00_geoprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Geoprocessing\n",
    "\n",
    "Spatial data comes in a rich variety of forms and corresponding file formats. At the beginning of most geocomputational workflows, one is typically reading these different formats and applying different forms of spatial data processing (or geoprocessing) methods to the data.\n",
    "\n",
    "In this notebook we cover a subset of geoprocessing methods:\n",
    "\n",
    "- Reading geopackages and shapefiles\n",
    "- Coordinate reference system transformations\n",
    "- Spatial joins\n",
    "- Spatial aggregations\n",
    "\n",
    "Along the way we introduce the package [geopandas](https://geopandas.org) which provides key spatial data processing functionality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading GeoPackages\n",
    "\n",
    "We begin with our first dataset that contains socieconomic data for census tracts in Southern California. The data are for the census year 2010 and are provided from the package [GeoSNAP](https://github.com/spatialucr/geosnap) as a **GeoPackage**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A [GeoPackage](https://www.geopackage.org/) is an open format for geospatial information that uses an [SQLite database](https://www.sqlite.org/index.html) to store:\n",
    "\n",
    "- vector features\n",
    "- tile matrix sets of imagery and raster maps at various scales\n",
    "- attributes (non-spatial data)\n",
    "- extensions\n",
    "\n",
    "We will use **geopandas** to read the GeoPackage for our data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/carlos/anaconda3/envs/pysal-spatialapi20/lib/python3.7/site-packages/geopandas/geodataframe.py:422: RuntimeWarning: Sequential read of iterator was interrupted. Resetting iterator. This can negatively impact the performance.\n",
      "  for feature in features_lst:\n"
     ]
    }
   ],
   "source": [
    "gp = gpd.read_file(\"data/scag_region.gpkg\") # read the geopackage "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['geoid', 'n_asian_under_15', 'n_black_under_15', 'n_hispanic_under_15',\n",
       "       'n_native_under_15', 'n_white_under_15', 'n_persons_under_18',\n",
       "       'n_asian_over_60', 'n_black_over_60', 'n_hispanic_over_60',\n",
       "       ...\n",
       "       'year', 'n_total_housing_units_sample', 'p_nonhisp_white_persons',\n",
       "       'p_white_over_60', 'p_black_over_60', 'p_hispanic_over_60',\n",
       "       'p_native_over_60', 'p_asian_over_60', 'p_disabled', 'geometry'],\n",
       "      dtype='object', length=194)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our dataset there is only one layer in the GeoPackage. To show this, we can use the package [fiona](https://github.com/Toblerity/Fiona):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fiona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "layers = fiona.listlayers('data/scag_region.gpkg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tracts']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we did have more than a single layer in our geopackage, we would have to specify the layer that we want to read:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "scag = gpd.read_file(\"data/scag_region.gpkg\", layer=\"tracts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(scag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geoid</th>\n",
       "      <th>n_asian_under_15</th>\n",
       "      <th>n_black_under_15</th>\n",
       "      <th>n_hispanic_under_15</th>\n",
       "      <th>n_native_under_15</th>\n",
       "      <th>n_white_under_15</th>\n",
       "      <th>n_persons_under_18</th>\n",
       "      <th>n_asian_over_60</th>\n",
       "      <th>n_black_over_60</th>\n",
       "      <th>n_hispanic_over_60</th>\n",
       "      <th>...</th>\n",
       "      <th>year</th>\n",
       "      <th>n_total_housing_units_sample</th>\n",
       "      <th>p_nonhisp_white_persons</th>\n",
       "      <th>p_white_over_60</th>\n",
       "      <th>p_black_over_60</th>\n",
       "      <th>p_hispanic_over_60</th>\n",
       "      <th>p_native_over_60</th>\n",
       "      <th>p_asian_over_60</th>\n",
       "      <th>p_disabled</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06037128702</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>986.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>2010</td>\n",
       "      <td>2903.0</td>\n",
       "      <td>64.726214</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((-118.44870 34.16485, -118.43997 34.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06037131600</td>\n",
       "      <td>83.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>777.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>1355.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>2010</td>\n",
       "      <td>1487.0</td>\n",
       "      <td>28.679979</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((-118.56229 34.22033, -118.55792 34.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06037134104</td>\n",
       "      <td>287.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1323.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>2010</td>\n",
       "      <td>1388.0</td>\n",
       "      <td>14.846188</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((-118.57976 34.21558, -118.57539 34.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06037134304</td>\n",
       "      <td>90.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>2010</td>\n",
       "      <td>928.0</td>\n",
       "      <td>33.378933</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((-118.61472 34.21952, -118.61039 34.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06037242000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1164.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>2010</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>0.058565</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((-118.25416 33.93882, -118.25413 33.9...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 194 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         geoid  n_asian_under_15  n_black_under_15  n_hispanic_under_15  \\\n",
       "0  06037128702              58.0               0.0                223.0   \n",
       "1  06037131600              83.0              62.0                777.0   \n",
       "2  06037134104             287.0              17.0                816.0   \n",
       "3  06037134304              90.0              24.0                298.0   \n",
       "4  06037242000               0.0             229.0                681.0   \n",
       "\n",
       "   n_native_under_15  n_white_under_15  n_persons_under_18 n_asian_over_60  \\\n",
       "0                0.0             475.0               986.0            None   \n",
       "1                0.0             135.0              1355.0            None   \n",
       "2                0.0              61.0              1323.0            None   \n",
       "3                0.0              89.0               520.0            None   \n",
       "4                0.0               0.0              1164.0            None   \n",
       "\n",
       "  n_black_over_60 n_hispanic_over_60  ...  year  n_total_housing_units_sample  \\\n",
       "0            None               None  ...  2010                        2903.0   \n",
       "1            None               None  ...  2010                        1487.0   \n",
       "2            None               None  ...  2010                        1388.0   \n",
       "3            None               None  ...  2010                         928.0   \n",
       "4            None               None  ...  2010                        1054.0   \n",
       "\n",
       "  p_nonhisp_white_persons  p_white_over_60  p_black_over_60  \\\n",
       "0               64.726214             None             None   \n",
       "1               28.679979             None             None   \n",
       "2               14.846188             None             None   \n",
       "3               33.378933             None             None   \n",
       "4                0.058565             None             None   \n",
       "\n",
       "   p_hispanic_over_60  p_native_over_60  p_asian_over_60  p_disabled  \\\n",
       "0                None              None             None        None   \n",
       "1                None              None             None        None   \n",
       "2                None              None             None        None   \n",
       "3                None              None             None        None   \n",
       "4                None              None             None        None   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-118.44870 34.16485, -118.43997 34.1...  \n",
       "1  POLYGON ((-118.56229 34.22033, -118.55792 34.2...  \n",
       "2  POLYGON ((-118.57976 34.21558, -118.57539 34.2...  \n",
       "3  POLYGON ((-118.61472 34.21952, -118.61039 34.2...  \n",
       "4  POLYGON ((-118.25416 33.93882, -118.25413 33.9...  \n",
       "\n",
       "[5 rows x 194 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scag.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4580, 194)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scag.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tells us there are 4580 census tracts and 194 attributes measured for each tract."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['geoid', 'n_asian_under_15', 'n_black_under_15', 'n_hispanic_under_15',\n",
       "       'n_native_under_15', 'n_white_under_15', 'n_persons_under_18',\n",
       "       'n_asian_over_60', 'n_black_over_60', 'n_hispanic_over_60',\n",
       "       ...\n",
       "       'year', 'n_total_housing_units_sample', 'p_nonhisp_white_persons',\n",
       "       'p_white_over_60', 'p_black_over_60', 'p_hispanic_over_60',\n",
       "       'p_native_over_60', 'p_asian_over_60', 'p_disabled', 'geometry'],\n",
       "      dtype='object', length=194)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scag.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At least one of the attributes (columns) in the GeoDataFrame holds the geometries for the observations. In our case, this column is named `geometry`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       POLYGON ((-118.44870 34.16485, -118.43997 34.1...\n",
       "1       POLYGON ((-118.56229 34.22033, -118.55792 34.2...\n",
       "2       POLYGON ((-118.57976 34.21558, -118.57539 34.2...\n",
       "3       POLYGON ((-118.61472 34.21952, -118.61039 34.2...\n",
       "4       POLYGON ((-118.25416 33.93882, -118.25413 33.9...\n",
       "                              ...                        \n",
       "4575    POLYGON ((-118.50373 34.42607, -118.50050 34.4...\n",
       "4576    POLYGON ((-118.20731 33.90754, -118.20641 33.9...\n",
       "4577    POLYGON ((-119.22134 34.18130, -119.21727 34.1...\n",
       "4578    POLYGON ((-116.51068 33.80502, -116.51069 33.8...\n",
       "4579    POLYGON ((-118.41379 34.17940, -118.41160 34.1...\n",
       "Name: geometry, Length: 4580, dtype: geometry"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scag.geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows us that each of our observations is encoded as a Polygon geometry. \n",
    "\n",
    "We can call the `plot` method of the GeoDataFrame to visualize these geometries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scag.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the tract identifier column is `geoid` which encodes the [11-digit FIPS code](https://www.fcc.gov/general/form-477-census-tract-information)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading shapefiles\n",
