geospatial-plotting/geospatial-plotting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "from shapely.geometry import MultiPolygon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Connecticut annotated tile boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct_tiles = gpd.read_file('sample-data/ct_labeled_tile_boundaries_gcs.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>GM_LAYER</th>\n",
       "      <th>GM_TYPE</th>\n",
       "      <th>NAME</th>\n",
       "      <th>LAYER</th>\n",
       "      <th>MAP_NAME</th>\n",
       "      <th>FILENAME</th>\n",
       "      <th>DESCRIPTIO</th>\n",
       "      <th>UPPER_LE_X</th>\n",
       "      <th>UPPER_LE_Y</th>\n",
       "      <th>LOWER_RI_X</th>\n",
       "      <th>...</th>\n",
       "      <th>TILE_HEIGH</th>\n",
       "      <th>COMPRESSIO</th>\n",
       "      <th>PIXEL_SCAL</th>\n",
       "      <th>TIEPOINTS</th>\n",
       "      <th>MODEL_TYPE</th>\n",
       "      <th>RASTER_TYP</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>tile_name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>995820_ne.tif</td>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>shape-1.shp</td>\n",
       "      <td>S:\\0000\\TEST\\995820_ne.tif</td>\n",
       "      <td>995820_ne.tif</td>\n",
       "      <td>997500.00</td>\n",
       "      <td>825000.00</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>...</td>\n",
       "      <td>256</td>\n",
       "      <td>None</td>\n",
       "      <td>( 20, 20, 0 )</td>\n",
       "      <td>( 0.00, 0.00, 0.00 ) --&gt; ( 997510.00, 824990.0...</td>\n",
       "      <td>Projection Coordinate System</td>\n",
       "      <td>Pixel is Point</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6250000.0</td>\n",
       "      <td>995820_ne</td>\n",
       "      <td>POLYGON ((-72.75916 41.72530, -72.75000 41.725...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>990755_nw.tif</td>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>shape-1.shp</td>\n",
       "      <td>S:\\0000\\TEST\\990755_nw.tif</td>\n",
       "      <td>990755_nw.tif</td>\n",
       "      <td>990000.00</td>\n",
       "      <td>760000.00</td>\n",
       "      <td>992500.00</td>\n",
       "      <td>...</td>\n",
       "      <td>256</td>\n",
       "      <td>None</td>\n",
       "      <td>( 20, 20, 0 )</td>\n",
       "      <td>( 0.00, 0.00, 0.00 ) --&gt; ( 990010.00, 759990.0...</td>\n",
       "      <td>Projection Coordinate System</td>\n",
       "      <td>Pixel is Point</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6250000.0</td>\n",
       "      <td>990755_nw</td>\n",
       "      <td>POLYGON ((-72.78653 41.54691, -72.77740 41.546...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>980760_sw.tif</td>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>shape-1.shp</td>\n",
       "      <td>S:\\0000\\TEST\\980760_sw.tif</td>\n",
       "      <td>980760_sw.tif</td>\n",
       "      <td>980000.00</td>\n",
       "      <td>762500.00</td>\n",
       "      <td>982500.00</td>\n",
       "      <td>...</td>\n",
       "      <td>256</td>\n",
       "      <td>None</td>\n",
       "      <td>( 20, 20, 0 )</td>\n",
       "      <td>( 0.00, 0.00, 0.00 ) --&gt; ( 980010.00, 762490.0...</td>\n",
       "      <td>Projection Coordinate System</td>\n",
       "      <td>Pixel is Point</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6250000.0</td>\n",
       "      <td>980760_sw</td>\n",
       "      <td>POLYGON ((-72.82307 41.55375, -72.81394 41.553...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>975745_sw.tif</td>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>shape-1.shp</td>\n",
       "      <td>S:\\0000\\TEST\\975745_sw.tif</td>\n",
       "      <td>975745_sw.tif</td>\n",
       "      <td>975000.00</td>\n",
       "      <td>747500.00</td>\n",
       "      <td>977500.00</td>\n",
       "      <td>...</td>\n",
       "      <td>256</td>\n",
       "      <td>None</td>\n",
       "      <td>( 20, 20, 0 )</td>\n",
       "      <td>( 0.00, 0.00, 0.00 ) --&gt; ( 975010.00, 747490.0...</td>\n",
       "      <td>Projection Coordinate System</td>\n",
       "      <td>Pixel is Point</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6250000.0</td>\n",
       "      <td>975745_sw</td>\n",
       "      <td>POLYGON ((-72.84128 41.51257, -72.83215 41.512...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>965765_nw.tif</td>\n",
       "      <td>Coverage/Quad</td>\n",
       "      <td>shape-1.shp</td>\n",
       "      <td>S:\\0000\\TEST\\965765_nw.tif</td>\n",
       "      <td>965765_nw.tif</td>\n",
       "      <td>965000.00</td>\n",
       "      <td>770000.00</td>\n",
       "      <td>967500.00</td>\n",
       "      <td>...</td>\n",
       "      <td>256</td>\n",
       "      <td>None</td>\n",
       "      <td>( 20, 20, 0 )</td>\n",
       "      <td>( 0.00, 0.00, 0.00 ) --&gt; ( 965010.00, 769990.0...</td>\n",
       "      <td>Projection Coordinate System</td>\n",
       "      <td>Pixel is Point</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>6250000.0</td>\n",
       "      <td>965765_nw</td>\n",
       "      <td>POLYGON ((-72.87792 41.57429, -72.86878 41.574...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        GM_LAYER        GM_TYPE           NAME          LAYER     MAP_NAME  \\\n",
       "0  Coverage/Quad  Coverage/Quad  995820_ne.tif  Coverage/Quad  shape-1.shp   \n",
       "1  Coverage/Quad  Coverage/Quad  990755_nw.tif  Coverage/Quad  shape-1.shp   \n",
       "2  Coverage/Quad  Coverage/Quad  980760_sw.tif  Coverage/Quad  shape-1.shp   \n",
       "3  Coverage/Quad  Coverage/Quad  975745_sw.tif  Coverage/Quad  shape-1.shp   \n",
       "4  Coverage/Quad  Coverage/Quad  965765_nw.tif  Coverage/Quad  shape-1.shp   \n",
       "\n",
       "                     FILENAME     DESCRIPTIO UPPER_LE_X UPPER_LE_Y  \\\n",
       "0  S:\\0000\\TEST\\995820_ne.tif  995820_ne.tif  997500.00  825000.00   \n",
       "1  S:\\0000\\TEST\\990755_nw.tif  990755_nw.tif  990000.00  760000.00   \n",
       "2  S:\\0000\\TEST\\980760_sw.tif  980760_sw.tif  980000.00  762500.00   \n",
       "3  S:\\0000\\TEST\\975745_sw.tif  975745_sw.tif  975000.00  747500.00   \n",
       "4  S:\\0000\\TEST\\965765_nw.tif  965765_nw.tif  965000.00  770000.00   \n",
       "\n",
       "   LOWER_RI_X  ... TILE_HEIGH COMPRESSIO     PIXEL_SCAL  \\\n",
       "0  1000000.00  ...        256       None  ( 20, 20, 0 )   \n",
       "1   992500.00  ...        256       None  ( 20, 20, 0 )   \n",
       "2   982500.00  ...        256       None  ( 20, 20, 0 )   \n",
       "3   977500.00  ...        256       None  ( 20, 20, 0 )   \n",
       "4   967500.00  ...        256       None  ( 20, 20, 0 )   \n",
       "\n",
       "                                           TIEPOINTS  \\\n",
       "0  ( 0.00, 0.00, 0.00 ) --> ( 997510.00, 824990.0...   \n",
       "1  ( 0.00, 0.00, 0.00 ) --> ( 990010.00, 759990.0...   \n",
       "2  ( 0.00, 0.00, 0.00 ) --> ( 980010.00, 762490.0...   \n",
