0_DHSdata.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "61287f6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "from os import path\n",
    "\n",
    "import geopandas as gpd\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e49de4",
   "metadata": {},
   "source": [
    "# Directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d10850aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define temp directory\n",
    "input_dir = \"data/raw/\"# input data directory\n",
    "dest_dir = \"data/temp/\"# output data directory\n",
    "\n",
    "# REMOVE invisible DS_Store file (mac issue) - do it ONCE only\n",
    "list_dir = ['data/output/.DS_Store',\n",
    "            'data/output/prep_regr_tabs/.DS_Store',\n",
    "            'data/output/regressions/.DS_Store',\n",
    "            'data/output/regressions/controlled_Climate/.DS_Store',\n",
    "            'data/output/regressions/controlled_WatSanEdu/.DS_Store',\n",
    "            'data/output/regressions/GWR/.DS_Store',\n",
    "            'data/output/regressions/NON_controlled/.DS_Store',\n",
    "            'data/output/regressions/sp_autocorr/.DS_Store',\n",
    "            'data/raw/.DS_Store',\n",
    "            'data/raw/dhs/.DS_Store',\n",
    "            'data/raw/dhs/CIGE61FL/.DS_Store',\n",
    "            'data/raw/dhs/CIKR62DT_children/.DS_Store',\n",
    "            'data/raw/osm/.DS_Store',\n",
    "            'data/raw/osm/input/.DS_Store',\n",
    "            'data/raw/osm/output/.DS_Store',\n",
    "            'data/raw/raster_to_clip/.DS_Store',\n",
    "            'data/temp/.DS_Store']\n",
    "\n",
    "for f_dir in list_dir:\n",
    "    if os.path.exists(f_dir)==True:\n",
    "        os.remove(f_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03537fa0",
   "metadata": {},
   "source": [
    "# DHS data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11cb7a06",
   "metadata": {},
   "source": [
    "### Retrieve children questionnaire data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4555ad5f",
   "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>caseid</th>\n",
       "      <th>midx</th>\n",
       "      <th>v000</th>\n",
       "      <th>v001</th>\n",
       "      <th>v002</th>\n",
       "      <th>v003</th>\n",
       "      <th>v004</th>\n",
       "      <th>v005</th>\n",
       "      <th>v006</th>\n",
       "      <th>v007</th>\n",
       "      <th>...</th>\n",
       "      <th>s1221f</th>\n",
       "      <th>idx94</th>\n",
       "      <th>s423a</th>\n",
       "      <th>s423b</th>\n",
       "      <th>s446a</th>\n",
       "      <th>idx95</th>\n",
       "      <th>s506y</th>\n",
       "      <th>sd506y</th>\n",
       "      <th>sm506y</th>\n",
       "      <th>sy506y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1     1  1</td>\n",
       "      <td>1</td>\n",
       "      <td>CI6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1803150</td>\n",
       "      <td>5</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9998.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1     6  3</td>\n",
       "      <td>1</td>\n",
       "      <td>CI6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1803150</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1     7  2</td>\n",
       "      <td>1</td>\n",
       "      <td>CI6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1803150</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1    16  3</td>\n",
       "      <td>1</td>\n",
       "      <td>CI6</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1803150</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1    16  3</td>\n",
       "      <td>2</td>\n",
       "      <td>CI6</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1803150</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7771</th>\n",
       "      <td>352    46  2</td>\n",
       "      <td>1</td>\n",
       "      <td>CI6</td>\n",
       "      <td>352</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>352</td>\n",
       "      <td>604654</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7772</th>\n",
       "      <td>352    46  4</td>\n",
       "      <td>1</td>\n",
       "      <td>CI6</td>\n",
       "      <td>352</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "      <td>352</td>\n",
       "      <td>604654</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7773</th>\n",
       "      <td>352    46  4</td>\n",
       "      <td>2</td>\n",
       "      <td>CI6</td>\n",
       "      <td>352</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "      <td>352</td>\n",
       "      <td>604654</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7774</th>\n",
       "      <td>352    47  2</td>\n",
       "      <td>1</td>\n",
       "      <td>CI6</td>\n",
       "      <td>352</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "      <td>352</td>\n",
       "      <td>604654</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7775</th>\n",
       "      <td>352    49  5</td>\n",
       "      <td>1</td>\n",
       "      <td>CI6</td>\n",
       "      <td>352</td>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>352</td>\n",
       "      <td>604654</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7776 rows × 1017 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               caseid  midx v000  v001  v002  v003  v004     v005  v006  v007  \\\n",
       "0          1     1  1     1  CI6     1     1     1     1  1803150     5  2012   \n",
       "1          1     6  3     1  CI6     1     6     3     1  1803150     1  2012   \n",
       "2          1     7  2     1  CI6     1     7     2     1  1803150     1  2012   \n",
       "3          1    16  3     1  CI6     1    16     3     1  1803150     1  2012   \n",
       "4          1    16  3     2  CI6     1    16     3     1  1803150     1  2012   \n",
       "...               ...   ...  ...   ...   ...   ...   ...      ...   ...   ...   \n",
       "7771     352    46  2     1  CI6   352    46     2   352   604654     1  2012   \n",
       "7772     352    46  4     1  CI6   352    46     4   352   604654     1  2012   \n",
       "7773     352    46  4     2  CI6   352    46     4   352   604654     1  2012   \n",
       "7774     352    47  2     1  CI6   352    47     2   352   604654     1  2012   \n",
       "7775     352    49  5     1  CI6   352    49     5   352   604654     1  2012   \n",
       "\n",
       "      ...  s1221f  idx94  s423a   s423b  s446a  idx95  s506y  sd506y  sm506y  \\\n",
       "0     ...     0.0      1   11.0  9998.0    2.0      1      0     NaN     NaN   \n",
       "1     ...     0.0      1    NaN     NaN    NaN      1      0     NaN     NaN   \n",
       "2     ...     0.0      1   22.0  1275.0    2.0      1      2     NaN     NaN   \n",
       "3     ...     0.0      1    NaN     NaN    NaN      1      0     NaN     NaN   \n",
       "4     ...     0.0      2    NaN     NaN    NaN      2      0     NaN     NaN   \n",
       "...   ...     ...    ...    ...     ...    ...    ...    ...     ...     ...   \n",
       "7771  ...     NaN      1   22.0  1080.0    1.0      1      0     NaN     NaN   \n",
       "7772  ...     0.0      1    NaN     NaN    NaN      1      0     NaN     NaN   \n",
       "7773  ...     0.0      2    NaN     NaN    NaN      2      0     NaN     NaN   \n",
       "7774  ...     0.0      1    NaN     NaN    1.0      1      1     1.0     4.0   \n",
       "7775  ...     NaN      1   11.0  1090.0    2.0      1      0     NaN     NaN   \n",
       "\n",
       "      sy506y  \n",
       "0        NaN  \n",
       "1        NaN  \n",
       "2        NaN  \n",
       "3        NaN  \n",
       "4        NaN  \n",
       "...      ...  \n",
       "7771     NaN  \n",
       "7772     NaN  \n",
       "7773     NaN  \n",
       "7774  2008.0  \n",
       "7775     NaN  \n",
       "\n",
       "[7776 rows x 1017 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert .dta file\n",
    "dhs_civ_df_kr = pd.read_stata(path.join(input_dir,\"dhs/CIKR62DT_children/CIKR62FL.DTA\"),convert_categoricals=False)\n",
    "dhs_civ_df_kr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3da24344",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N° households w/ children under-5: 5431\n",
      "N° clusters: 351\n",
      "dtype cluster name var: int16\n",
      "min: 1\n",
      "max: 352\n",
      "Histogram of answers to diarrhoea question (by code):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# N° households & clusters\n",
    "print(\"N° households w/ children under-5:\",dhs_civ_df_kr['caseid'].unique().shape[0])\n",
    "print(\"N° clusters:\",dhs_civ_df_kr['v001'].unique().shape[0])\n",
    "print(\"dtype cluster name var:\",dhs_civ_df_kr['v001'].dtype)\n",
    "print(\"min:\",dhs_civ_df_kr['v001'].unique().min())\n",
    "print(\"max:\",dhs_civ_df_kr['v001'].unique().max())\n",
    "\n",
    "# See answers for diarrhoea\n",
    "print(\"Histogram of answers to diarrhoea question (by code):\")\n",
    "dhs_civ_df_kr.h11.hist()#histogram with answers to diarrhoea question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e38f57a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Under-5 population (unweighted, valid obs.): 6732\n",
      "N° cases (unweighted, valid obs.): 1247 ( 18.523469994058228 %)\n",
      "--------------------- EXCLUDED OBS. ---------------------\n",
      "Don't know (excluded from analysis): 176\n",
