notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed07e2e1-3a95-49d1-98ff-e0effb9d94b1",
   "metadata": {
    "tags": []
   },
   "source": [
    "# random-tract\n",
    "\n",
    "By [Ben Welsh](https://palewi.re/who-is-ben-welsh/)\n",
    "\n",
    "A Python hack to respond to a [Twitter challenge](https://twitter.com/veltman/status/1439677491338121218) to \"select a random geographic point in the US, with the probability weighted by population.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47cb7770-d894-4b9c-8239-c6881c34ad25",
   "metadata": {},
   "source": [
    "Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "746ba241-1739-479e-addd-e84f2cff3705",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "import pathlib\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from census_data_downloader import tables\n",
    "from census_map_downloader.geotypes.tracts import TractsDownloader\n",
    "from census_map_downloader.geotypes.states_carto import StatesCartoDownloader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ab18e3d-67b5-44b9-a8bc-85517d8ee3c5",
   "metadata": {},
   "source": [
    "Configure environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2e6c7060-57b6-4bce-bbb7-b5b5943e52fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = pathlib.Path(\"./data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d81c2a7-6370-48a4-82bb-9033ee0ba93c",
   "metadata": {},
   "outputs": [],
   "source": [
    "census_api_key = os.getenv(\"CENSUS_API_KEY\") or ''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e5965b-cc9f-4a0f-ab70-6978f24ae92a",
   "metadata": {},
   "source": [
    "Download tract data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7d2e0ef1-d214-4568-a53e-fd4d1ddeec64",
   "metadata": {},
   "outputs": [],
   "source": [
    "tables.PopulationDownloader(\n",
    "    census_api_key,\n",
    "    data_dir=data_dir,\n",
    "    years=2019\n",
    ").download_tracts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8535692d-6f11-450f-a790-e3f3ea5f2827",
   "metadata": {},
   "source": [
    "Download the tract map file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c028841d-5910-4bab-bcb6-325a3e3d110b",
   "metadata": {},
   "outputs": [],
   "source": [
    "TractsDownloader(data_dir, year=2020).run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da27558-18bf-4745-8ac2-f336931c5686",
   "metadata": {},
   "source": [
    "Download state map file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c804b756-4d49-4823-91fc-5409bed836b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "StatesCartoDownloader(data_dir).run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c20115-7f12-424a-b595-4bb57ffe69a4",
   "metadata": {},
   "source": [
    "Read in tract data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1359da0e-9c8d-4edb-9c0e-ab72e2eede2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "tract_df = pd.read_csv(data_dir / \"processed/acs5_2019_population_tracts.csv\", dtype={\"geoid\": str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1fbf7d2e-aa05-4781-87b3-2f0551bd7a2d",
   "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>name</th>\n",
       "      <th>universe</th>\n",
       "      <th>universe_annotation</th>\n",
       "      <th>universe_moe</th>\n",
       "      <th>universe_moe_annotation</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>tract</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01071951100</td>\n",
       "      <td>Census Tract 9511, Jackson County, Alabama</td>\n",
       "      <td>6662.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>626.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>951100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01097002900</td>\n",
       "      <td>Census Tract 29, Mobile County, Alabama</td>\n",
       "      <td>4342.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>428.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01121010302</td>\n",
       "      <td>Census Tract 103.02, Talladega County, Alabama</td>\n",
       "      <td>4441.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>449.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>121</td>\n",
       "      <td>10302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01097003404</td>\n",
       "      <td>Census Tract 34.04, Mobile County, Alabama</td>\n",
       "      <td>2597.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>377.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>3404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01097000200</td>\n",
       "      <td>Census Tract 2, Mobile County, Alabama</td>\n",
       "      <td>1306.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>235.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         geoid                                            name  universe  \\\n",
       "0  01071951100      Census Tract 9511, Jackson County, Alabama    6662.0   \n",
       "1  01097002900         Census Tract 29, Mobile County, Alabama    4342.0   \n",
       "2  01121010302  Census Tract 103.02, Talladega County, Alabama    4441.0   \n",
       "3  01097003404      Census Tract 34.04, Mobile County, Alabama    2597.0   \n",
       "4  01097000200          Census Tract 2, Mobile County, Alabama    1306.0   \n",
       "\n",
       "   universe_annotation  universe_moe universe_moe_annotation  state  county  \\\n",
       "0                  NaN         626.0                     NaN      1      71   \n",
       "1                  NaN         428.0                     NaN      1      97   \n",
       "2                  NaN         449.0                     NaN      1     121   \n",
