{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# raster to geodataframe #\n",
    "\n",
    "import geopandas as gpd\n",
    "import rasterio as rio\n",
    "from rasterio.plot import show\n",
    "from rasterio.features import geometry_mask\n",
    "from rasterio.mask import mask\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import glob\n",
    "import xarray as xr\n",
    "import rioxarray as rxr\n",
    "\n",
    "# path for output\n",
    "outputPath = r\"C:/Users/jtrum/Desktop/wb_outputs\"\n",
    "os.chdir(r'C:/Users/jtrum/world_bank/data/')\n",
    "aoi = gpd.read_file('aoiLuanda.geojson')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fathom  \n",
    "**IHME (access to improved water sources)**  \n",
    "**IHME (access to improved sanitation)**  \n",
    "**IHME (reliance on open defecation)**  \n",
    "HDSL (children)  \n",
    "HDSL (elderly)  \n",
    "HDSL (women of reproductive age)  \n",
    "**WSF 2019 (built-up area)**  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IHME (water)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_17780\\2764959518.py:1: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\n",
      "  improved_water_sources = xr.open_rasterio(r\"C:/Users/jtrum/world_bank/data/IHME_LMIC_WASH_2000_2017_W_IMP_PERCENT_MEAN_2017_Y2020M06D02.tif\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File saved to improved_water_sources_gdf_aoi.geojson\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>pct</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>-9.312501</td>\n",
       "      <td>13.187516</td>\n",
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       "      <td>13.145849</td>\n",
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       "      <td>POINT (13.14585 -9.31250)</td>\n",
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       "      <td>POINT (13.14585 -9.27083)</td>\n",
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       "      <td>13.187516</td>\n",
       "      <td>36.074020</td>\n",
       "      <td>POINT (13.18752 -9.27083)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9964425</th>\n",
       "      <td>-9.229168</td>\n",
       "      <td>13.104183</td>\n",
       "      <td>32.597126</td>\n",
       "      <td>POINT (13.10418 -9.22917)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9885114</th>\n",
       "      <td>-8.729168</td>\n",
       "      <td>13.479183</td>\n",
       "      <td>99.981781</td>\n",
       "      <td>POINT (13.47918 -8.72917)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9885116</th>\n",
       "      <td>-8.729168</td>\n",
       "      <td>13.562516</td>\n",
       "      <td>98.460533</td>\n",
       "      <td>POINT (13.56252 -8.72917)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9878503</th>\n",
       "      <td>-8.687501</td>\n",
       "      <td>13.437516</td>\n",
       "      <td>99.396202</td>\n",
       "      <td>POINT (13.43752 -8.68750)</td>\n",
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       "    <tr>\n",
       "      <th>9878505</th>\n",
       "      <td>-8.687501</td>\n",
       "      <td>13.520849</td>\n",
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       "      <td>POINT (13.52085 -8.68750)</td>\n",
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       "    <tr>\n",
       "      <th>9878504</th>\n",
       "      <td>-8.687501</td>\n",
       "      <td>13.479183</td>\n",
       "      <td>99.813766</td>\n",
       "      <td>POINT (13.47918 -8.68750)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         latitude  longitude        pct                   geometry\n",
       "9977647 -9.312501  13.187516  33.094032  POINT (13.18752 -9.31250)\n",
       "9977646 -9.312501  13.145849  37.536205  POINT (13.14585 -9.31250)\n",
       "9971036 -9.270834  13.145849  36.304882  POINT (13.14585 -9.27083)\n",
       "9971037 -9.270834  13.187516  36.074020  POINT (13.18752 -9.27083)\n",
       "9964425 -9.229168  13.104183  32.597126  POINT (13.10418 -9.22917)\n",
       "...           ...        ...        ...                        ...\n",
       "9885114 -8.729168  13.479183  99.981781  POINT (13.47918 -8.72917)\n",
       "9885116 -8.729168  13.562516  98.460533  POINT (13.56252 -8.72917)\n",
       "9878503 -8.687501  13.437516  99.396202  POINT (13.43752 -8.68750)\n",
       "9878505 -8.687501  13.520849  96.345879  POINT (13.52085 -8.68750)\n",
       "9878504 -8.687501  13.479183  99.813766  POINT (13.47918 -8.68750)\n",