      "Missing or not applicable (excluded from analysis): 724\n",
      "Not de jure resident (excluded from analysis): 144\n"
     ]
    }
   ],
   "source": [
    "# Prevalence of diarrhoea (excluding invalid obs. & non-'de jure' residents)\n",
    "\n",
    "# Subset groups (valid obs. & 'de jure' residents only)\n",
    "cases_subset = dhs_civ_df_kr[['v001','v005','h11']][(dhs_civ_df_kr['h11'].isin([1,2])) # 1: diarrhoea in last 24h / 2: diarrhoea in last 2 weeks\n",
    "                                                    &(~dhs_civ_df_kr['v139'].isin([97,99])) # 97: not de jure resident / 99: missing info\n",
    "                                                   ]\n",
    "cases_subset['n_count'] = 1\n",
    "cases_subset['n_count'] = cases_subset['n_count'].astype('int')\n",
    "n_cases = cases_subset.n_count.sum()\n",
    "pop_subset = dhs_civ_df_kr[['v001','v005','h11']][(dhs_civ_df_kr['h11'].isin([0,1,2]))\n",
    "                                                  &(~dhs_civ_df_kr['v139'].isin([97,99]))\n",
    "                                                 ]\n",
    "pop_subset['wt'] = pop_subset['v005']/1000000\n",
    "pop_subset['n_count'] = 1\n",
    "pop_subset['n_count'] = pop_subset['n_count'].astype('int')\n",
    "n_pop = pop_subset.n_count.sum()\n",
    "print(\"Under-5 population (unweighted, valid obs.):\",n_pop)\n",
    "print(\"N° cases (unweighted, valid obs.):\",n_cases,\"(\",(n_cases/n_pop)*100,\"%)\")\n",
    "\n",
    "# Excluded observations\n",
    "# Don't know\n",
    "print(\"--------------------- EXCLUDED OBS. ---------------------\")\n",
    "n_dont_know = dhs_civ_df_kr[dhs_civ_df_kr['h11']==8].shape[0]\n",
    "print(\"Don't know (excluded from analysis):\",n_dont_know)\n",
    "# Non-applicable / missing obs.\n",
    "n_missing_or_na = dhs_civ_df_kr['h11'].isna().sum()+dhs_civ_df_kr[dhs_civ_df_kr['h11']==9].shape[0]\n",
    "print(\"Missing or not applicable (excluded from analysis):\",n_missing_or_na)\n",
    "# Not de jure resident\n",
    "n_not_dejure = dhs_civ_df_kr[(dhs_civ_df_kr['v139'].isin([97,99]))&\n",
    "                             (~dhs_civ_df_kr['h11'].isin([8,9]))&\n",
    "                             (~dhs_civ_df_kr['h11'].isna())\n",
    "                            ].shape[0]\n",
    "print(\"Not de jure resident (excluded from analysis):\",n_not_dejure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b898b762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Under-5 population (unweighted, valid obs.): 6876\n",
      "N° cases (unweighted, valid obs.): 1276 ( 18.557300756253635 %)\n",
      "--------------------- EXCLUDED OBS. ---------------------\n",
      "Don't know (excluded from analysis): 176\n",
      "Missing or not applicable (excluded from analysis): 724\n"
     ]
    }
   ],
   "source": [
    "# Prevalence of diarrhoea (excluding invalid obs. only)\n",
    "\n",
    "# Subset groups (valid obs. only)\n",
    "cases_subset_bis = dhs_civ_df_kr[['v001','v005','h11']][(dhs_civ_df_kr['h11'].isin([1,2]))] # 1: diarrhoea in last 24h / 2: diarrhoea in last 2 weeks\n",
    "cases_subset_bis['n_count'] = 1\n",
    "cases_subset_bis['n_count'] = cases_subset_bis['n_count'].astype('int')\n",
    "n_cases_bis = cases_subset_bis.n_count.sum()\n",
    "pop_subset_bis = dhs_civ_df_kr[['v001','v005','h11']][(dhs_civ_df_kr['h11'].isin([0,1,2]))]\n",
    "pop_subset_bis['n_count'] = 1\n",
    "pop_subset_bis['n_count'] = pop_subset_bis['n_count'].astype('int')\n",
    "n_pop_bis = pop_subset_bis.n_count.sum()\n",
    "print(\"Under-5 population (unweighted, valid obs.):\",n_pop_bis)\n",
    "print(\"N° cases (unweighted, valid obs.):\",n_cases_bis,\"(\",(n_cases_bis/n_pop_bis)*100,\"%)\")\n",
    "\n",
    "# Excluded observations\n",
    "# Don't know\n",
    "print(\"--------------------- EXCLUDED OBS. ---------------------\")\n",
    "n_dont_know = dhs_civ_df_kr[dhs_civ_df_kr['h11']==8].shape[0]\n",
    "print(\"Don't know (excluded from analysis):\",n_dont_know)\n",
    "# Non-applicable / missing obs.\n",
    "n_missing_or_na = dhs_civ_df_kr['h11'].isna().sum()+dhs_civ_df_kr[dhs_civ_df_kr['h11']==9].shape[0]\n",
    "print(\"Missing or not applicable (excluded from analysis):\",n_missing_or_na)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a5f5c02",
   "metadata": {},
   "source": [
    "### Retrieve household questionnaire data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eda75d3e",
   "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>hhid</th>\n",
       "      <th>hv000</th>\n",
       "      <th>hv001</th>\n",
       "      <th>hv002</th>\n",
       "      <th>hv003</th>\n",
       "      <th>hv004</th>\n",
       "      <th>hv005</th>\n",
       "      <th>hv006</th>\n",
       "      <th>hv007</th>\n",
       "      <th>hv008</th>\n",
       "      <th>...</th>\n",
       "      <th>chl8_08</th>\n",
       "      <th>chl8_09</th>\n",
       "      <th>chl8_10</th>\n",
       "      <th>chl8_11</th>\n",
       "      <th>chl8_12</th>\n",
       "      <th>chl8_13</th>\n",
       "      <th>chl8_14</th>\n",
       "      <th>chl8_15</th>\n",
       "      <th>chl8_16</th>\n",
       "      <th>chl8_17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1     1</td>\n",
       "      <td>CI6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1817406</td>\n",
       "      <td>5</td>\n",
       "      <td>2012</td>\n",
       "      <td>1349</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1     6</td>\n",
       "      <td>CI6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1817406</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1345</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1     7</td>\n",
       "      <td>CI6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1817406</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1345</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1    10</td>\n",
       "      <td>CI6</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1817406</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1345</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1    11</td>\n",
       "      <td>CI6</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1817406</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1345</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9681</th>\n",
       "      <td>352    43</td>\n",
       "      <td>CI6</td>\n",
       "      <td>352</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>352</td>\n",
       "      <td>620842</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1345</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9682</th>\n",
       "      <td>352    45</td>\n",
       "      <td>CI6</td>\n",
       "      <td>352</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>352</td>\n",
       "      <td>620842</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1345</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9683</th>\n",
       "      <td>352    46</td>\n",
       "      <td>CI6</td>\n",
       "      <td>352</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>352</td>\n",
       "      <td>620842</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1345</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9684</th>\n",
       "      <td>352    47</td>\n",
       "      <td>CI6</td>\n",
       "      <td>352</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "      <td>352</td>\n",
       "      <td>620842</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1345</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9685</th>\n",
       "      <td>352    49</td>\n",
       "      <td>CI6</td>\n",
       "      <td>352</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>352</td>\n",
       "      <td>620842</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1345</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9686 rows × 3900 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              hhid hv000  hv001  hv002  hv003  hv004    hv005  hv006  hv007  \\\n",
       "0          1     1   CI6      1      1      1      1  1817406      5   2012   \n",
       "1          1     6   CI6      1      6      3      1  1817406      1   2012   \n",
       "2          1     7   CI6      1      7      2      1  1817406      1   2012   \n",
       "3          1    10   CI6      1     10      1      1  1817406      1   2012   \n",
       "4          1    11   CI6      1     11      2      1  1817406      1   2012   \n",
       "...            ...   ...    ...    ...    ...    ...      ...    ...    ...   \n",
       "9681     352    43   CI6    352     43      1    352   620842      1   2012   \n",
       "9682     352    45   CI6    352     45      1    352   620842      1   2012   \n",
       "9683     352    46   CI6    352     46      1    352   620842      1   2012   \n",
       "9684     352    47   CI6    352     47      2    352   620842      1   2012   \n",
       "9685     352    49   CI6    352     49      1    352   620842      1   2012   \n",
       "\n",
       "      hv008  ...  chl8_08  chl8_09  chl8_10  chl8_11  chl8_12  chl8_13  \\\n",
       "0      1349  ...      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "1      1345  ...      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2      1345  ...      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "3      1345  ...      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "4      1345  ...      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "...     ...  ...      ...      ...      ...      ...      ...      ...   \n",
