energy_overlay_SIMPLE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "spare-dictionary",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import os\n",
    "import rasterio\n",
    "from rasterio.plot import show as Show\n",
    "import pylab as plt\n",
    "data = os.getenv('data')\n",
    "from rasterstats import point_query\n",
    "from rasterio import mask as mask\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incomplete-compact",
   "metadata": {},
   "source": [
    "## Overlaying infrastructure locations with hazard layers\n",
    "This demo shows how to do the following in Jupyter Notebooks:\n",
    "* Load infrastructure data (from file or online)\n",
    "* View flood or landslide layers\n",
    "* Intersect the two and produce summary statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "owned-broadcasting",
   "metadata": {},
   "source": [
    "#### 1. Example workflow: landslide risk of schools in Ghana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "acceptable-grove",
   "metadata": {},
   "outputs": [],
   "source": [
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))  # natural earth dataset used for country outlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "vulnerable-survivor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-3.499885236007076 4.454946991387557 4.815522086607817 6.898909130886979\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,15.808803113662092)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.1379781826177396\" opacity=\"0.6\" d=\"M 0.023802524423700785,11.018681748900804 L -0.04978471515994442,10.706917832883931 L 0.3675799902453889,10.19121287682718 L 0.3659005061958851,9.465003973829482 L 0.46119184734212126,8.677222601756014 L 0.7120292496868785,8.31246450442383 L 0.4909574723422451,7.411744289576475 L 0.5703841487748491,6.914358628767189 L 0.8369311865363329,6.279978745952149 L 1.0601216976049272,5.928837388528876 L -0.5076379052659377,5.3434726017426755 L -1.0636246402941936,5.000547797053812 L -1.9647065901675944,4.710462144383371 L -2.856125047202397,4.994475816259509 L -2.8107014632178395,5.38905121502411 L -3.244370083011262,6.250471503113502 L -2.9835849674503265,7.379704901555513 L -2.562189500326241,8.219627793811483 L -2.8274963037127065,9.642460842319778 L -2.9638962467471117,10.395334784380083 L -2.9404093082704605,10.962690334512558 L -1.2033577132114317,11.009819240762738 L -0.7615758935481834,10.936929633015055 L -0.43870154458858224,11.098340969278722 L 0.023802524423700785,11.018681748900804 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x7fa30f0a3c90>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country = 'Ghana'\n",
    "geom = world.geometry[world.name == country].reset_index(drop=True)[0]\n",
    "geom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "synthetic-formula",
   "metadata": {},
   "outputs": [],
   "source": [
    "ghana_schools = gpd.read_file('shp/ghana_schools.shp')  # read from disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "level-drawing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of schools:  2055\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nsein Senior High School</td>\n",
       "      <td>POINT (-2.21922 4.87712)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Essiama Senior High Technical School</td>\n",
       "      <td>POINT (-2.34812 4.93584)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Living Stone Preparatory School</td>\n",
       "      <td>POINT (-2.36531 4.94724)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   name                  geometry\n",
       "0              Nsein Senior High School  POINT (-2.21922 4.87712)\n",
       "1  Essiama Senior High Technical School  POINT (-2.34812 4.93584)\n",
       "2       Living Stone Preparatory School  POINT (-2.36531 4.94724)"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('number of schools: ', len(ghana_schools))\n",
    "ghana_schools.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "reliable-asset",
   "metadata": {},
   "outputs": [],
   "source": [
    "landslide_pth = 'LS_RF_Mean_1980-2018.tif'\n",
    "#landslide_pth = data + '/energy/LS_RF_Mean_1980-2018.tif'  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "spread-piece",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the landslide data for Ghana only\n",
    "with rasterio.open(landslide_pth) as src:\n",
    "    maskData, maskTransform = mask.mask(src, [geom], crop=True, nodata=src.nodatavals[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "expressed-wonder",
   "metadata": {},
   "outputs": [],
   "source": [
    "# administrative boundaries\n",
    "boundaries = gpd.read_file('shp/gadm36_GHA_1.shp')[['NAME_1','geometry']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "offshore-resource",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=[12,10])\n",
    "Show(maskData, transform=maskTransform,cmap='magma',ax=ax,vmax=0.01)\n",
    "boundaries.plot(edgecolor='w',facecolor='None',alpha=0.25,ax=ax)\n",
    "ghana_schools.plot(ax=ax,color='b',markersize=2)\n",
    "plt.title('Schools in Ghana (blue) overlaid with Global Landslide Hazard Map');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "functional-finger",
   "metadata": {},
   "outputs": [],
   "source": [
    "# query the raster at school locations\n",
    "ghana_schools['landslide_risk'] = point_query(ghana_schools, landslide_pth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "talented-converter",
   "metadata": {},
   "outputs": [],
   "source": [
    "ghana_schools.landslide_risk = ghana_schools.landslide_risk * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "christian-monroe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ghana_schools.landslide_risk.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "royal-empire",
   "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>name</th>\n",
       "      <th>geometry</th>\n",
       "      <th>landslide_risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1697</th>\n",
       "      <td>None</td>\n",
       "      <td>POINT (0.45588 6.91584)</td>\n",
       "      <td>2.205068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1781</th>\n",
       "      <td>None</td>\n",
       "      <td>POINT (0.45707 6.91587)</td>\n",
       "      <td>2.205068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1792</th>\n",
       "      <td>None</td>\n",
       "      <td>POINT (0.47387 6.82539)</td>\n",
       "      <td>2.187342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>Have R. C Basic Schools</td>\n",
       "      <td>POINT (0.35353 6.74714)</td>\n",
       "      <td>2.168775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>RIJNBEEK School Complex</td>\n",
       "      <td>POINT (0.35188 6.74456)</td>\n",
       "      <td>2.168775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name                 geometry  landslide_risk\n",
       "1697                     None  POINT (0.45588 6.91584)        2.205068\n",
       "1781                     None  POINT (0.45707 6.91587)        2.205068\n",
       "1792                     None  POINT (0.47387 6.82539)        2.187342\n",
       "1684  Have R. C Basic Schools  POINT (0.35353 6.74714)        2.168775\n",
       "1702  RIJNBEEK School Complex  POINT (0.35188 6.74456)        2.168775"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghana_schools.sort_values(by='landslide_risk',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "authorized-ordering",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots()\n",