    "\n",
    "[shapefiles](https://doc.arcgis.com/en/arcgis-online/reference/shapefiles.htm) are a common legacy spatial data format for vector data. \n",
    "Let's explore a second data set that encodes mental health provider locations in Riverside County, California as points in a shapefile:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading a point shapefile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['OBJECTID', 'SITE_TYPE', 'SITE_NAME', 'ADDRESS', 'CITY', 'ZIPCODE',\n",
       "       'PHONE', 'geometry'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinics_df = gpd.read_file('data/behavioralHealth.shp')\n",
    "clinics_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>OBJECTID</th>\n",
       "      <th>SITE_TYPE</th>\n",
       "      <th>SITE_NAME</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>CITY</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>PHONE</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>149.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Older Adult Services</td>\n",
       "      <td>6355 Riverside Ave</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92506</td>\n",
       "      <td>951-369-0219</td>\n",
       "      <td>POINT (6216487.156 2291913.664)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Children'S Treatment Services</td>\n",
       "      <td>9990 County Farm Rd</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92503</td>\n",
       "      <td>951-358-4840</td>\n",
       "      <td>POINT (6195566.226 2280519.190)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Family, Adolescent, And Children'S Treatment</td>\n",
       "      <td>1195 Magnolia Ave</td>\n",
       "      <td>Corona</td>\n",
       "      <td>91719</td>\n",
       "      <td>951-273-0608</td>\n",
       "      <td>POINT (6168252.101 2261023.951)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>447.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Blythe Clinic</td>\n",
       "      <td>1297 W Hobson Way</td>\n",
       "      <td>Blythe</td>\n",
       "      <td>92225</td>\n",
       "      <td>760-770-2222</td>\n",
       "      <td>POINT (7059944.605 2169652.046)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>152.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Interagency Services For Families</td>\n",
       "      <td>9890 County Farm Rd</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92503</td>\n",
       "      <td>951-358-4850</td>\n",
       "      <td>POINT (6195949.672 2280510.302)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID          SITE_TYPE                                     SITE_NAME  \\\n",
       "0     149.0  Behavioral Health                          Older Adult Services   \n",
       "1     150.0  Behavioral Health                 Children'S Treatment Services   \n",
       "2     151.0  Behavioral Health  Family, Adolescent, And Children'S Treatment   \n",
       "3     447.0  Behavioral Health                                 Blythe Clinic   \n",
       "4     152.0  Behavioral Health             Interagency Services For Families   \n",
       "\n",
       "               ADDRESS       CITY  ZIPCODE         PHONE  \\\n",
       "0   6355 Riverside Ave  Riverside    92506  951-369-0219   \n",
       "1  9990 County Farm Rd  Riverside    92503  951-358-4840   \n",
       "2    1195 Magnolia Ave     Corona    91719  951-273-0608   \n",
       "3    1297 W Hobson Way     Blythe    92225  760-770-2222   \n",
       "4  9890 County Farm Rd  Riverside    92503  951-358-4850   \n",
       "\n",
       "                          geometry  \n",
       "0  POINT (6216487.156 2291913.664)  \n",
       "1  POINT (6195566.226 2280519.190)  \n",
       "2  POINT (6168252.101 2261023.951)  \n",
       "3  POINT (7059944.605 2169652.046)  \n",
       "4  POINT (6195949.672 2280510.302)  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinics_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clinics_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     POINT (6216487.156 2291913.664)\n",
       "1     POINT (6195566.226 2280519.190)\n",
       "2     POINT (6168252.101 2261023.951)\n",
       "3     POINT (7059944.605 2169652.046)\n",
       "4     POINT (6195949.672 2280510.302)\n",
       "5     POINT (6195566.226 2280519.190)\n",
       "6     POINT (6255240.101 2281392.265)\n",
       "7     POINT (6162865.861 2269381.489)\n",
       "8     POINT (6195182.779 2280509.032)\n",
       "9     POINT (6342387.266 2220024.356)\n",
       "10    POINT (6195566.226 2280519.190)\n",
       "11    POINT (6577094.185 2205388.907)\n",
       "12    POINT (6195566.226 2280519.190)\n",
       "13    POINT (6283871.161 2136729.378)\n",
       "14    POINT (6229918.194 2307140.803)\n",
       "15    POINT (6496477.603 2228753.546)\n",
       "16    POINT (6342390.651 2220029.434)\n",
       "17    POINT (6265042.293 2237006.918)\n",
       "18    POINT (6341877.001 2233915.961)\n",
       "19    POINT (6283867.341 2136731.288)\n",
       "20    POINT (6367147.411 2280861.442)\n",
       "21    POINT (6496480.142 2228757.355)\n",
       "22    POINT (6570450.368 2198847.190)\n",
       "23    POINT (6234956.516 2303352.150)\n",
       "24    POINT (6195566.226 2280520.460)\n",
       "25    POINT (6216489.695 2291912.394)\n",
       "26    POINT (6229918.194 2307139.534)\n",
       "27    POINT (6229920.733 2307139.534)\n",
       "Name: geometry, dtype: geometry"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinics_df.geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         6355 Riverside Ave\n",
       "1        9990 County Farm Rd\n",
       "2          1195 Magnolia Ave\n",
       "3          1297 W Hobson Way\n",
       "4        9890 County Farm Rd\n",
       "5        9990 County Farm Rd\n",
       "6       23119 Cottonwood Ave\n",
       "7              623 N Main St\n",
       "8       10000 County Farm Rd\n",
       "9             650 N State St\n",
       "10       9990 County Farm Rd\n",
       "11             83-912 Ave 45\n",
       "12       9990 County Farm Rd\n",
       "13    41002 County Center Dr\n",
       "14          1827 Atlanta Ave\n",
       "15           68-615 Perez Rd\n",
       "16            650 N State St\n",
       "17        1688 N Perris Blvd\n",
       "18         950 N Ramona Blvd\n",
       "19    41002 County Center Dr\n",
       "20          1330 W Ramsey St\n",
       "21           68-615 Perez Rd\n",
       "22           47-825 Oasis St\n",
       "23             769 Blaine St\n",
       "24       9990 County Farm Rd\n",
       "25        6355 Riverside Ave\n",
       "26          1827 Atlanta Ave\n",
       "27          1827 Atlanta Ave\n",
       "Name: ADDRESS, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinics_df.ADDRESS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(clinics_df.ADDRESS.unique()) # note some clinics have the same address (different types of services provided at same location)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Integrating spatial datasets\n",
    "Let's assume the role of a public health epidemiologist who is interested in equity issues surrounding access to mental health services. We have two datasets to work with here:\n",
    "\n",
    "- the clinics (points in Riverside County)\n",
    "- the tracts for all of Southern California\n",
    "\n",
    "We will thus want to narrow down the focus to Riverside county. So first, we need to extract the tracts for the county from the larger region:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting Riverside County Tracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rc = scag[scag['geoid'].str.match(\"^06065\")]\n",
    "\n",
    "rc.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial Joins\n",
    "\n",
    "With the two datasets in hand, we would like to know the answer to the following question:\n",
    "\n",
    "> How many clinics are in each census tract in Riverside County?\n",
    "\n",
    "To get at this answer will can use a [spatial join](https://gisgeography.com/spatial-join/). \n",
    "The idea here is to create a linkage between each clinic (point) and the tract (polygon) that the clinic lies within.\n",
    "\n",
    "The answer to our question will be the number of matches found for each census tract.\n",
    "\n",
    "In geopandas the spatial join is done with the `sjoin` method of the GeoDataFrame:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/carlos/anaconda3/envs/pysal-spatialapi20/lib/python3.7/site-packages/ipykernel_launcher.py:1: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.\n",
      "Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.\n",
      "\n",
      "Left CRS: EPSG:2230\n",
      "Right CRS: EPSG:4326\n",
      "\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "clinics_tracts = gpd.sjoin(clinics_df, rc, op='within')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a warning here, that alerts us to a mismatch between the CRS: [Coordinate Reference Systems](https://www.w3.org/2015/spatial/wiki/Coordinate_Reference_Systems) of the two GeoDataFrames. We can inspect these to see what is going on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: EPSG:2230>\n",
       "Name: NAD83 / California zone 6 (ftUS)\n",
       "Axis Info [cartesian]:\n",
       "- X[east]: Easting (US survey foot)\n",
       "- Y[north]: Northing (US survey foot)\n",
       "Area of Use:\n",
       "- name: USA - California - SPCS - 6\n",
       "- bounds: (-118.15, 32.53, -114.42, 34.08)\n",
       "Coordinate Operation:\n",
       "- name: SPCS83 California zone 6 (US Survey feet)\n",
       "- method: Lambert Conic Conformal (2SP)\n",
       "Datum: North American Datum 1983\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinics_df.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4326>\n",
       "Name: WGS 84\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: World\n",
       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
       "Datum: World Geodetic System 1984\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rc.crs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the clinics have a unit of feet, while the tracts are in degrees. The spatial join will be incorrect because of this since the observations from the two dataframes are in different coordinate systems:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>OBJECTID</th>\n",
       "      <th>SITE_TYPE</th>\n",
       "      <th>SITE_NAME</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>CITY</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>PHONE</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>geoid</th>\n",
       "      <th>...</th>\n",
       "      <th>n_white_persons</th>\n",
       "      <th>year</th>\n",
       "      <th>n_total_housing_units_sample</th>\n",
       "      <th>p_nonhisp_white_persons</th>\n",
       "      <th>p_white_over_60</th>\n",
       "      <th>p_black_over_60</th>\n",
       "      <th>p_hispanic_over_60</th>\n",
       "      <th>p_native_over_60</th>\n",
       "      <th>p_asian_over_60</th>\n",
       "      <th>p_disabled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 202 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty GeoDataFrame\n",
