cover-photo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5eb2b68b",
   "metadata": {},
   "source": [
    "# National Flood Insurance Program Data for NYC\n",
    "## Cover Photo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fbc9148c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import urllib\n",
    "import urllib.request\n",
    "import requests\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "plt.rcParams['savefig.facecolor'] = 'white'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f54d766c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "printing packages and versions:\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.8.13\n",
      "IPython version      : 8.4.0\n",
      "\n",
      "numpy     : 1.23.1\n",
      "pandas    : 1.4.3\n",
      "geopandas : 0.11.1\n",
      "matplotlib: 3.5.2\n",
      "seaborn   : 0.11.2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('printing packages and versions:\\n')\n",
    "\n",
    "%reload_ext watermark\n",
    "%watermark -v -p numpy,pandas,geopandas,matplotlib,seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2bc8ad97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (5, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boro_code</th>\n",
       "      <th>boro_name</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>1.623621e+09</td>\n",
       "      <td>325917.353950</td>\n",
       "      <td>MULTIPOLYGON (((970217.022 145643.332, 970227....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>6.365205e+08</td>\n",
       "      <td>357713.308162</td>\n",
       "      <td>MULTIPOLYGON (((981219.056 188655.316, 980940....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>1.187175e+09</td>\n",
       "      <td>463180.579449</td>\n",
       "      <td>MULTIPOLYGON (((1012821.806 229228.265, 101278...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>1.934138e+09</td>\n",
       "      <td>728146.574928</td>\n",
       "      <td>MULTIPOLYGON (((1022227.320 152028.146, 102207...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Queens</td>\n",
       "      <td>3.041419e+09</td>\n",
       "      <td>888199.731385</td>\n",
       "      <td>MULTIPOLYGON (((1032452.015 154469.237, 103245...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   boro_code      boro_name    shape_area     shape_leng  \\\n",
       "0        5.0  Staten Island  1.623621e+09  325917.353950   \n",
       "1        1.0      Manhattan  6.365205e+08  357713.308162   \n",
       "2        2.0          Bronx  1.187175e+09  463180.579449   \n",
       "3        3.0       Brooklyn  1.934138e+09  728146.574928   \n",
       "4        4.0         Queens  3.041419e+09  888199.731385   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((970217.022 145643.332, 970227....  \n",
       "1  MULTIPOLYGON (((981219.056 188655.316, 980940....  \n",
       "2  MULTIPOLYGON (((1012821.806 229228.265, 101278...  \n",
       "3  MULTIPOLYGON (((1022227.320 152028.146, 102207...  \n",
       "4  MULTIPOLYGON (((1032452.015 154469.237, 103245...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing borough boundaries\n",
    "path = 'https://data.cityofnewyork.us/api/geospatial/tqmj-j8zm?method=export&format=Shapefile'\n",
    "borough_gdf = gpd.read_file(path)\n",
    "borough_gdf = borough_gdf.to_crs(2263)\n",
    "\n",
    "# preview data\n",
    "print('shape of data: {}'.format(borough_gdf.shape))\n",
    "borough_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4bb87b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "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": [
    "borough_gdf = (\n",
    "    borough_gdf\n",
    "    .loc[borough_gdf['boro_name'].isin(['Manhattan', 'Brooklyn'])]\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "borough_gdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "71bec110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "claims-36005.json    claims-36085.json    policies-36061.json\r\n",
      "claims-36047.json    fema-pfirm.zip       policies-36081.json\r\n",