    "ghana_schools.sort_values(by='landslide_risk',ascending=False)[:50].plot(column='landslide_risk',legend=True,cmap='hot',vmax=1,ax=ax)\n",
    "boundaries.plot(edgecolor='w',facecolor='b',alpha=0.25,ax=ax)\n",
    "plt.title('50 schools with highest landslide risk');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "bizarre-addiction",
   "metadata": {},
   "outputs": [],
   "source": [
    "ghana_schools.landslide_risk.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "sunrise-focus",
   "metadata": {},
   "outputs": [],
   "source": [
    "# spatial join: schools and admin boundaries\n",
    "ghana_schools = gpd.sjoin(ghana_schools,boundaries).drop(columns=['index_right'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "stuffed-tourism",
   "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>name</th>\n",
       "      <th>geometry</th>\n",
       "      <th>landslide_risk</th>\n",
       "      <th>NAME_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nsein Senior High School</td>\n",
       "      <td>POINT (-2.21922 4.87712)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Essiama Senior High Technical School</td>\n",
       "      <td>POINT (-2.34812 4.93584)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Living Stone Preparatory School</td>\n",
       "      <td>POINT (-2.36531 4.94724)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>POINT (-2.41583 4.97510)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Western</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ransom Foundation's Hope Center</td>\n",
       "      <td>POINT (-2.34163 4.93198)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Western</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   name                  geometry  \\\n",
       "0              Nsein Senior High School  POINT (-2.21922 4.87712)   \n",
       "1  Essiama Senior High Technical School  POINT (-2.34812 4.93584)   \n",
       "2       Living Stone Preparatory School  POINT (-2.36531 4.94724)   \n",
       "3                                  None  POINT (-2.41583 4.97510)   \n",
       "4       Ransom Foundation's Hope Center  POINT (-2.34163 4.93198)   \n",
       "\n",
       "   landslide_risk   NAME_1  \n",
       "0             0.0  Western  \n",
       "1             0.0  Western  \n",
       "2             0.0  Western  \n",
       "3             0.0  Western  \n",
       "4             0.0  Western  "
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghana_schools.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "limited-lewis",
   "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>landslide_risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_1</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Volta</th>\n",
       "      <td>0.167265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eastern</th>\n",
       "      <td>0.051941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western</th>\n",
       "      <td>0.013622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greater Accra</th>\n",
       "      <td>0.004846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central</th>\n",
       "      <td>0.003027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ashanti</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brong Ahafo</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northern</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper East</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper West</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               landslide_risk\n",
       "NAME_1                       \n",
       "Volta                0.167265\n",
       "Eastern              0.051941\n",
       "Western              0.013622\n",
       "Greater Accra        0.004846\n",
       "Central              0.003027\n",
       "Ashanti              0.000000\n",
       "Brong Ahafo          0.000000\n",
       "Northern             0.000000\n",
       "Upper East           0.000000\n",
       "Upper West           0.000000"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarize school landslide risk by region\n",
    "\n",
    "regional_summary = ghana_schools.groupby('NAME_1').mean().sort_values(by='landslide_risk',ascending=False)\n",
    "regional_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "comic-research",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'dtype'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-330-1750ca4fbcff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mboundaries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mboundaries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mregional_summary\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mleft_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'NAME_1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mright_on\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregional_summary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mboundaries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'landslide_risk'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'mean_landslide_prob_at_schools'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mboundaries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'mean_landslide_prob_at_schools'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Ghana: Mean landslide risk for schools by region'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/GPD3/lib/python3.7/site-packages/geopandas/geodataframe.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    919\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mthere\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    920\u001b[0m         \"\"\"\n\u001b[0;32m--> 921\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    922\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m     \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_dataframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/GPD3/lib/python3.7/site-packages/geopandas/plotting.py\u001b[0m in \u001b[0;36mplot_dataframe\u001b[0;34m(df, column, cmap, color, ax, cax, categorical, legend, scheme, k, vmin, vmax, markersize, figsize, legend_kwds, categories, classification_kwds, missing_kwds, aspect, **style_kwds)\u001b[0m\n\u001b[1;32m    635\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 637\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_categorical_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    638\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcategories\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    639\u001b[0m             raise ValueError(\n",
      "\u001b[0;32m~/miniconda3/envs/GPD3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5458\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5459\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5460\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5462\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'dtype'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# join back with boundaries and visualize mean values\n",
    "boundaries = boundaries.merge(regional_summary,left_on='NAME_1',right_on = regional_summary.index)\n",
    "boundaries.rename(columns = {'landslide_risk':'mean_landslide_prob_at_schools'},inplace=True)\n",
    "boundaries.plot(column = 'mean_landslide_prob_at_schools',legend=True)\n",
    "plt.title('Ghana: Mean landslide risk for schools by region');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "protecting-death",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "GPD3",
   "language": "python",
   "name": "gpd3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}