       "3  ( 0.00, 0.00, 0.00 ) --> ( 975010.00, 747490.0...   \n",
       "4  ( 0.00, 0.00, 0.00 ) --> ( 965010.00, 769990.0...   \n",
       "\n",
       "                     MODEL_TYPE      RASTER_TYP Shape_Leng Shape_Area  \\\n",
       "0  Projection Coordinate System  Pixel is Point    10000.0  6250000.0   \n",
       "1  Projection Coordinate System  Pixel is Point    10000.0  6250000.0   \n",
       "2  Projection Coordinate System  Pixel is Point    10000.0  6250000.0   \n",
       "3  Projection Coordinate System  Pixel is Point    10000.0  6250000.0   \n",
       "4  Projection Coordinate System  Pixel is Point    10000.0  6250000.0   \n",
       "\n",
       "   tile_name                                           geometry  \n",
       "0  995820_ne  POLYGON ((-72.75916 41.72530, -72.75000 41.725...  \n",
       "1  990755_nw  POLYGON ((-72.78653 41.54691, -72.77740 41.546...  \n",
       "2  980760_sw  POLYGON ((-72.82307 41.55375, -72.81394 41.553...  \n",
       "3  975745_sw  POLYGON ((-72.84128 41.51257, -72.83215 41.512...  \n",
       "4  965765_nw  POLYGON ((-72.87792 41.57429, -72.86878 41.574...  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct_tiles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ct_tiles.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Connecticut annotated tiles' train/valid splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct_split = pd.read_csv('sample-data/tile_assignments.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>tile_name</th>\n",
       "      <th>training_split</th>\n",
       "      <th>density_split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>975745_sw</td>\n",
       "      <td>train</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>185895_se</td>\n",
       "      <td>train</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>205770_ne</td>\n",
       "      <td>train</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>075860_sw</td>\n",
       "      <td>train</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>015750_sw</td>\n",
       "      <td>train</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tile_name training_split density_split\n",
       "0  975745_sw          train           NaN\n",
       "1  185895_se          train           NaN\n",
       "2  205770_ne          train           NaN\n",
       "3  075860_sw          train           NaN\n",
       "4  015750_sw          train           NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct_split.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Connecticut town/municipality boundaries. Downloaded from [2010 U.S. Census](http://magic.lib.uconn.edu/magic_2/vector/37800/townct_37800_0000_2010_s100_census_1_t.htm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct_town_boundaries = gpd.read_file('sample-data/townct_37800_0000_2010_s100_census_1_shp/wgs84/townct_37800_0000_2010_s100_census_1_shp_wgs84.shp')"
   ]
  },
  {
   "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",
       "    }\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>STATEFP10</th>\n",
       "      <th>COUNTYFP10</th>\n",
       "      <th>COUSUBFP10</th>\n",
       "      <th>COUSUBNS10</th>\n",
       "      <th>GEOID10</th>\n",
       "      <th>NAME10</th>\n",
       "      <th>NAMELSAD10</th>\n",
       "      <th>LSAD10</th>\n",
       "      <th>CLASSFP10</th>\n",
       "      <th>MTFCC10</th>\n",
       "      <th>...</th>\n",
       "      <th>NECTAFP10</th>\n",
       "      <th>NCTADVFP10</th>\n",
       "      <th>FUNCSTAT10</th>\n",
       "      <th>ALAND10</th>\n",
       "      <th>AWATER10</th>\n",
       "      <th>INTPTLAT10</th>\n",
       "      <th>INTPTLON10</th>\n",
       "      <th>GEOID_AFF2</th>\n",
       "      <th>GEOID_AFF1</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09</td>\n",
       "      <td>001</td>\n",
       "      <td>00000</td>\n",
       "      <td>00000000</td>\n",
       "      <td>0900100000</td>\n",
       "      <td>County subdivisions not defined</td>\n",
       "      <td>County subdivisions not defined</td>\n",
       "      <td>00</td>\n",
       "      <td>Z9</td>\n",
       "      <td>G4040</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>302060009</td>\n",
       "      <td>+41.0779471</td>\n",
       "      <td>-073.2165184</td>\n",
       "      <td>0600000US0900100000</td>\n",
       "      <td>06000US0900100000</td>\n",
       "      <td>POLYGON ((-73.10137 41.16388, -73.09614 41.159...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09</td>\n",
       "      <td>001</td>\n",
       "      <td>04720</td>\n",
       "      <td>00213390</td>\n",
       "      <td>0900104720</td>\n",
       "      <td>Bethel</td>\n",
       "      <td>Bethel town</td>\n",
       "      <td>43</td>\n",
       "      <td>T1</td>\n",
       "      <td>G4040</td>\n",
       "      <td>...</td>\n",
       "      <td>72850</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>43741119</td>\n",
       "      <td>100805</td>\n",
       "      <td>+41.3697777</td>\n",
       "      <td>-073.3895032</td>\n",
       "      <td>0600000US0900104720</td>\n",
       "      <td>06000US0900104720</td>\n",
       "      <td>POLYGON ((-73.43371 41.36737, -73.43423 41.368...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09</td>\n",
       "      <td>001</td>\n",
       "      <td>08070</td>\n",
       "      <td>00213396</td>\n",
       "      <td>0900108070</td>\n",
       "      <td>Bridgeport</td>\n",
       "      <td>Bridgeport town</td>\n",
       "      <td>43</td>\n",
       "      <td>T5</td>\n",
       "      <td>G4040</td>\n",
       "      <td>...</td>\n",
       "      <td>71950</td>\n",
       "      <td>None</td>\n",
       "      <td>C</td>\n",
       "      <td>41372169</td>\n",
       "      <td>8719037</td>\n",
       "      <td>+41.1873858</td>\n",
       "      <td>-073.1957339</td>\n",
       "      <td>0600000US0900108070</td>\n",
       "      <td>06000US0900108070</td>\n",
       "      <td>POLYGON ((-73.23338 41.14300, -73.23361 41.143...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09</td>\n",
       "      <td>001</td>\n",
       "      <td>08980</td>\n",
       "      <td>00213399</td>\n",
       "      <td>0900108980</td>\n",
       "      <td>Brookfield</td>\n",
       "      <td>Brookfield town</td>\n",
       "      <td>43</td>\n",
       "      <td>T1</td>\n",
       "      <td>G4040</td>\n",
       "      <td>...</td>\n",
       "      <td>72850</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>51211648</td>\n",
       "      <td>1543913</td>\n",
       "      <td>+41.4698845</td>\n",
       "      <td>-073.3936666</td>\n",
       "      <td>0600000US0900108980</td>\n",
       "      <td>06000US0900108980</td>\n",
       "      <td>POLYGON ((-73.42154 41.44794, -73.42258 41.451...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09</td>\n",
       "      <td>001</td>\n",
       "      <td>18500</td>\n",
       "      <td>00213415</td>\n",
       "      <td>0900118500</td>\n",
       "      <td>Danbury</td>\n",
       "      <td>Danbury town</td>\n",