      "claims-36061.json    policies-36005.json  policies-36085.json\r\n",
      "claims-36081.json    policies-36047.json\r\n"
     ]
    }
   ],
   "source": [
    "# url path from the web\n",
    "url = 'https://msc.fema.gov/portal/downloadProduct?productID=360497_PRELIMDB'\n",
    "file = 'data/fema-pfirm.zip'\n",
    "\n",
    "# download to data/ folder\n",
    "urllib.request.urlretrieve(url, file)\n",
    "\n",
    "%ls data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb3e6da5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (3985, 15)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FLD_AR_ID</th>\n",
       "      <th>FLD_ZONE</th>\n",
       "      <th>FLOODWAY</th>\n",
       "      <th>SFHA_TF</th>\n",
       "      <th>STATIC_BFE</th>\n",
       "      <th>V_DATUM</th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>LEN_UNIT</th>\n",
       "      <th>VELOCITY</th>\n",
       "      <th>VEL_UNIT</th>\n",
       "      <th>AR_REVERT</th>\n",
       "      <th>BFE_REVERT</th>\n",
       "      <th>DEP_REVERT</th>\n",
       "      <th>SOURCE_CIT</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>AE</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NAVD88</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>FEET</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>STUDY4</td>\n",
       "      <td>POLYGON ((1032001.691 263867.839, 1032006.047 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>X</td>\n",
       "      <td>None</td>\n",
       "      <td>F</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>STUDY4</td>\n",
       "      <td>POLYGON ((1033748.952 263778.464, 1033752.656 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83</td>\n",
       "      <td>0.2 PCT ANNUAL CHANCE FLOOD HAZARD</td>\n",
       "      <td>None</td>\n",
       "      <td>F</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>STUDY4</td>\n",
       "      <td>POLYGON ((1033309.701 263544.181, 1033322.210 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84</td>\n",
       "      <td>0.2 PCT ANNUAL CHANCE FLOOD HAZARD</td>\n",
       "      <td>None</td>\n",
       "      <td>F</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>STUDY4</td>\n",
       "      <td>POLYGON ((1033414.148 262658.868, 1033414.934 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>0.2 PCT ANNUAL CHANCE FLOOD HAZARD</td>\n",
       "      <td>None</td>\n",
       "      <td>F</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>STUDY4</td>\n",
       "      <td>POLYGON ((1034089.115 262466.034, 1034089.402 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FLD_AR_ID                            FLD_ZONE FLOODWAY SFHA_TF  STATIC_BFE  \\\n",
       "0        81                                  AE     None       T        13.0   \n",
       "1        82                                   X     None       F     -9999.0   \n",
       "2        83  0.2 PCT ANNUAL CHANCE FLOOD HAZARD     None       F     -9999.0   \n",
       "3        84  0.2 PCT ANNUAL CHANCE FLOOD HAZARD     None       F     -9999.0   \n",
       "4        85  0.2 PCT ANNUAL CHANCE FLOOD HAZARD     None       F     -9999.0   \n",
       "\n",
       "  V_DATUM   DEPTH LEN_UNIT  VELOCITY VEL_UNIT AR_REVERT  BFE_REVERT  \\\n",
       "0  NAVD88 -9999.0     FEET   -9999.0     None      None     -9999.0   \n",
       "1    None -9999.0     None   -9999.0     None      None     -9999.0   \n",
       "2    None -9999.0     None   -9999.0     None      None     -9999.0   \n",
       "3    None -9999.0     None   -9999.0     None      None     -9999.0   \n",
       "4    None -9999.0     None   -9999.0     None      None     -9999.0   \n",
       "\n",
       "   DEP_REVERT SOURCE_CIT                                           geometry  \n",
       "0     -9999.0     STUDY4  POLYGON ((1032001.691 263867.839, 1032006.047 ...  \n",
       "1     -9999.0     STUDY4  POLYGON ((1033748.952 263778.464, 1033752.656 ...  \n",
       "2     -9999.0     STUDY4  POLYGON ((1033309.701 263544.181, 1033322.210 ...  \n",
