491020306</td>\n",
       "      <td>203.06</td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((3083784.449301289 10164193.53322234,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>48491</td>\n",
       "      <td>020307</td>\n",
       "      <td>48491020307</td>\n",
       "      <td>203.07</td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((3087057.804743127 10152944.63629382,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GIST_ID FIPSSTCO TRT2000        STFID TRACTID EditedBy  \\\n",
       "0        6    48491  020301  48491020301  203.01            \n",
       "1        8    48491  020303  48491020303  203.03            \n",
       "2        9    48491  020305  48491020305  203.05            \n",
       "3       10    48491  020306  48491020306  203.06            \n",
       "4       11    48491  020307  48491020307  203.07            \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((3045090.606274828 10229355.30075569,...  \n",
       "1  POLYGON ((3078201.24957263 10174754.79228604, ...  \n",
       "2  POLYGON ((3075502.688383114 10174268.29249388,...  \n",
       "3  POLYGON ((3083784.449301289 10164193.53322234,...  \n",
       "4  POLYGON ((3087057.804743127 10152944.63629382,...  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read file Census_Tracts.shp from the data folder\n",
    "ctAustin = gpd.read_file(r\"C:\\Users\\casca\\Downloads\\SimpleAccess\\DataEx\\Census_Tracts.shp\")\n",
    "ctAustin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x175669b0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fkTdv69CqBVtqHAAEIrHkmhY7s2FonwA+LRR2AB4pZZcQQgc8jpJEe3TkYKkEhV9BidcC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\nhoaGRo75/zNhv5lSHzHkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17485470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# explore the ctAustin using .plot()\n",
    "ctAustin.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GIST_ID</th>\n",
       "      <th>FIPSSTCO</th>\n",
       "      <th>TRT2000</th>\n",
       "      <th>STFID</th>\n",
       "      <th>TRACTID</th>\n",
       "      <th>EditedBy</th>\n",
       "      <th>geometry</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>48491</td>\n",
       "      <td>020301</td>\n",
       "      <td>48491020301</td>\n",
       "      <td>203.01</td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((3045090.606274828 10229355.30075569,...</td>\n",
       "      <td>1.572039e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>48491</td>\n",
       "      <td>020303</td>\n",
       "      <td>48491020303</td>\n",
       "      <td>203.03</td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((3078201.24957263 10174754.79228604, ...</td>\n",
       "      <td>3.248904e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>48491</td>\n",
       "      <td>020305</td>\n",
       "      <td>48491020305</td>\n",
       "      <td>203.05</td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((3075502.688383114 10174268.29249388,...</td>\n",
       "      <td>9.791339e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>48491</td>\n",
       "      <td>020306</td>\n",
       "      <td>48491020306</td>\n",
       "      <td>203.06</td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((3083784.449301289 10164193.53322234,...</td>\n",
       "      <td>9.771745e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>48491</td>\n",
       "      <td>020307</td>\n",
       "      <td>48491020307</td>\n",
       "      <td>203.07</td>\n",
       "      <td></td>\n",
       "      <td>POLYGON ((3087057.804743127 10152944.63629382,...</td>\n",
       "      <td>1.033356e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GIST_ID FIPSSTCO TRT2000        STFID TRACTID EditedBy  \\\n",
       "0        6    48491  020301  48491020301  203.01            \n",
       "1        8    48491  020303  48491020303  203.03            \n",
       "2        9    48491  020305  48491020305  203.05            \n",
       "3       10    48491  020306  48491020306  203.06            \n",
       "4       11    48491  020307  48491020307  203.07            \n",
       "\n",
       "                                            geometry          area  \n",
       "0  POLYGON ((3045090.606274828 10229355.30075569,...  1.572039e+09  \n",
       "1  POLYGON ((3078201.24957263 10174754.79228604, ...  3.248904e+08  \n",
       "2  POLYGON ((3075502.688383114 10174268.29249388,...  9.791339e+07  \n",
       "3  POLYGON ((3083784.449301289 10164193.53322234,...  9.771745e+07  \n",
       "4  POLYGON ((3087057.804743127 10152944.63629382,...  1.033356e+08  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate Area and add a column 'area'\n",
    "ctAustin['area'] = ctAustin.area\n",
    "ctAustin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import pysal\n",
    "# Please refer to In[52]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Equal interval classifier with 6 classes\n",
    "# using pysal.CLASSIFIER.make(k=?)\n",
    "classifier = ps.Equal_Interval.make(k=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area\n",
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Classify the data by applying the classifiyer to the area column\n",
    "classifications =  ctAustin[['area']].apply(classifier)\n",
    "classifications.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Rename the column so that we know that it was classified with euqal interval\n",
    "classifications.columns = ['EqInt_area']\n",
    "# Join with our original data (here index is the key)\n",
    "ctAustin_ei = ctAustin.join(classifications)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17718310>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZFGU2YGiugu72wBhKay1X3jeD+Anhm1bSF8+ibd8RdCYTkz68huvK13HV7hfI/fhVALTt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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17701bb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the ctAustin_ei using the new column values.\n",
    "ctAustin_ei.plot(column=\"EqInt_area\", cmap=\"RdYlBu\", legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": true,
   "toc_cell": false,
   "toc_position": {
    "height": "262px",
    "left": "576px",
    "right": "20px",
    "top": "188px",
    "width": "353px"
   },
   "toc_section_display": "none",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}