       "Columns: [OBJECTID, SITE_TYPE, SITE_NAME, ADDRESS, CITY, ZIPCODE, PHONE, geometry, index_right, geoid, n_asian_under_15, n_black_under_15, n_hispanic_under_15, n_native_under_15, n_white_under_15, n_persons_under_18, n_asian_over_60, n_black_over_60, n_hispanic_over_60, n_native_over_60, n_persons_over_60, n_white_over_60, n_asian_over_65, n_black_over_65, n_hispanic_over_65, n_native_over_65, n_white_over_65, n_persons_over_75, n_persons_over_15, n_civilians_over_16, n_civilians_over_18, n_persons_over_25, n_age_5_older, n_asian_age_distribution, n_black_age_distribution, n_hispanic_age_distribution, n_native_age_distribution, n_white_age_distribution, n_asian_persons, n_black_persons, n_chinese_persons, n_labor_force, n_civilians_16_64, n_edu_college_greater, n_cuban_pop, n_poverty_determined_asian, n_poverty_determined_black, n_total_pop_sample, n_female_over_16, n_poverty_determined_families, n_poverty_determined_hispanic, n_disabled, n_housing_units_multiunit_structures_denom, n_poverty_determined_native, n_poverty_determined_persons, n_poverty_determined_white, n_employed_over_16, n_total_families, n_foreign_born_pop, n_female_headed_families, n_filipino_persons, n_female_labor_force, n_german_pop, n_german_born_pop, n_household_recent_move, n_structures_30_old, n_hawaiian_persons, n_total_households, n_asian_households, n_black_households, n_hispanic_households, n_white_households, median_household_income, median_income_asianhh, median_income_blackhh, median_income_hispanichh, median_income_whitehh, n_hispanic_persons, n_edu_hs_less, n_total_housing_units, per_capita_income, n_asian_indian_persons, n_irish_pop, n_irish_born_pop, n_italian_pop, n_italian_born_pop, n_japanese_persons, n_korean_persons, n_limited_english, n_employed_manufacturing, n_married, n_mexican_pop, median_home_value, median_contract_rent, n_housing_units_multiunit_structures, n_recent_immigrant_pop, n_poverty_over_65, n_poverty_asian, n_naturalized_pop, n_poverty_black, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 202 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinics_tracts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To rectify this, we need to have both GeoDataFrames in the same CRS. Let's change the CRS of the tracts GeoDataFrame which is currently:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rc.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To change this, we notice the helpful suggestion in the warning above to use the `to_crs` method. Let's try it to see how this works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rc.to_crs(clinics_df.crs).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of the two plots shows us that the CRS has been changed. We did not assign the object yet (which is good practice when experimenting). But it does what we need, so lets assign the result to redefine our tracts GeoDataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "rc = rc.to_crs(clinics_df.crs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "clinics_tracts = gpd.sjoin(clinics_df, rc, op='within')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>OBJECTID</th>\n",
       "      <th>SITE_TYPE</th>\n",
       "      <th>SITE_NAME</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>CITY</th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>PHONE</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>geoid</th>\n",
       "      <th>...</th>\n",
       "      <th>n_white_persons</th>\n",
       "      <th>year</th>\n",
       "      <th>n_total_housing_units_sample</th>\n",
       "      <th>p_nonhisp_white_persons</th>\n",
       "      <th>p_white_over_60</th>\n",
       "      <th>p_black_over_60</th>\n",
       "      <th>p_hispanic_over_60</th>\n",
       "      <th>p_native_over_60</th>\n",
       "      <th>p_asian_over_60</th>\n",
       "      <th>p_disabled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>149.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Older Adult Services</td>\n",
       "      <td>6355 Riverside Ave</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92506</td>\n",
       "      <td>951-369-0219</td>\n",
       "      <td>POINT (6216487.156 2291913.664)</td>\n",
       "      <td>661</td>\n",
       "      <td>06065031100</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1715.0</td>\n",
       "      <td>49.159311</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>146.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Adult Mental Health Services - Central Clinic</td>\n",
       "      <td>6355 Riverside Ave</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92506</td>\n",
       "      <td>951-369-5714</td>\n",
       "      <td>POINT (6216489.695 2291912.394)</td>\n",
       "      <td>661</td>\n",
       "      <td>06065031100</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1715.0</td>\n",
       "      <td>49.159311</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Children'S Treatment Services</td>\n",
       "      <td>9990 County Farm Rd</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92503</td>\n",
       "      <td>951-358-4840</td>\n",
       "      <td>POINT (6195566.226 2280519.190)</td>\n",
       "      <td>1798</td>\n",
       "      <td>06065041201</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>25.836431</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>152.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Interagency Services For Families</td>\n",
       "      <td>9890 County Farm Rd</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92503</td>\n",
       "      <td>951-358-4850</td>\n",
       "      <td>POINT (6195949.672 2280510.302)</td>\n",
       "      <td>1798</td>\n",
       "      <td>06065041201</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>25.836431</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>448.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Children'S Evaluation Services Unite</td>\n",
       "      <td>9990 County Farm Rd</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92503</td>\n",
       "      <td>951-358-7380</td>\n",
       "      <td>POINT (6195566.226 2280519.190)</td>\n",
       "      <td>1798</td>\n",
       "      <td>06065041201</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>25.836431</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>154.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Van Horn Counseling Center</td>\n",
       "      <td>10000 County Farm Rd</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92503</td>\n",
       "      <td>951-358-4390</td>\n",
       "      <td>POINT (6195182.779 2280509.032)</td>\n",
       "      <td>1798</td>\n",
       "      <td>06065041201</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>25.836431</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>155.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Inpatient Treatment Facility</td>\n",
       "      <td>9990 County Farm Rd</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92503</td>\n",
       "      <td>951-358-4700</td>\n",
       "      <td>POINT (6195566.226 2280519.190)</td>\n",
       "      <td>1798</td>\n",
       "      <td>06065041201</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>25.836431</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>156.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Emergency Treatment Services</td>\n",
       "      <td>9990 County Farm Rd</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92503</td>\n",
       "      <td>951-358-4881</td>\n",
       "      <td>POINT (6195566.226 2280519.190)</td>\n",
       "      <td>1798</td>\n",
       "      <td>06065041201</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>25.836431</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>145.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Adult Mental Health Services - West Clinic</td>\n",
       "      <td>9990 County Farm Rd</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92503</td>\n",
       "      <td>951-358-4647</td>\n",
       "      <td>POINT (6195566.226 2280520.460)</td>\n",
       "      <td>1798</td>\n",
       "      <td>06065041201</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>25.836431</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Family, Adolescent, And Children'S Treatment</td>\n",
       "      <td>1195 Magnolia Ave</td>\n",
       "      <td>Corona</td>\n",
       "      <td>91719</td>\n",
       "      <td>951-273-0608</td>\n",
       "      <td>POINT (6168252.101 2261023.951)</td>\n",
       "      <td>517</td>\n",
       "      <td>06065041813</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>2084.0</td>\n",
       "      <td>28.686088</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>447.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Blythe Clinic</td>\n",
       "      <td>1297 W Hobson Way</td>\n",
       "      <td>Blythe</td>\n",
       "      <td>92225</td>\n",
       "      <td>760-770-2222</td>\n",
       "      <td>POINT (7059944.605 2169652.046)</td>\n",
       "      <td>242</td>\n",
       "      <td>06065046102</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>887.0</td>\n",
       "      <td>14.184009</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>153.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Children'S Interagency Program</td>\n",
       "      <td>23119 Cottonwood Ave</td>\n",
       "      <td>Moreno Valley</td>\n",
       "      <td>92553</td>\n",
       "      <td>951-413-5678</td>\n",
       "      <td>POINT (6255240.101 2281392.265)</td>\n",
       "      <td>1818</td>\n",
       "      <td>06065042512</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>968.0</td>\n",
       "      <td>9.930314</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>449.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Substance Abuse Outpatient Program</td>\n",
       "      <td>623 N Main St</td>\n",
       "      <td>Corona</td>\n",
       "      <td>92880</td>\n",
       "      <td>951-737-2962</td>\n",
       "      <td>POINT (6162865.861 2269381.489)</td>\n",
       "      <td>1875</td>\n",
       "      <td>06065040809</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>29.670330</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>450.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Substance Abuse Outpatient Program</td>\n",
       "      <td>650 N State St</td>\n",
       "      <td>Hemet</td>\n",
       "      <td>92543</td>\n",
       "      <td>951-791-3350</td>\n",
       "      <td>POINT (6342387.266 2220024.356)</td>\n",
       "      <td>1212</td>\n",
       "      <td>06065043507</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>2674.0</td>\n",
       "      <td>39.315175</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>137.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Hemet Clinic</td>\n",
       "      <td>650 N State St</td>\n",
       "      <td>Hemet</td>\n",
       "      <td>92543</td>\n",
       "      <td>951-971-3300</td>\n",
       "      <td>POINT (6342390.651 2220029.434)</td>\n",
       "      <td>1212</td>\n",
       "      <td>06065043507</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>2674.0</td>\n",
       "      <td>39.315175</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>451.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Substance Abuse Outpatient Program</td>\n",
       "      <td>83-912 Ave 45</td>\n",
       "      <td>Indio</td>\n",
       "      <td>92201</td>\n",
       "      <td>760-347-0754</td>\n",
       "      <td>POINT (6577094.185 2205388.907)</td>\n",
       "      <td>2466</td>\n",
       "      <td>06065045303</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>843.0</td>\n",
       "      <td>4.670699</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>452.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Substance Abuse Outpatient Program</td>\n",
       "      <td>41002 County Center Dr</td>\n",
       "      <td>Temecula</td>\n",