       "9681   1345  ...      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "9682   1345  ...      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "9683   1345  ...      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "9684   1345  ...      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "9685   1345  ...      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "      chl8_14  chl8_15  chl8_16  chl8_17  \n",
       "0         NaN      NaN      NaN      NaN  \n",
       "1         NaN      NaN      NaN      NaN  \n",
       "2         NaN      NaN      NaN      NaN  \n",
       "3         NaN      NaN      NaN      NaN  \n",
       "4         NaN      NaN      NaN      NaN  \n",
       "...       ...      ...      ...      ...  \n",
       "9681      NaN      NaN      NaN      NaN  \n",
       "9682      NaN      NaN      NaN      NaN  \n",
       "9683      NaN      NaN      NaN      NaN  \n",
       "9684      NaN      NaN      NaN      NaN  \n",
       "9685      NaN      NaN      NaN      NaN  \n",
       "\n",
       "[9686 rows x 3900 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert .dta file\n",
    "dhs_civ_df_hr = pd.read_stata(path.join(input_dir,\"dhs/CIHR62DT_household/CIHR62FL.DTA\"),convert_categoricals=False)\n",
    "dhs_civ_df_hr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9a5d024e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median n° of participants 134.0\n",
      "Median n° of households 27.0\n"
     ]
    }
   ],
   "source": [
    "# Describe clusters\n",
    "\n",
    "# Median number of participants (surveyed household members) in each cluster\n",
    "print('Median n° of participants',dhs_civ_df_hr[['hv001','hv012']].groupby(['hv001']).sum().hv012.median())\n",
    "\n",
    "# Median number of surveyed households in each cluster\n",
    "print('Median n° of households',dhs_civ_df_hr[['hv001','hhid']].groupby(['hv001']).count().hhid.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e1d0732e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N° households interviewed: 9686\n",
      "N° clusters: 351\n",
      "dtype cluster name var: int16\n",
      "min: 1\n",
      "max: 352\n"
     ]
    }
   ],
   "source": [
    "# N° households & clusters\n",
    "print(\"N° households interviewed:\",dhs_civ_df_hr['hhid'].unique().shape[0])\n",
    "print(\"N° clusters:\",dhs_civ_df_hr['hv001'].unique().shape[0])\n",
    "print(\"dtype cluster name var:\",dhs_civ_df_hr['hv001'].dtype)\n",
    "print(\"min:\",dhs_civ_df_hr['hv001'].unique().min())\n",
    "print(\"max:\",dhs_civ_df_hr['hv001'].unique().max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7d9bc254",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No overlap; recoding improved/unimproved OK!\n",
      "Households with access to improved water (unweighted n°): 7681 ( 79.35737162930054 %)\n",
      "Households with unimproved water (unweighted n°): 1998 ( 20.642628370699452 %)\n",
      "Population with access to improved water (unweighted n°): 40029 ( 79.54414482443416 %)\n",
      "Population with unimproved water (unweighted n°): 10294 ( 20.455855175565844 %)\n",
      "Observations excluded from calculation (info missing): 7\n"
     ]
    }
   ],
   "source": [
    "# Source of drinking water (1)\n",
    "\n",
    "# Recoding info: https://dhsprogram.com/Data/Guide-to-DHS-Statistics/index.cfm\n",
    "# Water: https://dhsprogram.com/data/Guide-to-DHS-Statistics/index.htm#t=Household_Drinking_Water.htm\n",
    "\n",
    "# IMPROVED water subset\n",
    "imp_wat_df = dhs_civ_df_hr[['hhid','hv001','hv005','hv012','hv201']][dhs_civ_df_hr['hv201'].isin([11, 12, 13, 14, 21, 31, 41, 51, 61, 62, 71])]\n",
    "imp_wat_df['hh_count'] = 1\n",
    "imp_wat_df['hh_hab'] = dhs_civ_df_hr['hv012']# people that usually live in the surveyed household ('hv012')\n",
    "\n",
    "# Unimproved water subset\n",
    "unimp_wat_df = dhs_civ_df_hr[['hhid','hv001','hv005','hv012','hv201']][dhs_civ_df_hr['hv201'].isin([32, 42, 43, 96])]\n",
    "unimp_wat_df['hh_count'] = 1\n",
    "unimp_wat_df['hh_hab'] = unimp_wat_df['hv012']\n",
    "\n",
    "# Check that there are no overlaps (improved/unimproved categories must be exclusive)\n",
    "if len(list(set(imp_wat_df.hhid.to_list()) & set(unimp_wat_df.hhid.to_list())))>0:\n",
    "    print(\"overlapping hhid n° between imp & unimp. water\")\n",
    "else:\n",
    "    print(\"No overlap; recoding improved/unimproved OK!\")\n",
    "\n",
    "# Stats on access to improved water (households, UNWEIGHTED)\n",
    "n_imp_hh = imp_wat_df.hh_count.sum()\n",
    "n_unimp_hh = unimp_wat_df.hh_count.sum()\n",
    "print(\"Households with access to improved water (unweighted n°):\",\n",
    "      n_imp_hh,\"(\",\n",
    "      (n_imp_hh/(n_imp_hh+n_unimp_hh))*100,\"%)\")\n",
    "print(\"Households with unimproved water (unweighted n°):\",\n",
    "      n_unimp_hh,\"(\",\n",
    "      (n_unimp_hh/(n_imp_hh+n_unimp_hh))*100,\"%)\")\n",
    "\n",
    "# Stats on access to improved water (individuals, UNWEIGHTED)\n",
    "n_imp_pop = imp_wat_df.hh_hab.sum()\n",
    "n_unimp_pop = unimp_wat_df.hh_hab.sum()\n",
    "print(\"Population with access to improved water (unweighted n°):\",\n",
    "      n_imp_pop,\"(\",\n",
    "      (n_imp_pop/(n_imp_pop+n_unimp_pop))*100,\"%)\")\n",
    "print(\"Population with unimproved water (unweighted n°):\",\n",
    "      n_unimp_pop,\"(\",\n",
    "      (n_unimp_pop/(n_imp_pop+n_unimp_pop))*100,\"%)\")\n",
    "\n",
    "# Excluded obs.\n",
    "print(\"Observations excluded from calculation (info missing):\",\n",
    "      dhs_civ_df_hr[dhs_civ_df_hr['hv201'].isin([97,99])].shape[0]+dhs_civ_df_hr[dhs_civ_df_hr['hv201'].isna()].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cb5a313d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No overlap; recoding improved/unimproved OK!\n",
      "Households with access to BASIC water (unweighted n°): 7047 ( 72.84473847426091 %)\n",
      "Households WITHOUT access to basic water (unweighted n°): 2627 ( 27.155261525739093 %)\n",
      "Population with access to improved water (unweighted n°): 36302 ( 72.17240899421459 %)\n",
      "Population with unimproved water (unweighted n°): 13997 ( 27.8275910057854 %)\n",
      "Observations excluded from calculation (info missing): 12\n"
     ]
    }
   ],
   "source": [
    "# Source of drinking water (2)\n",
    "\n",
    "# BASIC water subset (improved source, within 30 min. both ways)\n",
    "bas_wat_df = dhs_civ_df_hr[['hhid','hv001','hv005','hv012','hv201','hv204']][(dhs_civ_df_hr['hv201'].isin([11, 12, 13, 14, 21, 31, 41, 51, 61, 62, 71]))\n",
    "                                                                             &((dhs_civ_df_hr['hv204']<=30)|(dhs_civ_df_hr['hv204']==996))]\n",
    "bas_wat_df['hh_count'] = 1\n",
    "bas_wat_df['hh_hab'] = dhs_civ_df_hr['hv012']# people that usually live in the surveyed household ('hv012')\n",
    "\n",
    "# Non-Basic water subset\n",
    "nobas_wat_df = dhs_civ_df_hr[['hhid','hv001','hv005','hv012','hv201','hv204']][((dhs_civ_df_hr['hv201'].isin([32, 42, 43, 96]))\n",
    "                                                                                |((((dhs_civ_df_hr['hv204']>30)&(dhs_civ_df_hr['hv204']<501))\n",
    "                                                                                  |(dhs_civ_df_hr['hv204'].isin([998,999])))\n",
    "                                                                                  &(~dhs_civ_df_hr['hv201'].isna())))]\n",
    "nobas_wat_df['hh_count'] = 1\n",
    "nobas_wat_df['hh_hab'] = nobas_wat_df['hv012']\n",
    "\n",
    "# Check that there are no overlaps (improved/unimproved categories must be exclusive)\n",
    "if len(list(set(bas_wat_df.hhid.to_list()) & set(nobas_wat_df.hhid.to_list())))>0:\n",
    "    print(\"overlapping hhid n° between basic & not basic water\")\n",
    "else:\n",
    "    print(\"No overlap; recoding improved/unimproved OK!\")\n",
    "\n",
    "# Stats on access to BASIC water (households, UNWEIGHTED)\n",
    "n_bas_hh = bas_wat_df.hh_count.sum()\n",
    "n_nobas_hh = nobas_wat_df.hh_count.sum()\n",
    "print(\"Households with access to BASIC water (unweighted n°):\",\n",
    "      n_bas_hh,\"(\",\n",
    "      (n_bas_hh/(n_bas_hh+n_nobas_hh))*100,\"%)\")\n",
    "print(\"Households WITHOUT access to basic water (unweighted n°):\",\n",
    "      n_nobas_hh,\"(\",\n",
    "      (n_nobas_hh/(n_bas_hh+n_nobas_hh))*100,\"%)\")\n",
    "\n",
    "# Stats on access to BASIC water (individuals, UNWEIGHTED)\n",
    "n_bas_pop = bas_wat_df.hh_hab.sum()\n",
    "n_nobas_pop = nobas_wat_df.hh_hab.sum()\n",
    "print(\"Population with access to improved water (unweighted n°):\",\n",
    "      n_bas_pop,\"(\",\n",
    "      (n_bas_pop/(n_bas_pop+n_nobas_pop))*100,\"%)\")\n",
    "print(\"Population with unimproved water (unweighted n°):\",\n",
    "      n_nobas_pop,\"(\",\n",
    "      (n_nobas_pop/(n_bas_pop+n_nobas_pop))*100,\"%)\")\n",
    "\n",
    "# Excluded obs.\n",
    "print(\"Observations excluded from calculation (info missing):\",\n",
    "      9686-(n_bas_hh+n_nobas_hh))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b99cf2aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No overlap; recoding improved/not improved OK!\n",
      "Households with access to improved sanitation (unweighted n°): 4398 ( 45.509105960264904 %)\n",