       "\n",
       "[116 rows x 4 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "improved_water_sources = xr.open_rasterio(r\"C:/Users/jtrum/world_bank/data/IHME_LMIC_WASH_2000_2017_W_IMP_PERCENT_MEAN_2017_Y2020M06D02.tif\")\n",
    "improved_water_sources = improved_water_sources.squeeze().drop(labels=\"band\")\n",
    "improved_water_sources = improved_water_sources.rename({\"x\":\"longitude\", \"y\":\"latitude\"})\n",
    "#convert to geodataframe\n",
    "improved_water_sources = improved_water_sources.to_dataframe(name=\"pct\")\n",
    "#turn latitude and longitude into columns\n",
    "improved_water_sources.reset_index(inplace=True)\n",
    "improved_water_sources_gdf = gpd.GeoDataFrame(improved_water_sources, geometry=gpd.points_from_xy(improved_water_sources.longitude, improved_water_sources.latitude))\n",
    "improved_water_sources_gdf.set_crs(epsg=4326, inplace=True)\n",
    "improved_water_sources_gdf_aoi = gpd.clip(improved_water_sources_gdf, aoi)\n",
    "improved_water_sources_gdf_aoi.to_file(os.path.join(outputPath, \"improved_water_sources_gdf_aoi.geojson\"))\n",
    "print(\"File saved to improved_water_sources_gdf_aoi.geojson\")\n",
    "improved_water_sources_gdf_aoi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "improved_water_sources = xr.open_rasterio(r\"C:/Users/jtrum/world_bank/data/IHME_LMIC_WASH_2000_2017_W_IMP_PERCENT_MEAN_2017_Y2020M06D02.tif\")\n",
    "improved_water_sources = improved_water_sources.squeeze().drop(labels=\"band\")\n",
    "improved_water_sources = improved_water_sources.rename({\"x\":\"longitude\", \"y\":\"latitude\"})\n",
    "#convert to geodataframe\n",
    "improved_water_sources = improved_water_sources.to_dataframe(name=\"pct\")\n",
    "#turn latitude and longitude into columns\n",
    "improved_water_sources.reset_index(inplace=True)\n",
    "improved_water_sources_gdf = gpd.GeoDataFrame(improved_water_sources, geometry=gpd.points_from_xy(improved_water_sources.longitude, improved_water_sources.latitude))\n",
    "improved_water_sources_gdf.set_crs(epsg=4326, inplace=True)\n",
    "improved_water_sources_gdf_aoi = gpd.clip(improved_water_sources_gdf, aoi)\n",
    "improved_water_sources_gdf_aoi.to_file(os.path.join(outputPath, \"improved_water_sources_gdf_aoi.geojson\"))\n",
    "print(\"File saved to improved_water_sources_gdf_aoi.geojson\")\n",
    "improved_water_sources_gdf_aoi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IHME (sanitation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_5004\\2086645591.py:1: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\n",
      "  improved_sanitation_sources = xr.open_rasterio(r\"C:/Users/jtrum/world_bank/data/IHME_LMIC_WASH_2000_2017_S_IMP_PERCENT_MEAN_2017_Y2020M06D02.tif\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File saved to improved_sanitation_sources_gdf_aoi.geojson\n"
     ]
    }
   ],
   "source": [
    "improved_sanitation_sources = xr.open_rasterio(r\"C:/Users/jtrum/world_bank/data/IHME_LMIC_WASH_2000_2017_S_IMP_PERCENT_MEAN_2017_Y2020M06D02.tif\")\n",
    "improved_sanitation_sources = improved_sanitation_sources.squeeze().drop(labels=\"band\")\n",
    "improved_sanitation_sources = improved_sanitation_sources.rename({\"x\":\"longitude\", \"y\":\"latitude\"})\n",
    "#convert to geodataframe\n",
    "improved_sanitation_sources = improved_sanitation_sources.to_dataframe(name=\"pct\")\n",
    "#turn latitude and longitude into columns\n",
    "improved_sanitation_sources.reset_index(inplace=True)\n",
    "improved_sanitation_sources_gdf = gpd.GeoDataFrame(improved_sanitation_sources, geometry=gpd.points_from_xy(improved_sanitation_sources.longitude, improved_sanitation_sources.latitude))\n",
    "improved_sanitation_sources_gdf.set_crs(epsg=4326, inplace=True)\n",
    "improved_sanitation_sources_gdf_aoi = gpd.clip(improved_sanitation_sources_gdf, aoi)\n",
    "improved_sanitation_sources_gdf_aoi.to_file(os.path.join(outputPath, \"improved_sanitation_sources_gdf_aoi.geojson\"))\n",
    "print(\"File saved to improved_sanitation_sources_gdf_aoi.geojson\")\n",
    "improved_sanitation_sources_gdf_aoi = improved_sanitation_sources_gdf_aoi[['pct', 'geometry']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IHME (open defacation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_5004\\4003020799.py:1: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\n",