       "      <td>92591</td>\n",
       "      <td>951-600-6360</td>\n",
       "      <td>POINT (6283871.161 2136729.378)</td>\n",
       "      <td>4266</td>\n",
       "      <td>06065049600</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>2443.0</td>\n",
       "      <td>58.274250</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>140.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Temecula Clinic</td>\n",
       "      <td>41002 County Center Dr</td>\n",
       "      <td>Temecula</td>\n",
       "      <td>92590</td>\n",
       "      <td>951-600-6355</td>\n",
       "      <td>POINT (6283867.341 2136731.288)</td>\n",
       "      <td>4266</td>\n",
       "      <td>06065049600</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>2443.0</td>\n",
       "      <td>58.274250</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>157.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Substance Abuse Outpatient Program</td>\n",
       "      <td>1827 Atlanta Ave</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92503</td>\n",
       "      <td>951-955-2105</td>\n",
       "      <td>POINT (6229918.194 2307140.803)</td>\n",
       "      <td>1546</td>\n",
       "      <td>06065042209</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1586.0</td>\n",
       "      <td>31.134793</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>147.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Adult System Of Care</td>\n",
       "      <td>1827 Atlanta Ave</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92507</td>\n",
       "      <td>951-955-8000</td>\n",
       "      <td>POINT (6229918.194 2307139.534)</td>\n",
       "      <td>1546</td>\n",
       "      <td>06065042209</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1586.0</td>\n",
       "      <td>31.134793</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>148.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Jefferson Wellness Center</td>\n",
       "      <td>1827 Atlanta Ave</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92507</td>\n",
       "      <td>951-955-8000</td>\n",
       "      <td>POINT (6229920.733 2307139.534)</td>\n",
       "      <td>1546</td>\n",
       "      <td>06065042209</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1586.0</td>\n",
       "      <td>31.134793</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>158.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Substance Abuse Outpatient Program</td>\n",
       "      <td>68-615 Perez Rd</td>\n",
       "      <td>Cathedral City</td>\n",
       "      <td>92234</td>\n",
       "      <td>760-770-2222</td>\n",
       "      <td>POINT (6496477.603 2228753.546)</td>\n",
       "      <td>2453</td>\n",
       "      <td>06065045000</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1820.0</td>\n",
       "      <td>43.139842</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>142.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Cathedral City Clinic</td>\n",
       "      <td>68-615 Perez Rd</td>\n",
       "      <td>Cathedral City</td>\n",
       "      <td>92234</td>\n",
       "      <td>760-770-2222</td>\n",
       "      <td>POINT (6496480.142 2228757.355)</td>\n",
       "      <td>2453</td>\n",
       "      <td>06065045000</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1820.0</td>\n",
       "      <td>43.139842</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>138.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Perris Clinic</td>\n",
       "      <td>1688 N Perris Blvd</td>\n",
       "      <td>Perris</td>\n",
       "      <td>92570</td>\n",
       "      <td>951-443-2200</td>\n",
       "      <td>POINT (6265042.293 2237006.918)</td>\n",
       "      <td>4515</td>\n",
       "      <td>06065042620</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>2671.0</td>\n",
       "      <td>17.630000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>139.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>San Jacinto Clinic</td>\n",
       "      <td>950 N Ramona Blvd</td>\n",
       "      <td>San Jacinto</td>\n",
       "      <td>92582</td>\n",
       "      <td>951-487-2674</td>\n",
       "      <td>POINT (6341877.001 2233915.961)</td>\n",
       "      <td>2955</td>\n",
       "      <td>06065051300</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>3064.0</td>\n",
       "      <td>45.269270</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>141.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Banning Clinic</td>\n",
       "      <td>1330 W Ramsey St</td>\n",
       "      <td>Banning</td>\n",
       "      <td>92220</td>\n",
       "      <td>951-849-7142</td>\n",
       "      <td>POINT (6367147.411 2280861.442)</td>\n",
       "      <td>231</td>\n",
       "      <td>06065044101</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>35.014233</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>143.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Indio Clinic</td>\n",
       "      <td>47-825 Oasis St</td>\n",
       "      <td>Indio</td>\n",
       "      <td>92201</td>\n",
       "      <td>760-863-8455</td>\n",
       "      <td>POINT (6570450.368 2198847.190)</td>\n",
       "      <td>2467</td>\n",
       "      <td>06065045501</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>1615.0</td>\n",
       "      <td>8.739635</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>144.0</td>\n",
       "      <td>Behavioral Health</td>\n",
       "      <td>Adult Mental Health Services - Blaine Clinic</td>\n",
       "      <td>769 Blaine St</td>\n",
       "      <td>Riverside</td>\n",
       "      <td>92507</td>\n",
       "      <td>951-358-4705</td>\n",
       "      <td>POINT (6234956.516 2303352.150)</td>\n",
       "      <td>172</td>\n",
       "      <td>06065042210</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>2010</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>26.254459</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28 rows × 202 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    OBJECTID          SITE_TYPE  \\\n",
       "0      149.0  Behavioral Health   \n",
       "25     146.0  Behavioral Health   \n",
       "1      150.0  Behavioral Health   \n",
       "4      152.0  Behavioral Health   \n",
       "5      448.0  Behavioral Health   \n",
       "8      154.0  Behavioral Health   \n",
       "10     155.0  Behavioral Health   \n",
       "12     156.0  Behavioral Health   \n",
       "24     145.0  Behavioral Health   \n",
       "2      151.0  Behavioral Health   \n",
       "3      447.0  Behavioral Health   \n",
       "6      153.0  Behavioral Health   \n",
       "7      449.0  Behavioral Health   \n",
       "9      450.0  Behavioral Health   \n",
       "16     137.0  Behavioral Health   \n",
       "11     451.0  Behavioral Health   \n",
       "13     452.0  Behavioral Health   \n",
       "19     140.0  Behavioral Health   \n",
       "14     157.0  Behavioral Health   \n",
       "26     147.0  Behavioral Health   \n",
       "27     148.0  Behavioral Health   \n",
       "15     158.0  Behavioral Health   \n",
       "21     142.0  Behavioral Health   \n",
       "17     138.0  Behavioral Health   \n",
       "18     139.0  Behavioral Health   \n",
       "20     141.0  Behavioral Health   \n",
       "22     143.0  Behavioral Health   \n",
       "23     144.0  Behavioral Health   \n",
       "\n",
       "                                        SITE_NAME                 ADDRESS  \\\n",
       "0                            Older Adult Services      6355 Riverside Ave   \n",
       "25  Adult Mental Health Services - Central Clinic      6355 Riverside Ave   \n",
       "1                   Children'S Treatment Services     9990 County Farm Rd   \n",
       "4               Interagency Services For Families     9890 County Farm Rd   \n",
       "5            Children'S Evaluation Services Unite     9990 County Farm Rd   \n",
       "8                      Van Horn Counseling Center    10000 County Farm Rd   \n",
       "10                   Inpatient Treatment Facility     9990 County Farm Rd   \n",
       "12                   Emergency Treatment Services     9990 County Farm Rd   \n",
       "24     Adult Mental Health Services - West Clinic     9990 County Farm Rd   \n",
       "2    Family, Adolescent, And Children'S Treatment       1195 Magnolia Ave   \n",
       "3                                   Blythe Clinic       1297 W Hobson Way   \n",
       "6                  Children'S Interagency Program    23119 Cottonwood Ave   \n",
       "7              Substance Abuse Outpatient Program           623 N Main St   \n",
       "9              Substance Abuse Outpatient Program          650 N State St   \n",
       "16                                   Hemet Clinic          650 N State St   \n",
       "11             Substance Abuse Outpatient Program           83-912 Ave 45   \n",
       "13             Substance Abuse Outpatient Program  41002 County Center Dr   \n",
       "19                                Temecula Clinic  41002 County Center Dr   \n",
       "14             Substance Abuse Outpatient Program        1827 Atlanta Ave   \n",
       "26                           Adult System Of Care        1827 Atlanta Ave   \n",
       "27                      Jefferson Wellness Center        1827 Atlanta Ave   \n",
       "15             Substance Abuse Outpatient Program         68-615 Perez Rd   \n",
       "21                          Cathedral City Clinic         68-615 Perez Rd   \n",
       "17                                  Perris Clinic      1688 N Perris Blvd   \n",
       "18                             San Jacinto Clinic       950 N Ramona Blvd   \n",
       "20                                 Banning Clinic        1330 W Ramsey St   \n",
       "22                                   Indio Clinic         47-825 Oasis St   \n",
       "23   Adult Mental Health Services - Blaine Clinic           769 Blaine St   \n",
       "\n",
       "              CITY  ZIPCODE         PHONE                         geometry  \\\n",
       "0        Riverside    92506  951-369-0219  POINT (6216487.156 2291913.664)   \n",
       "25       Riverside    92506  951-369-5714  POINT (6216489.695 2291912.394)   \n",
       "1        Riverside    92503  951-358-4840  POINT (6195566.226 2280519.190)   \n",
       "4        Riverside    92503  951-358-4850  POINT (6195949.672 2280510.302)   \n",
       "5        Riverside    92503  951-358-7380  POINT (6195566.226 2280519.190)   \n",
       "8        Riverside    92503  951-358-4390  POINT (6195182.779 2280509.032)   \n",
       "10       Riverside    92503  951-358-4700  POINT (6195566.226 2280519.190)   \n",
       "12       Riverside    92503  951-358-4881  POINT (6195566.226 2280519.190)   \n",
       "24       Riverside    92503  951-358-4647  POINT (6195566.226 2280520.460)   \n",
       "2           Corona    91719  951-273-0608  POINT (6168252.101 2261023.951)   \n",
       "3           Blythe    92225  760-770-2222  POINT (7059944.605 2169652.046)   \n",
       "6    Moreno Valley    92553  951-413-5678  POINT (6255240.101 2281392.265)   \n",
       "7           Corona    92880  951-737-2962  POINT (6162865.861 2269381.489)   \n",
       "9            Hemet    92543  951-791-3350  POINT (6342387.266 2220024.356)   \n",
       "16           Hemet    92543  951-971-3300  POINT (6342390.651 2220029.434)   \n",
       "11           Indio    92201  760-347-0754  POINT (6577094.185 2205388.907)   \n",
       "13        Temecula    92591  951-600-6360  POINT (6283871.161 2136729.378)   \n",
