notebooks/point_polygon_validation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial Validation for Census Points & Polygons\n",
    "_Latitude and Longitude coordinates provided by the Census should fall inside polygons provided by the Census, right?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# required packages, also need run homebrew for spatial join to work! \n",
    "# brew install spatialindex\n",
    "#!pip3 install geopandas descartes matplotlib shapely rtree\n",
    "\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from shapely import geometry, wkt\n",
    "from shapely.geometry import Point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Gazetteer Locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  USPS   GEOID  ANSICODE                NAME  LSAD FUNCSTAT      ALAND  \\\n",
      "0   CA  600135   2582926  Acalanes Ridge CDP    57        S    1193072   \n",
      "1   CA  600156   2629758          Acampo CDP    57        S    2428474   \n",
      "2   CA  600212   2407697           Acton CDP    57        S  101679341   \n",
      "3   CA  600296   2409663       Adelanto city    25        A  137220853   \n",
      "4   CA  600310   2582927            Adin CDP    57        S    8901691   \n",
      "\n",
      "   AWATER  ALAND_SQMI  AWATER_SQMI   INTPTLAT   INTPTLONG  \n",
      "0       0       0.461        0.000  37.904722 -122.078574  \n",
      "1       0       0.938        0.000  38.173529 -121.279892  \n",
      "2   60418      39.259        0.023  34.496070 -118.183897  \n",
      "3   45848      52.981        0.018  34.580902 -117.439458  \n",
      "4   15858       3.437        0.006  41.199312 -120.956779  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1057be198>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CSV converted from https://www2.census.gov/geo/docs/maps-data/data/gazetteer/2017_Gazetteer/2017_gaz_place_06.txt\n",
    "census_gaz = pd.read_csv(\"data/2017_gaz_place_06.csv\")\n",
    "print(census_gaz.head())\n",
    "\n",
    "# combine coordinates\n",
    "census_gaz['Coordinates'] = list(zip(census_gaz.INTPTLONG, census_gaz.INTPTLAT))\n",
    "census_gaz['Coordinates'] = census_gaz['Coordinates'].apply(Point)\n",
    "\n",
    "# convert to Pandas Geospatial Dataframe\n",
    "census_gaz_points = gpd.GeoDataFrame(census_gaz, geometry='Coordinates')\n",
    "\n",
    "# plot the points!\n",
    "census_gaz_points.plot(alpha=.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Cartographic Boundary Shapefile\n",
    "https://www.census.gov/geo/maps-data/data/cbf/cbf_place.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  STATEFP PLACEFP   PLACENS          AFFGEOID    GEOID           NAME LSAD  \\\n",
      "0      06   66000  02411782  1600000US0666000  0666000      San Diego   25   \n",
      "1      06   48354  02411130  1600000US0648354  0648354        Modesto   25   \n",
      "2      06   78582  02412065  1600000US0678582  0678582  Thousand Oaks   25   \n",
      "3      06   17498  02410272  1600000US0617498  0617498         Cudahy   25   \n",
      "4      06   21712  02410406  1600000US0621712  0621712       El Cajon   25   \n",
      "\n",
      "       ALAND     AWATER                                           geometry  \n",
      "0  841108238  122244240  (POLYGON ((-116.93264 32.649128, -116.931919 3...  \n",
      "1  111280668    4768868  (POLYGON ((-121.07441 37.699985, -121.068995 3...  \n",
      "2  143118041     387875  (POLYGON ((-118.911435 34.171963, -118.909328 ...  \n",
      "3    3043278     131925  POLYGON ((-118.201898 33.970611, -118.199612 3...  \n",
      "4   37516313          0  POLYGON ((-117.011281 32.820275, -117.005763 3...  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1180443c8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# downloaded from http://www2.census.gov/geo/tiger/GENZ2017/shp/cb_2017_06_place_500k.zip\n",
    "census_cb = gpd.read_file(\"data/cb_2017_06_place_500k/cb_2017_06_place_500k.shp\")\n",
    "print(census_cb.head())\n",
    "\n",
    "# plot the polygons!\n",
    "census_cb.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load TIGER/Line® Shapefile\n",
    "https://www.census.gov/cgi-bin/geo/shapefiles/index.php"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  STATEFP PLACEFP   PLACENS    GEOID              NAME               NAMELSAD  \\\n",
      "0      06   38520  02411544  0638520         King City         King City city   \n",
      "1      06   86300  02412299  0686300          Woodlake          Woodlake city   \n",
      "2      06   80994  02412119  0680994  Twentynine Palms  Twentynine Palms city   \n",