      "  open_def_sources = xr.open_rasterio(r\"C:/Users/jtrum/world_bank/data/IHME_LMIC_WASH_2000_2017_S_OD_PERCENT_MEAN_2017_Y2020M06D02.tif\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File saved to open_def_sources_gdf_aoi.geojson\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pct</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9977647</th>\n",
       "      <td>28.999447</td>\n",
       "      <td>POINT (13.18752 -9.31250)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977646</th>\n",
       "      <td>21.648537</td>\n",
       "      <td>POINT (13.14585 -9.31250)</td>\n",
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       "      <th>9971036</th>\n",
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       "    <tr>\n",
       "      <th>9971037</th>\n",
       "      <td>27.775961</td>\n",
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       "    <tr>\n",
       "      <th>9964425</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>9885114</th>\n",
       "      <td>24.031370</td>\n",
       "      <td>POINT (13.47918 -8.72917)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9885116</th>\n",
       "      <td>21.044580</td>\n",
       "      <td>POINT (13.56252 -8.72917)</td>\n",
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       "    <tr>\n",
       "      <th>9878503</th>\n",
       "      <td>26.772446</td>\n",
       "      <td>POINT (13.43752 -8.68750)</td>\n",
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       "    <tr>\n",
       "      <th>9878505</th>\n",
       "      <td>19.226595</td>\n",
       "      <td>POINT (13.52085 -8.68750)</td>\n",
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       "      <th>9878504</th>\n",
       "      <td>18.228943</td>\n",
       "      <td>POINT (13.47918 -8.68750)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               pct                   geometry\n",
       "9977647  28.999447  POINT (13.18752 -9.31250)\n",
       "9977646  21.648537  POINT (13.14585 -9.31250)\n",
       "9971036  26.851372  POINT (13.14585 -9.27083)\n",
       "9971037  27.775961  POINT (13.18752 -9.27083)\n",
       "9964425  26.202843  POINT (13.10418 -9.22917)\n",
       "...            ...                        ...\n",
       "9885114  24.031370  POINT (13.47918 -8.72917)\n",
       "9885116  21.044580  POINT (13.56252 -8.72917)\n",
       "9878503  26.772446  POINT (13.43752 -8.68750)\n",
       "9878505  19.226595  POINT (13.52085 -8.68750)\n",
       "9878504  18.228943  POINT (13.47918 -8.68750)\n",
       "\n",
       "[116 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open_def_sources = xr.open_rasterio(r\"C:/Users/jtrum/world_bank/data/IHME_LMIC_WASH_2000_2017_S_OD_PERCENT_MEAN_2017_Y2020M06D02.tif\")\n",
    "open_def_sources = open_def_sources.squeeze().drop(labels=\"band\")\n",
    "open_def_sources = open_def_sources.rename({\"x\":\"longitude\", \"y\":\"latitude\"})\n",
    "#convert to geodataframe\n",
    "open_def_sources = open_def_sources.to_dataframe(name=\"pct\")\n",
    "#turn latitude and longitude into columns\n",
    "open_def_sources.reset_index(inplace=True)\n",
    "open_def_sources_gdf = gpd.GeoDataFrame(open_def_sources, geometry=gpd.points_from_xy(open_def_sources.longitude, open_def_sources.latitude))\n",
    "open_def_sources_gdf.set_crs(epsg=4326, inplace=True)\n",
    "open_def_sources_gdf_aoi = gpd.clip(open_def_sources_gdf, aoi)\n",
    "open_def_sources_gdf_aoi.to_file(os.path.join(outputPath, \"open_def_sources_gdf_aoi.geojson\"))\n",
    "print(\"File saved to open_def_sources_gdf_aoi.geojson\")\n",
    "open_def_sources_gdf_aoi = open_def_sources_gdf_aoi[['pct', 'geometry']]\n",
    "open_def_sources_gdf_aoi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Children under 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raster = rxr.open_rasterio(r\"C:/Users/jtrum/world_bank/data/ago_children_under_five_2020.tif\")\n",
    "clip_bound = aoi.geometry\n",
    "clip_raster = raster.rio.clip(clip_bound, from_disk=True)\n",
    "children_under_five = clip_raster.squeeze().drop(labels=\"band\")\n",
    "children_under_five = children_under_five.rename({\"x\":\"longitude\", \"y\":\"latitude\"})\n",
    "children_under_five = children_under_five.to_dataframe(name=\"pct\")\n",
    "children_under_five = children_under_five.reset_index()\n",
    "children_under_five_gdf = gpd.GeoDataFrame(children_under_five, geometry=gpd.points_from_xy(children_under_five.longitude, children_under_five.latitude))\n",
    "children_under_five_gdf.set_crs(epsg=4326, inplace=True)\n",