       "3     -9999.0     STUDY4  POLYGON ((1033414.148 262658.868, 1033414.934 ...  \n",
       "4     -9999.0     STUDY4  POLYGON ((1034089.115 262466.034, 1034089.402 ...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading in shape file\n",
    "path = 'data/fema-pfirm.zip!s_fld_haz_ar.shp'\n",
    "pfirm_gdf = gpd.read_file(path)\n",
    "\n",
    "# examine data\n",
    "print('shape of data: {}'.format(pfirm_gdf.shape))\n",
    "pfirm_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "973e7f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Derived Projected CRS: EPSG:2263>\n",
       "Name: NAD83 / New York Long Island (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: United States (USA) - New York - counties of Bronx; Kings; Nassau; New York; Queens; Richmond; Suffolk.\n",
       "- bounds: (-74.26, 40.47, -71.8, 41.3)\n",
       "Coordinate Operation:\n",
       "- name: SPCS83 New York Long Island zone (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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if pfirm_gdf.crs is None:\n",
    "    # manually set crs to 2263\n",
    "    pfirm_gdf = pfirm_gdf.set_crs(2263)\n",
    "    \n",
    "pfirm_gdf.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8bd1e076",
   "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>FLD_AR_ID</th>\n",
       "      <th>FLD_ZONE</th>\n",
       "      <th>FLOODWAY</th>\n",
       "      <th>SFHA_TF</th>\n",
       "      <th>STATIC_BFE</th>\n",
       "      <th>V_DATUM</th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>LEN_UNIT</th>\n",
       "      <th>VELOCITY</th>\n",
       "      <th>VEL_UNIT</th>\n",
       "      <th>AR_REVERT</th>\n",
       "      <th>BFE_REVERT</th>\n",
       "      <th>DEP_REVERT</th>\n",
       "      <th>SOURCE_CIT</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>AE</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NAVD88</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>FEET</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>STUDY4</td>\n",
       "      <td>POLYGON ((1032001.691 263867.839, 1032006.047 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>83</td>\n",
       "      <td>0.2 PCT ANNUAL CHANCE FLOOD HAZARD</td>\n",
       "      <td>None</td>\n",
       "      <td>F</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>STUDY4</td>\n",
       "      <td>POLYGON ((1033309.701 263544.181, 1033322.210 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84</td>\n",
       "      <td>0.2 PCT ANNUAL CHANCE FLOOD HAZARD</td>\n",
       "      <td>None</td>\n",
       "      <td>F</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>STUDY4</td>\n",
       "      <td>POLYGON ((1033414.148 262658.868, 1033414.934 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>0.2 PCT ANNUAL CHANCE FLOOD HAZARD</td>\n",
       "      <td>None</td>\n",
       "      <td>F</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>STUDY4</td>\n",
       "      <td>POLYGON ((1034089.115 262466.034, 1034089.402 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>86</td>\n",
       "      <td>0.2 PCT ANNUAL CHANCE FLOOD HAZARD</td>\n",
       "      <td>None</td>\n",
       "      <td>F</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>STUDY4</td>\n",
       "      <td>POLYGON ((1033316.808 262437.816, 1033316.108 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  FLD_AR_ID                            FLD_ZONE FLOODWAY SFHA_TF  STATIC_BFE  \\\n",
       "0        81                                  AE     None       T        13.0   \n",
       "2        83  0.2 PCT ANNUAL CHANCE FLOOD HAZARD     None       F     -9999.0   \n",
       "3        84  0.2 PCT ANNUAL CHANCE FLOOD HAZARD     None       F     -9999.0   \n",
       "4        85  0.2 PCT ANNUAL CHANCE FLOOD HAZARD     None       F     -9999.0   \n",
       "5        86  0.2 PCT ANNUAL CHANCE FLOOD HAZARD     None       F     -9999.0   \n",
       "\n",
       "  V_DATUM   DEPTH LEN_UNIT  VELOCITY VEL_UNIT AR_REVERT  BFE_REVERT  \\\n",
       "0  NAVD88 -9999.0     FEET   -9999.0     None      None     -9999.0   \n",