      "3      06   81344  02412137  0681344            Upland            Upland city   \n",
      "4      06   87042  02412326  0687042           Yucaipa           Yucaipa city   \n",
      "\n",
      "  LSAD CLASSFP PCICBSA PCINECTA  MTFCC FUNCSTAT      ALAND   AWATER  \\\n",
      "0   25      C1       N        N  G4110        A   10090834   222052   \n",
      "1   25      C1       N        N  G4110        A    6305660  1178472   \n",
      "2   25      C1       N        N  G4110        A  152179753        0   \n",
      "3   25      C1       N        N  G4110        A   40432020    86983   \n",
      "4   25      C1       N        N  G4110        A   73279143        0   \n",
      "\n",
      "      INTPTLAT      INTPTLON  \\\n",
      "0  +36.2162833  -121.1319616   \n",
      "1  +36.4128251  -119.0995300   \n",
      "2  +34.1478314  -116.0659615   \n",
      "3  +34.1160732  -117.6599224   \n",
      "4  +34.0335713  -117.0429928   \n",
      "\n",
      "                                            geometry  \n",
      "0  (POLYGON ((-121.148837 36.208163, -121.146607 ...  \n",
      "1  POLYGON ((-119.134696 36.413689, -119.13468 36...  \n",
      "2  POLYGON ((-116.1946 34.13535, -116.194533 34.1...  \n",
      "3  POLYGON ((-117.704491 34.095262, -117.704433 3...  \n",
      "4  POLYGON ((-117.126232 34.028152, -117.126019 3...  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118076cc0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# downloaded/extracted from ftp://ftp2.census.gov/geo/tiger/TIGER2018/PLACE/tl_2018_06_place.zip\n",
    "census_tl = gpd.read_file(\"data/tl_2018_06_place/tl_2018_06_place.shp\")\n",
    "print(census_tl.head())\n",
    "\n",
    "# plot the polygons!\n",
    "census_tl.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge Cartographic Boundary with Gazetteer\n",
    "\n",
    "***Note that `sjoin` will return an error referencing `libspatialindex` if you have not used homebrew to install `spatialindex` as this is a requirement of [rtree](https://pypi.org/project/Rtree/)***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USPS</th>\n",
       "      <th>GEOID_left</th>\n",
       "      <th>ANSICODE</th>\n",
       "      <th>NAME_left</th>\n",
       "      <th>LSAD_left</th>\n",
       "      <th>FUNCSTAT</th>\n",
       "      <th>ALAND_left</th>\n",
       "      <th>AWATER_left</th>\n",
       "      <th>ALAND_SQMI</th>\n",
       "      <th>AWATER_SQMI</th>\n",
       "      <th>...</th>\n",
       "      <th>index_right</th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>PLACEFP</th>\n",
       "      <th>PLACENS</th>\n",
       "      <th>AFFGEOID</th>\n",
       "      <th>GEOID_right</th>\n",
       "      <th>NAME_right</th>\n",
       "      <th>LSAD_right</th>\n",
       "      <th>ALAND_right</th>\n",
       "      <th>AWATER_right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>CA</td>\n",
       "      <td>617918</td>\n",
       "      <td>2410291</td>\n",
       "      <td>Daly City city</td>\n",
       "      <td>25</td>\n",
       "      <td>A</td>\n",
       "      <td>19789731</td>\n",
       "      <td>0</td>\n",
       "      <td>7.641</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>CA</td>\n",
       "      <td>626000</td>\n",
       "      <td>2410545</td>\n",
       "      <td>Fremont city</td>\n",
       "      <td>25</td>\n",
       "      <td>A</td>\n",
       "      <td>200632981</td>\n",
       "      <td>26291597</td>\n",
       "      <td>77.465</td>\n",
       "      <td>10.151</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>CA</td>\n",
       "      <td>667000</td>\n",
       "      <td>2411786</td>\n",
       "      <td>San Francisco city</td>\n",
       "      <td>25</td>\n",
       "      <td>A</td>\n",
       "      <td>121485107</td>\n",
       "      <td>479107241</td>\n",
       "      <td>46.906</td>\n",
       "      <td>184.984</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>CA</td>\n",
       "      <td>673262</td>\n",
       "      <td>2411942</td>\n",
       "      <td>South San Francisco city</td>\n",
       "      <td>25</td>\n",
       "      <td>A</td>\n",
       "      <td>23769809</td>\n",
       "      <td>54435146</td>\n",
       "      <td>9.178</td>\n",
       "      <td>21.018</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     USPS  GEOID_left  ANSICODE                 NAME_left  LSAD_left FUNCSTAT  \\\n",
       "333    CA      617918   2410291            Daly City city         25        A   \n",