    "children_under_five_gdf_aoi = gpd.clip(children_under_five_gdf, aoi)\n",
    "children_under_five_gdf_aoi.dropna(inplace=True)\n",
    "children_under_five_gdf_aoi[['pct', 'geometry']]\n",
    "children_under_five_gdf_aoi.to_file(os.path.join(outputPath, \"children_under_five_gdf_aoi.geojson\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Elderly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_5004\\150416882.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  children_under_five_gdf_aoi.dropna(inplace=True)\n"
     ]
    }
   ],
   "source": [
    "raster = rxr.open_rasterio(r\"C:/Users/jtrum/world_bank/data/ago_elderly_60_plus_2020.tif\")\n",
    "clip_bound = aoi.geometry\n",
    "clip_raster = raster.rio.clip(clip_bound, from_disk=True)\n",
    "children_under_five = clip_raster.squeeze().drop(labels=\"band\")\n",
    "children_under_five = children_under_five.rename({\"x\":\"longitude\", \"y\":\"latitude\"})\n",
    "children_under_five = children_under_five.to_dataframe(name=\"pct\")\n",
    "children_under_five = children_under_five.reset_index()\n",
    "children_under_five_gdf = gpd.GeoDataFrame(children_under_five, geometry=gpd.points_from_xy(children_under_five.longitude, children_under_five.latitude))\n",
    "children_under_five_gdf.set_crs(epsg=4326, inplace=True)\n",
    "children_under_five_gdf_aoi = gpd.clip(children_under_five_gdf, aoi)\n",
    "children_under_five_gdf_aoi.dropna(inplace=True)\n",
    "children_under_five_gdf_aoi[['pct', 'geometry']]\n",
    "children_under_five_gdf_aoi.to_file(os.path.join(outputPath, \"elderly_over_60_gdf_aoi.geojson\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_5004\\2577157081.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  children_under_five_gdf_aoi.dropna(inplace=True)\n"
     ]
    }
   ],
   "source": [
    "raster = rxr.open_rasterio(r\"C:/Users/jtrum/world_bank/data/ago_women_of_reproductive_age_15_49_2020.tif\")\n",
    "clip_bound = aoi.geometry\n",
    "clip_raster = raster.rio.clip(clip_bound, from_disk=True)\n",
    "children_under_five = clip_raster.squeeze().drop(labels=\"band\")\n",
    "children_under_five = children_under_five.rename({\"x\":\"longitude\", \"y\":\"latitude\"})\n",
    "children_under_five = children_under_five.to_dataframe(name=\"pct\")\n",
    "children_under_five = children_under_five.reset_index()\n",
    "children_under_five_gdf = gpd.GeoDataFrame(children_under_five, geometry=gpd.points_from_xy(children_under_five.longitude, children_under_five.latitude))\n",
    "children_under_five_gdf.set_crs(epsg=4326, inplace=True)\n",
    "children_under_five_gdf_aoi = gpd.clip(children_under_five_gdf, aoi)\n",
    "children_under_five_gdf_aoi.dropna(inplace=True)\n",
    "children_under_five_gdf_aoi[['pct', 'geometry']]\n",
    "children_under_five_gdf_aoi.to_file(os.path.join(outputPath, \"women_reproductive_age_gdf_aoi.geojson\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "children_under_five_gdf_aoi['pct'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "351877"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(children_under_five_gdf_aoi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WSF 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#open wsf2019 raster\n",
    "built_up_area = xr.open_rasterio(r\"C:/Users/jtrum/world_bank/data/wsf2019.tif\")\n",
    "built_up_area = built_up_area.squeeze().drop(labels=\"band\")\n",
    "built_up_area = built_up_area.rename({\"x\":\"longitude\", \"y\":\"latitude\"})\n",
    "built_up_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Assuming 'built_up_area' is the raster you want to crop\n",
    "built_up_area_data = built_up_area.read(1)  # Read the raster data\n",
    "\n",
    "# Apply the mask to the raster data\n",
    "cropped_raster_data = np.ma.masked_array(built_up_area_data, mask)\n",
    "\n",
    "# Create a new rasterio dataset with the cropped data\n",
    "cropped_raster = rio.open(\n",
    "    \"C:/Users/jtrum/Desktop/wb_outputs/cropped_wsf2019.tif\",\n",
    "    \"w\",\n",
    "    driver=\"GTiff\",\n",
    "    height=cropped_raster_data.shape[0],\n",
    "    width=cropped_raster_data.shape[1],\n",
    "    count=1,  # Assuming a single band raster\n",
    "    dtype=str(cropped_raster_data.dtype),\n",
    "    crs=built_up_area.crs,\n",
    "    transform=built_up_area.transform,\n",
    ")\n",
    "\n",
    "# Write the cropped data to the new raster dataset\n",
    "cropped_raster.write(cropped_raster_data.filled(fill_value=0), 1)  # Fill masked values with 0 or another suitable value\n",