      "Households without access to improved sanitation (unweighted n°): 5266 ( 54.490894039735096 %)\n",
      "Population with access to improved sanitation (unweighted n°): 22912 ( 45.588761988141194 %)\n",
      "Population without access to improved sanitation (unweighted n°): 27346 ( 54.411238011858806 %)\n",
      "Observations excluded from calculation (info missing): 22\n"
     ]
    }
   ],
   "source": [
    "# Type of toilet facility (1)\n",
    "\n",
    "# Recoding info: https://dhsprogram.com/Data/Guide-to-DHS-Statistics/index.cfm\n",
    "# Sanitation: https://dhsprogram.com/data/Guide-to-DHS-Statistics/index.htm#t=Type_of_Sanitation_Facility.htm\n",
    "\n",
    "# Improved sanitation >> excludes \"flush don't know where\" (eventhough DHS recoding includes this category)\n",
    "# NOTE: only 4 obs. for \"flush don't know where\"\n",
    "imp_san_df = dhs_civ_df_hr[['hhid','hv001','hv005','hv012','hv205']][dhs_civ_df_hr['hv205'].isin([11, 12, 13, 21, 22, 41])]\n",
    "imp_san_df['hh_count'] = 1\n",
    "imp_san_df['hh_hab'] = imp_san_df['hv012']\n",
    "\n",
    "# Non-improved sanitation >> includes: flush don't know where ('hv205'==15) + open defecation ('hv205'==31)\n",
    "# NOTE: only 4 obs. for \"flush don't know where\"\n",
    "unimp_san_df = dhs_civ_df_hr[['hhid','hv001','hv005','hv012','hv205']][dhs_civ_df_hr['hv205'].isin([14, 15, 23, 31, 42, 43, 96])]\n",
    "unimp_san_df['hh_count'] = 1\n",
    "unimp_san_df['hh_hab'] = unimp_san_df['hv012']\n",
    "\n",
    "# Check that there are no overlaps (improved/unimproved categories must be exclusive)\n",
    "if len(list(set(imp_san_df.hhid.to_list()) & set(unimp_san_df.hhid.to_list())))>0:\n",
    "    print(\"overlapping hhid n° between imp & not imp. sanitation\")\n",
    "else:\n",
    "    print(\"No overlap; recoding improved/not improved OK!\")\n",
    "\n",
    "# Stats on access to improved sanitation (households, UNWEIGHETED)\n",
    "n_imps_hh = imp_san_df.hh_count.sum()\n",
    "n_unimps_hh = unimp_san_df.hh_count.sum()\n",
    "print(\"Households with access to improved sanitation (unweighted n°):\",\n",
    "      n_imps_hh,\"(\",\n",
    "      (n_imps_hh/(n_imps_hh+n_unimps_hh))*100,\"%)\")\n",
    "print(\"Households without access to improved sanitation (unweighted n°):\",\n",
    "      n_unimps_hh,\"(\",\n",
    "      (n_unimps_hh/(n_imps_hh+n_unimps_hh))*100,\"%)\")\n",
    "\n",
    "# Stats on access to improved sanitation (individuals, UNWEIGHETED)\n",
    "n_imps_pop = imp_san_df.hh_hab.sum()\n",
    "n_unimps_pop = unimp_san_df.hh_hab.sum()\n",
    "print(\"Population with access to improved sanitation (unweighted n°):\",\n",
    "      n_imps_pop,\"(\",\n",
    "      (n_imps_pop/(n_imps_pop+n_unimps_pop))*100,\"%)\")\n",
    "print(\"Population without access to improved sanitation (unweighted n°):\",\n",
    "      n_unimps_pop,\"(\",\n",
    "      (n_unimps_pop/(n_imps_pop+n_unimps_pop))*100,\"%)\")\n",
    "\n",
    "# Excluded obs:\n",
    "print(\"Observations excluded from calculation (info missing):\",\n",
    "      dhs_civ_df_hr[dhs_civ_df_hr['hv205'].isin([99])].shape[0]+dhs_civ_df_hr[dhs_civ_df_hr['hv205'].isna()].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "28f87425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No overlap; recoding improved/not improved OK!\n",
      "Households with access to BASIC sanitation (unweighted n°): 1687 ( 17.494555636212798 %)\n",
      "Households WITHOUT access to basic sanitation (unweighted n°): 7956 ( 82.5054443637872 %)\n",
      "Population with access to BASIC sanitation (unweighted n°): 10608 ( 21.152542372881356 %)\n",
      "Population WITHOUT access to basic sanitation (unweighted n°): 39542 ( 78.84745762711864 %)\n",
      "Observations excluded from calculation (info missing): 43\n"
     ]
    }
   ],
   "source": [
    "# Type of toilet facility (2)\n",
    "\n",
    "# Basic sanitation >> 'improved' excludes \"flush don't know where\" (eventhough DHS recoding includes this category)\n",
    "# NOTE: only 4 obs. for \"flush don't know where\"\n",
    "bas_san_df = dhs_civ_df_hr[['hhid','hv001','hv005','hv012','hv205','hv225']][(dhs_civ_df_hr['hv205'].isin([11, 12, 13, 21, 22, 41]))&\n",
    "                                                                             (dhs_civ_df_hr['hv225']==0)]\n",
    "bas_san_df['hh_count'] = 1\n",
    "bas_san_df['hh_hab'] = bas_san_df['hv012']\n",
    "\n",
    "# Non-basic sanitation >> 'unimproved' includes: flush don't know where ('hv205'==15) + open defecation ('hv205'==31)\n",
    "# NOTE: only 4 obs. for \"flush don't know where\"\n",
    "nobas_san_df = dhs_civ_df_hr[['hhid','hv001','hv005','hv012','hv205','hv225']][((dhs_civ_df_hr['hv205'].isin([14, 15, 23, 31, 42, 43, 96]))|(dhs_civ_df_hr['hv225']==1))\n",
    "                                                                               &(~dhs_civ_df_hr['hv205'].isna())]\n",
    "nobas_san_df['hh_count'] = 1\n",
    "nobas_san_df['hh_hab'] = nobas_san_df['hv012']\n",
    "\n",
    "# Check that there are no overlaps (improved/unimproved categories must be exclusive)\n",
    "if len(list(set(bas_san_df.hhid.to_list()) & set(nobas_san_df.hhid.to_list())))>0:\n",
    "    print(\"overlapping hhid n° between imp & not imp. sanitation\")\n",
    "else:\n",
    "    print(\"No overlap; recoding improved/not improved OK!\")\n",
    "\n",
    "# Stats on access to improved sanitation (households, UNWEIGHETED)\n",
    "n_bass_hh = bas_san_df.hh_count.sum()\n",
    "n_nobass_hh = nobas_san_df.hh_count.sum()\n",
    "print(\"Households with access to BASIC sanitation (unweighted n°):\",\n",
    "      n_bass_hh,\"(\",\n",
    "      (n_bass_hh/(n_bass_hh+n_nobass_hh))*100,\"%)\")\n",
    "print(\"Households WITHOUT access to basic sanitation (unweighted n°):\",\n",
    "      n_nobass_hh,\"(\",\n",
    "      (n_nobass_hh/(n_bass_hh+n_nobass_hh))*100,\"%)\")\n",
    "\n",
    "# Stats on access to improved sanitation (individuals, UNWEIGHETED)\n",
    "n_bass_pop = bas_san_df.hh_hab.sum()\n",
    "n_nobass_pop = nobas_san_df.hh_hab.sum()\n",
    "print(\"Population with access to BASIC sanitation (unweighted n°):\",\n",
    "      n_bass_pop,\"(\",\n",
    "      (n_bass_pop/(n_bass_pop+n_nobass_pop))*100,\"%)\")\n",
    "print(\"Population WITHOUT access to basic sanitation (unweighted n°):\",\n",
    "      n_nobass_pop,\"(\",\n",
    "      (n_nobass_pop/(n_bass_pop+n_nobass_pop))*100,\"%)\")\n",
    "\n",
    "# Excluded obs.\n",
    "print(\"Observations excluded from calculation (info missing):\",\n",
    "      9686-(n_bass_hh+n_nobass_hh))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "708ff442",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No overlap; recoding safe/not safe OK!\n",
      "Households with access to safe hygiene (unweighted n°): 1118 ( 18.055555555555554 %)\n",
      "Households without access to safe hygiene (unweighted n°): 5074 ( 81.94444444444444 %)\n",
      "Population with access to safe hygiene (unweighted n°): 6271 ( 19.552881017710153 %)\n",
      "Population without access to safe hygiene (unweighted n°): 25801 ( 80.44711898228985 %)\n"
     ]
    }
   ],
   "source": [
    "# Place for washing hands, with water and soap available\n",
    "\n",
    "# Subset groups\n",
    "imp_hyg_df = dhs_civ_df_hr[['hhid','hv001','hv005','hv012','hv230a','hv230a','hv230b','hv232']][((dhs_civ_df_hr['hv230a']==1)&\n",
    "                                                                                          (dhs_civ_df_hr['hv230b']==1)&\n",
    "                                                                                          (dhs_civ_df_hr['hv232']==1))]\n",
    "unimp_hyg_df = dhs_civ_df_hr[['hhid','hv001','hv005','hv012','hv230a']][(dhs_civ_df_hr['hv230a']!=1)&\n",
    "                                                                        (dhs_civ_df_hr['hv230a']!=9)&\n",
    "                                                                        (~dhs_civ_df_hr['hv230a'].isna())]\n",
    "imp_hyg_df['hh_count'] = 1\n",
    "imp_hyg_df['hh_hab'] = imp_hyg_df['hv012']\n",
    "\n",
    "unimp_hyg_df['hh_count'] = 1\n",
    "unimp_hyg_df['hh_hab'] = unimp_hyg_df['hv012']\n",
    "\n",
    "# Check that there are no overlaps (safely managed/other categories must be exclusive)\n",
    "if len(list(set(imp_hyg_df.hhid.to_list()) & set(unimp_hyg_df.hhid.to_list())))>0:\n",
    "    print(\"overlapping hhid n° between safe & not safe hygiene\")\n",
    "else:\n",
    "    print(\"No overlap; recoding safe/not safe OK!\")\n",
    "\n",
    "# Stats on access to safely managed hygiene (households, UNWEIGHETED)\n",
    "n_imph_hh = imp_hyg_df.hh_count.sum()\n",
    "n_unimph_hh = unimp_hyg_df.hh_count.sum()\n",
    "print(\"Households with access to safe hygiene (unweighted n°):\",\n",
    "      n_imph_hh,\"(\",\n",
    "      (n_imph_hh/(n_imph_hh+n_unimph_hh))*100,\"%)\")\n",
    "print(\"Households without access to safe hygiene (unweighted n°):\",\n",
    "      n_unimph_hh,\"(\",\n",
    "      (n_unimph_hh/(n_imph_hh+n_unimph_hh))*100,\"%)\")\n",
    "\n",
    "# Stats on access to safely managed hygiene (individuals, UNWEIGHETED)\n",
    "n_imph_pop = imp_hyg_df.hh_hab.sum()\n",
    "n_unimph_pop = unimp_hyg_df.hh_hab.sum()\n",
    "print(\"Population with access to safe hygiene (unweighted n°):\",\n",
    "      n_imph_pop,\"(\",\n",
    "      (n_imph_pop/(n_imph_pop+n_unimph_pop))*100,\"%)\")\n",
    "print(\"Population without access to safe hygiene (unweighted n°):\",\n",