       "2    None -9999.0     None   -9999.0     None      None     -9999.0   \n",
       "3    None -9999.0     None   -9999.0     None      None     -9999.0   \n",
       "4    None -9999.0     None   -9999.0     None      None     -9999.0   \n",
       "5    None -9999.0     None   -9999.0     None      None     -9999.0   \n",
       "\n",
       "   DEP_REVERT SOURCE_CIT                                           geometry  \n",
       "0     -9999.0     STUDY4  POLYGON ((1032001.691 263867.839, 1032006.047 ...  \n",
       "2     -9999.0     STUDY4  POLYGON ((1033309.701 263544.181, 1033322.210 ...  \n",
       "3     -9999.0     STUDY4  POLYGON ((1033414.148 262658.868, 1033414.934 ...  \n",
       "4     -9999.0     STUDY4  POLYGON ((1034089.115 262466.034, 1034089.402 ...  \n",
       "5     -9999.0     STUDY4  POLYGON ((1033316.808 262437.816, 1033316.108 ...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "condition = (\n",
    "    (pfirm_gdf['SFHA_TF'] == 'T')\n",
    "    | (pfirm_gdf['FLD_ZONE'] == '0.2 PCT ANNUAL CHANCE FLOOD HAZARD')\n",
    ")\n",
    "\n",
    "pfirm_gdf = pfirm_gdf.loc[condition]\n",
    "\n",
    "pfirm_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ff2615f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5mb4Fko01ufzwxQYGQ3HqZ+jFb7YxaSt94MeAEXgh85a7TUr5yflkpd/hiRBLpjjU5ecrNy2ckfNvqMrhrTYPt64oYs0cjKudLFOx0h82B0TmmDlvpR+IJFAUeGx3Bw6jjmgiNa1TvIPBGMFYgkRKIRhNUpplIX+eZkofDnXKd5LYzXp2NA8yEIwTiqfQTVPcq5SS3a0eookUl9XkEomnaHdHWDrOhCbzBVW4k2BrYz99/giPvdXBp66u4eZlxUzXqFSfP8Zjezr5t9uXEIgl6fFGWVHmVCcdzkKNVZgE66py2NbsYU1lNmsrstBqxLQJy2zQ8vVbFtPrj2HUaihymWZkQmO2owp3EnjCcU70B/nwJZWTdhMfjnhSwajXIIDtzW6a+gKjJqGez6jCnQQ//OtRUlLwf584QI8vMi3n9IbjHOj0YdRpeeZgN33+KAvyJm/6PNdRhTtBgtEEzQNh7CYdxU4Lj+7qoC8QndI5FUXy6rF+Vpe7ONTpQycEd6wqwTQPAsIniyrcCRBNJHntaC/bmj2cGAgRSaT4zLW1NPYF8UcTY59gBLY0DbCm3IWU8HpjP9X5dvLUoa9RUUcVJoCiwBtNg9TkW+n0RFhclJ7F2lidO+lzSilZXubCYdLTPBDkfevKcFrmZyadiaC2uBOg05ueKWvuDyGAUDw5ZftOXySBLbOwsSrXhtNinLehihNBFe4EONDpQyM0pCTUFljJsRoIxSbud5tIKfijCT70m+14QvFzwiDna6jiRFC7ChMgx6ZHIwR2o5bqfDu3Li/GOYmUpE/v6+JAl4/La3Ooypsew5D5hirccZJKKQgESMniYifecJKr6vMnfJ7BYIwn9neRSCp88br6M7adjAxVZ8nGRu0qjBOtVsMVdfk4rXo6vRG+ceuSSQksx2bkHStL2FSbh8V4ZrshxPTNwM11VOGOk5QieaOhH28oTo7VQIFzcm/+yZTCW60esi3qGO1UULsK40SrERzo9NIXiHP1wvxJhzDev7UFBCwqHnmxcn8gxp42D1qNYH1VtjrtOwyqcCfA4iI7W04M4g1PbrJhS+MAR3sCHO8NcOfasmH32dPq5qevnki3zG0eYkmF960v5yOXVVKRY51K9ecUaldhAiRl2pTjfesnviBzS+MA//VCA88f6sVm1KMbZsir1x/l5681UZVr4ZcfXsPltbksKXZg0mvY3NDPj186zlttnum4lFmPKtwJ0OGJUldgo75w4sEvWVYDep2G8mwz1yzKHzZr+v++eQKjXsvyEhcaIdBpBFqNYOOCXIpdJh7c1srH7tvF5ob+6bicWY0q3HHS0OMnGI1jmeSCyEVFDj59TQ2LCu1EE6lhvRcGAzH2tfv441sd6DQCm0lPMiU50uOnoTfI25cXsrzEyR92zq+E08OhCnec1BU60Gk1HOzyjb3zCNz7RjOrKrKJpxQU5cyp4mgiRVWuldXlLn75oTUIIfjKjQuxm3TsanHz1P5u9rR5cYdieMMJBoOxqV7SrEYV7jiIJVLc+aut7GxxE01MLjZhX7uXimwrBQ4jf9rdyS83N5+x3aTXUltoJxxLEst8h9Os58r6PKJJhVAsyYEOH197+2LuuWMZBzt9RBOTS686F1CFOw6SisJVdXkIBN5Igqb+4ISOTymSZw92s7fDy6d/vwetBvZ3eM/Zz2EyEE9J3mwaOBVo89HLFxCMJWkZDFOSZWFVeRZVuVaurM/njeP93LelhbbBMJ5QnNOeLHMfVbjjoMcfozrPRrbVgNWgnbA/rUbAYDDO7lYP4XiK5oEwxcPY7B/s9BKMJfntmy187c8H2N2aHkH4yftXsyDPSl8gekbc77WLCvjwpRWU51hwWfQMzKPugyrccVCdZ+O6xQXsbvWQVCTHev0oisJjb3WcynIzGrtaPXR4Ti/xqS+wDzupUJJlZlNNLk6Tlqb+ID95+TiNfUFKXGY+srGSVWVZ5NpOz9gNnSIWQvDEvq55Y/qsTkCMk1gyRTieIhxP8rttbTx7oBdPOM7iYgcLx7BFqsm3YdJrqMm3odMIHGYdNcNEhT2yq4NoQiGZUnCY9TT2BvmPvx7lFx9cy4cureTSMQLWOz0RGvsC1M0Dmya1xR0HvnCCI90BCpwmbEY9aypc7Gp1I4DaYcZjz8Zl1rMgz0anJ4JJp6XXF6W+4LRwFUVyuNOLWa/hM9fU8JMPrOZjly/AE47zxvFBfJF096BmjJxpK0pdvNk0OKVrnS1MxUo/WwjxQsbi/oWhTuFzyUo/kVJ4taGPP+7uwKARfPCSChxmI0VOM039QRp6A2OeQwhBXYGN2gIb8VSKQqf5jDjcpl4vD+9o5p47lnNpdS75dhOFThN2kx4hwKwfOy5iV4ubkiwzx7oDtLlDU7rm2cBUrPS/CrwkpawFXsr8f85Z6QeiSRYXOShzmdhUn09VrpX7t7Zw+8piPnFFNfdtaRnXefJsJgSwrNRFVY4FXSYjTzKlsLvNT4cvidN8ut/rCcfp9EYIx1N899kj/PK1phGXCXV4wnz2oT3pPi6SFw/3TvWyL3ombaXPmfb393GmLf6csdLf2jRAPJniucO9OE06/ri7g9tWFHP3pZW8e23puMdSd7W6CcdT7G33smDII38gEMGo1/Dru9eeEYubZzNiM+pQpCTLYmBBno1E6twXr5Qi+eFfG8iyGqgvsGE2aHnpaB+hWPKcfecSE3o5O8tKvyDjeYuUslsIcXI5QAnpnBAnOWl/n2CcVvpCiIvGSr/HF+Fgp5+97T7WVmTztbcvQpGg0