    "cropped_raster.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jtrum\\AppData\\Local\\Temp\\ipykernel_17780\\1584535359.py:1: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\n",
      "  built_up_area = xr.open_rasterio(r\"C:/Users/jtrum/world_bank/data/wsf2019.tif\")\n"
     ]
    },
    {
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       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
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       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
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       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
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       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (latitude: 22487, longitude: 22487)&gt;\n",
       "[505665169 values with dtype=uint8]\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 -7.99 -7.99 -7.99 ... -10.01 -10.01 -10.01\n",
       "  * longitude  (longitude) float64 11.99 11.99 11.99 11.99 ... 14.01 14.01 14.01\n",
       "Attributes:\n",
       "    transform:      (8.983152841195215e-05, 0.0, 11.989993760200077, 0.0, -8....\n",
       "    crs:            +proj=longlat +datum=WGS84 +no_defs=True\n",
       "    res:            (8.983152841195215e-05, 8.983152841195215e-05)\n",
       "    is_tiled:       0\n",
       "    nodatavals:     (nan,)\n",
       "    scales:         (1.0,)\n",
       "    offsets:        (0.0,)\n",
       "    AREA_OR_POINT:  Area</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 22487</li><li><span class='xr-has-index'>longitude</span>: 22487</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3c248b78-59f1-4ab4-9d5c-edc953b52778' class='xr-array-in' type='checkbox' checked><label for='section-3c248b78-59f1-4ab4-9d5c-edc953b52778' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[505665169 values with dtype=uint8]</pre></div></div></li><li class='xr-section-item'><input id='section-ea8810fb-f09d-4d78-b26c-b51fd3d9ef49' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ea8810fb-f09d-4d78-b26c-b51fd3d9ef49' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-7.99 -7.99 -7.99 ... -10.01 -10.01</div><input id='attrs-cc4cdc7e-ab06-4cc9-a830-797920462372' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc4cdc7e-ab06-4cc9-a830-797920462372' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9dff950f-218b-4fcf-8acc-84503f26a918' class='xr-var-data-in' type='checkbox'><label for='data-9dff950f-218b-4fcf-8acc-84503f26a918' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ -7.99002 ,  -7.99011 ,  -7.9902  , ..., -10.009792, -10.009882,\n",
       "       -10.009972])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>11.99 11.99 11.99 ... 14.01 14.01</div><input id='attrs-a9347c6c-a1eb-4b25-9a17-aaac50d9f374' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9347c6c-a1eb-4b25-9a17-aaac50d9f374' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-707c94ec-862e-4548-bf6a-d0971d057f02' class='xr-var-data-in' type='checkbox'><label for='data-707c94ec-862e-4548-bf6a-d0971d057f02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([11.990039, 11.990129, 11.990218, ..., 14.009811, 14.009901, 14.00999 ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e8d55b64-30a7-4ed2-8b18-5bcc7bd8c6b4' class='xr-section-summary-in' type='checkbox'  ><label for='section-e8d55b64-30a7-4ed2-8b18-5bcc7bd8c6b4' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f1e1f41b-2af7-4c3e-a4b6-35d6e5d98ef2' class='xr-index-data-in' type='checkbox'/><label for='index-f1e1f41b-2af7-4c3e-a4b6-35d6e5d98ef2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -7.990020378836878,   -7.99011021036529,  -7.990200041893702,\n",
       "        -7.990289873422114,  -7.990379704950525,  -7.990469536478938,\n",
       "         -7.99055936800735,  -7.990649199535762,  -7.990739031064174,\n",
       "        -7.990828862592585,\n",
       "       ...\n",
       "       -10.009163642952327, -10.009253474480738,  -10.00934330600915,\n",
       "       -10.009433137537563, -10.009522969065975, -10.009612800594386,\n",
       "       -10.009702632122798, -10.009792463651209, -10.009882295179622,\n",
       "       -10.009972126708034],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=22487))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-469328e6-9249-41c2-8e37-2a2379fa3ad3' class='xr-index-data-in' type='checkbox'/><label for='index-469328e6-9249-41c2-8e37-2a2379fa3ad3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([11.990038675964284, 11.990128507492695, 11.990218339021107,\n",