       "19        Temecula    92590  951-600-6355  POINT (6283867.341 2136731.288)   \n",
       "14       Riverside    92503  951-955-2105  POINT (6229918.194 2307140.803)   \n",
       "26       Riverside    92507  951-955-8000  POINT (6229918.194 2307139.534)   \n",
       "27       Riverside    92507  951-955-8000  POINT (6229920.733 2307139.534)   \n",
       "15  Cathedral City    92234  760-770-2222  POINT (6496477.603 2228753.546)   \n",
       "21  Cathedral City    92234  760-770-2222  POINT (6496480.142 2228757.355)   \n",
       "17          Perris    92570  951-443-2200  POINT (6265042.293 2237006.918)   \n",
       "18     San Jacinto    92582  951-487-2674  POINT (6341877.001 2233915.961)   \n",
       "20         Banning    92220  951-849-7142  POINT (6367147.411 2280861.442)   \n",
       "22           Indio    92201  760-863-8455  POINT (6570450.368 2198847.190)   \n",
       "23       Riverside    92507  951-358-4705  POINT (6234956.516 2303352.150)   \n",
       "\n",
       "    index_right        geoid  ...  n_white_persons  year  \\\n",
       "0           661  06065031100  ...             None  2010   \n",
       "25          661  06065031100  ...             None  2010   \n",
       "1          1798  06065041201  ...             None  2010   \n",
       "4          1798  06065041201  ...             None  2010   \n",
       "5          1798  06065041201  ...             None  2010   \n",
       "8          1798  06065041201  ...             None  2010   \n",
       "10         1798  06065041201  ...             None  2010   \n",
       "12         1798  06065041201  ...             None  2010   \n",
       "24         1798  06065041201  ...             None  2010   \n",
       "2           517  06065041813  ...             None  2010   \n",
       "3           242  06065046102  ...             None  2010   \n",
       "6          1818  06065042512  ...             None  2010   \n",
       "7          1875  06065040809  ...             None  2010   \n",
       "9          1212  06065043507  ...             None  2010   \n",
       "16         1212  06065043507  ...             None  2010   \n",
       "11         2466  06065045303  ...             None  2010   \n",
       "13         4266  06065049600  ...             None  2010   \n",
       "19         4266  06065049600  ...             None  2010   \n",
       "14         1546  06065042209  ...             None  2010   \n",
       "26         1546  06065042209  ...             None  2010   \n",
       "27         1546  06065042209  ...             None  2010   \n",
       "15         2453  06065045000  ...             None  2010   \n",
       "21         2453  06065045000  ...             None  2010   \n",
       "17         4515  06065042620  ...             None  2010   \n",
       "18         2955  06065051300  ...             None  2010   \n",
       "20          231  06065044101  ...             None  2010   \n",
       "22         2467  06065045501  ...             None  2010   \n",
       "23          172  06065042210  ...             None  2010   \n",
       "\n",
       "    n_total_housing_units_sample  p_nonhisp_white_persons  p_white_over_60  \\\n",
       "0                         1715.0                49.159311             None   \n",
       "25                        1715.0                49.159311             None   \n",
       "1                         1135.0                25.836431             None   \n",
       "4                         1135.0                25.836431             None   \n",
       "5                         1135.0                25.836431             None   \n",
       "8                         1135.0                25.836431             None   \n",
       "10                        1135.0                25.836431             None   \n",
       "12                        1135.0                25.836431             None   \n",
       "24                        1135.0                25.836431             None   \n",
       "2                         2084.0                28.686088             None   \n",
       "3                          887.0                14.184009             None   \n",
       "6                          968.0                 9.930314             None   \n",
       "7                         1026.0                29.670330             None   \n",
       "9                         2674.0                39.315175             None   \n",
       "16                        2674.0                39.315175             None   \n",
       "11                         843.0                 4.670699             None   \n",
       "13                        2443.0                58.274250             None   \n",
       "19                        2443.0                58.274250             None   \n",
       "14                        1586.0                31.134793             None   \n",
       "26                        1586.0                31.134793             None   \n",
       "27                        1586.0                31.134793             None   \n",
       "15                        1820.0                43.139842             None   \n",
       "21                        1820.0                43.139842             None   \n",
       "17                        2671.0                17.630000             None   \n",
       "18                        3064.0                45.269270             None   \n",
       "20                        1223.0                35.014233             None   \n",
       "22                        1615.0                 8.739635             None   \n",
       "23                        2047.0                26.254459             None   \n",
       "\n",
       "    p_black_over_60 p_hispanic_over_60 p_native_over_60 p_asian_over_60  \\\n",
       "0              None               None             None            None   \n",
       "25             None               None             None            None   \n",
       "1              None               None             None            None   \n",
       "4              None               None             None            None   \n",
       "5              None               None             None            None   \n",
       "8              None               None             None            None   \n",
       "10             None               None             None            None   \n",
       "12             None               None             None            None   \n",
       "24             None               None             None            None   \n",
       "2              None               None             None            None   \n",
       "3              None               None             None            None   \n",
       "6              None               None             None            None   \n",
       "7              None               None             None            None   \n",
       "9              None               None             None            None   \n",
       "16             None               None             None            None   \n",
       "11             None               None             None            None   \n",
       "13             None               None             None            None   \n",
       "19             None               None             None            None   \n",
       "14             None               None             None            None   \n",
       "26             None               None             None            None   \n",
       "27             None               None             None            None   \n",
       "15             None               None             None            None   \n",
       "21             None               None             None            None   \n",
       "17             None               None             None            None   \n",
       "18             None               None             None            None   \n",
       "20             None               None             None            None   \n",
       "22             None               None             None            None   \n",
       "23             None               None             None            None   \n",
       "\n",
       "   p_disabled  \n",
       "0        None  \n",
       "25       None  \n",
       "1        None  \n",
       "4        None  \n",
       "5        None  \n",
       "8        None  \n",
       "10       None  \n",
       "12       None  \n",
       "24       None  \n",
       "2        None  \n",
       "3        None  \n",
       "6        None  \n",
       "7        None  \n",
       "9        None  \n",
       "16       None  \n",
       "11       None  \n",
       "13       None  \n",
       "19       None  \n",
       "14       None  \n",
       "26       None  \n",
       "27       None  \n",
       "15       None  \n",
       "21       None  \n",
       "17       None  \n",
       "18       None  \n",
       "20       None  \n",
       "22       None  \n",
       "23       None  \n",
       "\n",
       "[28 rows x 202 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinics_tracts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we see the spatial join has worked. There are 28 matches (one for each clinic) and this is stored in a new GeoDataFrame called: `clinics_tracts`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determine the number of clinics in each tract\n",
    "\n",
    "Returning to our question, we can find the number of clinics in each of the tracts in this new GeoDataFrame by using the `group_by` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index_right</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geoid</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>06065031100</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065040809</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065041201</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065041813</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065042209</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065042210</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065042512</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065042620</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065043507</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065044101</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065045000</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065045303</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065045501</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065046102</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065049600</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06065051300</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             index_right\n",
       "geoid                   \n",
       "06065031100            2\n",
       "06065040809            1\n",
       "06065041201            7\n",
       "06065041813            1\n",
       "06065042209            3\n",
       "06065042210            1\n",
       "06065042512            1\n",
       "06065042620            1\n",
       "06065043507            2\n",
       "06065044101            1\n",
       "06065045000            2\n",
       "06065045303            1\n",
       "06065045501            1\n",
       "06065046102            1\n",
       "06065049600            2\n",
       "06065051300            1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clinics_tracts[['geoid', 'index_right']].groupby('geoid').agg('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This works, so let's create another DataFrame to store these counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct = clinics_tracts[['geoid', 'index_right']].groupby('geoid').agg('count')\n",