       "3                  NaN         377.0                     NaN      1      97   \n",
       "4                  NaN         235.0                     NaN      1      97   \n",
       "\n",
       "    tract  \n",
       "0  951100  \n",
       "1    2900  \n",
       "2   10302  \n",
       "3    3404  \n",
       "4     200  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tract_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5399dc1f-0665-44a1-ac94-7a46021c821f",
   "metadata": {},
   "source": [
    "Use population as a weight to randomly select a tract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2f66ca44-9c42-4595-a369-cdd6597e78ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "population = tract_df.geoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7e270b74-c171-49b4-a477-b76da0b1a977",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = tract_df.universe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "24a1b695-3cd3-416c-83ba-5b99c8f3a808",
   "metadata": {},
   "outputs": [],
   "source": [
    "random_tract = random.choices(population, weights=weights)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0db67b1f-df30-4d3f-80de-29f02b7dde44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'12095016301'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_tract"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a77144c-f6f4-40f0-9a20-7aee611ad1a7",
   "metadata": {},
   "source": [
    "Read in the tract map file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2167c1ff-bfc8-4086-8aad-97bb87e17afe",
   "metadata": {},
   "outputs": [],
   "source": [
    "tracts_gdf = gpd.read_file(data_dir / \"processed/tracts_2020.geojson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aab99b53-7ffb-4afa-8545-54dfd6376eb6",
   "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>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>TRACTCE</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>NAMELSAD</th>\n",
       "      <th>MTFCC</th>\n",
       "      <th>FUNCSTAT</th>\n",
       "      <th>ALAND</th>\n",
       "      <th>AWATER</th>\n",
       "      <th>INTPTLAT</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>045</td>\n",
       "      <td>020801</td>\n",
       "      <td>01045020801</td>\n",
       "      <td>208.01</td>\n",
       "      <td>Census Tract 208.01</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>19261326</td>\n",
       "      <td>0</td>\n",
       "      <td>+31.3971437</td>\n",
       "      <td>-085.6615959</td>\n",
       "      <td>POLYGON ((-85.70064 31.38509, -85.70043 31.385...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01</td>\n",
       "      <td>017</td>\n",
       "      <td>954200</td>\n",
       "      <td>01017954200</td>\n",
       "      <td>9542</td>\n",
       "      <td>Census Tract 9542</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>62998696</td>\n",
       "      <td>34641</td>\n",
       "      <td>+32.8341436</td>\n",
       "      <td>-085.2678516</td>\n",
       "      <td>POLYGON ((-85.31549 32.80580, -85.31354 32.808...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01</td>\n",
       "      <td>017</td>\n",
       "      <td>953800</td>\n",
       "      <td>01017953800</td>\n",
       "      <td>9538</td>\n",
       "      <td>Census Tract 9538</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>515692207</td>\n",
       "      <td>11516898</td>\n",
       "      <td>+33.0302580</td>\n",
       "      <td>-085.3867880</td>\n",
       "      <td>POLYGON ((-85.59345 33.00012, -85.59333 33.000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01</td>\n",
       "      <td>017</td>\n",
       "      <td>954300</td>\n",
       "      <td>01017954300</td>\n",
       "      <td>9543</td>\n",
       "      <td>Census Tract 9543</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>132642536</td>\n",
       "      <td>178276</td>\n",
       "      <td>+32.7766468</td>\n",
       "      <td>-085.2734917</td>\n",
       "      <td>POLYGON ((-85.38077 32.78301, -85.37985 32.782...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>017</td>\n",
       "      <td>954700</td>\n",
       "      <td>01017954700</td>\n",
       "      <td>9547</td>\n",
       "      <td>Census Tract 9547</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>38036820</td>\n",
       "      <td>264376</td>\n",
       "      <td>+32.7702191</td>\n",
       "      <td>-085.1744682</td>\n",
       "      <td>POLYGON ((-85.22897 32.74543, -85.22894 32.745...</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "  STATEFP COUNTYFP TRACTCE        GEOID    NAME             NAMELSAD  MTFCC  \\\n",
       "0      01      045  020801  01045020801  208.01  Census Tract 208.01  G5020   \n",
       "1      01      017  954200  01017954200    9542    Census Tract 9542  G5020   \n",
       "2      01      017  953800  01017953800    9538    Census Tract 9538  G5020   \n",
       "3      01      017  954300  01017954300    9543    Census Tract 9543  G5020   \n",
       "4      01      017  954700  01017954700    9547    Census Tract 9547  G5020   \n",
       "\n",
       "  FUNCSTAT      ALAND    AWATER     INTPTLAT      INTPTLON  \\\n",
       "0        S   19261326         0  +31.3971437  -085.6615959   \n",
       "1        S   62998696     34641  +32.8341436  -085.2678516   \n",
       "2        S  515692207  11516898  +33.0302580  -085.3867880   \n",
       "3        S  132642536    178276  +32.7766468  -085.2734917   \n",
       "4        S   38036820    264376  +32.7702191  -085.1744682   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-85.70064 31.38509, -85.70043 31.385...  \n",
       "1  POLYGON ((-85.31549 32.80580, -85.31354 32.808...  \n",