       "      <td>43</td>\n",
       "      <td>T5</td>\n",
       "      <td>G4040</td>\n",
       "      <td>...</td>\n",
       "      <td>72850</td>\n",
       "      <td>None</td>\n",
       "      <td>C</td>\n",
       "      <td>108499386</td>\n",
       "      <td>5829369</td>\n",
       "      <td>+41.4018389</td>\n",
       "      <td>-073.4722848</td>\n",
       "      <td>0600000US0900118500</td>\n",
       "      <td>06000US0900118500</td>\n",
       "      <td>POLYGON ((-73.42378 41.45487, -73.42294 41.452...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP10 COUNTYFP10 COUSUBFP10 COUSUBNS10     GEOID10  \\\n",
       "0        09        001      00000   00000000  0900100000   \n",
       "1        09        001      04720   00213390  0900104720   \n",
       "2        09        001      08070   00213396  0900108070   \n",
       "3        09        001      08980   00213399  0900108980   \n",
       "4        09        001      18500   00213415  0900118500   \n",
       "\n",
       "                            NAME10                       NAMELSAD10 LSAD10  \\\n",
       "0  County subdivisions not defined  County subdivisions not defined     00   \n",
       "1                           Bethel                      Bethel town     43   \n",
       "2                       Bridgeport                  Bridgeport town     43   \n",
       "3                       Brookfield                  Brookfield town     43   \n",
       "4                          Danbury                     Danbury town     43   \n",
       "\n",
       "  CLASSFP10 MTFCC10  ... NECTAFP10 NCTADVFP10 FUNCSTAT10    ALAND10  \\\n",
       "0        Z9   G4040  ...      None       None          F          0   \n",
       "1        T1   G4040  ...     72850       None          A   43741119   \n",
       "2        T5   G4040  ...     71950       None          C   41372169   \n",
       "3        T1   G4040  ...     72850       None          A   51211648   \n",
       "4        T5   G4040  ...     72850       None          C  108499386   \n",
       "\n",
       "    AWATER10   INTPTLAT10    INTPTLON10           GEOID_AFF2  \\\n",
       "0  302060009  +41.0779471  -073.2165184  0600000US0900100000   \n",
       "1     100805  +41.3697777  -073.3895032  0600000US0900104720   \n",
       "2    8719037  +41.1873858  -073.1957339  0600000US0900108070   \n",
       "3    1543913  +41.4698845  -073.3936666  0600000US0900108980   \n",
       "4    5829369  +41.4018389  -073.4722848  0600000US0900118500   \n",
       "\n",
       "          GEOID_AFF1                                           geometry  \n",
       "0  06000US0900100000  POLYGON ((-73.10137 41.16388, -73.09614 41.159...  \n",
       "1  06000US0900104720  POLYGON ((-73.43371 41.36737, -73.43423 41.368...  \n",
       "2  06000US0900108070  POLYGON ((-73.23338 41.14300, -73.23361 41.143...  \n",
       "3  06000US0900108980  POLYGON ((-73.42154 41.44794, -73.42258 41.451...  \n",
       "4  06000US0900118500  POLYGON ((-73.42378 41.45487, -73.42294 41.452...  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct_town_boundaries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BFNJlcXj1gnfX8gfCENfmEsyxPEngsJmvIRmSAJiZ/+XMwc/VLUbc8sZHwjLm00G8tZIburqNm/76C1NRKCKPQlXDRr6KXEV1HeJ5sFHAxakY7jEOoWO9F5TqWt/uqIgs4qXZePNcM883Sk1DSpvD3gxqfk2azV2iYJ6/pDmwrYVBKWjzPI0oIl5/tg99QIs5zBElLPgG0wXDGk8BmCdY64+t6hpYWp+9iEMMKto6LMHkfqVUbnZ7Dc04cHHIQgBBkUOlmTmOB0XLGtfdUh27pTouTEaxuFdGuSvJMUg/spvjIwv8UG64AVeKSmwH+eFWETcXku36zRZ2YaMb8wnc8vh5rs8nhlJ0ftQepj+o2QVehWL7EVMkV49Phbwp6ex5EKHoZFjb8XS4V818kImAqZB04PknGLLV9zcK4HmCGwtJBEQO0YCAl2bjiAxQ6uTmgh1WPPCNpSwmY2x1nG5M0t21HGIKe9hnEHM3DM9wNCJjJOzuOhoU32C6YNBscyduvdXphHsdwaDIYXkAVaBoQPA0mbKbsCz0LccaxHzUNQPjsQDmmh40q/EtVDXcWswgEZQwnw7hzZXcQCELN07SMPMn3V7yMKiqBs6OR5gfP4jBfGctjwtTUdfPW0iHcGMhiVOjIewU61gcsOrCLf6W3AWlIYgHt0iHZTxzIbfmJZY4nw4hHBCxW6x7knabTgQtu3KCIod4SEI0IEKReIgcAYhZu9jQDCSCIt7bLCJbqaNU1/CoT7LG6wWnSDyW9spQm1vpi5NRLLn8fK0Z5YD30ploQEDBxTkxjIT6VDyAqCIy970LLj/bvbUcIrLAJjIz4OdRGM5pQoDTo2HEgyJWM1Us7pUPnMtHMRepE99gumAYRetAKxDv7mzzIlwcCYh4fTGDqCIgpojMNYw8RzCbDGIiFkAkIEBvdppUGzoKNbOQu6IaqORq2ED/Lfv1+YSzfJnHC+6FsQjeXs21v8/VVMcRH3Z4bYkMSi4N5hDUIBWJd1Um5lbNp6IauLkQ74llHgZWNbIiT/DCeAQBgcez3ZJtZYSf9DnGDHJRdnJtPolbLk5IAmDJQ/F4K4RQqGq4PB3DO2v9M6tTCQVjERkCR1Coqe27eCIoelZWYom3ejUgYlf3ykRUwWrG+7ba641Qcjk0bRhSZCLPuYql1zX3W/f3t4ue9ETdkghJWG82ChACXJmOgxDg/a0icyuvX1Z0jBnW0Hjqcm82lwp66pDp9EQKNQ2SwGE6oSARlMAR02tdzVSwnq32FefwKpLAETBtkb3PVj94kdRUHRenYniyU/IkrlzzeNF1G24nBt2SL6Sd++i78XKIsxUVc6ngQA0PLHRuya/NJTxVEHi5IQyCbzBdMCyD6faumA7Lrg3mbFLBSofXVW1oUDUDz3bLYFEeCoicZ69tJsl2sXm5mMejgbayeovW9xJP2rWOa9kqc4ZfN2irIe8AIk+a8VkOUlNNvtWvzRNzimc/EZWwzGMyrrQzzpWGjs18beAdymhEdh2LdntzbnFYHT6dFGsaXpyIICwLuOOhtx7AoQmSWOEbTBcMw2CmQhLes1EL74eXc3c0EjhgMN3OMZ9NBm1jR3aMRWQmg+lFmmsuFcSWhYBHQ6cHtnLTcQWjUdkxrNCqb5UFDhRmf7jRFLlQq9YxysszH3hIL4xFmsX1VeyVGn2P3aAJCi8VC17DAIfVQ97JWrYKiSd4bwB9htohVAvY4ZcVuWAYBvPESKid3T3K93WLm3q8bljv+m4vZp6z7yXvZi1XZdJzbJVs1ZvF8azLEjgOE7EArs7GMRKRcW89bxu7LdY0s5rAI4t7Zdd/l8Oc4DDoS58eDQ88quKwJ2924xtMFwwy47mF27id15EUVl7YYUMImBXV3RrMUyNh18X7BsNlLXscF/HmShab+RreWskxJ47Skd4CfjdMMBast/AqDnIU5TrDMHatduCjwjeYLhh0eBngfnTpbCroYfohP1AxNuB9nLDAEeYkiluDKbvMSgNsNyivep+dtoj13Bikq2gmobhOxBkebclhjNjthucIYsrgUcGjLC3yDSYjNVUfSoDZ7V21JTbhhvmUN1HbTrwmfFSd4sZCEjcXkpAF++2nWyEML7FcJ6MclMxav0EQecKso6kMMC0yJAuuPWydejtnj8IIPdgoYCruvoOtm6MsLfKTPowMw7s8kQ65HmDmJQYVHGC+NgBMxgOuhXQ7adWYzjuUQ7m9KL0UmDsZ5fOT0YEFMSZiCnPYxO0AMJ4jmIwHkA7JiAdF1FQdYvM1TKUgUz1Ia81+1w1oTXFhranM7kVGrbt0qx9eM/CdvLtZxNXZON4aYJLqUZYW+QaTkWEorase9ke7HjKj5QF73kcjgxlMwPSMnUqh8kPqnLKjapNFPTkSOtAx5JVUSGI2mFYjH6IBAWPRAGKKCI4jaGgGcpUGVjIVrGaqWM1UcXk65rq8zGpHwxFz5POjrSIIIQiKPITWrKOgBFAKAwChOND0/8EIXlMj4Np8AoWq6rmiAjDbVK0EjFlgMe7DwjeYjAzDYI5FAqg2dOZtlcQTrGUHK1j3gpdYYTcL6ZBjdr/U0B07Sjhi1l6mwrK5fXZZh2h38zCHmQ3uJbnt+Lk2nwBHTKOYKdexW6yjUNNQqNkbnWEkHVsY1Pz8RlPostU73j02hXWUSj9pOAA4Ox5BtJnZN2tdKUDJASMs8gTvDHDjOspMuW8wGak0BhfeuL2cxbW5BLPBnEuH8NjlnXsuqfQI67plGJ+VtRogGZKwXaiDI+b0wrFoADxHUNd05Moq1nMV1FSDWTC3m5rFdk0R+YHnowPm+t3EYisN3fNnGUZrZSciQ3iAMGpKdXuHLdGPkCw4tgHPJRWU6t6N3lEmfXyDyYhXgYZOAiLnqm854aEWciQaGNhgDpph5wjwZKe/MeI5gomYufUMywI0w4AimnPT15r/umno3i8m1eJiWkiHPM9HH4nIWEiHUKppeG+zgLiLv5PX6oOZhOJpAJ4dLOFU1hEVi3vlAypHV2fjeGMp69jBQ2DOvU+HZaxbzHB34ijLipgMJiHkLIC/Syn9IiHkNQBfpZR+rs/j/gMAqea3FUrpvxjeUp8vg46aHY8GwHPAExfzyL2MUxg0Dp8KSQO38J0aDWNxr4y5VBDRgIgnOyWcn4pit1DHeq5iaRitGKTnxCox4XY++ngsgNlkEJlSHU92ywdiy+mwhJgiolBTsZat4vxk1LK7yOu2ujyAB2aN85FlkWBrMZP8QA6Q1TOlQFsU5uJUzJPBPFZbckKIDOALAEIAQCn9NiHEqhbgL1FKP9t83r8F8JExmPkBYpg3FtypE7XY9hAE93LCdTKdUFwZTEngMBkLIBGUIAocGqqB3VINmk6xvF/B+ckokiERtwfIRBfrOm4uJKE3M8KtzLDeHDSn6RSqYUDVKKYTATzaLqGm6lB1inOTMVCYnm2+qiKqiMiWG0ze1VhUxlwqiP1SA093y5bHtntMQ+dWVxY4TCUUJEMSCIBMqYGGZrgWAG5oOs5NRPCuS/ENO1jCJgEXBnM0IkHgY2hohuUOwx7qKflz3LbkPwPgGwB+suNnVlfUHULI3wTwmwD+sdULEkK+AuArADA7O8u20ueMV89gPBbAXQ8B7bDE91UQsiOmCK5GsAKmd7SQDrXFJ4KSgOvzCbSSo51ZUQJA4AkMCjRUA3vlOtazVSztVyyztyJP2hJeXhE5wqzPaFB6IHyynKkciBm3PudUXMH1ZvIFMD1RnZp95A3dgMRzeGctj22XPfiAmdi4Pp/ATqGG5UwVz3bLTdETk1Z22g2lho5IQMRoREahpg6lJlhjqNpwMz9ov9zAPUZZtn7cWy/g0nTMtcE8Nh4mIeTzAL5DKa0wjgD4RQD/BMB/B+BPWD2IUvp1AF8HgGvXrh2u6N6Q0A2KiVigXcDO8keKKSJ44k0+bCYVdC3lNZcM4f2dIkYjAcSDIgICDxDYercnRsKuvN/r8+5kuHY8GJxuJuPscdmdYh0CB7QOeXeCrXWy1TT7CaAcMb1SL4mWUk23jY967fZ5fTEDnpjn1fnJmGeFnxYsRpclMdTCi8h1N16aE4Y5a8sJp7/clwGMNY3lFULIL1BKv2bz+K8B+D8AeA3AvwTw+aGs8hiw2kcuTBYIFEmAzHOQRA6ywEPiCYTmmNygxON7T/c9vV8kYJ1ISIfNIHlYFsARM6NcqmtYyZRRUw2sZCpY6bCBJ9KhvuU447EA3l5xd9G5TX4N4+4fUUQA7J5zVJEcS5qciq4NCoxHZU8hjoxDYo8bICqrUyBTUVFezw2sWckyV8qNapGXIX3deInBH5ukD6X0p1pfE0K+1c9YEtOaRimleQCXKKVFAN8khPyVoa/2OdIvu13XKOpa/7sqIaYo6tXZOAzD1BbXWx0Yzf83NB2aTlHXDTRUHXXNaHdk1FUd6bCEsWgAIYmHQc2wwFrOlA9zc3Lulet4eS4BTTfwcKuIumZAEkylHbcGYafo7vHZioqLU9GBtmp2xef9iMiCrcFMhkQ8ZMiQj4S9GcydQg0cse7SGsa45rpGMRJmk9GzgmWoH+twuUhAGKiS5ORICMmQhPsupQ+BY164Tgi5COAkIeQCpfQ+gEsAfh7AlwD8EiHkZwGsA/gfh7rS54zbGCalOLDlY038cMRUzwmIvGvDaEWhqrW3bxJPcGk6hmRQwrfe33X9WpmyipDEu0pabBfquLGQhG4YuLOcc/2ebjuXBBsl9AuTUewW68jUnf+e7uZ/f0DbO7UIRwxr1MmDjbzl7oGFYRbCT8QCKDoU3tuRrTT6ijGzcGw8zE5a2W9K6T0AMx0/fwemsQSl9N8MeX3HhtyAJxdr/aVBzW3sYQWyGzrF3bU80mEJIkdca3MCZoH54x32i2OnWMdOsY6TIyHIAkFdc/eebjwXReQtL7yYIuLRVtHTZ3ZLMiRZGszNfNXWA2WlqhoD6ZaqOnX8e7DKw0VtQkhOnJuI4uGWvWcpcOZ5lwzJkAUODd1sHV3P1Y5VDNOnySB3Y5EnrkcLDCOAbsdeqYGIzEP1UN/n9SKNKSIigZgpQEHMcpB8VcV6toKGTYtiuaFD4AhTXapd5lfk3d0gBlHBsRNAMSjw6dNp1NRmSEYzwHMEisRBN8xM/bubBaa2zY1cFbLAeV5rTJFss9Ks4RCvCu0CB2zkq+2bRyQgYCwiI6ZI4HmCuqpjr9zAVs7MIaz0Sf6xZPuHhW8wGaip+kC1Xldm4q4VcdyMb/WKl8J4oDUm2D0iz/UtDyLETGSNRmSEZAEEBIVaAzXVQEDk26VJLMrzqk4RVQQU+oyW2Cs1cGEyyhwnG+SmZSWy0SJfUXtmE3Vi1ZvdTabSgOBSAamTkCwANgaTNUaqSBwW0iEs75ddec7X55NQdYp8VcV2oYZCTWvuKA46GDFFQN5iXIh2yNMtO/ENJgODqP+cHAnhtofyj2H0cztxciTsKche8RgusNreUQrHeO3JEWcxjxaKwKOA/sdvNVt1lJ1rsT2Aar1TXC3goIv5+mIGL4xF8Mih313VKa7ORnFrke0ce2k2DoMCDzcLqGkGJuMBqJqBtT71uxOxANMguXMTEXzr0R4AM+47lwwhqgjQDYr1bNW2AWMlU8VOseboTSeCkqXBPAqx4xa+wWRgkBiJQb2VStRU40A94WEQ8qib6bWbaJAtbjosI6aIeLhVdPx72F18+aqKmqrj5EjIMclQVQ1mtZ5+72MHS31nsa5CEjjH3Q3r+RVTBNxdyyMkC5hOBsER4HtPzLI3ReIxmwgiqgioNnTc3yhgJCI7GkyOHGzbrKlGj5FPhyVMJxRIAo9STcPKfhml5t+wUGW7CUZtwkC+gPAxY5AykD2XZTidJIIydj3OBmfBqV7Qir1Sw3Lba8fuAMeitT09NxFlEM2wtyB1zUCQUfl8NBKwNZg8RzAeC2AkLEMSOBiUotrQkas0bBM7LNv9jVwNN+aTuLVkvzVn7dteSIfx9moO+araY9CrDf2AoTs7FgGBdQ1vi5cZ5on32z1MJRSMRWQEBB5/+My5VjlgI6E3jDItVnyDyUBxgC15sa4jLAtMNW/dxILCoRrMfEV1FdPrZDoexLtVd8/bLtQRELmB2voCIocb82ZfuVWnC0tMb5+xXCssCyDEFCVJh2WEAwI4mJ7UXrmO7XwN69lq3zbWqXjAsi2UteriwXoO0wnFVqzkrZXsAaUgK54wVjZEAgIeNo1nq5742W655wYbD4p4z6PiU+uY3VhIsj3BJiY8DE1TVnyDycCgGeuz4xHXcUxJ4IZSg2mH2UZIbC9sK0Ky+9k0BgVmEkFXJUnd6AbFmysZXJiKWj7Gzt8SONMjm04GsdFnuzkelTEWCyAg8GjoBvbLdfDEOcbaj1RYxnquBgKzq2okIiMSENHQdBRrKpOa/rmpGJ7ulnFzIYlSXcODPjc31aB4cSSEu2vWSaTJeADTiSBTLfB8KtTW7KQU7XN3PBrAaFSGIvLm7HbA82iJ6/MJPNwqMrfl2sWErST8DgPfYDIwaMbaS6EypRTVI0j8bORrSIclBEUOFReeH+s2sJtBpiYCH0iB2cUAg5KA8ZjZXhqWBUg8h7qmI1tRsZGrQuI5UEpxZToGjjNbWUs1DavZCrYKdcv6SbfEFRGzySA282ZJzGa+hqm4wiyQcnY80t7utkISl6bMNVcbOp7tldre1d21PD59Oo31bBWENLP0HcLmlYbObJysBrVtFWrt8c2nRkKupAoBc/u+uFfGRCzgumqkYlP+pg9hthArvsFkYNCOiMW9Ms6MhV3NPVF1ihcnIq4FOLywV2q4lqBzKqxPhSSMNMuEWjWUNVUfyvgLAKAGRTosYTKuICDyoJSiXNeRKdexuF+2jXWquln/+LaNRzYMKqreM+tnPVfFy3MJR+EMsSlH101nKdJ0QsFU3NyuT8UV5CoNhGTBs6J7CxYpQ7dtkBJP8P52EZSir5fsRM4mOaT5WfLjxTCKyMMeMtJeb5xhicdINICwzEMRBRiUOoYEbi9lcHIkhIZmOCp7j0VkKCKHoMRjvKmeLvEcNJ2iVFeRq6jYLtb7etaXp2PePlSTmYSCkYgZS3y4XbLcJjvd5J7tlXF+MortQu3QQh9W5WgrmYpjBcTVuYTjDawlxGzWqZp/s8vTMc+ZfcAMBT3ddb5J2xmwbnhiqmI9dClp14ldzNmPYR4zskPo/X1nNYeYIjJ7q9GA0M6wRwICxqIyBI6DIvEQOAKOEFBQGIbZ6VDXDNRUs10sW1FR6shsJoLOnTkGBZ7ulhEQOZwdDyNTVpEIiogEzCmGdU1HvqJiM1/DdrGOnVIdAsEBnUcW9gZIYrUyxqvZKuIOn6mq2ntAnZni0YgMoHcuzaBYSdvtFuu2Mnlhicd9F15ip8FQdaPdpujFaCoij3zV3mNTJN6VIIpOBxsi6DQF4LgJCH/s8Xq37kSnZkzKrnsjKHI4NRaBJHB4sFFAUBIwEeMxnVAGmp2drag4PRZmGqh2diyKlWwZmbKKVFiy9EwpBSYTQSwzjpdt4bUY/Pp84kB5Ta6iIijxljWZo5EAMmW2v1tV1Qca82rFfrlhKVTy/nbJ8ndnJyK47UGk5EQ6BNWgWM5UMBVXIPKc68/EMnMooYiuFaRmkko7/umWpJPB9Lfkxws3g8vsEDiCGwsJAGY7V0011b2TIQnFmorH26UDmc5qw7yQhxH3SwYl29+nwxJmk8EDs2ic6iyTIRHLLidvaAbw4ngEYvMzLe6VHeNhVm2CoxHZsmOHJbmUDptx1oebxaEMuevHRDyAJzu9Xni+quL6fAL1ZvsnhRmD3SvVwbNOHuvgxkISu8V6+6a4nqtiLCJjMu5uxrxB4ZgA9FIozioT14+wQ0WG72EeM4Ylg/V4p+T6jr9TrIM6yIWxYPUZeAJcm0/i7dVcz+Cu7YJ9LFMS3JcWAWaxd+vGQAgwmwx2bItrWM1U26Xndj3VdkaR5QIdiciHnlSzU/Gx2jU4hRs6OTESQkDgcHc116Psv12sI6oIrhOOQVlARbV2EvbLDddx0mpDw3g04MnLFHn78+xYyrt9nBl0YmQLq3INJ3ZLdUzGAgNtG5/slBAQuAMX1enRMFTd6GuQWKZHem1J6zwOlKKpEP+Bp9jqR56IBWw1O+0GdDn1/6fDpsf9qVMpM0ar9j8OXjg3EUFYFkBhapu65elu2ZXm6PvbRcsEUqGqQdUqODseYU66sAw+S4VlVwbz3noBNxeSnrfldvhb8mOG1xGn7VESAQEEZnDeq0L2Rr6Gl2bjng2mZlCcnYjg/noBAYHDxemYbVzUKW4EAPseEzhOvl9NNbCWrWBxz94rsnudfgkXReRxcTqGjWy1rVzfyeXpWLvO0yvdyZzzk9YF9lboBsWZsQjeYhie92y37GgMq6qBxb0SY1spTPk9BzIe/vZ1zdt15PQ8f0t+zCjbFJAngxJGIhKCsgCR56DqRjub3N0dcn0+cRTLtUQReVyajmG7UHNMItmJHbTw6i2wiCOfGAk71hPatVhWVa1HQ/P0mP3ANydJNha6xyWwqP30w81SWOK1dY3i6W6RqRWWc9DvS4clT6VYukE9jbJwavn0y4qOGZWGDpEjeHEiCkXioRsUpbqGzXwNmUqDWcRikMA34Hwit1AkHtGAgKDII6qICIjmmgmAe2t5B2mK5nsxvJWqU4xFZNfz01kuNpZEl91xPzcZQ0PT21tiO4GG85NRBASuJ4brhfe2CqZQcfMizpQbridtAmYIpTUS2Iqrs3FwYNesrGsUDzYKjutxOvRTccWTwby3XjAl62ru4sZOsn4Nj56rF3yD6UBN1XB+IoqoIuAP3t8b8LUG+8NKPIcb8wmAEHOOtkHR0A3UVB3Vho5iXUOhqqHaML+fiAWw2LXFvD6fwF6pgZX9MuxuzKzGPRWWXBvMrXwNhNgX5rPEpbZtXodSirdXnbfXk7GAp86TbsIyj3OTMazsV3o87zeWsrgwFcVGtooMY9yvUNPwwlgYjyySNdfmEp50VmlzPTfmk3hjOYNTI2EkQxKqqo6tfK0Z8rH/27PEOK1wW4cr8cSxhtPLGGuv+AbTgXJdx1urOYgcwY2FJCp1zZO6D2AaikGm69U1w9Us6s18DYrIH9gCv7GUBUeAuVQQiaCEp7ulvsKsBqNstqcOJpheSj+FnxYsF6VqUIxbTL5k9eVrQ/JOJmKK7Xb//nrBFEF2kSiJW5SCefFYu7m1lMHlmRgMAweSXSGJRywo4vp8ApSaYZdupSS7EJUdIkdc6yqMRGRHYZiGZoBSOvAOjgXfYDrQkmVTDYpbixlcmY57fq2dYh2pkFnv2N1jzIIXD3U2GewRdDUosLhXwSIq4IhZmrJbqKF4QAiW7b28Ro/SIaltMMMSj4mEgmhAAM9xzRHEbF7DaMco3GRIRCIoISDyzGMSMmUVp0bDzNJn/VhIh5gUmJ7ulnF2PIyHW2zv1V3kH5YFnJuMuur5t2M9W+1pOSw39J6Jp/GgiNlEEIrEo1jTkPHYSjoaDTALj3zw3hKTkpZBzRK5w8Y3mA50e4N313M4PRr2LFG2X26AUoqphL2H1Q8vozKiiv2f2KBmpnUkIuP0WKQdx2PV72RRo29N/IsERCgiD76ZjDk1GsZ+qY5sRe3pQjIVdwBZ5JEKSYgGRMgi124LNShFXTUQCQh4eS4BniPYztfwtPlZWKTTWgwyeTEZkjAWkZmH3AVE9ktuab/Srk+dT4Vwfz03NGMJmIm1vVLv6+ldsZpcRUWucjC8MZNUMBYJADC32SuZiuNNKhEUXRtMFqFnQrwPYXOLbzAd6DSYIkcwEpUH0nMEgExFxc2xiGuD6aVFk/U02i3WwXdsaVi3Ti0vSOIJJmIBpMJyu1qg3NAQC0hYyVaa0/7YPy+lwCdOpPBHz/ax1nD33LDMg0E/AoDZfbWRs/f2FZHDVKJp8AUetaZU3Fq2gky54WqY3DtrOXcSbxMRfPfx3tDbNkWe4LHFvKDdkrNHt5qpYrU5wXEmqUAWOMynQ4gGRFRVvXlsDp6vLGGWdFhCKiwjLAvgOYKGquPqbLx9o2ztPuq6gVJNQ76q+mVFx4lOEQcvWworWGa6dJOvqq7nWbtRet8q1NoF69MJxbELpqVCo+pF5KsqljNVLHeMQR2LyHjEuP3sZiQi480Vb96U3YU5EQtgPBqAKHCoNXQERA63lrIQmwY/HpQQEHgYoKjUNewU69grNdrtjTcXkj3Z9LdXcxgJs40TodQcz8B6HhWq2sDzy/txYTJmWee5mnE3unc7X0NDpz3ny1hExtmJiDmyo6pC4AlEnmAsGkAiaFZvEJjlYdlKA1uFGoo11XUGPuawixomvsF0oLPWbz1XZeqAYXtdb8mGeFBinp4IoK+quB0L6ZApGuGQzJmKK6axsdkixoKi6wx6ex2pIG4teXtuSDLXPpNUMBoOgOcIyg0NK5lKW8i3xSdPpdodVCuZat+51528u1no6ZjSDIqFdIh5nMiD9TwmYjI2886Pf7Y72G7GCrt2QgrzPGDpDLJrXd0u1pEKy+1ieZ6YIjQtWbrhcTTbcQAYjprrR5huw3ZiJDSU190vezMGbuNtuYqKZIj9ObeXs7g+n8Dj7VJPLWZY5nFxKob5VBA7xZrj1MWQx1ZQwJx54/X5NU2HIvJYzVRxZyWLW0sZPNgo9K1OaGiGq+1usabhUh9Nz/sbeYQZ11tu6JiKB5keW6hpODfhvlvIjvFowLHSg+U8iwYEx3bSzi4d1vpyt+e4eBTZnia+wXSguytlGGLCALCZr0PxUM/GOu2wk7FowNXj767lcXosjHNdbX2luo7HO0Us7VeYuitEhhY7KxqawdxL3Q0BWzcRYBbys+iFdtLPO6o0dJyfYhdHdrPD8FK6ZcdsytlYsxSDF2oaJmKy5e/HYwFP1SCtPn9WBjnP3OIbTAe6xYMfbZcwHnNngKyYiLt/HS9GltXzaVHXDCzulttdMpemYrgxn8TLs3FMxRX2FxqgLq5QU6GI3k5PRWI3MDXNlNcLML7XjfkEZIu/wdPdkmOXTAs3iSIQ4IKHnvR+8BzBEwblIlZlrOmEtfGdiAY8tS2ytOV2MqyxJywwvRMh5Cwh5JvNr18jhPw7h8d+mRDyyrAW+Tzpl5meS7Jtp5xIKO7upEBve6TYnPp4biKKa3MJ3FxI4tpcAucmophKKE3VHPeGS+LNjOSp0TDurudxaymDOys5JBx0NTsZZNZKpqwiHbb2XuxwKwrydLeMGZsLvxPNoJYlRHulBi7PsOoFsBsSTTfweKeIl2bjGIt6OyYtLk7FmFp5N/O1viGRqYRyYAts1X8v8gSa4e3vzyL+0ckgnUducbwVE0JkAF8AEAIASum3CSF93QxCyAsAvkwp/ctDXeVzpN94isc7JdfZ6n7wDLEXReIxFpERVUTIglmHeHk6hlJdQ6ZsjqNYz9Vsi3uzHgSQY0ERb/fJorqpdxukNs5L6990XMF4c4t4djyCpzslqAaFyBGoNn+s+VQQa4xZa6eZ56z1nyyz2QkBrs7E8XinhLpG8eZKDlPxwEAze1SdPRQwmwqiphpIhyVouoHlfXOW+ExSQammQRF5S/WjkyNhPPSoNeq2guRYGUwAPwPgGwB+suNnVlfgfw/gm4SQfwDg1yil3+33IELIVwB8BQBmZ2fZV/sc6GdsMuUGLk5FcW99sB7kWjNGFwkIiAZEjEZkSAKHhqaj0jCwW6ojU25gab+CmaTSrntzy2q26ijk0E1Y7r8t6lbjsWOQ6adulYMknphtfB2GTxF5cBzFbDKIREjqyeiLPMHlmThuu2gzbDh8/pVMBecnI3iwYW0sZpNBRAPCAZGOqzNxlBsaDEqRDMmglGK3WO8pYVrP1TCfCoLC/ZycdFiyXVc3Dc3A4l65x6NezVRxbT4BVTMsqzAebhXx8mwcdzwImlRctl669UgHwdZgEkI+D+A7lNKKU58mISQEYB7ALwOYBvA6IWSOUtpjcSilXwfwdQC4du3a0WkzecCq93cYgebdYg0ST1CsaSjWNNvavOwAkw0bmoGpeICpxazzOf3Yr7Bvd91023TD2uN9cyGJXNWcN96dBW8lfh7vlJAMST0F45en3RnLyVgA7205C3rwxP7cGI3IuL2cxXhUxkRzTO57m4WOUiX76oOl/Qrmkgp0g7rSJTiR7t/Z0w874Q8AeHsl63hD9NrbnXOZWPUqzO0FJw/zywDGmh/8CiHkFyilX7N4rASgSik1AKwQQjYAjANYGdpqnwNW83webhZcFff2Y7tYZz6pSg0dYVlwVYjeSSrsLGIwHpUxlwpBMyhUi8+13hzr6hTMF3mC1aw3seR4UGTugrq7lmfKiLdk1loGM6YIeGct52pdY7EAU13r3fU8To2GsZKpYDqhIBmUwBGCQk3Feq7aDjVsFerYKtQxGpFdK+4sZ6o4NRqGZlSZBpIpEo+1bAXX5hKoqjre2yzYhpSCDpl5zYBjLanT5E4rdlzqrB6bpA+l9KcopZ+llH4WwNv9jCUxiVFKswDqhJBw81e7ANaHvuIjxmrEbkU18OJEZKDXNihcBfFTLsstOmGJ8yRDEl5fzODOchZ3LcR7DWp6Zk5MJ4KeYryt3mnW5gDW7DZglsGMNV//hbGIqwzuqdEw3nKxvQxLAhqagWe7ZdxeNmtBH271H7Y2yNiRhVSIqQ7x/EQUG/kabi9n8WCjgKhiKhKdHY/0FDMkgiLuMSjP22XIAyLnebSuZpjnIitHGcN0bZoJIRcBnCSEXGj+6BKA/6H59V8E8FVCyJcA/B1K6dEpex4Cqm6gYLvlGbxgNhViN5h2A7Wc6BZU6ObkSAjvMgbp767lsJC2zyo7Tam0Yj4V6nuhJUMSLkxGcWPerAI4Ox7BaER2dbE82iqiWNcQUwTm+TYt3BhmwBRpaRn/w+TdzQLOTUQdE2zd43NzFRVvLGXxcKuIhCLixkKy/bvTYxGmsie7OPN41F0IqBs3x87t32YQmAvWml4mKKX3AMx0/PwdAF9qfv0GgDeGu8Tnh1N86N2NvOuh9t24+WMPcmIU69Z3+4V0yFVxdEOnqNR1RAOC5Q3FYdBfX0SOICBxPRnyKzMxvL2a79sSemrUXedVpaEjEhBcJ6RYstqdGNQ0/sMWzejHO2t5W43M0YhsG4/MVFTcWszg9GgYT3dLWGJUXuoOVykih6AkYL/c6JGNc4ubBg3pCLfkfi+5DU5yag2d4qWpCPNoA44AV2biH5SmUOqqyHoQgdSl/cqBTPlMQsFkXMFGrorFvTJOj4btnt7DdrGOcxPm8K1+zojVseOI2XmkiDyedV2Y8+kQvv3+Hk6OhNqzsU+NhGyV071MZSzWNFydifeITwREztIwehn6da/ZLlka4IbKyhtLWUujOZ9mM9yjERmJoIRbS2yJoe4C87PjEby/XTLl9poSfK1/ukEREHlm4WOn8q1OAh7HPXvBN5g2sMRgNMY42HwqCJ4jPcb1cp++ZCsMD3U6PEF7rs90QgEBwWa+itWs+a8FyyCtbt7dLFqKL+QrGl4YiyASEMBxBDVVR6bcOCB+cWMheaDUJ9xcw9PdMuaSQVBQxyFiXgP+d9dyuDQdayvLb+RNpZwbC0nkKg0QQlBtmDJlJ0e86Z9WGzpuzCeZDdCgvL2a6zsZcodRgEXVDdxyUTVAukJSHMeh3NBtpwLYiXV04qbp4bjVYX5syTOUN7y7mWcaO6EbFEt9hlW5ybJbZa7tuDTd8qRUW+PjtUzq9cUMbi4k21tcReJwb73AVAh+azFzYERsZz1dPChiu1B37CcXOG/r1qmZYe+3pk5MfUYJ+aq3mfBL++UedaPDQtUpHu8UcXEq1p64OZ8K9j3v+uJyB6NTAzcXkqhrBiSB69vo0M3rixm8PJvAnRV7w+ymXOqoxIMB32Da0h0o74dmmBlXp64UK0/IjVSbl1kqrK/PMnTMipbHwBEzieXmM3HE9LJXM5UDHjTzfPDmtaJIPE6mQwhKAkDMH2sGxb31/EACs+ao5AxGI7Lr4n/AzIAPYwYPK6pO8e5GHldm4nh7NYfRaIDdYLpAETmsZqqebiJ313Pm9EgLAWMA2CmyJ4z8GOYxgXV8LrW5jM6MhSHyxLLDYqdYZ57z7KVMYyNXdZzQCLjvv+7Hucko7rvsfmpl5q/MxPA+gyhENxwxx+Tul+p9Jcu8TlfsZqdYx0xCORDGYMVLp9Ug6BS4v57DixMR5gROJCDgvsMc+E7mUiE89ajVqeoU28UaxqOypchHvqox1x0fmzrMjzusmb5nu2UIFtuCgMA7tqNNxtgUgPZLDdfbD9WgmGBQV1rNVBH1EMfsZJATV+Q5phBINw3NwIONgvWFx7BLYMVtu2ZLYWgrX8OV2fjQ1uHEWETGldkERqPsYYQXJyJM85laPNwqDqTalauokAQeQZvKj1HGGuWjNJi+h2mD3dYyEhDwwlgEDc3A+9tFvDgR6dtbbud9tmBt7aIAJuMB1z3lI2EZGw41cRTA2Qn3EwnPT0YBUIRlEffXc66e22I0IjuOw7DCKQb8eLuEl2bjzJUMViSCItZcdC7dXEji/voH8W0nHdVkSEIyKCIkC5BFHpRSqDpFua6B54ht3ajEE5wZjyAoCdjImWrm28U6Ls+wJxS99P2PhmXP+gaA2Xdvp8nAKiRsJbd3GPgG04bOkzwRFDGbDEISOGwValjNVA9s9aySJixlQ27UWdIh9ycp6wn1dKfEXFd6fjLa9u5aTMYCODuh4M3lrKvtZ13TMRqVUdp1H6NlaYscxrzqkyNh5q39y7OJdly3VQmwU6zjxnwCqk4hChwMSlFTdeSrKvaKDWTKDcsbdD9PbiapYCKqoNzQ8Hin1BMKiQQE3GeNA8ObohXrcR2NyAjJApJBETzPoVLXsF2sY7dYx731gmUlAauohu9hHhNCsoCrs3FsNO/Y2Yr1CWhlxFYyFSgih6pN4bMb9W0vAW6nLp8W++UGLs/E8I5F3eNkPIC55kz1B33ihRv5GjbyNcylgtjKV1HX2N43X9XwwlgUzxxGXvSDZRs5iC5nC5ZQSFDk8OJkDG92GNa3VrKIKQLyVc1VyU4nmm60h7fxHMHSfvnA1MZ+TMQCrmLCz3bLUESeWan+7LhzorPFTrGOQFXtUT0KSzymEkFQSvHJkynslxtYz1ZQrJtrYBVZ9g3mMeG9zQJz//BuqY6ILKDYFaTeytcwGpFxZSaEP3rWf7vrJtNY03SEZR6UwrHkhiPmhSMzdAhdnIq26+pePZlCQzNMsWIK5KoNbOaq2MjVMJMIOra8Le9XXGeGH20XcHMhifVslVmbEgCTNzyIQEqL1qXLE2AipjRV2nlQUFQbOvZK5tTD5iFro+oU+ao3EYoWxZqGumY41qR2Um12NJ0aCYPnCNZzFXCEICgLiCsiNvO1A6M2DGrOq+p3I+zkxkISO4Waq0oIAJhOKHjSdUMsNfS+mfJ0WMJEzByyd3U2jmy5gdVs1XIn5m/JjwluZKaSof7THF+eS2AjV7H1hHaK9Z5azpDEYzwWQEwRIfIcGrqBQlXFs90ySs078EhExlhEhiKZ40p1g0LgOdRUc272Zr6K9VzNViQBMJMErNqeTt1PLd5Yyh6osXQiX9WwlqtiI1/FxakoOELMmeFdUx69rGeHcdxCJwJHMJsKIhmSQGCWmE3EAthuam5aGXU3iRNW6prBPMa3xWq2ivlU8EA3U6fotSLyuD6fwG6x3i47YmlHvMeoDtVNPCjBSbauhVnKdfBakgWCuWQIiea2vlTTsJmvIltR/U6f44KbrG13X+1sMoigxLe7HgSew/nJqOUdfCEdQjIkYS1bRa5injBOUxl3m3EgJ5wC+t1jL+xY2iszlSkB7qf/jUVkrGerPcZ7NhnEeEzG/bU8Kl2hDc2gkAViu/3PVMzqAisPJRoQMJMMIiQJMCjFfrmBlf0ynu2WXYUJJuMBvOvgoXklHZFcGUygaaQ6ajA7P35V1ds7gLlUEKMRGSLP4ex4BKW6Bk2n2OqSWWuNI/aC2wqDbuoa7WmlBYCoIiAk+wbzWODGYLak2mKKCEXkewqvVzNVSHytqajeu0W8u5bHuYkonnhowXPCSYx3NCozb/dKDZ2pe0QWOFfdGqmQZBlzW8lUsJKpICwLuDYfw8p+pV2/OhlXsJ6toK7Z/62iAQGEEIxH5fbM9UpDh25QPNwqOm5FWYgrkmM1gldCLjQHWryzloPAmc0VdizvV7Dc8ffkiPnvxnwSy5kyshUVDc1AOuzdYHpJKrFQqGqe9AS84htMCxqa4bpD5MWJKL77eNfyBG3oFNOJQN8xrVNxxXI+yqDYBcXPjkcskzxWjERkR4N5cSrmqmB8YSTkqH5eqmsHHtPaus0mFeyXDxrMkYiMmYQCniMo13Us75dQbhg9YZPr86xDy5xZz1WHMuupH252AS0oBeIhtqaITgxq/ntjKQNZ4BCSeFyajjoqyduxtF92nK3klWMp7/Zxw4uyeaWuOd7N44qIoMT3eFOH2Q0rcASnR8NQRB4iz4HjzIuppuptwQs3sAiO6C4K+6bjiitx3m6CkoDxqIzpRBCUAuu5CrYKbOGKQaT5uslXVUQCAhSRH7qsW91D3BDwNse+BYU5hrimGag2DDzYyLV/p4g8BI6g1NAOhGdkgTOPgcRDEXkEBB6iwIHnCLLlOh7vuK+EcMLL6Gmv+AbTAtbkRics2+lW+971+QS28rV2q91+ue5OKMEFVVW3VNsZjwUQD4qu2i6X9p1P+gbjTB7AVJJ3kxnvZrdYx6mRMF73oAo07JKUYk0bSOjZCq9TIocxewoAHmwUcGkqhrvreVyYiuLRZhFVlSIVkkCpGUvcLZpiKXULj/bGQgKsiR83HOVMH7810gI38TfAvPDcZNXfWMpiI2dmhC9Px1BVDUQV0a1gDBOSjZrvVr4Giecwk2BrzwTMi/fcRNTy92fHI3iXcTrhixMRdqENC/bLDRgeO7XdeMKseBRQsmUtW8ElF1KALYZpvFuGKSQJ7a31frmBTMWcbOpU5uZm4qirdR3nERUfF3JVd3Gfumbg8nQc1+bYY2I6Be6tF7BfquPmQgLZSgM3F5K4uZBsthwOB6dZ1DvFOnIVFecnD84oUkQe49FAXy9sJCLhxnwCL05EenrQY4rIbL6GUSMJWE+5tEPi7VsOvVKu67Y90l4wqDfdR3GIHnQrcbPnUahlOTP83ZPEc67EhgfFN5gWlFx6mACwmq1gab+M+RS7t8YTICAJeH0xi9VMFbcWM3h9MYPQELcZLBdNsa7h0VYJNzqSIBenYtgq1DARC/QkRzbyNdxayuK9zSIKNQ2jERmXpmO4sZCELBCcGQtDcTAaV2dinrp7+uGl/vH0WMT16AkWMuUGTrpUsGfBi4A0HaIHvZ6t4vp8wrHczYpcRcV8yr4m2C1HuR0H/BimJaxbcpEnOD1qKovnqyre3y7i5EgSS/tsMblr8wcVqFtJRBcDDW2ZiAWwvMd2Z9cMit1SHdfmEtgp1pFvetlL+xUs7Vfw2TNpbBfrWMtW8bgrabVTrLcTHa1RBSJPcHEqhoDIoVhT8XDr4HOG5V0C3rRCH2wUcGMhgVuLw9eqdDN6hJWah5uCMcT7wUwyOLCuZzrsXGHhhmE6Fiz4BtOCik1WMh4UcXo0jHJdw+JeuacciNXYnh61FnRQm/3PkYCAVEjyfJLNJIPMCkQcMb2IRQsDW6hpjqpCAvdB37Wq07byN2B6rKXmMbs8HRs4dtlJ3mNS5NZidmiamZ14SRo64SZGDvSOABmEmCJ6qhzppu4iGciClyqPQfC35BZ03s1lgcO5iShuLCSxkA61R5S+u1nsK6qxuFeGU9lcUORQbmiW3SeGQXFjIYlaQ8fSfgUXp9wH/AF35UqzySAaNq4tS7fGS3NJyyLwe+t5LO6VcWUmPlQxXZEnjgkHO24vZw+MmR0GDzYKmIqzh2ZYYCmTOgwIMWPW/eqH3TJox083h+HJ2+F7mBaUGxpenktA1Q083Cy4KiqvqjpOjYZty4zOjEdtZ6B0q4e7kYBrcX0+wTRwqkVqwO2SxJsD1pxofe6rMzHslBpYH/BCDMuC57IbWSCYiCmoNXS8ejKFP3y6P9BaOplKKFgfoFyqm7pmIBmUDkwCGI2Y3WUh2ayxNQv1Naxmq0PzLl8c7x2sNggCR5iViJw4SqUiwDeYlmzlq7bT75xIhiTL312ajjENjOpkaa8EWeCwkA6h2tCwU2rYFl172Y45JRWcTvHTYxFXLYZvreZBYI6RWMtWe3qXWRmNBBwNZkDgMJMKIhGUEBR5LO6VkCmrKNa1AzcJ1qmGLDzeLrqucbUipogYi8qgMPvKFZHH+9tFVFW9p0j+2lxiKO/Zet+Kh/iwFe+s5ZlHT7DgG8xjglePpUWhqiIs821loRZRxVTFdktFNZAMSXi6U4JqUIzHAmhoBmaTCtayVajNrXRYFnB6NOzaWHIEeM/B2Dmd5Iu7JZwZC7vSYaQA3lnN4caJpGeDGVMOnsY8AU6NhhEPSqhrOvaKdWwX6+1EFc8RnJuIYLmPnuTrixnPsT9CzHEj6YiEgMCjoRso1lScGYvgzeWso1fVMoqRgAiBI2hoBgo1FduFGvJVFfmqekC1KCz3n1a6tF+GLHBDSaqdHY8M7QbS4sxYGIYBPNjMt89br7CKDA8L32BaMGi86OFWETxHcHk6Blnkka+oeLxTxJlRduHVbjr7oNMhCTJPsLhXwdlxcwLfTCIIVdcPSHqxspAO2ZaLEADLDh0+FdXA+9slW1WmFhwBrs7GwRMOpbqG7z3Zx9XZOASOYLtQxwpjzR4hZs3s1dk4RJ6DwBHcWc7iUYfR7hag0A1qTpe04BaD0ZxLBZEOy6Zh0w3kKirWsxWs56p9tuFlnJ+I4tleCYokYDQiQxI4BAQemmEgX1Wxnf/AKNrRqViUDkt9b2J7pYYnT5knwHTClLQzO4QoRH64McfOEbutHvW6puPdjYKnHvyjnEkO+AbTErdSWv3QDYrF/TIKTQFZt4IUdnTGOB9uFXFpOorVTNWzZ5wKy22DKXCmNFhA5JEOywiI5kgF1vIbliLya3O9Ywk6+8mdRCxEnuDSdByZch3vb5vrjgQEUIP2eFZmouGDF4sEBNxZtjcm3UYzKHI4MRJGJCDi2W6pR+HHiQebBVyaiuLuesG1+K4VdjWID9bziCmipQGOKQImYgFEAxIMSpEpN7CarWA5UzlQYO5mLpATFyajB+aRlxt6O+wVU8zKk0pDd4yXnpuIIhES8b0n+/6W/DhAKfUkOtuPgMCjANNgPtiwP4kH4e5aAVdn4shWcp6e3znG4eW5D7yTfEXtUZF3olBVHUfSqg4FgjxHYPTZrsUUES+MR/B4u3ggxhwJCBA5gkyfLWq3wSzWzITe4l7Z1njdWszg8kwMqmZ6zq2b1GiEbZphNwUPzRB22Em+lRo6rs1F8Gy3jNFoABLPISDxUHUD24UaNnI15KvOoZNhFfY7KRXlq2rbmUiGJJwcCaFY0/Bkp9jeHfAcwedfHMU/+OmrCIg8NnJVz11HXmEymISQswD+LqX0i4SQ1wB8lVL6OZvH/wqAf04p/dZwlnm0FOva0Iqq51LBdlDeoPa1l4Py1mquLUTsNmHV6pS5sZA8INQxlVBctw9uF+s47dDpsuqw5e6uCkiGRJwejeD+er7vVvnUaNhS8ahfiVfr+LQEJazoJ303kwh6UiNa2q9gLCJje0jlQU6Sb28u5zAeCwzU/ulWBNqKS9PxA96lHZ0D4RSJx6XxCF6aS+DnfvCFA9qXk3EFk0Mu3XLC0Z8lhMgAvgAgBACU0m8DsFwlIeTHAAy/L+wIGZZ3CaA9J6dFVR1+QXMni3tlZD1s+R5uFfHpU2ncWswc8Lq8iDdcnIo69g3bZeT5ju34iXQI1+cTKNU0vL6Y6VtveWMhYSsPZ2dYvLTWeS0llIUhK6s4xPwMAGNR77PDgSHKDnp8oWpDx5srOVQa+pEKBVvB4mH+DIBvAPjJjp/1vSIJIQvN13zP7gUJIV8B8BUAmJ2dZVroUZJ3KbxhRVDi8c7awbvquxtFJILiwFl4O0Yicl85/26SQRGnRsMwqKnK/p0nez2P0al7T/vZbtk2jhkUOdvPz3EEL83EwXME33+Wsf0ss8mg41gIO4N5ZzmLmaTianSxm2FkLW4uJPHGUma4xecMRuit1ZxjTbDTW0zFA+AIadZ5AmjOW+IJASGmU0CIuWXmzB+AmA8DAQEFEBwwOTNMJ2YQbA0mIeTzAL5DKa04zSAmhAgAfphS+o8JIZftHksp/TqArwPAtWvXDkGfejCGFWs6Pxnt6b2lAE6Ohh3VxQdhPVthqnU7MRJ2HP2qG2YB/JOd0gEjFw+KSAQlRGQBssiBI2YZTCQg4NuPew1vJ+enYrY9yapO8cZSFpcYuptGIzJzRr0fmkERFAVX9ZLruWpPAbkVHAGuzMRxZzkLg5pfu63BtUJlHB8seFBrb9HQDccpoSwMmjx6b7OAYk1F5BC0Rt3g5GF+GcBY01heIYT8AqX0axaPfQ3Af0II+ZMA5gH8BCHki5TS9aGt9oio1Afvd7UrS6kOsRC4m5sLSaxkKphJKnhq4+mJHGFK5hRq5qTKdFjCQjqEQlVFrqoiV1F7DEw6LDn2dBOgZz61FSxtdCwTDAWH0QqPtoumIXORMJtLBR0N5qmREEp1c0vZ4u3VHOJBEamQhHBAQEAwkxd2CTIrWDULHm4VcXEqyjwZtEVEFoa2DR5UNGk9V8VPf/37+OZ/8emhrMcrtgaTUvpTra8JId/qZyyJaU2jlNJ/D+DV5s/+OoBvfRiNJeBN+aZFQORwaSpuW8P3aKuIoMQPfSSrKdybR7GuYzNfQ0wxa/5GI/KBizYZFBEPSnjEkAxoJb/6jT7t5sRIyLH06Pp8bznRILAYDYGhltCtnmb3awqcqcQTD4pQRAGiwIFSisX9XM9zO282QZHD1bkE9kr1vroEdhRcVFsUqhrztE8AuDGfwJ3lLJYzw5HfY/WG7cg1h7FJR1xK1InrsiJCyEUAJwkhFyil9wFcAvDzAL407MU9L3TDwI35JAgx597oBkVQFpi6P85PRB0NgmYAlyeiA7Ve9iOqiHi49YERzlc15KvagYz1mbEwMuUGU4wTYG89kwXOMZZ4bS7hylhS4nx1s7TYFWvOhuXxThFT8QDz9nOnWMe1+QSqDd0cd1yqY6tg/nMiKHI4NxlDQzfwdKeE7z3Zx8Wp2AFlJxbcTAVYzlTw0mz8wI3TCvPvZJ6bLPObWPCiL9vNfrnuSRN0mDAbTErpZ5v/vwdgpuPn76DLWFJK//pwlvd8WM1Uey7sEcbaO1bvlOUiduLKTAxvN8tephNK31GmQZHDW6s5XJ+Lg3Ac7ixnXQl5sMr/sxTluxVc4BiyGizHsaoajh69qlNzZC+jwVzeryBXaSBfdWcIFJHDVCLYc6xqHoacVVUdAYFDjdE7ZlEmP5EO4e5arv39TrGOiZiMzbz3pIvbpJoVgebgteeJL+/Wh341drvFOpJB54BzTLEW3ejk/e0S0mG2x3YTDQi4OhPHe5sF3FhI4uWmeEW3qC8AXJyOm3J0yzmoumFrLCdigbaWZQvWXl0Wb0c3DLw8l7AVJulcC8tWWtUpJKbHORsVN1tcAFhIuaueG43ImE+H+g6kizOcWy1iioCrMzFcm08gHWE/h1hMjaobPRJ/M8nBVNLlIfV7/70/eflIx1H0w+/06YNVHWNIFnByNIy7aznUtf6Gp+EiVjOXCrmeGX1pOoa1TKXdL24XJkiHpQMZ2ftrefAc6TGaC+kQeA54slNGMihhLqWAEAJKAZlxNs0Gg6xbZ9LBqde5VFNhULai5EhAxL5D7SnPEVuhh0RQPNB/zoLIWFcZCQh4ccKU87MqeLdLcEUCAhbSIQQEHuv5KtazVbzV3FlcnolhLcvmFbOUNPUTPh40/Nh9E/bKsIroB8H3MPtglf1czVbNcpfpuOVz3XSAuNmGRQICLk/HcHctjwxj+ct8KnSgY0k1aM9Mlam4AkopnjTnRWcqDby1msebKzm8tZpjFkRwO7lPpxRnxqw9tGJdZ45XhWXn9z4zFrH9vRd9RhZ1pWtzCYCaNza7xFL3+0dkHi9ORHBqNIxiTcPdtTxuLWV6tEMFFyMqWVpyF0Z6/yaDzm6PBb3tpLr59bfWYQxJR9MrvsHsg1NpzKOtIqxyIevZKiIOsvnJoGgq9XAEEYaL/cJkFAGBH8pIh/1yAy/NxnFuIoqLU1Gs5+zXy+obvDgRwVwyiDNjYYyEneO9720UkLC4kJIh05NgvTZYFGsCDuUxNVWH6NITWs1UcaE5JvnkSOjA7+aSCs6MmW2wLOVbnW99eTqGckPHe5tFx4Lzt1dzzKGd/XLDcRLAneVsz8C7QRMtw0rU/E/fX7FtYz0KfIPZh37Jk04KNQ0XJuOWv5/r8uIiMo8r03Fcn09gOqEgU1Hx1koOd9fyOG3j+YRlAdfmEri/UfCkntTPA8pVVLy5kkO+2mhvke22gyzF2TxHzGxxqY6aakBzENbgCHB+MtZ3S54OSziRNr0cg3GCF0ut4FurWVvDooi865ZHReQRlAS8s5bH0n7lgKGpNqXuWHl7NYfPnEnjykwc76zlmW8WukFxso9XaMVUXHE0mm+v5jAV/6Cl0ssI407cxobtGKaYsRf8GGYXqm4wtS3qNnfNuCK2tR13i3Us7VfwdkfmsROrreD5ySi2CzXPQh0TsYDtDJbx2AclNPc3CpYlJ4+3SxiLyti2KJdRJB7pkNQuQ6mpOk6PRdBQDeSqDewV6yh1beleGI+0qxAW0kGMRU0x5I1cFVuFejuuy9pxJTHEWVWdYiahYL/c6FuLOJMMMqvFnx2PQOI5vLdVgNo0Jrphdiddn09gM19ljiu2OD0awa2lLMajMsIS33PM7FjOVHo0P61YzVYxGQ9gw6YaQNUppuLB9vkxiNThC2Nh17FhK/73r8zh1ZPpobyWV3yD2QWrcMW99XxbeWY+FcRIRIZmUKzsV/DdJ+xzYd7bLBy4QBSJx8uzCTzeLrpOCHUyGpFte57vLOfa0xJ1g+LNlRwuTcewW6z1lJDMJIKWBvPS1EFPsXPcbgtJ4HAiHcJ+qYHdUh2JoIipuIKdQg2LexXIAt+jqKOIHHPLI8/oGr61msf1+UT7JtYJS0Ze4AhemkscSLR1a2K+sZQFzxGMxwLmrB2Jh8Bz0HTjwA3p9GgYiZAIwzCLyd9czYJSYHGv4nqK5Va+hquzcVsBkk5YjldnfL1Y01yVL3Uiu4htSzwHQnrHL0s8h7/0+dP485856fr9h41vMLtwM8r0zHgEVVVvz+32gqpTXJo2i9jPjJkB/u8+2YPEE9xYSOK9jYJrPcqFdIgp3nl72ezXXs9VsV9u4G5z3sr1+QSKNQ2abiARkixLi16eS+ANhkL0hmbg4ZY532YsKuP1xQ9qQdPh/h1H8+lQe6RvMiQiU7b+u7iJkL2xlEUiKGIuFTxg7N5ZzWM+FUQiJAEUeLRVQKWj82YqoUDmuZ6qhEylgYV08MBoYt2g2MrXsNV1w7q5kERd1VFrHg8r7qxkMZ8Kujqn3Gybo4oIOLRiruUqB0Sc0xHZ9dRIkSN46kL044cvjuMv/7EX8Au/cQ/f6dAj+LkffAFffu2Eq/c+LPwYZhduMoLFmjYUoY5yXcONhQRqDb3tFTZ0iluLGRAOrkfABl1Ilt1dz6Ou6W2hi1JdwxtLWTzcKuLJbhlvLGUtZ7Qv75ddjRXIVVRsF+oHypoW0qG+Bi8ZknBzIYmTIyFkyipenktYFi27naiZrajIlBs9tY9L+xW8tZLDg80CLnRUQlyfT2C/WLfsjhqJsEmoZSsNvL2Wd9SnpNS9SDFrXmUsIjvG6AEgU1bxwvgH8XUvJT2qQXF2Isr8+D//2ZOYTQXxz/7sDfzyn7qKH78yif/2P7yE/+zTC67f+7DwPcwu3AgHD2uEausCujwdw0rXXbxQ1XBrMcOs1H5hMtozoteJUl3Hs90Szo5Hei7msMwjJAlIhaQDtY7JkLmtDssictWG5ymF3UH8dFjCdDyI73WFNe4sZ3F6NIxcVe2pJ1zzoFZUrGk4NxHpu+6GZqDW0DGfCiIk87bKSgB7UsPVwC+XGail/TJ4Aji9RTIstT13JzqrD7zOznESim7x6skUzo6bxpUQgh+9NIkfvTTp6T0PE9/D7MKNt7JbrGMuFcSp0ZDzgxl4Zy2PS9P9ZbCmGJSlOeIupNBJqaHj4VaxXRt5bS6BRFCEbgDffbKH/XIDI2EZp8fCOD8ZRbGm4Z21PEYjMnIVFYrIYyEd7ClJsSMZEvHuxsGL98RI2DJB9ninBIkniAYEJIIiXp6L45WTSc8K5u9uFnFipP/fLh4UsbRfwYMNZ+Py/naRyatPuOjmcStWUWnothUXLdy0FnZu870Un4sc6Ylnh2UBf+zcGP7Wj5/Hn//syfZ5/Wc/eXy8SDt8D7MLN506gBn0ZzFmrKxlK4gpwoEe5XRYwlbB2Zt9adZdsqAfpbqGKzP9+8J3S/WejGmh2ctdVXUs7lWQYqjBbHFyJIw3ygffR3dwkdZztba3e2c5B8DcvnsdLGblGX/78R6uzyccvUvAjPPNp0KOw7vcoHlor2FpHmCRzOsH9VBLqRoUqZCEiXgAnzkzgtdOj+CluURzIqXJz33hBbyzlsOVmbindR01vsHswosi9nquitGI7GnOSze5itpMpnxwoXbKpgkcEJQFRGQBqZA50RHEVJXxOjqhk41cDapF22c/umsu31nN4fRouG+/dCfJkIQ3u4xyMiRhLeu8hetugzTjnO4N5kI6ZDsj/s5yFldn4kxji8MBZ2PFqgPqFaexIIA7g/lgo4B0WMJeyZyxw1q6lA5LeO30CF47M4JPnU4jbXMT5TiCq7Psu5LnjW8wu3h5LgFJ4FwX6yZD4kAGMyjxuDAVxdJepcerKdc1zKeC2Cs1UKprKFTNf93KOmFZwHRCcZ3N7CYZkphr78LSwW2mqlOEZMGxlvDUaPhAxvnGfBJ313KeSle8FFbPJYPYLdZtY9YGBZ7tlTEdV7DmEK9mGaGQrajM2W/Rg8iEynAcnAandbOQNvUOnu6WbeXhQhKPH700