QisBh3/8Z4V4zpPKJbEZtSxp93L+iHGzMd6Q9y6ouScAPKkInGa9bxnbSmfvbZ2xPMe7wvQF4iRZzOyt93H5uP9LCl2kprjY7rjfjkbyUp/uF2HKZtxK30hxC4hxK7+/ukNQOn2RvjtmyewGrTYTTqEEKcWM2o14tQjfyz+/poail0mavKtLC897bzoCSeGPUdVrpWH/+4SNtWOPpLwxvEBPKEYFTkWWgfDOEx6Ch1GLOPoF89mpmKl35t5/JP5ty9TPqes9C+vzcVm1pNjM7B0hKR7ff7oObNWZ3ch8u0mjDptJrnJ6f4tcuRx4NIsC9csLBi1flW5VipyrKQUyeJiO9995zKurM/HM8mY4dnCmF2Fkaz0OW1//73Mv0Nt8X8vhPghUMxpK/2UECIghLiEdFfjw8D/O+tcWxlipS+EeB74zpAXsuvJ5FM7HyiKgtWoZ31lNoe6/Fy76FwRxZMK33zyMDoN1BTYKLCbaHNHODEQ5LYVxYRiSQaDcTo8YRYV2XnHytM9ncNdPq6sH12YY1GSZQZE+ntCcQ53+3FZDMSn6PdwsTMVK/3vAY8IIT4KtJFxFJ9LVvpd3ghfeGQflblWLhkmW2Q4lkwH3sSTaDTQ6YkST0qq8yzoNII3GgcocZrJsRn40KWLMA/JaB6JJ/FH4jjNrinVsb7AzkAoxtJiB8f7g+TbTWysycExx5f7iLkWmLF27Vq5a9euaTnXsR4/n7h/N6F4km/dvoSblxUTiSd56UgvHd4Iu1s8eEIxPryxkpuWFaMfZ3+3oddPU18Qp9nAxprJ2zedpD8QAwlOi55ESsFqnBsTokKI3VLKtcNtmxtXOEO81eah0GnEG9ZyRV0+mxv62FSbx+N7u9jd6sag1fL1Wxfy9uWlo54nHEviDYbJtZsxGPS4gzF6fRH6AzGq860UOMxj1kVKOeLS9Tz76fgFg25+TIaqwh2FEpcJvVZQlWvlj7vbefFwH7taPPgice5YVcqqchfl2acXMLYMBOlwh0GmW9WGviBd/hhryl24QwkW5Ru5aUUl5TlWvvPMUdZWuvjN6yf457cvGbUev32zmXyHiWsW5mOa46MF40UV7ihcUVfA1sZBtpwY5JWjSfa0eUCmuGlpEXeuK8di0J5qBY/3+vn15iayrEaO9QQpyTLz9uUlbKpLj3K82TjAS0d6WeYNs7jIwd9fuYAvPXaQ9ZUu/v3ZI3zuujoMZy15TykSjYBoUuG/XmjArNdy9cKJu+fMRVThjsH7N5TT5g7jjya5ZXkhK8uyeOeaUvQazSnRhmJJXjraz7vXVbCuMpt4Ujnnkb2jeZAuX4Rfv36Cd64sYG+Hl9tXFNPqDvPsoV78sSRfur6epCLJsRr45eYT7Gpxk2szsu3EILetKGZt5UWZOP6CMO+E+8KhHl462sunr6mlNGvs/A1lOTa+dvNCDnX5WVqWhdOsP8MMJJpI8pe9neiQrMvMiA3XzzTqtLxxfIAra7P5w44OTHotA6E48YTk/evL8EeS/PCFBo50+4klFUx6LSf6gwwE46ypyMIdjhOMJVVzkAzzSrhd3gj/+UIDb1uUT4c7NC7hAhRn2yjOHj6kcEezh6Pd/mGHy07SH4ixpWmQlIQOb5zjvQGWlrpoHggRjCb47NvquKwml50tg+i1GjZUZXO0J8DiIgcfv2IBBzt93LCkcFLXPFeZV8LNthpYUuzgPWvKqMidHleY2nwbT+9XRu17GvUabliSz9ISB5++phYpJb5IAm84gdmgpSrjULOuMod1lek/gOuXFKIoEo1GUOIae9RhvjGvhGvSa/nP966ctvNtbujDHYyTbzeeMblwNg6TnluWl2Axak91M+wmPaVjdFmH815QSTM/Bv0mSSiaoMcXJpE8N3QxmVQ42h3glWN9rK4Y+6Upy2qY1ly/8x1VuCMQjSf5+P27ONYT4mCnj0OdHsJDYlwPdvno9kXxhhNsqBq5f6syM8wL4UbiCb779EEa+wLjWgUrpeTnrzah12rIthpYVZFNIJrCOGTwf2vTACUuIzlWA74ppIpSmRzzQrigYUG+g9QwKwiG4+GdbexocfNG0yDPHOxGUSS9/tipONx97V7coTg7mj1IAe5QfCYrrzIM8+LlzGzQcue68VmD7mwe5NmDPext91KRY6GpN8Bv3jjBlfWn43xfPNzDQDBGSZaZREqSZ1Pzkp1v5oVwx0s0keKXm08gEKwqc7GizMV715VS5LRg0mtRFIVHdnXgCccIxJLEU/D56+rIV7NAnndU4Q7BoNVQnm2hNxDlI2sq2bAg51RQi5SSn7zSxL1vNuMPJ1ha6uRvLxt5+Vu7O8zxvsCYKxhUJocq3CFoNIKv37oEfySBzag7NY7a6Q3z4NYW3n9JJZsb+kmkFDRCsLTEOawHWDSR4ouP7KMvEOXSBbmY1WTS0848eTmbGA6z/pRoj/f4+O5Th9lUl89H/ncnu1o9WA1a1lZmke8wYhlm4mFvuxchIMdmoGVw7ptzXAjUFncUntzXxQNbW/CEE3zh4X30+KMAhBMKDb1B7COsNGh3hxkMxlha4qR2DNsklcmhtrijUOQ0YTZoKXSaKHKaWFhox6AVHO720zoY4pFd7eccE4mnePpAN/3BGBsW5Ix7+brKxFBb3FFo94TRIIinUvgiCYKxJMtKXQQiCfKdJl480ntqmE1RJI/ubmdni5stjf3cc8dy3rV69CU9KpNHFe4oXF2fzwNbWznc7SeaUDBoBUadhpQi6W73Updv574tzSwvdbG9eZD/euE4saTCpQvSeR7UIJmZQxXuKLgsBlaVuRBCMBiMEY6naPdEWFPhwhRO0BeM8tyhXp7c101fIEYsqbCyzEWWxcDl1fMzfuG1hn6urJteU5bhUDtgo9DU5+eu9WV86fo6FAl9gRiLi+wEo0nybAYKHSaOdPlZWuJAqxEsK3Fy7aJ8fvbBNVTkzs+XsoFMOtdwfGZN91ThjkIkIdnT5mN5qYufvn8VlyzI5nB3AEVCXYEDq0FLsctMXYGNymwLd64r5TPXjGxQN9f59esneOFwL3f8bAtvtXpn9LvUrsIolGWb+euhHqxGHctKXdy1rpzqPBv5dgOHuwNE4imurs+jeSBMhzfMu1YPn593PtAfiLG42MHyUhdrKrJoGQxx+RiGfVNBFe4oOM0GwkPM6xr7gtxzxzIaev3cvLSQNneYve0+tp9ws6TYOa9nyL755CE+sKGC9VXZrK/KJjLDXQVVuGOQbTXw1P4u2t1hSrPSa7+qcm1ohUCr0fCLzc34owmqh0lGMpfxhRMc7PKxviqbE/0hFuRaWVuZRa8/isOkH3Up03QwnhwQ9woh+oQQB4eUrRRCbBNC7M340q4fsm3O5H8AuPuSSqpyrHzokgruymRN39/h474tzRzvD/H9dy2jOs/G25cVXYjqXTDsJh2BaJLNDf08f6iH6nwbeq2GTm+ENxsHZvz7x/Ny9lvOta//d+CbUsqVwNcz/59z+R8ArCYdS0qcaAR4QjFu+K/NtA8GafdE+OVrTfzguQbu3lhxhj3+fECjESQVhdca+jnRH+TW5cVAOidcZe74lv1P6fvH2kFKuZm09ecZxcDJZFpOTpstz6n8D5D2yG0fDPGjF4/xx92dfPv2xXz76aPsbfdiNmhxWvRcUTc/bZFq8m2sqcjiaE+AB7e38vT+bgxaDS8d6SOenFl/3sl2RD4HPC+E+A/S4t+YKb8g+R+EEJ8g3ZpTXj6+lQ7j5blDvXz+4b28d20pzx7oZH9nemWERkDrYJi/u6J6Wr9vNrGw0EGO1cjCAgfff+4oFTlmdBq4dlE+eu3MzhpOdhz3U8DnpZRlwOdJGzPDBcj/ADNnpd86GMJh1PCpqxbwwLY24gr0+WIEY0laB8P84oNr2DhHZsiiiRR9geip5NfjJc9upKbAxvIyJ/l2EzcsLaIm3z6iJep0MVnh3g2czAXxKOk+KFyA/A8ziQbY1jTAwQ4f2RY9UgJC4jDpef+GCpaWOOdE9Nerx/r4zEN7WH/PS9x97w6eOdBNbBgviZHQagS3rSg+r74Rk73rXcCVmc/