    "ct.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table Join\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now know for the tracts that contain at least one clinic, how many clinics they contain. This implies that the tracts that are not in the `ct` GeoDataFrame do not contain a clinic. What we would like to have is an additional attribute on our `rc` tracts GeoDataFrame that stores the number of clinics in each tract - for all tracts in Riverside County.\n",
    "\n",
    "We can do this in two remaining steps:\n",
    "\n",
    "- a table join on the `geoid` column \n",
    "- setting NA values to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geoid</th>\n",
       "      <th>n_asian_under_15</th>\n",
       "      <th>n_black_under_15</th>\n",
       "      <th>n_hispanic_under_15</th>\n",
       "      <th>n_native_under_15</th>\n",
       "      <th>n_white_under_15</th>\n",
       "      <th>n_persons_under_18</th>\n",
       "      <th>n_asian_over_60</th>\n",
       "      <th>n_black_over_60</th>\n",
       "      <th>n_hispanic_over_60</th>\n",
       "      <th>...</th>\n",
       "      <th>n_total_housing_units_sample</th>\n",
       "      <th>p_nonhisp_white_persons</th>\n",
       "      <th>p_white_over_60</th>\n",
       "      <th>p_black_over_60</th>\n",
       "      <th>p_hispanic_over_60</th>\n",
       "      <th>p_native_over_60</th>\n",
       "      <th>p_asian_over_60</th>\n",
       "      <th>p_disabled</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06065042210</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>26.254459</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6231281.575 2304380.321, 6231334.142...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06065044101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>643.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>1223.0</td>\n",
       "      <td>35.014233</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6360916.783 2282317.173, 6368904.531...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06065046102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>887.0</td>\n",
       "      <td>14.184009</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((7056901.330 2172059.833, 7059549.717...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06065041813</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>2055.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>2084.0</td>\n",
       "      <td>28.686088</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6163401.216 2262775.124, 6163930.656...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06065031100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>686.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>1383.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>1715.0</td>\n",
       "      <td>49.159311</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6212596.752 2292503.659, 6212608.744...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06065043507</td>\n",
       "      <td>13.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>2674.0</td>\n",
       "      <td>39.315175</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6338337.237 2228280.410, 6339766.118...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>06065042209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>1586.0</td>\n",
       "      <td>31.134793</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6227628.717 2309774.907, 6229897.578...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>06065041201</td>\n",
       "      <td>66.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>704.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1235.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>25.836431</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6191322.967 2284972.576, 6191482.577...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>06065042512</td>\n",
       "      <td>115.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>968.0</td>\n",
       "      <td>9.930314</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6254926.395 2282777.381, 6255161.824...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>06065040809</td>\n",
       "      <td>92.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>29.670330</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6161525.203 2270923.394, 6162129.754...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>06065045000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>1820.0</td>\n",
       "      <td>43.139842</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6491233.519 2220787.193, 6491907.742...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>06065045303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>822.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>843.0</td>\n",
       "      <td>4.670699</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6566733.011 2209587.422, 6567651.623...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>06065045501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1621.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2097.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>1615.0</td>\n",
       "      <td>8.739635</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6566573.193 2201041.736, 6566586.787...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>06065051300</td>\n",
       "      <td>109.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1348.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>2563.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>3064.0</td>\n",
       "      <td>45.269270</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6331082.740 2256322.074, 6333703.972...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>06065049600</td>\n",
       "      <td>224.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>521.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>989.0</td>\n",
       "      <td>2182.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>2443.0</td>\n",
       "      <td>58.274250</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6280386.730 2140388.891, 6280920.607...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>06065042620</td>\n",
       "      <td>77.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>2143.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>2671.0</td>\n",
       "      <td>17.630000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((6254341.091 2261739.127, 6256666.895...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 195 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          geoid  n_asian_under_15  n_black_under_15  n_hispanic_under_15  \\\n",
       "0   06065042210              14.0               4.0                392.0   \n",
       "1   06065044101               0.0               0.0                441.0   \n",
       "2   06065046102               0.0              16.0                409.0   \n",
       "3   06065041813              22.0               0.0               1385.0   \n",
       "4   06065031100               0.0              35.0                686.0   \n",
       "5   06065043507              13.0              56.0               1180.0   \n",
       "6   06065042209               0.0              17.0                276.0   \n",
       "7   06065041201              66.0              14.0                704.0   \n",
       "8   06065042512             115.0             220.0                674.0   \n",
       "9   06065040809              92.0              79.0                328.0   \n",
       "10  06065045000               0.0               0.0                696.0   \n",
       "11  06065045303               0.0               0.0                822.0   \n",
       "12  06065045501               0.0              12.0               1621.0   \n",
       "13  06065051300             109.0              58.0               1348.0   \n",
       "14  06065049600             224.0              53.0                521.0   \n",
       "15  06065042620              77.0             383.0               2143.0   \n",
       "\n",
       "    n_native_under_15  n_white_under_15  n_persons_under_18 n_asian_over_60  \\\n",
       "0                 0.0              49.0               626.0            None   \n",
       "1                 0.0              90.0               643.0            None   \n",
       "2                 0.0               8.0               500.0            None   \n",
       "3                 0.0             218.0              2055.0            None   \n",
       "4                 0.0             322.0              1383.0            None   \n",
       "5                62.0             148.0              1806.0            None   \n",
       "6                 0.0              99.0               604.0            None   \n",
       "7                 0.0              94.0              1235.0            None   \n",
       "8                 0.0              56.0              1239.0            None   \n",
       "9                12.0             129.0               952.0            None   \n",
       "10                0.0             115.0              1013.0            None   \n",
       "11                0.0               0.0              1190.0            None   \n",
       "12               10.0              96.0              2097.0            None   \n",
       "13              164.0             635.0              2563.0            None   \n",
       "14               37.0             989.0              2182.0            None   \n",
       "15               43.0             237.0              3436.0            None   \n",
       "\n",
       "   n_black_over_60 n_hispanic_over_60  ... n_total_housing_units_sample  \\\n",
       "0             None               None  ...                       2047.0   \n",
       "1             None               None  ...                       1223.0   \n",
       "2             None               None  ...                        887.0   \n",
       "3             None               None  ...                       2084.0   \n",
       "4             None               None  ...                       1715.0   \n",
       "5             None               None  ...                       2674.0   \n",
       "6             None               None  ...                       1586.0   \n",
       "7             None               None  ...                       1135.0   \n",
       "8             None               None  ...                        968.0   \n",
       "9             None               None  ...                       1026.0   \n",
       "10            None               None  ...                       1820.0   \n",
       "11            None               None  ...                        843.0   \n",
       "12            None               None  ...                       1615.0   \n",
       "13            None               None  ...                       3064.0   \n",
       "14            None               None  ...                       2443.0   \n",
       "15            None               None  ...                       2671.0   \n",
       "\n",
       "    p_nonhisp_white_persons p_white_over_60  p_black_over_60  \\\n",
       "0                 26.254459            None             None   \n",
       "1                 35.014233            None             None   \n",
       "2                 14.184009            None             None   \n",
       "3                 28.686088            None             None   \n",
       "4                 49.159311            None             None   \n",