    "      n_unimph_pop,\"(\",\n",
    "      (n_unimph_pop/(n_imph_pop+n_unimph_pop))*100,\"%)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01659fe8",
   "metadata": {},
   "source": [
    "### Retrieve women's questionnaire data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "59d47142",
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10059</th>\n",
       "      <td>352    49  6</td>\n",
       "      <td>CI6</td>\n",
       "      <td>352</td>\n",
       "      <td>49</td>\n",
       "      <td>6</td>\n",
       "      <td>352</td>\n",
       "      <td>604654</td>\n",
       "      <td>1</td>\n",
       "      <td>2012</td>\n",
       "      <td>1345</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10060 rows × 4364 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                caseid v000  v001  v002  v003  v004     v005  v006  v007  \\\n",
       "0           1     1  1  CI6     1     1     1     1  1803150     5  2012   \n",
       "1           1     6  3  CI6     1     6     3     1  1803150     1  2012   \n",
       "2           1     7  2  CI6     1     7     2     1  1803150     1  2012   \n",
       "3           1    10  2  CI6     1    10     2     1  1803150     5  2012   \n",
       "4           1    13  2  CI6     1    13     2     1  1803150     5  2012   \n",
       "...                ...  ...   ...   ...   ...   ...      ...   ...   ...   \n",
       "10055     352    47  2  CI6   352    47     2   352   604654     1  2012   \n",
       "10056     352    49  2  CI6   352    49     2   352   604654     1  2012   \n",
       "10057     352    49  4  CI6   352    49     4   352   604654     1  2012   \n",
       "10058     352    49  5  CI6   352    49     5   352   604654     1  2012   \n",
       "10059     352    49  6  CI6   352    49     6   352   604654     1  2012   \n",
       "\n",
       "       v008  ...  sm506y_3  sm506y_4  sm506y_5  sm506y_6  sy506y_1  sy506y_2  \\\n",
       "0      1349  ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1      1345  ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2      1345  ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "3      1349  ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "4      1349  ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "...     ...  ...       ...       ...       ...       ...       ...       ...   \n",
       "10055  1345  ...       NaN       NaN       NaN       NaN    2008.0       NaN   \n",
       "10056  1345  ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "10057  1345  ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "10058  1345  ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "10059  1345  ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "       sy506y_3  sy506y_4  sy506y_5  sy506y_6  \n",
       "0           NaN       NaN       NaN       NaN  \n",
       "1           NaN       NaN       NaN       NaN  \n",
       "2           NaN       NaN       NaN       NaN  \n",
       "3           NaN       NaN       NaN       NaN  \n",
       "4           NaN       NaN       NaN       NaN  \n",
       "...         ...       ...       ...       ...  \n",
       "10055       NaN       NaN       NaN       NaN  \n",
       "10056       NaN       NaN       NaN       NaN  \n",
       "10057       NaN       NaN       NaN       NaN  \n",
       "10058       NaN       NaN       NaN       NaN  \n",
       "10059       NaN       NaN       NaN       NaN  \n",
       "\n",
       "[10060 rows x 4364 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert .dta file\n",
    "dhs_civ_df_ir = pd.read_stata(path.join(input_dir,\"dhs/CIIR62DT_ind_women/CIIR62FL.DTA\"),convert_categoricals=False)\n",
    "dhs_civ_df_ir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2bb6fee0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N° of interviewed women: 10060\n",
      "N° clusters: 351\n",
      "dtype cluster name var: int16\n",
      "min: 1\n",
      "max: 352\n"
     ]
    }
   ],
   "source": [
    "# N° households & clusters\n",
    "print(\"N° of interviewed women:\",dhs_civ_df_ir['caseid'].unique().shape[0])\n",
    "print(\"N° clusters:\",dhs_civ_df_ir['v001'].unique().shape[0])\n",
    "print(\"dtype cluster name var:\",dhs_civ_df_ir['v001'].dtype)\n",
    "print(\"min:\",dhs_civ_df_ir['v001'].unique().min())\n",
    "print(\"max:\",dhs_civ_df_ir['v001'].unique().max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "483965fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population of women (unweighted, valid obs.): 9832\n",
      "N° women with NO education (unweighted, valid obs.): 5631 ( 57.272172497965826 %)\n",
      "--------------------- EXCLUDED ---------------------\n",
      "Missing or not applicable (excluded from analysis): 0\n",
      "Not de jure resident (excluded from analysis): 228\n"
     ]
    }
   ],
   "source": [
    "# Educational attainment (excluding invalid obs. & non-'de jure' residents)\n",
    "\n",
    "# Subset groups (valid obs. only) and add weight column\n",
    "nedu_subset = dhs_civ_df_ir[['v001','v005','v139','v149']][(dhs_civ_df_ir['v149'].isin([0]))& # 0: no education\n",
    "                                                           (~dhs_civ_df_ir['v139'].isin([97,99])) # 97: not de jure resident / 99: missing info\n",
    "                                                          ]\n",
    "nedu_subset['n_count'] = 1\n",
    "\n",
    "n_nedu = nedu_subset.n_count.sum()\n",
    "\n",
    "# Total women population\n",
    "wpop_subset = dhs_civ_df_ir[['v001','v005','v139','v149']][(dhs_civ_df_ir['v149'].isin([0,1,2,3,4,5]))&\n",
    "                                                           (~dhs_civ_df_ir['v139'].isin([97,99]))]\n",
    "\n",
    "wpop_subset['n_count'] = 1\n",
    "n_wpop = wpop_subset.n_count.sum()\n",
    "\n",
    "# Educational attainment statistics (UNWEIGHTED)\n",
    "print(\"Population of women (unweighted, valid obs.):\",n_wpop)\n",
    "print(\"N° women with NO education (unweighted, valid obs.):\",n_nedu,\"(\",(n_nedu/n_wpop)*100,\"%)\")\n",
    "\n",
    "# Excluded observations\n",
    "# Non-applicable / missing obs.\n",
    "print(\"--------------------- EXCLUDED ---------------------\")\n",
    "n_missing_or_na = dhs_civ_df_ir['v149'].isna().sum()+dhs_civ_df_ir[dhs_civ_df_ir['v149']==9].shape[0]\n",
    "print(\"Missing or not applicable (excluded from analysis):\",n_missing_or_na)\n",
    "# Not de jure resident\n",
    "n_not_dejure = dhs_civ_df_ir[(dhs_civ_df_ir['v139'].isin([97,99]))&\n",
    "                             (~dhs_civ_df_ir['v149'].isin([9]))&\n",
    "                             (~dhs_civ_df_ir['v149'].isna())\n",
    "                            ].shape[0]\n",
    "print(\"Not de jure resident (excluded from analysis):\",n_not_dejure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d1709a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population of women (unweighted, valid obs.): 10060\n",
      "N° women with NO education (unweighted, valid obs.): 5744 ( 57.09741550695825 %)\n",
      "--------------------- EXCLUDED ---------------------\n",
      "Missing or not applicable (excluded from analysis): 0\n"
     ]
    }
   ],
   "source": [
    "# Educational attainment (excluding invalid obs. only)\n",
    "\n",
    "# Subset groups (valid obs. only) and add weight column\n",
    "nedu_subset_bis = dhs_civ_df_ir[['v001','v005','v149']][(dhs_civ_df_ir['v149'].isin([0]))] # 0: no education\n",
    "\n",
    "nedu_subset_bis['n_count'] = 1\n",
    "\n",
    "n_nedu_bis = nedu_subset_bis.n_count.sum()\n",
    "\n",
    "# Total women population\n",
    "wpop_subset_bis = dhs_civ_df_ir[['v001','v005','v149']][(dhs_civ_df_ir['v149'].isin([0,1,2,3,4,5]))]\n",
    "\n",
    "wpop_subset_bis['n_count'] = 1\n",
    "n_wpop_bis = wpop_subset_bis.n_count.sum()\n",
    "\n",
    "# Educational attainment statistics (UNWEIGHTED)\n",
    "print(\"Population of women (unweighted, valid obs.):\",n_wpop_bis)\n",
    "print(\"N° women with NO education (unweighted, valid obs.):\",n_nedu_bis,\"(\",(n_nedu_bis/n_wpop_bis)*100,\"%)\")\n",
    "\n",
    "# Excluded observations\n",
    "# Non-applicable / missing obs.\n",
    "print(\"--------------------- EXCLUDED ---------------------\")\n",
    "n_missing_or_na = dhs_civ_df_ir['v149'].isna().sum()+dhs_civ_df_ir[dhs_civ_df_ir['v149']==9].shape[0]\n",
    "print(\"Missing or not applicable (excluded from analysis):\",n_missing_or_na)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d967f014",
   "metadata": {},
   "source": [
    "### Retrieve GPS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2088157c",
   "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>DHSID</th>\n",
       "      <th>DHSCC</th>\n",
       "      <th>DHSYEAR</th>\n",
       "      <th>DHSCLUST</th>\n",
       "      <th>CCFIPS</th>\n",
       "      <th>ADM1FIPS</th>\n",
       "      <th>ADM1FIPSNA</th>\n",
       "      <th>ADM1SALBNA</th>\n",
       "      <th>ADM1SALBCO</th>\n",
       "      <th>ADM1DHS</th>\n",
       "      <th>...</th>\n",
       "      <th>DHSREGCO</th>\n",
       "      <th>DHSREGNA</th>\n",
       "      <th>SOURCE</th>\n",
       "      <th>URBAN_RURA</th>\n",
       "      <th>LATNUM</th>\n",
       "      <th>LONGNUM</th>\n",
       "      <th>ALT_GPS</th>\n",
       "      <th>ALT_DEM</th>\n",
       "      <th>DATUM</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CI201200000001</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sud sans Abidjan</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>5.973453</td>\n",