XAMczn58A7sqMFFRxOv9DNxAQQlyS6b9+mDNzRpwcMTiV/wF4HrheCJGVeSm7PlM24yRSCh3uMD2+CPGUgj+aZEN1DkuKHayuyOb9G8r59DU156MqM05KkbzW0I/FoMVp1nPD0kLKsy38+3PHzkjIEk2keP5gz7DnUBRJjy9Kjz/KY7vPDfWcCcaTvOQh4CogVwjRQfpN/+PAjzItZJRM/3Iu5H+IJlL0+qN0esK8eqyPdm+MH7xnBRU50+M1drGh1Qj+7opqPv37t7iiLo9vPHGId64qIRRP8p9/beAL19Wh0Qja3CGeOdBNWY6FFw/3cvvKYipyrETiKVoGgxh0GvIdRoKxFJ2eMCXjNBScLGMKV0r5vhE2rRlh/3uAe4Yp3wUsHaY8SibxyTDb7gXuHauO08mxHj/d3ig7W91sO+Hmi9fVz1nRAuxscfOJ+3dxVX0+9YV2ntzXxaO7Oyh2mrhrXfmp0MzafDtGvYZnD3RjM+oocZnp8IQ53hvk2YPd3LmujI9eXgXAy0f7yLeb0M+grb86c3YWB7t8bD7Wz42LC3jv2jLqCx1jHzSLqc23cfvKEu5aX8avXjsBgN2o5Us31HPFkPBEIQR/e3kV977RzKAQXPvD14gnFZaVOPnSDfXUFZzOIr+6IouWwSC1BTN371ThZjja4yffbqLYaUYjBDevLJkXJnUHOn2npqw/tmkBu1o9LCyys7LURSCaOMNIushppj8QoyLbQpbFQI7NwI/uWnVOjIZZr6V8hp9Ss/+VeJpYWOjgYKePwWCMW8/zG/KFRCsEH9hQztP7u/ntlmba3GH2tXn54YsNHO46M/Ot06zn5x9cgz+W5KOXV/L5a2uHDSzqD8Rm/P6pLS7pqVx/NJ0kuimW5N1rp3cS42JmbWU2Bp2GDk8nBzr91ObbsBi0bG7oJ9dqYHGxA5tRd2pc1qTXcuvyQhRFMNLkWJbVQCKljDvv22SY1y2uPxLnv184yg+eP8oXHt7HC4d6+OCGigtdrfPKyXwV6yqzCUTiZFsNNPQG8UeTvNrQzx93d9DuDnG0O936RuIpDnb60Wk1LC91EoydGb7oDcfTiVo8kRmt97xtcaWU/OfzxxgIx6nNtwOCtVXZ6OdJF+FsGvuCuCwGAtEkikynqXKYdDxzoJtANMEty4vp80fZemKQv718AX2BKIe7feTYjNiMOuJJhXA8icOkPy+2U/NWuAAaDbx3bTmXVeeg1QhaB8MXukoXjA0LsvGE4+TajFTmWjHpNOzr8HHJgmx+/moTBQ4T3kgCm0GL1aijymjj+UPdFDvNeEJxDDoNLovhvNV33nYVmvpDFLosXFmXh06bzllWOU0JTWYjRp2WO1aVYtJrMek0eCMJSlxmtje7WVORxRN7u1hd4mBJiePUjFqR08zxviA2k27M/MFSSo72+EfdZyLMW+Ee6fbxnjXzx7DjeG+An73aSDSzFOlEf5B/+MOeM6Z1P/e2Wm5YUog7FEcjoMsXQacRLCl2oNXAi0f7yLOZ6PBECEWTNPeH6PSG0Y4joOalo7185Y/7Od4bmJbrmbddBYNWQ7Z1fhgy9/ii7Gr18JOXGynLslDsMnPP04fpC8T4+P27eP+GchYXOdnR4qbQYaLYZcYfTXBlbR5l2Ra+etMidrd6eGBbKztb3Ny+soQOd4TbVhaPOwpsW5ObEwMhGvuC1A6ZrJgs81K4rYMh2t3zpz9r1KWDu3/7kfVsPzHI77a3Ek2kKHaaCMaSNPQEaOgN8vT+bkCiEYK+QIz/eu9K6grs7Gnz8NT+Low6weU1eQghKMsZOxYhGEviiyRwmHQ8vLOdb9y2hJumaYnTvBTuswe6WVQ0t6dyT/QHeavNS38gisWg46s31bO92c3LR3vZ0+6jJs9KZzhMQoFEUqGhL0i2xUCRy0QknmRxkYOSLDMajeDFI30EY0m+dvNisqxjv4AlUwo/eaWRZw/0sLLMxaeuqmZJiYM7Vo1shD1R5l0fV0qJSadlwygpTOcCJ/pDPL6nk1hC4W2LC6jIsWIz6Njf6cdq0HJiIAQI/NEE3kgCh0nPomI74XiKWDKd+fLfnjrMn3a3k2XW8Z13LBuXaAGO9wV5ZFc7zYMhzAYt5dkW/vCJS6fVS23etbjNA0GuX1xwyiJ/rnL1wnwuq8llS9MAWRY9gWiSsmwz6yuz0Go17Gv30ulL+/029YeozLGg02hYWuJka9MgVoMWRaazZ64sy8IwgftVnm3hKzcuZEWpa8ZGauadcFsGwggBxdkzvxL1QtLuDvO5h/fSPBDCYtBy09Ii7lhVzJdvWMhbbR48oRh2k55tJ9xkWfTUFdj59DU1fP4Pe8mzGUmkUiwrcbBhQQ6P7+kc9bueP9hDodPIirL0ImyrUcftK0fuFiRTCpFEakqZ4OedcKWUXFo9c9ZAFwsVORZuW1HMQzvaKMkyc++bzfQFonzztiWY9Vouqcri8X3dGHUagrEk3kicb/zlEHqdhqSS4oOXVPKuzHDhV24a/n0gHE/y4xePs6VpkEsWZJ8S7lgEokm2nRic0ovavOvj6nTaGXdZuRg4GT97aXUOZr2Wz15Tw0Awhi+SYFGxg+O9QQrtRr54bQ2Liuw4jHqWljqpy7dRlWvjnatHf5E63OXjHx7awy82n8Bh1rGpLo9kSjljXPhsookUjX1BsqyGKY8uzP3f4FkkUjO73v9iIplScFkM1BXYybEauGVFMfdtbSHXZuSvh3ow67RYDBq+eesSnj3YQ7bFQKs7zKbavFHHZ5/a18nvd7Sxr93HglwLWo2Gy2tyeavNy5qzEnIHogke3NbG312xAJNey/1bW/jyDfVndBPiSeVUsM94mXfC7fVHSaaUObE6dyx+8koj929tIZpIcc87lvL5R/YigGM9AUpcZiwOLY/v7cEfS1GWZaGu0MbG2tGX9/9uWyuP7u5gb7uX1eUuFCmJpxQe2NbKhy+tPLVf22A6z9vV9fn8ZW8nVbkWblxaxLduP2f1FvdvbeFjmxZM6Nrm/m/vLE6ucJgPfGzTAr552xIAfvTScXQagS+cYFWZi4psC+G4gl4reOVoPylFsr9j9FiCNxv7eXxvJ23uMPUFdpr6QyAFGiASO3M5+yvH+vjL3i72dnj5+i2LuXHp8F2DYCyJ3TjxEZ551+KW51ho6p+eaceLmWAsyRvH+/ncw3uREtrcEewmPWXZFqSUhGJJrEYtGgGKlDy2p5Prl4wejrij2YM7FMdq0NLrj7CsxIlJp0Wng7VV2Wfse/fGSu7eWDlmPfVaQUNvcMLXN++EW5Fl5i97O+eccFsyw152k55Ob4TvPHOE7ScGOfmuZNBqyLLoMeu1LCl28NyhXuKROGa9DpdZj6JIFo6xuFGrSb8jlLjMJBUjRS4zq8pcbMrENEwGdzDOBM1zgHkoXJ1Oy552H7etmtmlJeeb//dyI/5ogpuXFXKgw8/+Di+h+OnHd1JRONDpoybfTnm2mY01uTy+p5MToTAbq7OJJSXHewOsrhh5SOvuS6uozLGi0whKsswsL3VNyWopmkjxu+2tTObXMO+EC1BTYOOVo31cv6TwQldlWtl+YpDXGvpPOSUKAdkWA9X5NpaVOHjpSB/+SIL7t7Vz28oirlmYT8tAiEg8RYnLwhtNA2gELC11DRvL4bTouW2UiYWxkFLyl71d3LaiGI1GYNJr+cJ19XR6J77MZ+40ORPgfevK2d3qQZnMM+oiossbIZpI0eEJ4zDreOSTl/JPNy085ZRo0mupzLFQl28jFE9S6DDR4QnT2BfkpcN9bKrOIRhLEktKgrEke9u8/OSVRn79ejO9meng6WB/h/fUvf7JK43c/OPX2dXi5r2/3Mof3+qYVDdjXgrXqNfy8SsW8MrR3gtdlUmRFl+APW1ePvK/OxFC8Kkrq+kPxHhoRxs5ViOVORaurc8nx2rghqUF6IQGBNiM+kxKAMlf9nfxzdsWke8wsvl4P7k2I0tKnASiCV6a4r3p8IRpd4fZ3erhPb/YSiypkFQknlCcoz0B3v2LrbzV6uHymsnNYk7KSj9T/pmMXf4hIcS/DymfFVb6uTYjvmiC7kk8pi40x3uD3PnLbfznX4+x9cQgH/rNdj754G4+cf9uGnqDFDiNmDJeXvGU5Eh3gJbBEC6zgf5gDKNOQyyZTqS9o9lLicvM2ops9rR7cQdjLC5ynEr1OlkKHaa0r/Avt/KF6+rQawXheAp3OA6kXxY/clklxS7zpM4/KSt9IcTVpJ3El0splwD/kSmfVVb6t68s5bWGvlGnKS9GNtbk8KFLK9BlugRGnQZFpg3s1ldm4Q3HsZv1PHOgh09dXU2Jy4RWo2HriUGyLXqsRh0dngiLi+xE4ik84ThVuVYK7EZaBsPcuLQA7RRDEHVaDR/bVMXvP7aBv7sybX7901ca0WvSkltZ7jpVPqnzj7WDlHLz0FYww6eA70kpY5l9+jLlp6z0geaMA+N6IUQLGSt9ACHESSv9ZzPHfCNz/B+Bn5xtpZ855qSV/kOTutJh0GoEm2rz+NNb7bxzVdmsyb1r1GlZW5HNf7+Ydndtd0eozbexuMjBiYHgqQxAPf4oP325kR/fuYK6AgfPH+rhtYZ+PnxJBQa9ljXlLlKK5McvH2d5qROdVvCOlcUsLHKe8X2TnWkcOunwzIFuXj7ax6baXF462senr64h1zb5pVOTHVWoAzYJIe4hbTP6JSnlTmahlX5JloW3LSrghcO9tLlDNPcHURCUZpn50CWVOC2TD72bbhIphYFgjCKnmcpcCzcuKeS5Qz0EY0ma+oPk2owsLLTjNBtQpOTBj27gxSO9/OqNFlaUOunyRbEadFiMWi6vySOeUjDqNHzjtqUkUgrvzCQYVBTJ5uO9PLmvh6SiYNHr+O67lk+p7tcuKsATShBNprhmUf6UV6BMVrg6IAu4BFgHPCKEWMAFtNIHfgWwdu3aCT/3XRYjNyw9PTQWjiU53hfkr0d6ONLt54qaPDbV5U358TlVvvvMUY73BajJt9HuDpPMvKmXZZu572/W8y9PHESr0bCxOocn9nVxsNNHlsXALSuKeKvFw6cuL8duMeO06OnxR/FFErxytI9siwFPOIGipIgkFLp9UTq9YaJJiU4jWDwBkfUFouTZjOeM71qNOj50aQXxlDItQfyTFW4H8FjGOXyHEEIBcpmalX7HMFb6V511zKuTrO+EsBh1rChzsaLMBUC7O8Sju9oYDMVZVZbFJQtyhu1WeEJxfr+jjXZ3mP9zVQ3l41hQOF6CsSSvHuujLxCjzR2mwxOhyGk6tf13O9r47h3L+fKf9vGL15q4oi4PfzROQ0+QW1cUMRCK8a1nGnjXmlIe3tnOK8f6AdhYnUNtvo2nD3RTm2ejeSBIRa6N7c0eANZUuOj2RcblBfZWi5vH9nRg1mtJSjDrBDctK2ZZqQtIp5gyaaZn5clkh8MeJ22hjxCiDjAAA8wRK/2zKcu2ctf6Cu5cV8brx/t5cHvrsPtpNIIn9nbxh53tXPvDV+n1p8dCo4mR8ylIKXn+0PAW9UM52u0nllROJU7JthpYXZ5+V213R3j1WB//+Md9fOzyBdy6opiv37qYO1aV8ulrath+ws3LR/tIKBKDVkPrYJg8u5GN1TmkpKTTGyGRTNHhi5BjN7G9+bTx+/HeIPGkwlutnjHruLPFTbcvCgg+sWkBjf1hvv/MEdyh2JjHTpTxDIc9BGwF6oUQHUKIj5J2CV+QGSL7A3C3THMIOGml/xznWun/mnTusybOtNLPybzIfQH4KqSt9IGTVvo7OU9W+qORazPxpevriSdTfOvJQ+eMRjy4rRWnRc/6ymyWFDv5wP9s4x//uI/fvNE84jkf2NbKt548zBP7hs/LIqVkV4ubTm+Eaxflo8u09JF46tRQUkmWmSKniQOdPv77xQY2LMimdTDEbT95kw/+ZgcWg4bFRXa6vBG+8+xRqnLMlLpMSCkZCMTIsxvJc6Rb70Ndft6+vIjV5S5cFj12k55ESqGxb+xAmLvWVyAlNPYF+OaTh4gnU+zp8J76A55OxGwbChqLtWvXyl27ds349zyyo4VHd3fxf29ZRJ7NRHGWmcu+9zK5dgNmvZZQNIVBryGlKHz5hnouqzk3zlVKyecf3stLR/r4zd+sY2eLm49tqjrlLXtyxcAPX2jghiUFVOZY+fC9O4gN8fdcVuLApNdiNWoZDCawGLQsyLPyrtWlPLq7g4d3tpNjNfDTD6zm16+fYGmJk5SisLfdRyKl0O4O0+OLsrAovSriu+9cxtrKLBp6gxzvC5BrNfI/r5/gkgU5fPsd58bSns3+di/feOoQg8E49QU2PnllzajxD6MhhNgtpVw73LZ5GaswHbx3fSU9gTj//OcDbKzOwaTXkWczpGN9JRzv9RHNPGt+v72djdW5wwakLC52EI6nsBq1FDpMvHasn6Qiybcb+f5zRxEiHUP70I52kopyhmiBtFNiSmEgGEev1bCmIotbVxTz6K527r60kkKHiR+9dJxfv36C1WUuagvtfOfpo+m+qz+KJxRnTUUWB7v83Li08NQ6s4ocK9ctLuBnrzaSbzfii8THdV+Wl7l47FOXTe3mjgNVuFPg/RsqONDhYzAUJxSNkJISnRB4wnHKc2wMBNOG0Y19QVoHw1TmWs8YE93SNMh9W1rJtxv5zjNHWFjo4IGtrVTlWVle6mBfh494UsFu1JFrN56TZjQdXigpz7byTzfXc8dPt/LC4V50mnSM6/++2cw1C/N41+oS/vRWJ7csL+KFw70YMqZ21yws4NblxXzrqcPcsryIf7pp0TnXeN2iAuJJhe3NgwwGY+RMYex1OlGFOwVybUa+fstiOrwR/rCjjcFAlAKnGaNeiycUwx1OUJ5twWnRs715kHAsyYM72vjHG+ppHQzz7acOk1IkQsBgMM4T+7qIpxScZh2eUAKnWY/VoMUfTaIRnIqiEgKkTP+/2GUCJM8d7EYI6PFF2NPmxROO82/vWMK/PXOEd68uo9MbpSbfxq3Li/nsH/bytsX5LC5yUp1n5bKaXAqdJna2uBkIxilxmU+tHastsPPJbAtP7u1iT5uHty2+OCLqVOFOkUKXmU5vlMFgnJoCB6F4kj2tXvLt6ZapzR2mzR1GURSayoOEY0m+/Og+9nf6sBh0lGaZOdLtY2GRk25fNL2OS0kb1a2tcNHhiZBl0dPli7K0xIFeq0FKSZs7gtWgRasR9Ppj9PvTL1mFDhN5diOxZIqvPnYAjRA8vLON79yxFIdZR8tgCEVKLAYd9YXpYPqT0VkbM8v2z46aM+m1bKzJZUez+6IR7ryMDptO9FoNx/v8lGSZKXaZqS+wk2XRo9dqsBl1lGdbKHGZ0Gs17GnzcKjbhz+aZEGejXSXV5JIKZzoD7Igz0pjX4BdrR4Odvlxh+LEkgrH+4KEYkmCkQQn+gJohGBZsYOqHAuBaJJYUiEQS/HetaWUZJl5bE8nO1o8pBTJ25cXkWc3srPFgy8UpzTLgjcc55WjfSQSSdrdYZoHQmdc08kx6lgyxQuHe1EUyeJiB32B6R/WmiyqcKdIXyDK2spsLlmQTbc3QmN/kOp8G+XZFswGLUadBr1WQyShkJJg0mkZDMbSyZ4lCAQaIVAUhXgiRSR+uh/b7YvR0BskGEsRjKXwRRPk2Ix0uMMgBP5oEl84DjIdv7CwwEFFtpn1VdkU2I3savHwxvEBvnh9PXeuK8Ns1KLTCAqdRhYX2/nfLS3sbnETT6XYPEwYYzCa5IFtraRk2kusyxfBGx7fS9pMo3YVpkiWxcAz+7vp9Uf5yOVVLC1x8M+PHSQYS6IoEoc5Hf/a6YnQ5g6zuNhBIJKk1x/l9hXFbGt2E01KoskUBYpErxXUFdiQCHRaQduQkWtfJEmWxcjVC/Mx6DT4zDrc4TgL8ixcVZfHd549wgc2VJCScOPSAuLxFAVOMw9sbWV1hYulxQ5aBkJ85x3LMZ/lIL6r2UMknkKjgUd3dZBSJDajDoNW8J1njvD0/m70Wg33bWnhH95Wd57v8rmowp0ix3sDHO0J8L1MEEqHJ0RNvpWG3iA3LCmk0xuh2xuheSDEYChOljeCEGAxaNnZ4mZ1mZNFhXZePNJHUpEUusw0D4S4ZmE+ezt83LikML3I0aAlkVLo9EZo6g8RSaTQazVkWQ20eyI8vq+TYz1+7n2zmTy7EX84gc2kY39HC0tLnJRmm9jbLkdcrrSxJod9HR7WlGfx5z2d+CMJwvHUGctqqnKtp+JpLzSqcCeBlPLUmGyOzciiIgcH272YjFpyrQaaB8IkUpJubzqmIN9uJMui57LqHPZ1+qjJs9HhidDti9LQm07g3H3WUpnBUIIranO5rCaXbKuBJUUOFCCRkihSMhhMC8gbidPnj6HXSP71liWYDVqkhG5fhDcaByh0moglFI52+YknFa6uzx82x25Vro0fv9TI84d6+fkHV9M6GOZ7zx49Q7iFDiO3rSieuRs7AVThTgB/JM7vtrWyr9OLTqOlwG4knkqPsy66pAKtRuAJxbAaNOxoHuAD68p5pWEgbZwBLCt10Z+JyipxmklpNdTk286IDTiJRgMP7Wjn9zvSkZ0Ok471VTmsr8rCZkxPw2o0Aqko2Axa3r7qdDinEOlwzTvXlXPnunJ6fVG6vWFyHKMnhi6wG3m1oR+7SY9eI4gmkmyoyiaRVPBHE5RlW1hTkT3i8ecTdcp3nLhDMT7/8D5ea+jHqNOgFVCeY+VoT4D1VdkgJbUFdi6tzsGkE8SSCg9ua2PridHDK5aXOtnf4TujrMRlRqeBcDxFf3DkR/P6qmyKHEYOdPp4/nNXTjlb+S9ebeTty4uxGXUZz69WfvNmC6vKXFxSncMHNpRT5JzcUpvJoE75TgObGwaIJZKsr8qm3R2m25duOddXZiGA7S0edrR4+N32Nkpc6YCVTu/px7/LoscbTpxzXtNZCQF1GqgvsJFU0i9sZwvXqBPpTOUStAKSSnoipKk/cM7KhYliMer4l78coDLHSp7dxGW1Oawsd7GxOve85jAbD6pwx0lFjgWDVsvRXj99gbSYUpmB+rMdIIcK9iTDiRYgJdOP+iybAbtRT12+lZIsC/dtbcVp1mHRa0gpErNesLYyB61WQ8tAmCyLHgkoKBQ4TLQMhKYs3JuWFnFlbR4VsyDfmyrccbKw0IFGk36MG7QaChwmevwxYikFTyjB+spsdrRMPOpSpxGsrUxPryoSfNEk/W1urqzJJpaS2M16Ch1G8h0mrq7PJ9dmpG0wjNmYtluSikSv1WA3Tf1XmWc3gv3iiEUYC1W448Rs0PKj963mUKePYz1+jnQHaOoLUOwyIZEc7PSOeY5si57qPCt6vQaD0JJj11PqNJNjN5JlNVDoMJGd8URACDSC4SPKSqbWss4FVOFOAKdZz8aaXDbW5JJSJFV5Vp472IPVoCVm0mPSpycQXBZ9+sekw2Uz4jDpsRg02I16HGY9NZkVuQaddtasLL7YUIU7SbQawY1LC8mx6jHotMSSaRt5q0FHodNEXYEDp0VPJJ7CqNOoAp1mVOFOgarcdL6E0TAb5nZaqguFGmSjMitRhasyK1GFqzIrUYWrMitRhasyK1GFqzIrUYWrMitRhasyK1GFqzIrmXOB5EKIfmB4O8WRySXtNjkbmE11hanVt0JKOWxy4Tkn3MkghNg1UqT9xcZsqivMXH3VroLKrEQVrsqsRBVuml9d6ApMgNlUV5ih+qp9XJVZidriqsxKVOGqzErmnHCFEDdm8gg3CiG+Osz2LCHEn4UQ+4UQO4QQSzPl9UKIvUN+/EKIz2W2fUMI0Tlk283TVNdh8yQP2S4yeY8bM/VdPdZ1CiGyhRAvZPIfvzCdaWQnW18hRJkQ4hUhxJFM7ud/GHLM5O6tlHLO/ABa0hl9FpBOYbUPWHzWPj8A/jXzeSHw0gjn6SE9AA7plK1fmoH6XgGsBg6OsP1m0tmJBOlkiNvHuk7g34GvZj5/Ffj+RVDfImB15rMdaBhS30nd27nW4q4HGqWUJ6SUcdKprG4/a5/FwEsAUsqjQKUQouCsfa4FmqSUE52BmxBSys2kkxGOxO3A/TLNNsAlhChi9Ou8Hbgv8/k+0jmTL2h9pZTdUsq3MucIAEcYIb3teJlrwh1P/t99wDsBhBDrgQrOzHoJ6QzwZye7/nTm8XfvdGdxH4WRrme06yyQ6YSIZP7NPw/1PMmY9z+T0HwVsH1I8YTv7VwT7njy/34PyBJC7AU+A+wBkqdOIIQBuA14dMgxPweqgZVAN/Cf01bj0ZlynuPzzKj1EkLYgD8Bn5NS+jPFk7q3c215+ki5hE+RuWEfgfTLBNCc+TnJTcBbUsreIcec+iyE+B/gqWmv+fCMdD2GEcoBek8+njPdir7zUtM0I95/IYSetGh/J6V87OQOk723c63F3QnUCiGqMi3nXaRzBZ9CCOHKbAP4GLB5yF8/wPs4q5uQEcBJ7gCGfaueAZ4APpx5W78E8GUe/6Nd59DcyHdzOmfyBatvpoH4DXBESvnDoQdM+t5O95vyhf4h/WbbQPqt+2uZsk8Cn8x8vhQ4DhwFHgOyhhxrAQYB51nnfAA4AOzP/HKKpqmuD5F+PCZIt1YfPauuAvhp5loOAGtHu85MeQ7pl8/jmX+zp/HeTqq+wOWkuwz7gb2Zn5uncm/VKV+VWclc6yqozBNU4arMSlThqsxKVOGqzEpU4apMO2MF4wyz/3uFEIczATi/H9cx6qiCynQjhLgCCJKOW1g6xr61wCPANVJKjxAiX0o55qSJ2uKqTDtymGAcIUS1EOI5IcRuIcTrQoiFmU0fB34qpfRkjh3XTJ8qXJXzxa+Az0gp1wBfAn6WKa8D6oQQbwohtgkhbhzPyeZarILKRUgmuGYj8Kg4nUXoZF4qHVALXEU6tuF1IcRSKaV3tHOqwlU5H2gAr5Ry5TDbOoBtUsoE0CyEOEZayDvHOqGKyoySCWJqFkK8B04t8VmR2fw4cHWmPJd01+HEWOdUhasy7QghHgK2AvVCiA4hxEeBDwAfFULsAw5xesXG88CgEOIw8ArwZSnl4JjfoQ6HqcxG1BZXZVaiCldlVqIKV2VWogpXZVaiCldlVqIKV2VWogpXZVby/wGFj2fzJycstQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# clip flood polygon against borough geometry\n",
    "clipped_pfirm = gpd.clip(pfirm_gdf, borough_gdf)\n",
    "\n",
    "clipped_pfirm.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "29d1a065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "colors = ['deepskyblue', 'orange']\n",
    "zones = [['VE', 'AE', 'A', 'AO'], ['0.2 PCT ANNUAL CHANCE FLOOD HAZARD']]\n",
    "\n",
    "for zone, color in zip(zones, colors):\n",
    "    \n",
    "    (clipped_pfirm\n",
    "     .loc[clipped_pfirm['FLD_ZONE'].isin(zone)]\n",
    "     .plot(color=color, ax=ax)\n",
    "    )\n",
    "\n",
    "\n",
    "borough_gdf.plot(\n",
    "    ax=ax,\n",
    "    facecolor='None',\n",
    "    zorder=0,\n",
    "    linewidth=1\n",
    ")\n",
    "\n",
    "plt.ylim(180000, 220000)\n",
    "plt.xlim(960000, 1000000)\n",
    "plt.axis('off')\n",
    "plt.savefig('figures/cover-photo.png', bbox_inches='tight', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb1069b3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}