iR+/MombJ1JD6x8/bvgxzC4W0iH8xR845fp5QZuRp3ZEZAE3F5LgANxazPY1urphZnCdtB9LdQ08RwYatnV6LIyaxl4p0K9o/O3VHE7a9ImHJB5vdUwQvDhlaoh6MZYAewzxwBpknklLM19VoUg8nMo0l/YrTJnkUcaM+turOdeZchbn0a0d6xTjeHMlh5t9KjZ+4sokfv//9Fn8nf/oEl49lf7IGkvAN5h9+c8/c6KnN9gJt7JTiaCImwtJ6IaB1xcztt7Y8n6ZuV94eb+Ci1NxV2tpcWM+icfbpZ5ibDsaqoHxPpMJn+6UcXM+gevNfy/Pxk0hCpiqT50ZY68Z2BZV1cBFhvk/nbgZvfB4p4SrcwlcnY3bPo7lnFnLVqAwdCYZ1Kz9dIPT3C0vPNwqHkhWvb6YaSf2To+G8atf/gT+/k9fxeiA0yk/LPhb8j7IAo//209ewp/8H/+I+TmsHULxoIgXxyN4cyVrK2/WSVU1cHY8yNz5c3s5y6yu3ULkCAgHV2MMADN+2693vFTXkK9pB8qUzjVr8px6zb3gJgwxnwri4ba7Wd23l8zpkgTWhfIsx20jX8OLExGm0p5CVYUsEEspQUXiD0xjnEsFHbfyYrMhov05mi9NQZsTHnvfiwOBTj/4jchz+Bt//Dz+1M1ZT6GDDzO+wbTgxkISX7oxi1+9tcL0+Mc7JZyfjFr2I88mFYxEAri3nkddMywvAiveWjVbF+8yKhY93S3j3EQUqm6AI+jbz8sRs8ykoVOMRQN4/VkGp0fDyFdVpnisxBNEFQFPd/snM2SBQ0DgIHAEpYYOkSfgSK++optRC1as56qO42RPjISQrzSgG7TdoPDSbBwNzUBdM9qF7ARmPa7AcVjPVdsiJhLP2XYVPd1j62qpM87uebpbxvW5BGqaDo4QEEJgGBTFuoZMuY58VTvQaOGkCToMfuTiOH7xR89hIja8ypAPE77BtOHnf/gsfu/dbewxJEB4gr7b5nMTEXAcwf31AlaaMv1vruRc9f622MhVEZJ4lBm6kfJV9UCh+0I61M7SRmQeL07GsLRXxkI61AwJmEbr8U4JQZFj6meejCuWxhIw60pPpEPtDpl31vK4MZ/oKY6va0bfonk3TCcULO9br2U+FURN1RGSBUQVEUGJx2qm0uOFi/xBoeGAwOHCVBT31wuOcepCVWNK6rBMumytJSQLeGPIs5+8sJAO4W/88fN47czI817Kc8U3mDbEFBFf/bFz+Nlffcv2cZemYshUGviD9/dwdTaOu6s5XJ4xR0O8a7H1erJdwkhYdqUEs1dqMNcGdpMIitCSCsajAdxfL7Qz1JWGjhvzSTza+sBzragGbi9ncXY8At2gliVCVsF9niM4MxZGSBJ6jO6tpV6ZtcW9Mq65KHjvRhE5ZMsN206apf1Kh8q79fa9+zVqmoH3Ngq4MhPDk50yzo6H8XCr93iIPMHl6Th2itaCJ0GJx3wqCIOa5WvdSlWjERnJkISYIqLa0LGUKdu+3mEi8RyuLyTwqVMj+PTpNM5NRF1n2D+K+AbTgR+9NIFfu7OGb7+/2/O7iZiM8ahyoE5vPVvFaDTgGD8s1jXMJIOupbPeWMp68sbeXs1hNCr3GNtSXcOtpQxiAaHHu2q9R0uNaTVTPaCx2X35tIzGs91y3xhdMighFhR6xsnyHMFGrtqe5T6dUPqW/FyejkHgOPAcwZPdUrv2sqoatuGQEyMhPNst943PsaBT4O5aHucnY3h/u4hXT6RQ13Tc2yi0S5quzsbx7nqhJ3kXU0ScHg2jrul4b7PQvoEKnHlcY4oISeCwvF/pq/SEuKcle+LseASfPp3Gp06P4MZ8EorLyoOPA8RLBf8wuXbtGr19+/ZzXYMTy/tl/LFf+nb74ghLPM5NxnBnpf8ExtaFz8KNhYTjLO9uZpMKtgp11/WHdkbl5bkE09jf6YTppTZ0A3vFOjY6FHmctvEnR0IYich4ayV7IIZ7fjKKtWwVPCE4NRbGdr6GmCIeUNdeSAeRr2htQePOpNbFqSh2CnXMNmtVl/bLoNSMz748l0CloUHiOeyU6gNNLwyKHCaaYYhL01E0NKM99O71xQwErnO6qHk72crXPJppE9YEkRdeGIvgxkISNxaSuHkiyVzy9FGBEHKHUnrNzXN8D5OBuVQI/8fPnsIv/dv38dJsHM/2yrbzte+u5XrEKqx4ayXnuth8JVN1HCLWj5Dc/8/98mwC9xml/9ey1fZar83F2wZzJqkcqK3sh0GB7z/rXXNUEVHZLkJtKjQBpjc6GQtAFjmkwzLuLGd7lJFOjoQgC1xbOb7VUx6SeCRCErbyVdfHyI6KarRjtgSkZ2uuGRhoHO1hwnME5yejuDGfbBvJ+JBm7Xyc8A0mI//5Z07g7dUsfv9R79a8m5pq4MJkiMlgqjpFMii57s5xuzMw42K92ejL06an7IWqquPkSAib+RomYgFH720rX0U6LIHSzpIWituLGahd1rBzNEan1mQLu5BHuaGj3BiOktSHFYnncGUm3jaOL88lLG+YPuz4R5CRgMjjz3xygclgAqYI7FxSwTLDFvDuet5yOxuWeMSCUltKThYIPnEihT94f6/nsUGJR6VPBj0o8ZhOBA6MuQXMTeMgpSgPNooQOIIrMzG8wRBWqKoGqurhl758HAmIHK7NJXFzIYmbJ1K4NB0buCHApxffYLrgM2dG8MWLE/jmvU3Hx1LaHC/KGDPbyFUPKFwDpkE7Oxk9oLd4YTKOB+sFXJtLoFhToRnUVEc3KEo1FRenoni4VWwnb16eTaCqan1lytJh9yra3WgGRU0zBorT+bhHFji8PJfAJ06k8MrJFC5PxyFZjTL1GRq+wXTJL/7oOXzr0Q5TLeTdtTxeGIvgEUNXSbZqakp2vu71juTRjfkEbi1lUayr2Cs3MJ8O9S1Gv7dewKWpGBq6gZ1CDUv7ZUsvcrdUR0QWXI/A6GQ0IuNdl4LFPu6ReA5XZuN4pWkgr8zEfQ/yOeAbTJeMxwL4Cz9wCv/d7z5iejzrTOZzExHslxpIGBQEwERcOZBpv7dROJB9v72cxbmJSN86z+7ZzdGA0HeUhlkE795YTsQCmE0GYVCKh5sFV2MqfNgwQx1x/MDZUfziF+N4aS7hG8hjAJPBJIScBfB3KaVfJIS8BuCrlNLP9XnclwD8lwCiAP40pfR41wu5xDAo/pe31vH6s30oEs80/+fxTglXZ2J4a9U+C11XjQMdIqtdW+VqQ+8pVdovN6CIXE9dYzfzqVCPEQXM7LSdp/zSbBxCc/BXKiQhJAtYyVQwnVCGmn32MbPYl6ZjeOVECp84kcK1+YRnBSyfw8PxL0IIkQF8AUAIACil3yaE9DSSElMqpUIpvUkI+XMA/gaALw55vc+NN1ey+Oq/foB7TcPjpuNmr9ToiU92EpR45pnYnWwX6rgxn7QtcQKs1YB2CvZdJO9uFjCfCuFas0Zzv9zA5ZmYp04jn4NwBLgwFcMrJ00DeX0+ibCfxT72sPyFfgbANwD8ZMfPeoJi1Kxz+dfNb98A8IrVCxJCvgLgKwAwOzvLutbnQr6i4r/57ffw/729euDne6UGs7LParZqa9jOT0Y9G6HbyxlMxQM9YsKd7Jf71wZenulVNEoERWSbYxtqqoHlpkfZ+pjvOHjKPv0hxPw7f2LBjEFeX0giGnA/idHn+WJrMAkhnwfwHUppxaXW3ucB/D2rX1JKvw7g64DZ6ePmhY8KSin+9dsb+L9+872+4huLe2VMJRSsM2aZH20X+26fX55NuBus3YVBnVW0+ykPhSS+Z+rl1dk47q/lcW3e9CgpBWYSCmqMYhE+B3lxItpO0tyYTyLmYgiaz/HEycP8MoCxprG8Qgj5BUrp1+yeQAg5BWCZUvrukNZ45DzdLeGv/+aDA8Pk+xENCFhnfM18VcUrJ1L4o2fm+FiOoD27Jx2WbHUPnXAqJgkIPIo4mNw51+XVvjAewd3VHHRqaj9enY1D4Ii//XbBmbHwBwZyIYVkyO+k+ahhazAppT/V+poQ8q1+xrIZu4xSSvOEkDEAlyml/4oQEjZfgh7u5KchUm3o+B/+4Cn+8beeMM2QLtfdeV4NXUcyJEE3DEzElLYx2is1XPWfdxMOiLBT4NH7CPZ2FrgrEo9yTTsw09qt9NzHkZMjIbxyMoVXTqRx80TSdtiXz0cD11FmQshFACcJIRcopfcBXALw84SQvwjgdwFohJD/M8y6a1eN7c+T33+4g6/+5gMkQxKTsQRMpR9WfcqAyOH+eh5nxqLYLtR61IYerOcRD4qWY1+tuDgVayeirDg1GumJn0oCB5En4DnSVIDPuXrfjyNzqSBePZnCKyfTeGUhhZGobyA/bjAbTErpZ5v/vwdgpuPn7wD4UvPbK0Nc25Gwnqvib/zmA/xv724DgKthWplyAyGHx0/EAphOKAjJAr71aNfSuJUbOq57SP6wzKLuF4N9ayWHU6MhpEKyXyJkwUQsgFdPpptGMoXJIc6f9/lw8rGtY6hrOn7lu4v4h//uyQEF7IdbRWYlIIknkEWur4cZlgWcm4ji9nIGm/kaXp5zFsi9s5x1rVzklJCJB8W24nk3T3bKGD0ht2dPf9wZicjtGOSrJ1OYTQYPZbCYz4eXj6XB/MOne/hr/8s9y1ECd5azTMXmDZ1CFjhcn0/gvc0iSnUNp0bDSIUkPNs9KAEnMKhVGxRIhdiVi1jingvpkG088t5GAamgBITxsTOa8aB4wECeHAn7BtLHlo+VwdzK1/C1334Pv/XOhu3jNIM6zmVpsZmvYzNfx2fOpPFws4gnOyU86fM41vz3O2t5XJmJ4+0OFfd+JEMiHm31L3bnOYJzE1EEJR4cR3B5Oob3t0t9Z8kUaxpeGIugnjMwGpGZhp99WInIAm4sJM1EzckUXhz3xy74uONjYTAbmoFf+e4ifvnfP2ZK0ACm0bQbqdpJQODw1kqub7+2F4o1tWdcRDeZsopLU7EDLY/psIQTI2E82y31xEpFnuDiVBSyyLcTSxwBOELAEYLNfA3jURljURnbhY+G0VREHtfmE+045PnJqOv58T4+nXzkDeZ3H+/hX9xaxm/f23L1vGJNw+mxMB73UQTq5uK0c7vgm8sZjMcC2Mo7D7V6ultm2m4nQiJOjIQQEHhIPIe76znslfo/R9Vpjx5mN1uFOsYiMvM6jxuSwOGl2ThePZn2Jc98DoWPrMHcLtTwN3/rXXzz3iZ4Aqbxp90kGCT8CYANm7bEFpphth2yGqKKg4pQMiTiD5/s9yiVD8p2sY7RiIyJWACbx9xoCi3BipMpvHoyjZd9RR+fQ+YjZzA13cA//aNl/NLvvY9SU+dRp0A44P6jZhnUyK8ziF+0CLlQn3EacHZ6NHJo5UA7xTpGwjIm4wGmm8FRwRHg4vQHmpDX/LELPkfMR+pse3s1h1/49Xt9lX/urxdwYTKK+y5UgZ7slhBVBBSq/b29eFDEu5vsr2c1x7sfIVnAbDKIlUyvV2xOfzxcEYzdUh2pkOSqX37YEAKcm4ji1Zaijy9Y4fOc+UgYzFJdw//9dx/hn/7Rkq16UK6qgudI39G4/aAUOJkOWZYXnR4NH1qvtWFQrGQqWEiHEFMEvL2aByFNj/aICs33yw2kiXSk2/PTo+F2mc8nTqT8yYY+x4oPvcH8nfub+Fv/5r0e5Z1+rGWrrnQsATNZcnMhCUrR1rSkoOA5gnccSn+6yVfZ2x61plFfbBadzyUVKJJwZMayxV6pgbHI4WXPpxMKXm3GIF89mcJo9OM1G9vnw8WH1mCuZir46m8+wL9/uOPqeQ/W88wzwwHg/kYBisjj1GioJ8vsdjY4i1Fv0e0Fs0yfPCy2i3VMxIbTEdTqpvnkKdNIziSDQ1qlj8/h86EzmKpu4J985xn++3/3GDWH0Qz9qKgGXpwMuhovq+oGlvq0F76+mMHZ8UiPkIYlLhLaqu7+sx0mm/k6puIKkiEJGRfHLqYc7KY5Nep30/h8ePnQGcyaquMb31n0ZCxb3FnO4cRICM922ZTnzk1GcXetfxyzVNMci8xbRALsExpZFZOOkvVcFTNJBQa1VlUKSTxuLCTbtZDnJvxuGp+PDh86gxkJiPjqj53DX/qXbw/0OpwLL0e2KX5ey1WZtSwbLrzG4+ZhtljNVDGfCsIwKAo1DZLA4eXZBD55yvQiL03HIfrdND4fUT50BhMAfvzKFL55d7MtyeaFJzslXJtL4PaycwLIKav+eLvINH/cTY153aEO83miU4qvvHYCV2cTfrG4z8eKD6XBBID/yxfP4Vvv7zoWeNvx/k7RcmZ3J051iNmKilOjYc/r6IQQs8Mo5TJWeNicSIfwQxfG8SMXJ3B+MurHIX0+lnxoDeZsKoivfPoEfvn3+2kDsVGoakxlRmPRALaHrOJzYyEJUApVp6iqOsoNDcWahlxFRabcOBbG8sxYGD90YQJfvDiBM2N+ssbH50NrMAHgL/zASfz6W+uuynW6ubOcxUxSwapN2c6DzQLCEo+SjdJRscZeY5kMiUdeT8nKuYkofvjCOH744sTQvGYfn48KH2qDGZQEfPXHzuEr//yO59cwKJrtdnZDxCgmEwreZ1Ausn8vM4g5GVOQKbub3XOYXJ6O4YcuTOCHL4xjPh163svx8Tm2fKgNJgB84fw4vnBubKAE0IONAk6PhvF4x9oghh1EHkKSc49zK3l0HAQjXpqN40cuTuAHz4/7xeM+Pow8/yt3CHz1j5/Hdx7v9VUUZyURtDd4b67kcHUmjrcs2iF1uyb2Fs2HuCkvGhaEANfnkviRi+P4oQsTGI/5LYg+Pm75SBjMqbiCn/3cKfy3v/PI82uw6EouZ6wL3Z/YeKdtiGm4NgeIubqB5wg+cSKJH7owgR88P4bRiG8kfXwG4SNhMAHgz31qAf/mnU1Xcmud7BSc1XgyZRVBkUOlq8toIR1qi2Q4cW3OnfiHW3iO4NWTKXzx4gS+cH4cyZCv9uPjMyw+MgZTFnj87f/wIn7iH33PVYF4i/VcDadGw46eYj9PdCQsMxnMZFDCuy70OFnxjaSPz9HwkTGYAHBpOo6fvjGLf/H6iqfnByXnjpV+Pd6UUVUjFZKw3EcQ2AsiT/DJU2l84dw4fuiCbyR9fI6Cj5TBBID/8vOn8VvvbKDoYYLjg/U8YoroSrcScG6dBICLUzG86VI/s5uFdAg3F5L45Kk0PvPCiK8+7uNzxHzkDOZoJIBf/OI5/JV/ddf1c3UKjEZk1wYzGhBxfT4BwFRppwDQFBqmzW+86FlOxAL49Ok0PnkqjVdO+OK6Pj7Pm4+cwQSAP3l9Br/zYMu1uDBgrxJECPqOwNgv1x1H2LIQknh84kQKnzqdxqdPp3FyxG9H9PE5TjAZTELIWQB/l1L6RULIawC+Sin9XJ/HnQHwUwAqAH6LUvr+UFfrgrGoPPTXJHClAewIR4DLM3F8+lQanzo9gquzvjSaj89xxtFgEkJkAF8AEAIASum3CSGKxcP/AYA/AUAF8KsAfnJI63RFXdPx2/e2hv66xMrFdMFsMojXzqTxqVMjeOVkCjHFj0P6+HxYYPEwfwbAN3DQ+PVI6TSN6ElKaan5/QIhRKCU9mRfCCFfAfAVAJidnfWybltqDcN1HLKF3RbYy+Y4JPF45aS5xfZHNPj4fLixNZiEkM8D+A6ltMJwkScAdAbyNAAjADa7H0gp/TqArwPAtWvXhj6LISTzA0w5tF4Oq507Ox7BZ14YwWfOjODaXBKSjWK7j4/PhwcnD/PLAMaaxvIKIeQXKKVfs3jsPoDONG4QQG7gFXqAI8RzLNBudIV5HHoNakgW8MWLE/jMmRG8dmbE79P28fmIYmswKaU/1fqaEPKtfsaSmFYkSinNE0KWCSFBAAaAVUrpc5kNy3EE/68/cx0//fXvu5oO6UTLlBJiFsl/5rRZD3lpKgZR8Mc0+Ph81HFdVkQIuQjgJCHkAqX0PoBLAH4ewJcA/FUAfwVAHcB/NcyFuuX0WAT/ny/fxJe+/n1kLSYcuiEdlvHZMyP49Jk0PnUqjVR4+Fl4Hx+f4w2hA2Z9B+XatWv09u3bh/b6Dzby+FP/5HXmJFBr/K7IE1ybS+K1MyN47UwaL47742J9fD5KEELuUEqvuXnOR7JwvZPzkzH8T3/uJv7UN77v2C45mwzih86P4+psAq+eTB0LoV8fH5/jw8fCIlycjuGf/dkb+NO/cgul+gdGUxF5vHIyhddOp/GZF0ax4I9n8PHxseFjYTAB4OpsAv/0z17HL/7GA7xyMoXPvjCC6/NJf6a2j48PMx8bgwkAL88l8dt/6dPPexk+Pj4fUvyKah8fHx9GfIPp4+Pjw4hvMH18fHwY8Q2mj4+PDyO+wfTx8fFhxDeYPj4+Poz4BtPHx8eHEd9g+vj4+DDiG0wfHx8fRp67WhEhZBfAMsND0wD2Dnk5H4Y1AP46jtsaAH8dx20NgPM65iilI25e8LkbTFYIIbfdSjF9FNfgr+P4rcFfx/Fbw2Gtw9+S+/j4+DDiG0wfHx8fRj5MBvPrz3sBOB5rAPx1dHIc1gD46+jkOKwBOIR1fGhimD4+Pj7Pmw+Th+nj4+PzXPENpo+Pjw8jvsH08fHxYeRYGExCyD8hhKwQQpYIIRlCSJgQ8oOEkN8ihLxBCBnrevxc83FLhJB1QsjXmj+fJ4RsEEK2CCGfP+x1WL0nIeQ/I4T8DCHk5wghro6xh2PBE0K+QQh5RAj5V4QQ6Tkei57PPcixsFnH5wgh3yGEPCOE/HDX44d+brhdg9X7PYdjcZTnhtPxGOq5YbEGhRDy1wghP9/n8cM5Lyilz/UfAALgZsf3v9T8/4vN//81AH+86zkvAZCaX/8nAF5ufv1XAYhHtY5+7wlgHsA/a379nwL4qUM+Fq8CGAPAA/h9AH/ieRyLfp97kGPhsI7/oPn/zwF4cJjnhpc1DPu8GOBYHOW5YbeOoZ4bVmtofv0FAH+9z3OGcl48dw+TmrwOAISQSwDeaf78veZDigD+bddz3qSUNprfvkQpvdO8e34BwDIh5D8+inVYvOcXADxufv0AwI8c5hoopX9IKd2mlOoA3gKw+TyOBfp/bs/HwmEdv958yBsANrueM9Rzw8sahn1eeF3HEZ8bluvAkM8NqzU0aVg8ZyjnxZFPjSSE/DUAZ7p+/BuU0t8A8GMA/h8dj/2PYHozqwB+o89rBQGUAaB5MD5HCJkG8E1CyBuU0vcPcx393hNm/2q2+ZAagPHDXEPH73kAIUrpd5s/OtJjgf6fm/lYuF1Hkx8B8LctXsvTuTGMNQx6XgxrHR2vdSTnhs06Bjo3PKzBkkFsBvAcDCal9L+x+XWSUprpeOz/TAhZAfBV9DESAH4YwP/a9fprzfjEBQCWH36Y6+h6z10AieavIgD2j2INAH4awN+0WddhH4t+n5v5WLhdByEkDdMI/EuLx3s6N4a5Bq/nxbDXgSM6N2zWMdC54WYNDHi2GcAxSfoAACHkFPov9hlMlx2EkEjzbtniEwD+qOM1SPNLBcD3j2Idfd7zdwGcb/7sHIDfOYI1/ACANyml66SZjHkOx6Lf5x74WPRbByEkBOBHKKW/QggRCCGpwz433K7hMM4Lj+s4knPDYR2Hcm7YnJ+djxnqeXHkHqYNPwHgnwPtD/HbAP4dgDyAv9V8zH8N4Fsw3WcJQIPSZhSYkFcA/CNCyK8B+B6ldOOw10EIyfR7T2Jmkf8czG1G323SENfAA/iHAHabX/86IeT3+q3rMNdBKf1m9+emlOpDOBbd65ABfBNAhBDyXwCIwQzoH/a5wbyGQzwv3K7jqM4N23Uc4rnRXkNzHQLMRNd5QkiCUprFkM8LvzXSx8fHh5FjsyX38fHxOe74BtPHx8eHEd9g+vj4+DDiG0wfHx8fRnyD6ePj48OIbzB9fHx8GPENpo+Pjw8j/3+1s7/izZWrBwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ct_town_boundaries.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task: To plot sampling locations of all annotated CT tiles on CT town boundary map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1: Get centroids of each annotated tile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-59b322ba9c75>:1: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  ct_tiles_centroids = ct_tiles.centroid\n"
     ]
    }
   ],
   "source": [
    "ct_tiles_centroids = ct_tiles.centroid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make centroids of tiles a new GeoPandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-00cd2c42220a>:1: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  ct_tiles_centroids = gpd.GeoDataFrame({'tile_name': ct_tiles['tile_name']}, geometry=ct_tiles.centroid)\n"
     ]
    }
   ],
   "source": [
    "ct_tiles_centroids = gpd.GeoDataFrame({'tile_name': ct_tiles['tile_name']}, geometry=ct_tiles.centroid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Append train/valid split information to the centroids GeoDataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct_split_dict = dict(zip(\n",
    "    ct_split['tile_name'], ct_split['training_split']\n",
    "))\n",
    "\n",
    "ct_tiles_centroids['split'] = ct_split['tile_name'].map(ct_split_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>tile_name</th>\n",
       "      <th>geometry</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>995820_ne</td>\n",
       "      <td>POINT (-72.75458 41.72187)</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>990755_nw</td>\n",
       "      <td>POINT (-72.78196 41.54348)</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>980760_sw</td>\n",
       "      <td>POINT (-72.81850 41.55032)</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>975745_sw</td>\n",
       "      <td>POINT (-72.83671 41.50915)</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>965765_nw</td>\n",
       "      <td>POINT (-72.87334 41.57086)</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tile_name                    geometry  split\n",
       "0  995820_ne  POINT (-72.75458 41.72187)  train\n",
       "1  990755_nw  POINT (-72.78196 41.54348)  train\n",
       "2  980760_sw  POINT (-72.81850 41.55032)  train\n",
       "3  975745_sw  POINT (-72.83671 41.50915)  train\n",
       "4  965765_nw  POINT (-72.87334 41.57086)  train"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct_tiles_centroids.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert CT town boundary map from filled polygons to only outlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct_shapefile_town = ct_town_boundaries.query('NAME10 != \"County subdivisions not defined\"')\n",
    "outer_boundary = ct_shapefile_town.geometry.unary_union"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# plot inner county boundaries\n",
    "for i, row in ct_shapefile_town.iterrows():\n",
    "    if isinstance(row.geometry, MultiPolygon):\n",
    "        for pol in row.geometry.geoms:\n",
    "            plt.plot(*pol.exterior.xy, c='black', linewidth=0.5, alpha=0.3)\n",
    "    else:\n",
    "        plt.plot(*row.geometry.exterior.xy, c='black', linewidth=0.5, alpha=0.3)\n",
    "\n",
    "# plot outer boundary\n",
    "plt.plot(*outer_boundary.geoms[0].exterior.xy, c='black', linewidth=0.5)\n",
    "        \n",
    "color_dict = {\n",
    "    'train': 'tab:blue',\n",
    "    'valid': 'tab:green'\n",
    "}\n",
    "\n",
    "full_name_dict = {\n",
    "    'train': 'Training',\n",
    "    'valid': 'Validation'  \n",
    "}\n",
    "\n",
    "# plot labeled ground truth points\n",
    "for split in ct_tiles_centroids.split.unique():\n",
    "    subset = ct_tiles_centroids.query(f'split == \"{split}\"')\n",
    "    subset_xs = [point.x for point in subset.geometry]\n",
    "    subset_ys = [point.y for point in subset.geometry]\n",
    "    plt.scatter(subset_xs, subset_ys, s=30, c=color_dict[split], label=f'{full_name_dict[split]} set locations')\n",
    "\n",
    "plt.legend(fontsize=18)\n",
    "plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
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