       "5                 39.315175            None             None   \n",
       "6                 31.134793            None             None   \n",
       "7                 25.836431            None             None   \n",
       "8                  9.930314            None             None   \n",
       "9                 29.670330            None             None   \n",
       "10                43.139842            None             None   \n",
       "11                 4.670699            None             None   \n",
       "12                 8.739635            None             None   \n",
       "13                45.269270            None             None   \n",
       "14                58.274250            None             None   \n",
       "15                17.630000            None             None   \n",
       "\n",
       "    p_hispanic_over_60  p_native_over_60  p_asian_over_60  p_disabled  \\\n",
       "0                 None              None             None        None   \n",
       "1                 None              None             None        None   \n",
       "2                 None              None             None        None   \n",
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       "11                None              None             None        None   \n",
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       "                                             geometry  index_right  \n",
       "0   POLYGON ((6231281.575 2304380.321, 6231334.142...            1  \n",
       "1   POLYGON ((6360916.783 2282317.173, 6368904.531...            1  \n",
       "2   POLYGON ((7056901.330 2172059.833, 7059549.717...            1  \n",
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       "5   POLYGON ((6338337.237 2228280.410, 6339766.118...            2  \n",
       "6   POLYGON ((6227628.717 2309774.907, 6229897.578...            3  \n",
       "7   POLYGON ((6191322.967 2284972.576, 6191482.577...            7  \n",
       "8   POLYGON ((6254926.395 2282777.381, 6255161.824...            1  \n",
       "9   POLYGON ((6161525.203 2270923.394, 6162129.754...            1  \n",
       "10  POLYGON ((6491233.519 2220787.193, 6491907.742...            2  \n",
       "11  POLYGON ((6566733.011 2209587.422, 6567651.623...            1  \n",
       "12  POLYGON ((6566573.193 2201041.736, 6566586.787...            1  \n",
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       "\n",
       "[16 rows x 195 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rc.merge(ct, on='geoid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this isn't quite what we want as it only matches the 16 tracts containing clinics. We can change the `how` argument to `outer` to include all tracts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>1194.0</td>\n",
       "      <td>27.105703</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>570.0</td>\n",
       "      <td>877.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
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      ],
      "text/plain": [
       "           geoid  n_asian_under_15  n_black_under_15  n_hispanic_under_15  \\\n",
       "0    06065042005               7.0             157.0                259.0   \n",
       "1    06065042210              14.0               4.0                392.0   \n",
       "2    06065042403              10.0              62.0                604.0   \n",
       "3    06065042405              17.0             570.0                877.0   \n",
       "4    06065042507               6.0             196.0               1132.0   \n",
       "..           ...               ...               ...                  ...   \n",
       "448  06065040603               0.0               0.0                354.0   \n",
       "449  06065040701              17.0               0.0                142.0   \n",
       "450  06065042738             149.0              65.0                549.0   \n",
       "451  06065043309               0.0              59.0                493.0   \n",
       "452  06065044804             178.0               0.0                452.0   \n",
       "\n",
       "     n_native_under_15  n_white_under_15  n_persons_under_18 n_asian_over_60  \\\n",
       "0                  0.0             540.0              1472.0            None   \n",
       "1                  0.0              49.0               626.0            None   \n",
       "2                  0.0             142.0              1153.0            None   \n",
       "3                  0.0             140.0              1949.0            None   \n",
       "4                  0.0              47.0              1778.0            None   \n",
       "..                 ...               ...                 ...             ...   \n",
       "448                0.0              26.0               544.0            None   \n",
       "449                0.0             332.0               669.0            None   \n",
       "450                0.0             531.0              1709.0            None   \n",
       "451                6.0             204.0               855.0            None   \n",
       "452                0.0              80.0               794.0            None   \n",
       "\n",
       "    n_black_over_60 n_hispanic_over_60  ... n_total_housing_units_sample  \\\n",
       "0              None               None  ...                       1511.0   \n",
       "1              None               None  ...                       2047.0   \n",
       "2              None               None  ...                       1194.0   \n",
       "3              None               None  ...                       1960.0   \n",
       "4              None               None  ...                       1209.0   \n",
       "..              ...                ...  ...                          ...   \n",
       "448            None               None  ...                        663.0   \n",
       "449            None               None  ...                        694.0   \n",
       "450            None               None  ...                       2076.0   \n",
       "451            None               None  ...                       1165.0   \n",
       "452            None               None  ...                       1162.0   \n",
       "\n",
       "     p_nonhisp_white_persons p_white_over_60  p_black_over_60  \\\n",
       "0                  52.240247            None             None   \n",
       "1                  26.254459            None             None   \n",
       "2                  27.105703            None             None   \n",
       "3                  12.679643            None             None   \n",
       "4                  11.965812            None             None   \n",
       "..                       ...             ...              ...   \n",
       "448                23.933856            None             None   \n",
       "449                78.002378            None             None   \n",
       "450                56.894592            None             None   \n",
       "451                44.564120            None             None   \n",
       "452                27.977189            None             None   \n",
       "\n",
       "     p_hispanic_over_60  p_native_over_60  p_asian_over_60  p_disabled  \\\n",
       "0                  None              None             None        None   \n",
       "1                  None              None             None        None   \n",
       "2                  None              None             None        None   \n",
       "3                  None              None             None        None   \n",
       "4                  None              None             None        None   \n",
       "..                  ...               ...              ...         ...   \n",
       "448                None              None             None        None   \n",
       "449                None              None             None        None   \n",
       "450                None              None             None        None   \n",
       "451                None              None             None        None   \n",
       "452                None              None             None        None   \n",
       "\n",
       "                                              geometry  index_right  \n",
       "0    POLYGON ((6220451.796 2273544.804, 6222975.728...          NaN  \n",
       "1    POLYGON ((6231281.575 2304380.321, 6231334.142...          1.0  \n",
       "2    POLYGON ((6265534.659 2293274.415, 6268193.791...          NaN  \n",
       "3    POLYGON ((6254900.866 2289559.203, 6257552.174...          NaN  \n",
       "4    POLYGON ((6260074.750 2265634.761, 6260130.720...          NaN  \n",
       "..                                                 ...          ...  \n",
       "448  POLYGON ((6170360.112 2300927.196, 6175629.419...          NaN  \n",
       "449  POLYGON ((6152825.484 2282403.533, 6153252.486...          NaN  \n",
       "450  POLYGON ((6281402.868 2194126.352, 6286714.408...          NaN  \n",
       "451  POLYGON ((6346253.351 2219037.313, 6347578.579...          NaN  \n",
       "452  POLYGON ((6482476.536 2236646.671, 6482483.425...          NaN  \n",
       "\n",
       "[453 rows x 195 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rc.merge(ct, on='geoid', how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok now we have all the tracts covered. But if you scroll to the right we will see `NaN` values for the tracts that do not contain a clinic. What remains is to replace the "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>n_asian_over_60</th>\n",
       "      <th>n_black_over_60</th>\n",
       "      <th>n_hispanic_over_60</th>\n",
       "      <th>...</th>\n",
       "      <th>n_total_housing_units_sample</th>\n",
       "      <th>p_nonhisp_white_persons</th>\n",
       "      <th>p_white_over_60</th>\n",
       "      <th>p_black_over_60</th>\n",
       "      <th>p_hispanic_over_60</th>\n",
       "      <th>p_native_over_60</th>\n",
       "      <th>p_asian_over_60</th>\n",
       "      <th>p_disabled</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06065042005</td>\n",
       "      <td>7.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>1472.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1511.0</td>\n",
       "      <td>52.240247</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((6220451.796 2273544.804, 6222975.728...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06065042210</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2047.0</td>\n",
       "      <td>26.254459</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((6231281.575 2304380.321, 6231334.142...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>06065042403</td>\n",
       "      <td>10.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>1153.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1194.0</td>\n",
       "      <td>27.105703</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((6265534.659 2293274.415, 6268193.791...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06065042405</td>\n",
       "      <td>17.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>877.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>12.679643</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((6254900.866 2289559.203, 6257552.174...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>06065042507</td>\n",
       "      <td>6.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>1132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1209.0</td>\n",