       "       11.990308170549518, 11.990398002077931, 11.990487833606343,\n",
       "       11.990577665134754, 11.990667496663168,  11.99075732819158,\n",
       "        11.99084715971999,\n",
       "       ...\n",
       "       14.009181940079731, 14.009271771608145, 14.009361603136556,\n",
       "       14.009451434664967, 14.009541266193379,  14.00963109772179,\n",
       "       14.009720929250204, 14.009810760778615, 14.009900592307027,\n",
       "        14.00999042383544],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=22487))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3d48ba0e-3c0a-468a-9685-0a6b5ff2ae23' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3d48ba0e-3c0a-468a-9685-0a6b5ff2ae23' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(8.983152841195215e-05, 0.0, 11.989993760200077, 0.0, -8.983152841195215e-05, -7.989975463072672)</dd><dt><span>crs :</span></dt><dd>+proj=longlat +datum=WGS84 +no_defs=True</dd><dt><span>res :</span></dt><dd>(8.983152841195215e-05, 8.983152841195215e-05)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(nan,)</dd><dt><span>scales :</span></dt><dd>(1.0,)</dd><dt><span>offsets :</span></dt><dd>(0.0,)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (latitude: 22487, longitude: 22487)>\n",
       "[505665169 values with dtype=uint8]\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 -7.99 -7.99 -7.99 ... -10.01 -10.01 -10.01\n",
       "  * longitude  (longitude) float64 11.99 11.99 11.99 11.99 ... 14.01 14.01 14.01\n",
       "Attributes:\n",
       "    transform:      (8.983152841195215e-05, 0.0, 11.989993760200077, 0.0, -8....\n",
       "    crs:            +proj=longlat +datum=WGS84 +no_defs=True\n",
       "    res:            (8.983152841195215e-05, 8.983152841195215e-05)\n",
       "    is_tiled:       0\n",
       "    nodatavals:     (nan,)\n",
       "    scales:         (1.0,)\n",
       "    offsets:        (0.0,)\n",
       "    AREA_OR_POINT:  Area"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "built_up_area = xr.open_rasterio(r\"C:/Users/jtrum/world_bank/data/wsf2019.tif\")\n",
    "built_up_area = built_up_area.squeeze().drop(labels=\"band\")\n",
    "built_up_area = built_up_area.rename({\"x\":\"longitude\", \"y\":\"latitude\"})\n",
    "built_up_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert to geodataframe\n",
    "built_up_area = built_up_area.to_dataframe(name=\"built_up_area\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                      built_up_area\n",
       "latitude   longitude               \n",
       "-7.990020  13.464713            255\n",
       "           13.464803            255\n",
       "           13.464893            255\n",
       "           13.464983            255\n",
       "           13.465072            255\n",
       "...                             ...\n",
       "-10.007726 12.498485            255\n",
       "           12.498575            255\n",
       "           12.630268            255\n",
       "           12.630358            255\n",
       "           12.630448            255\n",
       "\n",
       "[49377713 rows x 1 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop zero values\n",
    "built_up_area_255 = built_up_area[built_up_area.built_up_area != 0]\n",
    "built_up_area_0 = built_up_area[built_up_area.built_up_area == 0]\n",
    "built_up_area_255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "           latitude  longitude  built_up_area\n",
       "0         -7.990020  13.464713            255\n",
       "1         -7.990020  13.464803            255\n",
       "2         -7.990020  13.464893            255\n",
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       "49377710 -10.007726  12.630268            255\n",
       "49377711 -10.007726  12.630358            255\n",
       "49377712 -10.007726  12.630448            255\n",
       "\n",
       "[49377713 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#turn latitude and longitude into columns\n",
    "built_up_area_255.reset_index(inplace=True)\n",
    "built_up_area_255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "built_up_area_255_gdf = gpd.GeoDataFrame(built_up_area_255, geometry=gpd.points_from_xy(built_up_area_255.longitude, built_up_area_255.latitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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     "execution_count": 43,