       "      <td>-3.198473</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>POINT (-3.19847 5.97345)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CI201200000002</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.462503</td>\n",
       "      <td>-8.211979</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>POINT (-8.21198 6.46250)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CI201200000003</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>7.274597</td>\n",
       "      <td>-8.324563</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>POINT (-8.32456 7.27460)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CI201200000004</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Nord-Est</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>7.731449</td>\n",
       "      <td>-3.059561</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>POINT (-3.05956 7.73145)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CI201200000005</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Centre-Nord</td>\n",
       "      <td>GPS</td>\n",
       "      <td>U</td>\n",
       "      <td>7.713830</td>\n",
       "      <td>-5.040708</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>POINT (-5.04071 7.71383)</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>CI201200000348</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Centre-Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.361619</td>\n",
       "      <td>-5.715785</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>POINT (-5.71579 6.36162)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>CI201200000349</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Centre</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.987534</td>\n",
       "      <td>-5.277727</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>POINT (-5.27773 6.98753)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>CI201200000350</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Centre-Est</td>\n",
       "      <td>GPS</td>\n",
       "      <td>U</td>\n",
       "      <td>7.136385</td>\n",
       "      <td>-3.200418</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>POINT (-3.20042 7.13638)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>CI201200000351</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Centre-Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.658221</td>\n",
       "      <td>-6.512773</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>POINT (-6.51277 6.65822)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>CI201200000352</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>IV</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>NULL</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Centre-Nord</td>\n",
       "      <td>GPS</td>\n",
       "      <td>U</td>\n",
       "      <td>7.695010</td>\n",
       "      <td>-5.034002</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>POINT (-5.03400 7.69501)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              DHSID DHSCC  DHSYEAR  DHSCLUST CCFIPS ADM1FIPS ADM1FIPSNA  \\\n",
       "0    CI201200000001    CI   2012.0       1.0     IV     NULL       NULL   \n",
       "1    CI201200000002    CI   2012.0       2.0     IV     NULL       NULL   \n",
       "2    CI201200000003    CI   2012.0       3.0     IV     NULL       NULL   \n",
       "3    CI201200000004    CI   2012.0       4.0     IV     NULL       NULL   \n",
       "4    CI201200000005    CI   2012.0       5.0     IV     NULL       NULL   \n",
       "..              ...   ...      ...       ...    ...      ...        ...   \n",
       "346  CI201200000348    CI   2012.0     348.0     IV     NULL       NULL   \n",
       "347  CI201200000349    CI   2012.0     349.0     IV     NULL       NULL   \n",
       "348  CI201200000350    CI   2012.0     350.0     IV     NULL       NULL   \n",
       "349  CI201200000351    CI   2012.0     351.0     IV     NULL       NULL   \n",
       "350  CI201200000352    CI   2012.0     352.0     IV     NULL       NULL   \n",
       "\n",
       "    ADM1SALBNA ADM1SALBCO  ADM1DHS  ... DHSREGCO          DHSREGNA SOURCE  \\\n",
       "0         NULL       NULL   9999.0  ...      9.0  Sud sans Abidjan    GPS   \n",
       "1         NULL       NULL   9999.0  ...      8.0             Ouest    GPS   \n",
       "2         NULL       NULL   9999.0  ...      8.0             Ouest    GPS   \n",
       "3         NULL       NULL   9999.0  ...      6.0          Nord-Est    GPS   \n",
       "4         NULL       NULL   9999.0  ...      3.0       Centre-Nord    GPS   \n",
       "..         ...        ...      ...  ...      ...               ...    ...   \n",
       "346       NULL       NULL   9999.0  ...      4.0      Centre-Ouest    GPS   \n",
       "347       NULL       NULL   9999.0  ...      1.0            Centre    GPS   \n",
       "348       NULL       NULL   9999.0  ...      2.0        Centre-Est    GPS   \n",
       "349       NULL       NULL   9999.0  ...      4.0      Centre-Ouest    GPS   \n",
       "350       NULL       NULL   9999.0  ...      3.0       Centre-Nord    GPS   \n",
       "\n",
       "    URBAN_RURA    LATNUM   LONGNUM  ALT_GPS  ALT_DEM  DATUM  \\\n",
       "0            R  5.973453 -3.198473   9999.0    136.0  WGS84   \n",
       "1            R  6.462503 -8.211979   9999.0    255.0  WGS84   \n",
       "2            R  7.274597 -8.324563   9999.0    337.0  WGS84   \n",
       "3            R  7.731449 -3.059561   9999.0    223.0  WGS84   \n",
       "4            U  7.713830 -5.040708   9999.0    341.0  WGS84   \n",
       "..         ...       ...       ...      ...      ...    ...   \n",
       "346          R  6.361619 -5.715785   9999.0    264.0  WGS84   \n",
       "347          R  6.987534 -5.277727   9999.0    203.0  WGS84   \n",
       "348          U  7.136385 -3.200418   9999.0    242.0  WGS84   \n",
       "349          R  6.658221 -6.512773   9999.0    245.0  WGS84   \n",
       "350          U  7.695010 -5.034002   9999.0    360.0  WGS84   \n",
       "\n",
       "                     geometry  \n",
       "0    POINT (-3.19847 5.97345)  \n",
       "1    POINT (-8.21198 6.46250)  \n",
       "2    POINT (-8.32456 7.27460)  \n",
       "3    POINT (-3.05956 7.73145)  \n",
       "4    POINT (-5.04071 7.71383)  \n",
       "..                        ...  \n",
       "346  POINT (-5.71579 6.36162)  \n",
       "347  POINT (-5.27773 6.98753)  \n",
       "348  POINT (-3.20042 7.13638)  \n",
       "349  POINT (-6.51277 6.65822)  \n",
       "350  POINT (-5.03400 7.69501)  \n",
       "\n",
       "[351 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Open geodata\n",
    "dhs_civ_geodf = gpd.read_file(path.join(input_dir,\"dhs/CIGE61FL/CIGE61FL.shp\"))\n",
    "dhs_civ_geodf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "142eceed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N° clusters: 351\n",
      "dtype cluster name var: float64\n",
      "min: 1.0\n",
      "max: 352.0\n"
     ]
    }
   ],
   "source": [
    "# Global statistics\n",
    "print(\"N° clusters:\",dhs_civ_geodf['DHSCLUST'].unique().shape[0])\n",
    "print(\"dtype cluster name var:\",dhs_civ_geodf['DHSCLUST'].dtype)\n",
    "print(\"min:\",dhs_civ_geodf['DHSCLUST'].unique().min())\n",
    "print(\"max:\",dhs_civ_geodf['DHSCLUST'].unique().max())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43018aec",
   "metadata": {},
   "source": [
    "### Join datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "80cb0fa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subset OK\n"
     ]
    }
   ],
   "source": [
    "# Select variables of interest from GPS data\n",
    "gps_data_j = dhs_civ_geodf[['DHSID','DHSCC','DHSYEAR','DHSCLUST',\n",
    "                            'DHSREGCO','DHSREGNA','SOURCE',\n",
    "                            'URBAN_RURA','LATNUM','LONGNUM',\n",
    "                            'ALT_GPS','ALT_DEM','DATUM','geometry']]\n",
    "gps_data_j = gps_data_j.rename(columns={'DHSCLUST':'v001'})\n",
    "\n",
    "# Join cluster weight\n",
    "wt_data = pop_subset[['wt','v001']].groupby(['v001']).first()\n",
    "gps_data_j = pd.merge(gps_data_j,wt_data['wt'],on='v001',how='left')\n",
    "\n",
    "# Check subset (cluster stats)\n",
    "n_cl = dhs_civ_geodf['DHSCLUST'].unique().shape[0]\n",
    "n_cl_c = gps_data_j['v001'].unique().shape[0]\n",
    "v_cl_max = dhs_civ_geodf['DHSCLUST'].unique().max()\n",
    "v_cl_max_c = gps_data_j['v001'].unique().max()\n",
    "if ((n_cl==n_cl_c) and (v_cl_max==v_cl_max_c)):\n",
    "    print(\"subset OK\")\n",
    "else:\n",
    "    print(\"incoherent cluster values for subset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "205eb7f0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subset OK\n",
      "subset OK\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>DHSID</th>\n",
       "      <th>DHSCC</th>\n",
       "      <th>DHSYEAR</th>\n",
       "      <th>v001</th>\n",
       "      <th>DHSREGCO</th>\n",
       "      <th>DHSREGNA</th>\n",
       "      <th>SOURCE</th>\n",
       "      <th>URBAN_RURA</th>\n",
       "      <th>LATNUM</th>\n",
       "      <th>LONGNUM</th>\n",
       "      <th>...</th>\n",
       "      <th>geometry</th>\n",
       "      <th>wt</th>\n",
       "      <th>cU5_0</th>\n",
       "      <th>cU5_2</th>\n",
       "      <th>pop_U5</th>\n",
       "      <th>dPrevU5</th>\n",