       "498    CA      626000   2410545              Fremont city         25        A   \n",
       "1193   CA      667000   2411786        San Francisco city         25        A   \n",
       "1296   CA      673262   2411942  South San Francisco city         25        A   \n",
       "\n",
       "      ALAND_left  AWATER_left  ALAND_SQMI  AWATER_SQMI     ...       \\\n",
       "333     19789731            0       7.641        0.000     ...        \n",
       "498    200632981     26291597      77.465       10.151     ...        \n",
       "1193   121485107    479107241      46.906      184.984     ...        \n",
       "1296    23769809     54435146       9.178       21.018     ...        \n",
       "\n",
       "      index_right  STATEFP PLACEFP  PLACENS AFFGEOID GEOID_right NAME_right  \\\n",
       "333           NaN      NaN     NaN      NaN      NaN         NaN        NaN   \n",
       "498           NaN      NaN     NaN      NaN      NaN         NaN        NaN   \n",
       "1193          NaN      NaN     NaN      NaN      NaN         NaN        NaN   \n",
       "1296          NaN      NaN     NaN      NaN      NaN         NaN        NaN   \n",
       "\n",
       "     LSAD_right ALAND_right AWATER_right  \n",
       "333         NaN         NaN          NaN  \n",
       "498         NaN         NaN          NaN  \n",
       "1193        NaN         NaN          NaN  \n",
       "1296        NaN         NaN          NaN  \n",
       "\n",
       "[4 rows x 23 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# coordinate reference system warning averted!\n",
    "census_gaz_points.crs = census_cb.crs\n",
    "cenus_gaz_points_cb = gpd.sjoin(census_gaz_points, census_cb, how=\"left\", op='intersects')\n",
    "\n",
    "# check for non-joined geographies\n",
    "cenus_gaz_points_cb_unjoined = cenus_gaz_points_cb[cenus_gaz_points_cb.GEOID_right.isnull()]\n",
    "cenus_gaz_points_cb_unjoined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge TIGER/Line® Shapefile with Gazetteer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USPS</th>\n",
       "      <th>GEOID_left</th>\n",
       "      <th>ANSICODE</th>\n",
       "      <th>NAME_left</th>\n",
       "      <th>LSAD_left</th>\n",
       "      <th>FUNCSTAT_left</th>\n",
       "      <th>ALAND_left</th>\n",
       "      <th>AWATER_left</th>\n",
       "      <th>ALAND_SQMI</th>\n",
       "      <th>AWATER_SQMI</th>\n",
       "      <th>...</th>\n",
       "      <th>LSAD_right</th>\n",
       "      <th>CLASSFP</th>\n",
       "      <th>PCICBSA</th>\n",
       "      <th>PCINECTA</th>\n",
       "      <th>MTFCC</th>\n",
       "      <th>FUNCSTAT_right</th>\n",
       "      <th>ALAND_right</th>\n",
       "      <th>AWATER_right</th>\n",
       "      <th>INTPTLAT_right</th>\n",
       "      <th>INTPTLON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty GeoDataFrame\n",
       "Columns: [USPS, GEOID_left, ANSICODE, NAME_left, LSAD_left, FUNCSTAT_left, ALAND_left, AWATER_left, ALAND_SQMI, AWATER_SQMI, INTPTLAT_left, INTPTLONG, Coordinates, index_right, STATEFP, PLACEFP, PLACENS, GEOID_right, NAME_right, NAMELSAD, LSAD_right, CLASSFP, PCICBSA, PCINECTA, MTFCC, FUNCSTAT_right, ALAND_right, AWATER_right, INTPTLAT_right, INTPTLON]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 30 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# coordinate reference system warning averted!\n",
    "census_gaz_points.crs = census_tl.crs\n",
    "cenus_gaz_points_tl = gpd.sjoin(census_gaz_points, census_tl, how=\"left\", op='intersects')\n",
    "\n",
    "# check for non-joined geographies\n",
    "cenus_gaz_points_tl_unjoined = cenus_gaz_points_tl[cenus_gaz_points_tl.GEOID_right.isnull()]\n",
    "cenus_gaz_points_tl_unjoined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the FIPs codes for Points not found in Cartographic Boundary shapefile?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "617918 Daly City city\n",
      "626000 Fremont city\n",
      "667000 San Francisco city\n",
      "673262 South San Francisco city\n"
     ]
    }
   ],
   "source": [
    "for idx,row in cenus_gaz_points_cb_unjoined.iterrows():\n",
    "    geoid = row['GEOID_left']\n",
    "    name = row['NAME_left']\n",
    "    print(geoid,str(name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter both shapefiles for 4 places"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>PLACEFP</th>\n",
       "      <th>PLACENS</th>\n",
       "      <th>AFFGEOID</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>LSAD</th>\n",
       "      <th>ALAND</th>\n",
       "      <th>AWATER</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>06</td>\n",