       "      <td>11.965812</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((6260074.750 2265634.761, 6260130.720...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>06065040603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>354.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>663.0</td>\n",
       "      <td>23.933856</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((6170360.112 2300927.196, 6175629.419...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>06065040701</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>694.0</td>\n",
       "      <td>78.002378</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((6152825.484 2282403.533, 6153252.486...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>06065042738</td>\n",
       "      <td>149.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>549.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2076.0</td>\n",
       "      <td>56.894592</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((6281402.868 2194126.352, 6286714.408...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>06065043309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1165.0</td>\n",
       "      <td>44.564120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((6346253.351 2219037.313, 6347578.579...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>06065044804</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>794.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>27.977189</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((6482476.536 2236646.671, 6482483.425...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>453 rows × 195 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           geoid  n_asian_under_15  n_black_under_15  n_hispanic_under_15  \\\n",
       "0    06065042005               7.0             157.0                259.0   \n",
       "1    06065042210              14.0               4.0                392.0   \n",
       "2    06065042403              10.0              62.0                604.0   \n",
       "3    06065042405              17.0             570.0                877.0   \n",
       "4    06065042507               6.0             196.0               1132.0   \n",
       "..           ...               ...               ...                  ...   \n",
       "448  06065040603               0.0               0.0                354.0   \n",
       "449  06065040701              17.0               0.0                142.0   \n",
       "450  06065042738             149.0              65.0                549.0   \n",
       "451  06065043309               0.0              59.0                493.0   \n",
       "452  06065044804             178.0               0.0                452.0   \n",
       "\n",
       "     n_native_under_15  n_white_under_15  n_persons_under_18  n_asian_over_60  \\\n",
       "0                  0.0             540.0              1472.0                0   \n",
       "1                  0.0              49.0               626.0                0   \n",
       "2                  0.0             142.0              1153.0                0   \n",
       "3                  0.0             140.0              1949.0                0   \n",
       "4                  0.0              47.0              1778.0                0   \n",
       "..                 ...               ...                 ...              ...   \n",
       "448                0.0              26.0               544.0                0   \n",
       "449                0.0             332.0               669.0                0   \n",
       "450                0.0             531.0              1709.0                0   \n",
       "451                6.0             204.0               855.0                0   \n",
       "452                0.0              80.0               794.0                0   \n",
       "\n",
       "     n_black_over_60  n_hispanic_over_60  ...  n_total_housing_units_sample  \\\n",
       "0                  0                   0  ...                        1511.0   \n",
       "1                  0                   0  ...                        2047.0   \n",
       "2                  0                   0  ...                        1194.0   \n",
       "3                  0                   0  ...                        1960.0   \n",
       "4                  0                   0  ...                        1209.0   \n",
       "..               ...                 ...  ...                           ...   \n",
       "448                0                   0  ...                         663.0   \n",
       "449                0                   0  ...                         694.0   \n",
       "450                0                   0  ...                        2076.0   \n",
       "451                0                   0  ...                        1165.0   \n",
       "452                0                   0  ...                        1162.0   \n",
       "\n",
       "     p_nonhisp_white_persons  p_white_over_60  p_black_over_60  \\\n",
       "0                  52.240247                0                0   \n",
       "1                  26.254459                0                0   \n",
       "2                  27.105703                0                0   \n",
       "3                  12.679643                0                0   \n",
       "4                  11.965812                0                0   \n",
       "..                       ...              ...              ...   \n",
       "448                23.933856                0                0   \n",
       "449                78.002378                0                0   \n",
       "450                56.894592                0                0   \n",
       "451                44.564120                0                0   \n",
       "452                27.977189                0                0   \n",
       "\n",
       "     p_hispanic_over_60  p_native_over_60  p_asian_over_60  p_disabled  \\\n",
       "0                     0                 0                0           0   \n",
       "1                     0                 0                0           0   \n",
       "2                     0                 0                0           0   \n",
       "3                     0                 0                0           0   \n",
       "4                     0                 0                0           0   \n",
       "..                  ...               ...              ...         ...   \n",
       "448                   0                 0                0           0   \n",
       "449                   0                 0                0           0   \n",
       "450                   0                 0                0           0   \n",
       "451                   0                 0                0           0   \n",
       "452                   0                 0                0           0   \n",
       "\n",
       "                                              geometry  index_right  \n",
       "0    POLYGON ((6220451.796 2273544.804, 6222975.728...          0.0  \n",
       "1    POLYGON ((6231281.575 2304380.321, 6231334.142...          1.0  \n",
       "2    POLYGON ((6265534.659 2293274.415, 6268193.791...          0.0  \n",
       "3    POLYGON ((6254900.866 2289559.203, 6257552.174...          0.0  \n",
       "4    POLYGON ((6260074.750 2265634.761, 6260130.720...          0.0  \n",
       "..                                                 ...          ...  \n",
       "448  POLYGON ((6170360.112 2300927.196, 6175629.419...          0.0  \n",
       "449  POLYGON ((6152825.484 2282403.533, 6153252.486...          0.0  \n",
       "450  POLYGON ((6281402.868 2194126.352, 6286714.408...          0.0  \n",
       "451  POLYGON ((6346253.351 2219037.313, 6347578.579...          0.0  \n",
       "452  POLYGON ((6482476.536 2236646.671, 6482483.425...          0.0  \n",
       "\n",
       "[453 rows x 195 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rc.merge(ct, on='geoid', how='outer').fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great. Now we save our work and rename the column holding the clinic count:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "rc = rc.merge(ct, on='geoid', how='outer').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rc['index_right'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "rc.rename(columns={'index_right': 'clinics'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rc.clinics.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(rc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" style=\"font-size:120%\">\n",
    "<b>Exercise</b>: <br>\n",
    "Create a shapefile for each of the counties in the Southern California dataframe, and write to <code>data/FIPSCODE.shp</code>. Where <code>FIPSCODE</code> is the 5-digit\n",
    "<a href=\"https://www.nrcs.usda.gov/wps/portal/nrcs/detail/ca/home/?cid=nrcs143_013697\">FIPS Code for the county</a>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load solutions/00.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-\n",
    "sa/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\"\n",
    "src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" /></a><br /><span\n",
    "xmlns:dct=\"http://purl.org/dc/terms/\" property=\"dct:title\">Geoprocessing</span> by <a xmlns:cc=\"http://creativecommons.org/ns#\"\n",
    "href=\"http://sergerey.org\" property=\"cc:attributionName\"\n",
    "rel=\"cc:attributionURL\">Serge Rey</a> is licensed under a <a\n",
    "rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\">Creative\n",
    "Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "geopandas==0.8.1\n"
     ]
    }
   ],
   "source": [
    "# List of used packages to be added to the `requirements.txt` file (ref: https://stackoverflow.com/questions/40428931/package-for-listing-version-of-packages-used-in-a-jupyter-notebook)\n",
    "import pkg_resources\n",
    "import types\n",
    "def get_imports():\n",
    "    for name, val in globals().items():\n",
    "        if isinstance(val, types.ModuleType):\n",
    "            # Split ensures you get root package, \n",
    "            # not just imported function\n",
    "            name = val.__name__.split(\".\")[0]\n",
    "\n",
    "        elif isinstance(val, type):\n",
    "            name = val.__module__.split(\".\")[0]\n",
    "\n",
    "        # Some packages are weird and have different\n",
    "        # imported names vs. system/pip names. Unfortunately,\n",
    "        # there is no systematic way to get pip names from\n",
    "        # a package's imported name. You'll have to add\n",
    "        # exceptions to this list manually!\n",
    "        poorly_named_packages = {\n",
    "            \"PIL\": \"Pillow\",\n",
    "            \"sklearn\": \"scikit-learn\"\n",
    "        }\n",
    "        if name in poorly_named_packages.keys():\n",
    "            name = poorly_named_packages[name]\n",
    "\n",
    "        yield name\n",
    "imports = list(set(get_imports()))\n",
    "\n",
    "# The only way I found to get the version of the root package\n",
    "# from only the name of the package is to cross-check the names \n",
    "# of installed packages vs. imported packages\n",
    "requirements = []\n",
    "for m in pkg_resources.working_set:\n",
    "    if m.project_name in imports and m.project_name!=\"pip\":\n",
    "        requirements.append((m.project_name, m.version))\n",
    "\n",
    "for r in requirements:\n",
    "    print(\"{}=={}\".format(*r))"
   ]
  }
 ],
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