       "2  POLYGON ((-85.59345 33.00012, -85.59333 33.000...  \n",
       "3  POLYGON ((-85.38077 32.78301, -85.37985 32.782...  \n",
       "4  POLYGON ((-85.22897 32.74543, -85.22894 32.745...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracts_gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c7495c-9f52-4fab-b9e5-1606c8fd0e31",
   "metadata": {},
   "source": [
    "Reader in the states maps file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cb0d4963-2430-4aa0-a628-43a625b18d03",
   "metadata": {},
   "outputs": [],
   "source": [
    "states_gdf = gpd.read_file(data_dir / \"processed/states_carto_2018.geojson\").to_crs(epsg=4326)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fae0c18e-a93e-4730-8c59-1ca64803f159",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>state_fips</th>\n",
       "      <th>geoid</th>\n",
       "      <th>name</th>\n",
       "      <th>land_area</th>\n",
       "      <th>water_area</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>121533519481</td>\n",
       "      <td>3926919758</td>\n",
       "      <td>MULTIPOLYGON (((-88.50297 30.21523, -88.49176 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>125923656064</td>\n",
       "      <td>13466071395</td>\n",
       "      <td>MULTIPOLYGON (((-75.72681 35.93584, -75.71827 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>177662925723</td>\n",
       "      <td>3374587997</td>\n",
       "      <td>POLYGON ((-103.00257 36.52659, -103.00219 36.6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>102257717110</td>\n",
       "      <td>8528531774</td>\n",
       "      <td>MULTIPOLYGON (((-75.74241 37.80835, -75.74151 ...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>62266474513</td>\n",
       "      <td>489028543</td>\n",
       "      <td>POLYGON ((-82.64320 38.16909, -82.64300 38.169...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_fips geoid            name     land_area   water_area  \\\n",
       "0         28    28     Mississippi  121533519481   3926919758   \n",
       "1         37    37  North Carolina  125923656064  13466071395   \n",
       "2         40    40        Oklahoma  177662925723   3374587997   \n",
       "3         51    51        Virginia  102257717110   8528531774   \n",
       "4         54    54   West Virginia   62266474513    489028543   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((-88.50297 30.21523, -88.49176 ...  \n",
       "1  MULTIPOLYGON (((-75.72681 35.93584, -75.71827 ...  \n",
       "2  POLYGON ((-103.00257 36.52659, -103.00219 36.6...  \n",
       "3  MULTIPOLYGON (((-75.74241 37.80835, -75.74151 ...  \n",
       "4  POLYGON ((-82.64320 38.16909, -82.64300 38.169...  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states_gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e28b1e4-5ff6-4919-9ce2-73a68f58ff8d",
   "metadata": {},
   "source": [
    "Create a geodataframe with the randomly selected tract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0578c20d-60b6-4c70-a631-302de97048c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "point_gdf = tracts_gdf[tracts_gdf.GEOID == random_tract].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca883a55-cbf1-42c1-971d-2b7afc8d780a",
   "metadata": {},
   "source": [
    "Pull the centroid of the tract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "70e0c1da-f316-4a64-a12b-3cf3e96b2a53",
   "metadata": {},
   "outputs": [],
   "source": [
    "point_gdf.geometry = point_gdf.to_crs(epsg=900913).geometry.centroid.to_crs(epsg=4326)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b2aab327-92e7-4f2a-86bd-8009b8f80a40",
   "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>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>TRACTCE</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>NAMELSAD</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>16203</th>\n",
       "      <td>12</td>\n",
       "      <td>095</td>\n",
       "      <td>016301</td>\n",
       "      <td>12095016301</td>\n",
       "      <td>163.01</td>\n",
       "      <td>Census Tract 163.01</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>3514974</td>\n",
       "      <td>0</td>\n",
       "      <td>+28.6026207</td>\n",
       "      <td>-081.3183381</td>\n",
       "      <td>POINT (-81.31834 28.60262)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      STATEFP COUNTYFP TRACTCE        GEOID    NAME             NAMELSAD  \\\n",
       "16203      12      095  016301  12095016301  163.01  Census Tract 163.01   \n",
       "\n",
       "       MTFCC FUNCSTAT    ALAND  AWATER     INTPTLAT      INTPTLON  \\\n",
       "16203  G5020        S  3514974       0  +28.6026207  -081.3183381   \n",
       "\n",
       "                         geometry  \n",
       "16203  POINT (-81.31834 28.60262)  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b6b60b2-129b-46dc-b11a-ba6f511ea2df",
   "metadata": {},
   "source": [
    "Map the point over the states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "540be5dd-6b2f-42fc-a914-b03a0064dcab",
   "metadata": {},
   "outputs": [],
   "source": [
    "clip_list = ['Commonwealth of the Northern Mariana Islands', 'Guam', 'American Samoa', 'United States Virgin Islands', 'Alaska', 'Puerto Rico', 'Hawaii']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e55cb943-7628-40c2-aaea-e157e44bdbf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 79,
     "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": [
    "base = states_gdf[~states_gdf.name.isin(clip_list)].plot(color='white', edgecolor='black')\n",
    "point_gdf.plot(ax=base, marker='o', color='red', markersize=25)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}