       "      <td>26000</td>\n",
       "      <td>02410545</td>\n",
       "      <td>1600000US0626000</td>\n",
       "      <td>0626000</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>25</td>\n",
       "      <td>200632981</td>\n",
       "      <td>26291597</td>\n",
       "      <td>POLYGON ((-122.130211 37.563942, -122.124954 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>06</td>\n",
       "      <td>67000</td>\n",
       "      <td>02411786</td>\n",
       "      <td>1600000US0667000</td>\n",
       "      <td>0667000</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>25</td>\n",
       "      <td>121485107</td>\n",
       "      <td>479107241</td>\n",
       "      <td>(POLYGON ((-122.332045780727 37.7877603470098,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>06</td>\n",
       "      <td>73262</td>\n",
       "      <td>02411942</td>\n",
       "      <td>1600000US0673262</td>\n",
       "      <td>0673262</td>\n",
       "      <td>South San Francisco</td>\n",
       "      <td>25</td>\n",
       "      <td>23769809</td>\n",
       "      <td>54435146</td>\n",
       "      <td>POLYGON ((-122.47076 37.649406, -122.469055 37...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>06</td>\n",
       "      <td>17918</td>\n",
       "      <td>02410291</td>\n",
       "      <td>1600000US0617918</td>\n",
       "      <td>0617918</td>\n",
       "      <td>Daly City</td>\n",
       "      <td>25</td>\n",
       "      <td>19789731</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-122.50052918554 37.7008353023262, -...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    STATEFP PLACEFP   PLACENS          AFFGEOID    GEOID                 NAME  \\\n",
       "297      06   26000  02410545  1600000US0626000  0626000              Fremont   \n",
       "390      06   67000  02411786  1600000US0667000  0667000        San Francisco   \n",
       "393      06   73262  02411942  1600000US0673262  0673262  South San Francisco   \n",
       "719      06   17918  02410291  1600000US0617918  0617918            Daly City   \n",
       "\n",
       "    LSAD      ALAND     AWATER  \\\n",
       "297   25  200632981   26291597   \n",
       "390   25  121485107  479107241   \n",
       "393   25   23769809   54435146   \n",
       "719   25   19789731          0   \n",
       "\n",
       "                                              geometry  \n",
       "297  POLYGON ((-122.130211 37.563942, -122.124954 3...  \n",
       "390  (POLYGON ((-122.332045780727 37.7877603470098,...  \n",
       "393  POLYGON ((-122.47076 37.649406, -122.469055 37...  \n",
       "719  POLYGON ((-122.50052918554 37.7008353023262, -...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_cb_test = census_cb\n",
    "census_cb_test = census_cb_test.query('GEOID in (\"0667000\",\"0617918\",\"0626000\",\"0673262\")')\n",
    "census_cb_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>PLACEFP</th>\n",
       "      <th>PLACENS</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>NAMELSAD</th>\n",
       "      <th>LSAD</th>\n",
       "      <th>CLASSFP</th>\n",
       "      <th>PCICBSA</th>\n",
       "      <th>PCINECTA</th>\n",
       "      <th>MTFCC</th>\n",
       "      <th>FUNCSTAT</th>\n",
       "      <th>ALAND</th>\n",
       "      <th>AWATER</th>\n",
       "      <th>INTPTLAT</th>\n",
       "      <th>INTPTLON</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>06</td>\n",
       "      <td>67000</td>\n",
       "      <td>02411786</td>\n",
       "      <td>0667000</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>San Francisco city</td>\n",
       "      <td>25</td>\n",
       "      <td>C1</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>G4110</td>\n",
       "      <td>A</td>\n",
       "      <td>121375681</td>\n",
       "      <td>479216672</td>\n",
       "      <td>+37.7272391</td>\n",
       "      <td>-123.0322294</td>\n",
       "      <td>(POLYGON ((-122.612285 37.815224, -122.584588 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>06</td>\n",
       "      <td>17918</td>\n",
       "      <td>02410291</td>\n",
       "      <td>0617918</td>\n",
       "      <td>Daly City</td>\n",
       "      <td>Daly City city</td>\n",
       "      <td>25</td>\n",
       "      <td>C1</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>G4110</td>\n",
       "      <td>A</td>\n",
       "      <td>19789734</td>\n",
       "      <td>0</td>\n",
       "      <td>+37.7008350</td>\n",
       "      <td>-122.5007910</td>\n",