     "metadata": {},
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   "source": [
    "built_up_area_255_gdf['built'] = \"True\"\n",
    "built_up_area_255_gdf = built_up_area_255_gdf.drop(columns=['built_up_area', 'latitude', 'longitude'])\n",
    "built_up_area_255_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set up built_up_are_255_gdf to crs of aoi\n",
    "built_up_area_255_gdf = built_up_area_255_gdf.set_crs(epsg=4326)"
   ]
  },
  {
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   "execution_count": 40,
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    "built_up_area_255_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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       "   NAME_1 GID_0 COUNTRY     GID_1 NL_NAME_1       GID_2   NAME_2 VARNAME_2  \\\n",
       "0  Luanda   AGO  Angola  AGO.11_1        NA  AGO.11.1_1  Cacuaco        NA   \n",
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       "\n",
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      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aoi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jtrum\\miniconda3\\envs\\wash\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3448: FutureWarning: The `op` parameter is deprecated and will be removed in a future release. Please use the `predicate` parameter instead.\n",
      "  if await self.run_code(code, result, async_=asy):\n"
     ]
    }
   ],
   "source": [
    "#only keep points within aoi\n",
    "built_up_area_255_gdf_aoi = gpd.sjoin(built_up_area_255_gdf, aoi, how=\"inner\", op='intersects')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5539893\n"
     ]
    }
   ],
   "source": [
    "print(len(built_up_area_255_gdf_aoi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>16025944</th>\n",
       "      <td>POINT (13.41531 -8.63968)</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO.11_1</td>\n",
       "      <td>NA</td>\n",
       "      <td>AGO.11.1_1</td>\n",
       "      <td>Cacuaco</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>Município</td>\n",
       "      <td>Municpality|City Council</td>\n",
       "      <td>1108</td>\n",
       "      <td>AO.LU.CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16025945</th>\n",
       "      <td>POINT (13.41540 -8.63968)</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO.11_1</td>\n",
       "      <td>NA</td>\n",
       "      <td>AGO.11.1_1</td>\n",
       "      <td>Cacuaco</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>Município</td>\n",
       "      <td>Municpality|City Council</td>\n",
       "      <td>1108</td>\n",
       "      <td>AO.LU.CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16031698</th>\n",
       "      <td>POINT (13.41522 -8.63977)</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO.11_1</td>\n",
       "      <td>NA</td>\n",
       "      <td>AGO.11.1_1</td>\n",
       "      <td>Cacuaco</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>Município</td>\n",
       "      <td>Municpality|City Council</td>\n",
       "      <td>1108</td>\n",
       "      <td>AO.LU.CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48886381</th>\n",
       "      <td>POINT (13.15426 -9.34297)</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO.11_1</td>\n",
       "      <td>NA</td>\n",
       "      <td>AGO.11.1_1</td>\n",
       "      <td>Cacuaco</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>Município</td>\n",
       "      <td>Municpality|City Council</td>\n",
       "      <td>1108</td>\n",
       "      <td>AO.LU.CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48886382</th>\n",
       "      <td>POINT (13.15435 -9.34297)</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO.11_1</td>\n",
       "      <td>NA</td>\n",
       "      <td>AGO.11.1_1</td>\n",
       "      <td>Cacuaco</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>Município</td>\n",
       "      <td>Municpality|City Council</td>\n",
       "      <td>1108</td>\n",
       "      <td>AO.LU.CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48886383</th>\n",
       "      <td>POINT (13.15443 -9.34297)</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO.11_1</td>\n",
       "      <td>NA</td>\n",
       "      <td>AGO.11.1_1</td>\n",
       "      <td>Cacuaco</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>Município</td>\n",
       "      <td>Municpality|City Council</td>\n",
       "      <td>1108</td>\n",
