03-dd_centro_poblacion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from osgeo import ogr\n",
    "from shapely.geometry import MultiLineString, Polygon, MultiPolygon\n",
    "from shapely import wkt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "from functions.geograficas import *\n",
    "from functions.cargar_data import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4302, 12)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# capa de deptos\n",
    "deptos = gpd.read_file('capas/ine_deptos.gpkg')\n",
    "\n",
    "# zonas 2011\n",
    "zonas = gpd.read_file('capas/ine_zonas_11.gpkg')\n",
    "\n",
    "# personas censo 96\n",
    "personas96 = cargar_censo_96()\n",
    "\n",
    "# personas censo 2011\n",
    "# censo = pd.read_csv('tablas/personas_censo_2011.gz', compression='gzip',\n",
    "#                     header=0, sep=',', quotechar='\"')\n",
    "\n",
    "cols = ['DPTO', 'LOC', 'SECC', 'SEGM','PERPH02']\n",
    "censo = cargar_censo_vars(cols)\n",
    "\n",
    "# carga capa de segmentos censales\n",
    "seg = gpd.read_file('capas/ine_segm_11.gpkg')\n",
    "seg['CODSEG'] = seg['CODSEG'].astype(str)\n",
    "seg.drop_duplicates('CODSEG', inplace=True)\n",
    "seg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4283, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DPTO</th>\n",
       "      <th>LOC</th>\n",
       "      <th>SECC</th>\n",
       "      <th>SEGM</th>\n",
       "      <th>poblacion</th>\n",
       "      <th>CODSEG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>696</td>\n",
       "      <td>101001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>660</td>\n",
       "      <td>101002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>786</td>\n",
       "      <td>101003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>104</td>\n",
       "      <td>882</td>\n",
       "      <td>101104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>818</td>\n",
       "      <td>101105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DPTO  LOC  SECC  SEGM  poblacion  CODSEG\n",
       "0     1   20     1     1        696  101001\n",
       "1     1   20     1     2        660  101002\n",
       "2     1   20     1     3        786  101003\n",
       "3     1   20     1   104        882  101104\n",
       "4     1   20     1   105        818  101105"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calcula la población de cada segmento censal\n",
    "poblacion = censo[cols].groupby(by=['DPTO','LOC', 'SECC', 'SEGM']).count()\n",
    "poblacion.rename(columns={'PERPH02': 'poblacion'}, inplace=True)\n",
    "\n",
    "poblacion.reset_index(inplace=True)\n",
    "\n",
    "# genera cod seg\n",
    "poblacion['CODSEG'] = get_codseg(poblacion)\n",
    "\n",
    "print(poblacion.shape)\n",
    "poblacion.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "poblacion.drop_duplicates('CODSEG', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pobl_seg = pd.merge(seg, poblacion, on='CODSEG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ecuación del centro medio de población\n",
    "\n",
    "$$\n",
    "\\overline{X}_w=\\frac{\\sum_{i=1}^{n}w_{i}X_{i}}{\\sum_{i=1}^{n} w_{i}}\n",
    "$$\n",
    "\n",
    "\n",
    "$$\n",
    "\\overline{Y}_w=\\frac{\\sum_{i=1}^{n}w_{i}Y_{i}}{\\sum_{i=1}^{n} w_{i}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Centro medio de población 2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmean, ymean = centro_medio(pobl_seg, 'DPTO', 'poblacion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPTO</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>POINT (575497.000 6141786.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>POINT (517717.000 6636093.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>POINT (587353.000 6159278.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>POINT (777711.000 6409999.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>POINT (431654.000 6211872.000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DEPTO                        geometry\n",
       "0      1  POINT (575497.000 6141786.000)\n",