       "      <td>POLYGON ((-122.500791 37.700835, -122.499925 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>06</td>\n",
       "      <td>73262</td>\n",
       "      <td>02411942</td>\n",
       "      <td>0673262</td>\n",
       "      <td>South San Francisco</td>\n",
       "      <td>South San Francisco city</td>\n",
       "      <td>25</td>\n",
       "      <td>C1</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>G4110</td>\n",
       "      <td>A</td>\n",
       "      <td>23893943</td>\n",
       "      <td>54315250</td>\n",
       "      <td>+37.6551037</td>\n",
       "      <td>-122.3762341</td>\n",
       "      <td>POLYGON ((-122.471733 37.649159, -122.471517 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>06</td>\n",
       "      <td>26000</td>\n",
       "      <td>02410545</td>\n",
       "      <td>0626000</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>Fremont city</td>\n",
       "      <td>25</td>\n",
       "      <td>C1</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>G4110</td>\n",
       "      <td>A</td>\n",
       "      <td>200632981</td>\n",
       "      <td>26291597</td>\n",
       "      <td>+37.5010483</td>\n",
       "      <td>-122.0839341</td>\n",
       "      <td>POLYGON ((-122.149175 37.545835, -122.135769 3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    STATEFP PLACEFP   PLACENS    GEOID                 NAME  \\\n",
       "205      06   67000  02411786  0667000        San Francisco   \n",
       "302      06   17918  02410291  0617918            Daly City   \n",
       "304      06   73262  02411942  0673262  South San Francisco   \n",
       "479      06   26000  02410545  0626000              Fremont   \n",
       "\n",
       "                     NAMELSAD LSAD CLASSFP PCICBSA PCINECTA  MTFCC FUNCSTAT  \\\n",
       "205        San Francisco city   25      C1       Y        N  G4110        A   \n",
       "302            Daly City city   25      C1       N        N  G4110        A   \n",
       "304  South San Francisco city   25      C1       Y        N  G4110        A   \n",
       "479              Fremont city   25      C1       N        N  G4110        A   \n",
       "\n",
       "         ALAND     AWATER     INTPTLAT      INTPTLON  \\\n",
       "205  121375681  479216672  +37.7272391  -123.0322294   \n",
       "302   19789734          0  +37.7008350  -122.5007910   \n",
       "304   23893943   54315250  +37.6551037  -122.3762341   \n",
       "479  200632981   26291597  +37.5010483  -122.0839341   \n",
       "\n",
       "                                              geometry  \n",
       "205  (POLYGON ((-122.612285 37.815224, -122.584588 ...  \n",
       "302  POLYGON ((-122.500791 37.700835, -122.499925 3...  \n",
       "304  POLYGON ((-122.471733 37.649159, -122.471517 3...  \n",
       "479  POLYGON ((-122.149175 37.545835, -122.135769 3...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_tl_test = census_tl\n",
    "census_tl_test = census_tl_test.query('GEOID in (\"0667000\",\"0617918\",\"0626000\",\"0673262\")')\n",
    "census_tl_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform for Socrata Spatial Success\n",
    "- Convert Polygons to Multipolygons\n",
    "- Save as GEOJSON files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Handy function provided by https://github.com/geopandas/geopandas/issues/834\n",
    "upcast_dispatch = {geometry.Point: geometry.MultiPoint, \n",
    "                   geometry.LineString: geometry.MultiLineString, \n",
    "                   geometry.Polygon: geometry.MultiPolygon}\n",
    "\n",
    "def maybe_cast_to_multigeometry(geom):\n",
    "    caster = upcast_dispatch.get(type(geom), lambda x: x[0])\n",
    "    return caster([geom])\n",
    "\n",
    "census_tl.geometry = census_tl.geometry.apply(maybe_cast_to_multigeometry)\n",
    "census_cb.geometry = census_cb.geometry.apply(maybe_cast_to_multigeometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/geopandas/io/file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n",
      "  with fiona.drivers():\n"
     ]
    }
   ],
   "source": [
    "# Convert to GEOJSON\n",
    "census_tl.to_file(\"~/data/tl_2018_06_place.json\", driver=\"GeoJSON\")\n",
    "census_cb.to_file(\"~/data/cb_2018_06_place.json\", driver=\"GeoJSON\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visually inspect unjoined cities\n",
    "- Plot point on polygon for the GEOID\n",
    "- Get centroid of polygon\n",
    "- Plot centroid\n",
    "- Confirm spatial join between polygon and centroid"
   ]
  }
 ],
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