       "      <td>AO.LU.CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48886384</th>\n",
       "      <td>POINT (13.15452 -9.34297)</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO.11_1</td>\n",
       "      <td>NA</td>\n",
       "      <td>AGO.11.1_1</td>\n",
       "      <td>Cacuaco</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>Município</td>\n",
       "      <td>Municpality|City Council</td>\n",
       "      <td>1108</td>\n",
       "      <td>AO.LU.CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48886385</th>\n",
       "      <td>POINT (13.15461 -9.34297)</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO.11_1</td>\n",
       "      <td>NA</td>\n",
       "      <td>AGO.11.1_1</td>\n",
       "      <td>Cacuaco</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>Município</td>\n",
       "      <td>Municpality|City Council</td>\n",
       "      <td>1108</td>\n",
       "      <td>AO.LU.CC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5539893 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           geometry built  index_right  NAME_1 GID_0 COUNTRY  \\\n",
       "15924125  POINT (13.41441 -8.63807)  True            0  Luanda   AGO  Angola   \n",
       "16025943  POINT (13.41522 -8.63968)  True            0  Luanda   AGO  Angola   \n",
       "16025944  POINT (13.41531 -8.63968)  True            0  Luanda   AGO  Angola   \n",
       "16025945  POINT (13.41540 -8.63968)  True            0  Luanda   AGO  Angola   \n",
       "16031698  POINT (13.41522 -8.63977)  True            0  Luanda   AGO  Angola   \n",
       "...                             ...   ...          ...     ...   ...     ...   \n",
       "48886381  POINT (13.15426 -9.34297)  True            0  Luanda   AGO  Angola   \n",
       "48886382  POINT (13.15435 -9.34297)  True            0  Luanda   AGO  Angola   \n",
       "48886383  POINT (13.15443 -9.34297)  True            0  Luanda   AGO  Angola   \n",
       "48886384  POINT (13.15452 -9.34297)  True            0  Luanda   AGO  Angola   \n",
       "48886385  POINT (13.15461 -9.34297)  True            0  Luanda   AGO  Angola   \n",
       "\n",
       "             GID_1 NL_NAME_1       GID_2   NAME_2 VARNAME_2 NL_NAME_2  \\\n",
       "15924125  AGO.11_1        NA  AGO.11.1_1  Cacuaco        NA        NA   \n",
       "16025943  AGO.11_1        NA  AGO.11.1_1  Cacuaco        NA        NA   \n",
       "16025944  AGO.11_1        NA  AGO.11.1_1  Cacuaco        NA        NA   \n",
       "16025945  AGO.11_1        NA  AGO.11.1_1  Cacuaco        NA        NA   \n",
       "16031698  AGO.11_1        NA  AGO.11.1_1  Cacuaco        NA        NA   \n",
       "...            ...       ...         ...      ...       ...       ...   \n",
       "48886381  AGO.11_1        NA  AGO.11.1_1  Cacuaco        NA        NA   \n",
       "48886382  AGO.11_1        NA  AGO.11.1_1  Cacuaco        NA        NA   \n",
       "48886383  AGO.11_1        NA  AGO.11.1_1  Cacuaco        NA        NA   \n",
       "48886384  AGO.11_1        NA  AGO.11.1_1  Cacuaco        NA        NA   \n",
       "48886385  AGO.11_1        NA  AGO.11.1_1  Cacuaco        NA        NA   \n",
       "\n",
       "             TYPE_2                 ENGTYPE_2  CC_2    HASC_2  \n",
       "15924125  Município  Municpality|City Council  1108  AO.LU.CC  \n",
       "16025943  Município  Municpality|City Council  1108  AO.LU.CC  \n",
       "16025944  Município  Municpality|City Council  1108  AO.LU.CC  \n",
       "16025945  Município  Municpality|City Council  1108  AO.LU.CC  \n",
       "16031698  Município  Municpality|City Council  1108  AO.LU.CC  \n",
       "...             ...                       ...   ...       ...  \n",
       "48886381  Município  Municpality|City Council  1108  AO.LU.CC  \n",
       "48886382  Município  Municpality|City Council  1108  AO.LU.CC  \n",
       "48886383  Município  Municpality|City Council  1108  AO.LU.CC  \n",
       "48886384  Município  Municpality|City Council  1108  AO.LU.CC  \n",
       "48886385  Município  Municpality|City Council  1108  AO.LU.CC  \n",
       "\n",
       "[5539893 rows x 16 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "built_up_area_255_gdf_aoi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export as geojson to output folder\n",
    "built_up_area_255_gdf_aoi.to_file(r\"C:/Users/jtrum/Desktop/wb_outputs/built_up_area_255_gdf_aoi.geojson\", driver='GeoJSON')"
   ]
  }
 ],
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