       "1      2  POINT (517717.000 6636093.000)\n",
       "2      3  POINT (587353.000 6159278.000)\n",
       "3      4  POINT (777711.000 6409999.000)\n",
       "4      5  POINT (431654.000 6211872.000)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_center_gdf = gpd.GeoDataFrame({'DEPTO': np.arange(1,20)},\n",
    "                                   geometry=gpd.points_from_xy(xmean, ymean),\n",
    "                                   crs= 'epsg:32721')\n",
    "\n",
    "mean_center_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f45c384b190>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot de las capas de departamentos y localidades\n",
    "plt.style.use('default')\n",
    "\n",
    "f, ax = pl.subplots(figsize=(8,6))\n",
    "\n",
    "# deptos\n",
    "deptos.plot(linewidth = 0.7, edgecolor='black', color = 'grey', alpha=0.1, ax = ax)\n",
    "\n",
    "# localidades\n",
    "mean_center_gdf.plot(markersize=14, color = 'red', ax = ax, label='Centro medio de población')\n",
    "deptos.geometry.centroid.plot(markersize=5, color = 'grey', ax = ax, label='Centroide')\n",
    "\n",
    "# oculta ejes\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "\n",
    "# hide spines\n",
    "[ax.spines[i].set_visible(False) for i in ['right', 'top', 'left', 'bottom']]\n",
    "\n",
    "# título\n",
    "pl.title(\"Centroide vs. Centro de Población (datos Censo 2011)\", size=12)\n",
    "\n",
    "# referencias\n",
    "plt.legend(frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # exporta geopaquete\n",
    "# mean_center_gdf.to_file(\"capas/centro_poblacion.gpkg\",\n",
    "#                         layer='centro_de_poblacion', driver=\"GPKG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data para webmap\n",
    "mean_center_webmap = mean_center_gdf.to_crs(4326).rename({'DEPTO': 'id'}, axis = 1)\n",
    "mean_center_webmap['name'] = decode_depto_pretty(mean_center_webmap.id)\n",
    "mean_center_webmap['lat'] = round(mean_center_webmap.geometry.y, 5)\n",
    "mean_center_webmap['lon'] = round(mean_center_webmap.geometry.x, 5)\n",
    "mean_center_webmap = mean_center_webmap.drop('geometry', axis=1)\n",
    "mean_center_webmap.to_csv(\"webmap_flows/nodes.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Centro medio de población 1996"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "capa_96 = zonas.merge(personas96, left_on='CODCOMP', right_on='CCOMP04', how='inner')\n",
    "capa_96 = capa_96.loc[:, ['DEPTO', 'CODCOMP','CCOMP04','personas','geometry']]\n",
    "capa_96.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmean, ymean = centro_medio(capa_96, 'DEPTO', 'personas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_center_gdf_96 = gpd.GeoDataFrame({'DEPTO': np.arange(1,20)},\n",
    "                                   geometry=gpd.points_from_xy(xmean, ymean),\n",
    "                                   crs= 'epsg:32721')\n",
    "\n",
    "mean_center_gdf_96.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot de las capas de departamentos y localidades\n",
    "plt.style.use('default')\n",
    "\n",
    "f, ax = pl.subplots(figsize=(8,6))\n",
    "\n",
    "# deptos\n",
    "deptos.plot(linewidth = 0.7, edgecolor='black', color = 'grey', alpha=0.1, ax = ax)\n",
    "\n",
    "# localidades\n",
    "mean_center_gdf.plot(markersize=14, color = 'red', ax = ax, label='Centro medio de población 2011')\n",
    "mean_center_gdf_96.plot(markersize=14, color = 'blue', ax = ax, label='Centro medio de población 1996')\n",
    "deptos.geometry.centroid.plot(markersize=5, color = 'grey', ax = ax, label='Centroide')\n",
    "\n",
    "# oculta ejes\n",
    "ax.get_xaxis().set_visible(False)\n",
    "ax.get_yaxis().set_visible(False)\n",
    "\n",
    "# hide spines\n",
    "[ax.spines[i].set_visible(False) for i in ['right', 'top', 'left', 'bottom']]\n",
    "\n",
    "# título\n",
    "pl.title(\"Centroide vs. Centros de Población (Censos 1996 y 2011)\", size=12)\n",
    "\n",
    "# referencias\n",
    "# plt.legend(frameon=False)"
   ]
  }
 ],
 "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}