       "      <th>cU5bis_0</th>\n",
       "      <th>cU5bis_2</th>\n",
       "      <th>pop_U5bis</th>\n",
       "      <th>dPrevU5bis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CI201200000001</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sud sans Abidjan</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>5.973453</td>\n",
       "      <td>-3.198473</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (-3.19847 5.97345)</td>\n",
       "      <td>1.803150</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CI201200000002</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.462503</td>\n",
       "      <td>-8.211979</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (-8.21198 6.46250)</td>\n",
       "      <td>0.997934</td>\n",
       "      <td>23</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>26</td>\n",
       "      <td>7.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CI201200000003</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>7.274597</td>\n",
       "      <td>-8.324563</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (-8.32456 7.27460)</td>\n",
       "      <td>0.192455</td>\n",
       "      <td>20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>20</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CI201200000004</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Nord-Est</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>7.731449</td>\n",
       "      <td>-3.059561</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (-3.05956 7.73145)</td>\n",
       "      <td>0.470353</td>\n",
       "      <td>23</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.148148</td>\n",
       "      <td>23</td>\n",
       "      <td>5.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.178571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CI201200000005</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Centre-Nord</td>\n",
       "      <td>GPS</td>\n",
       "      <td>U</td>\n",
       "      <td>7.713830</td>\n",
       "      <td>-5.040708</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (-5.04071 7.71383)</td>\n",
       "      <td>0.922173</td>\n",
       "      <td>15</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>15</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.250000</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>CI201200000348</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Centre-Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.361619</td>\n",
       "      <td>-5.715785</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (-5.71579 6.36162)</td>\n",
       "      <td>1.532898</td>\n",
       "      <td>26</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>26</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>CI201200000349</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Centre</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.987534</td>\n",
       "      <td>-5.277727</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (-5.27773 6.98753)</td>\n",
       "      <td>0.537444</td>\n",
       "      <td>36</td>\n",
       "      <td>7.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.162791</td>\n",
       "      <td>37</td>\n",
       "      <td>7.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.159091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>CI201200000350</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Centre-Est</td>\n",
       "      <td>GPS</td>\n",
       "      <td>U</td>\n",
       "      <td>7.136385</td>\n",
       "      <td>-3.200418</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (-3.20042 7.13638)</td>\n",
       "      <td>0.299906</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>CI201200000351</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Centre-Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.658221</td>\n",
       "      <td>-6.512773</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (-6.51277 6.65822)</td>\n",
       "      <td>1.270933</td>\n",
       "      <td>15</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>15</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>CI201200000352</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Centre-Nord</td>\n",
       "      <td>GPS</td>\n",
       "      <td>U</td>\n",
       "      <td>7.695010</td>\n",
       "      <td>-5.034002</td>\n",
       "      <td>...</td>\n",
       "      <td>POINT (-5.03400 7.69501)</td>\n",
       "      <td>0.604654</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              DHSID DHSCC  DHSYEAR   v001  DHSREGCO          DHSREGNA SOURCE  \\\n",
       "0    CI201200000001    CI   2012.0    1.0       9.0  Sud sans Abidjan    GPS   \n",
       "1    CI201200000002    CI   2012.0    2.0       8.0             Ouest    GPS   \n",
       "2    CI201200000003    CI   2012.0    3.0       8.0             Ouest    GPS   \n",
       "3    CI201200000004    CI   2012.0    4.0       6.0          Nord-Est    GPS   \n",
       "4    CI201200000005    CI   2012.0    5.0       3.0       Centre-Nord    GPS   \n",
       "..              ...   ...      ...    ...       ...               ...    ...   \n",
       "346  CI201200000348    CI   2012.0  348.0       4.0      Centre-Ouest    GPS   \n",
       "347  CI201200000349    CI   2012.0  349.0       1.0            Centre    GPS   \n",
       "348  CI201200000350    CI   2012.0  350.0       2.0        Centre-Est    GPS   \n",
       "349  CI201200000351    CI   2012.0  351.0       4.0      Centre-Ouest    GPS   \n",
       "350  CI201200000352    CI   2012.0  352.0       3.0       Centre-Nord    GPS   \n",
       "\n",
       "    URBAN_RURA    LATNUM   LONGNUM  ...                  geometry        wt  \\\n",
       "0            R  5.973453 -3.198473  ...  POINT (-3.19847 5.97345)  1.803150   \n",
       "1            R  6.462503 -8.211979  ...  POINT (-8.21198 6.46250)  0.997934   \n",
       "2            R  7.274597 -8.324563  ...  POINT (-8.32456 7.27460)  0.192455   \n",
       "3            R  7.731449 -3.059561  ...  POINT (-3.05956 7.73145)  0.470353   \n",
       "4            U  7.713830 -5.040708  ...  POINT (-5.04071 7.71383)  0.922173   \n",
       "..         ...       ...       ...  ...                       ...       ...   \n",
       "346          R  6.361619 -5.715785  ...  POINT (-5.71579 6.36162)  1.532898   \n",
       "347          R  6.987534 -5.277727  ...  POINT (-5.27773 6.98753)  0.537444   \n",
       "348          U  7.136385 -3.200418  ...  POINT (-3.20042 7.13638)  0.299906   \n",
       "349          R  6.658221 -6.512773  ...  POINT (-6.51277 6.65822)  1.270933   \n",
       "350          U  7.695010 -5.034002  ...  POINT (-5.03400 7.69501)  0.604654   \n",
       "\n",
       "    cU5_0 cU5_2  pop_U5   dPrevU5  cU5bis_0  cU5bis_2  pop_U5bis  dPrevU5bis  \n",
       "0      23   1.0    24.0  0.041667        23       1.0       24.0    0.041667  \n",
       "1      23   7.0    30.0  0.233333        26       7.0       33.0    0.212121  \n",
       "2      20   2.0    22.0  0.090909        20       2.0       22.0    0.090909  \n",
       "3      23   4.0    27.0  0.148148        23       5.0       28.0    0.178571  \n",
       "4      15   4.0    19.0  0.210526        15       5.0       20.0    0.250000  \n",
       "..    ...   ...     ...       ...       ...       ...        ...         ...  \n",
       "346    26   4.0    30.0  0.133333        26       4.0       30.0    0.133333  \n",
       "347    36   7.0    43.0  0.162791        37       7.0       44.0    0.159091  \n",
       "348     8   3.0    11.0  0.272727        10       3.0       13.0    0.230769  \n",
       "349    15   8.0    23.0  0.347826        15       8.0       23.0    0.347826  \n",
       "350     8   1.0     9.0  0.111111        11       1.0       12.0    0.083333  \n",
       "\n",
       "[351 rows x 23 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select variables of interest from children's questionnaire (excluding non-de jure residents)\n",
    "q_data_j = pop_subset.copy()\n",
    "\n",
    "# Subset\n",
    "n_cases_c = q_data_j[q_data_j['h11'].isin([1,2])].n_count.sum()# 1: diarrhoea in last 24h / 2: diarrhoea in last 2 weeks\n",
    "n_pop_c = q_data_j.n_count.sum()\n",
    "\n",
    "# Check subset\n",
    "if ((n_cases==n_cases_c) and (n_pop==n_pop_c)):\n",
    "    print(\"subset OK\")\n",
    "else:\n",
    "    print(\"incoherent values for subset (based on diarrhoea stats)\")\n",
    "\n",
    "# Set groups of diarrhoea outcomes\n",
    "group_list = list(q_data_j['h11'].unique())\n",
    "\n",
    "# Aggregate data: under-5 population and cases (unweighted calculation)\n",
    "\n",
    "# Join data aggregated by cluster (unweighted counts)\n",
    "for g in group_list:\n",
    "    if pd.notna(g):\n",
    "        df = q_data_j[q_data_j['h11']==g]\n",
    "        dfc = df[['n_count','v001']].groupby(['v001']).sum()\n",
    "        dfc = dfc.rename(columns={'n_count': 'cU5_'+str(round(g))})\n",
    "        gps_data_j = pd.merge(gps_data_j,dfc,on='v001',how='left')\n",
    "        # Calculate under-5 population + diarrhoea prevalence\n",
    "        if group_list.index(g)==(len(group_list)-1):\n",
    "            col_list = [col for col in gps_data_j if col.startswith('cU5_')]\n",
    "            gps_data_j['pop_U5'] = gps_data_j[col_list].sum(axis=1)\n",
    "            # Prevalence of diarrhoea, by cluster\n",
    "            col_cases = [col for col in gps_data_j if ((col.endswith('U5_1'))or(col.endswith('U5_2')))]\n",
    "            gps_data_j['dPrevU5'] = gps_data_j[col_cases].sum(axis=1)/gps_data_j['pop_U5']\n",
    "    \n",
    "\n",
    "# ------------------------------------------------------------------------------------------\n",
    "# Select variables of interest from children's questionnaire (including non-de jure residents)\n",
    "q_data_j = pop_subset_bis.copy()\n",
    "\n",
    "# Subset\n",
    "n_cases_c = q_data_j[q_data_j['h11'].isin([1,2])].n_count.sum()# 1: diarrhoea in last 24h / 2: diarrhoea in last 2 weeks\n",
    "n_pop_c = q_data_j.n_count.sum()\n",
    "\n",
    "# Check subset\n",
    "if ((n_cases_bis==n_cases_c) and (n_pop_bis==n_pop_c)):\n",
    "    print(\"subset OK\")\n",
    "else:\n",
    "    print(\"incoherent values for subset (based on diarrhoea stats)\")\n",
    "\n",
    "# Set groups of diarrhoea outcomes\n",
    "group_list = list(q_data_j['h11'].unique())\n",
    "\n",
    "# Aggregate data: under-5 population and cases (unweighted calculation)\n",
    "\n",
    "# Join data aggregated by cluster (unweighted counts)\n",
    "for g in group_list:\n",
    "    if pd.notna(g):\n",
    "        df = q_data_j[q_data_j['h11']==g]\n",
    "        dfc = df[['n_count','v001']].groupby(['v001']).sum()\n",
    "        dfc = dfc.rename(columns={'n_count': 'cU5bis_'+str(round(g))})\n",
    "        gps_data_j = pd.merge(gps_data_j,dfc,on='v001',how='left')\n",
    "        # Calculate under-5 population + diarrhoea prevalence\n",
    "        if group_list.index(g)==(len(group_list)-1):\n",
    "            col_list = [col for col in gps_data_j if col.startswith('cU5bis_')]\n",
    "            gps_data_j['pop_U5bis'] = gps_data_j[col_list].sum(axis=1)\n",
    "            # Prevalence of diarrhoea, by cluster\n",
    "            col_cases = [col for col in gps_data_j if ((col.endswith('U5bis_1'))or(col.endswith('U5bis_2')))]\n",
    "            gps_data_j['dPrevU5bis'] = gps_data_j[col_cases].sum(axis=1)/gps_data_j['pop_U5bis']\n",
    "            \n",
    "# Check result\n",
    "gps_data_j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bf60fc2c",
   "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>DHSID</th>\n",
       "      <th>DHSCC</th>\n",
       "      <th>DHSYEAR</th>\n",
       "      <th>v001</th>\n",
       "      <th>DHSREGCO</th>\n",
       "      <th>DHSREGNA</th>\n",
       "      <th>SOURCE</th>\n",
       "      <th>URBAN_RURA</th>\n",
       "      <th>LATNUM</th>\n",
       "      <th>LONGNUM</th>\n",
       "      <th>...</th>\n",
       "      <th>pp_unim_s</th>\n",
       "      <th>pp_bas_s</th>\n",
       "      <th>pp_noba_s</th>\n",
       "      <th>pp_imp_h</th>\n",
       "      <th>pp_unim_h</th>\n",
       "      <th>pc_imp_w</th>\n",
       "      <th>pc_bas_w</th>\n",
       "      <th>pc_imp_s</th>\n",
       "      <th>pc_bas_s</th>\n",
       "      <th>pc_imp_h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CI201200000001</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sud sans Abidjan</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>5.973453</td>\n",
       "      <td>-3.198473</td>\n",
       "      <td>...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>27.160494</td>\n",
       "      <td>27.160494</td>\n",
       "      <td>39.506173</td>\n",
       "      <td>11.728395</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CI201200000002</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.462503</td>\n",
       "      <td>-8.211979</td>\n",
       "      <td>...</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>78.089888</td>\n",
       "      <td>69.662921</td>\n",
       "      <td>5.056180</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CI201200000003</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>7.274597</td>\n",
       "      <td>-8.324563</td>\n",
       "      <td>...</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>53.571429</td>\n",
       "      <td>48.214286</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CI201200000004</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Nord-Est</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>7.731449</td>\n",
       "      <td>-3.059561</td>\n",
       "      <td>...</td>\n",
       "      <td>104.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>87.591241</td>\n",
       "      <td>24.087591</td>\n",
       "      <td>2.919708</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CI201200000005</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Centre-Nord</td>\n",
       "      <td>GPS</td>\n",
       "      <td>U</td>\n",
       "      <td>7.713830</td>\n",
       "      <td>-5.040708</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>67.692308</td>\n",
       "      <td>18.461538</td>\n",
       "      <td>15.625000</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>CI201200000348</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Centre-Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.361619</td>\n",
       "      <td>-5.715785</td>\n",
       "      <td>...</td>\n",
       "      <td>115.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>76.595745</td>\n",
       "      <td>73.404255</td>\n",
       "      <td>38.829787</td>\n",
       "      <td>16.489362</td>\n",
       "      <td>8.403361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>CI201200000349</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Centre</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.987534</td>\n",
       "      <td>-5.277727</td>\n",
       "      <td>...</td>\n",
       "      <td>220.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.381579</td>\n",
       "      <td>65.131579</td>\n",
       "      <td>27.631579</td>\n",
       "      <td>22.368421</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>CI201200000350</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Centre-Est</td>\n",
       "      <td>GPS</td>\n",
       "      <td>U</td>\n",
       "      <td>7.136385</td>\n",
       "      <td>-3.200418</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>83.544304</td>\n",
       "      <td>83.544304</td>\n",
       "      <td>96.202532</td>\n",
       "      <td>11.392405</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>CI201200000351</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Centre-Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.658221</td>\n",
       "      <td>-6.512773</td>\n",
       "      <td>...</td>\n",
       "      <td>108.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>93.846154</td>\n",
       "      <td>93.846154</td>\n",
       "      <td>16.923077</td>\n",
       "      <td>9.230769</td>\n",
       "      <td>8.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>CI201200000352</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Centre-Nord</td>\n",
       "      <td>GPS</td>\n",
       "      <td>U</td>\n",
       "      <td>7.695010</td>\n",
       "      <td>-5.034002</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>94.488189</td>\n",
       "      <td>94.488189</td>\n",
       "      <td>94.488189</td>\n",
       "      <td>22.834646</td>\n",
       "      <td>28.358209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              DHSID DHSCC  DHSYEAR   v001  DHSREGCO          DHSREGNA SOURCE  \\\n",
       "0    CI201200000001    CI   2012.0    1.0       9.0  Sud sans Abidjan    GPS   \n",
       "1    CI201200000002    CI   2012.0    2.0       8.0             Ouest    GPS   \n",
       "2    CI201200000003    CI   2012.0    3.0       8.0             Ouest    GPS   \n",
       "3    CI201200000004    CI   2012.0    4.0       6.0          Nord-Est    GPS   \n",
       "4    CI201200000005    CI   2012.0    5.0       3.0       Centre-Nord    GPS   \n",
       "..              ...   ...      ...    ...       ...               ...    ...   \n",
       "346  CI201200000348    CI   2012.0  348.0       4.0      Centre-Ouest    GPS   \n",
       "347  CI201200000349    CI   2012.0  349.0       1.0            Centre    GPS   \n",
       "348  CI201200000350    CI   2012.0  350.0       2.0        Centre-Est    GPS   \n",
       "349  CI201200000351    CI   2012.0  351.0       4.0      Centre-Ouest    GPS   \n",
       "350  CI201200000352    CI   2012.0  352.0       3.0       Centre-Nord    GPS   \n",
       "\n",
       "    URBAN_RURA    LATNUM   LONGNUM  ...  pp_unim_s  pp_bas_s pp_noba_s  \\\n",
       "0            R  5.973453 -3.198473  ...       98.0      19.0     143.0   \n",
       "1            R  6.462503 -8.211979  ...      169.0       0.0     178.0   \n",
       "2            R  7.274597 -8.324563  ...      156.0       0.0     168.0   \n",
       "3            R  7.731449 -3.059561  ...      104.0       4.0     133.0   \n",
       "4            U  7.713830 -5.040708  ...       42.0      24.0     106.0   \n",
       "..         ...       ...       ...  ...        ...       ...       ...   \n",
       "346          R  6.361619 -5.715785  ...      115.0      31.0     157.0   \n",
       "347          R  6.987534 -5.277727  ...      220.0      68.0     236.0   \n",
       "348          U  7.136385 -3.200418  ...        3.0       9.0      70.0   \n",
       "349          R  6.658221 -6.512773  ...      108.0      12.0     118.0   \n",
       "350          U  7.695010 -5.034002  ...        7.0      29.0      98.0   \n",
       "\n",
       "    pp_imp_h  pp_unim_h    pc_imp_w    pc_bas_w   pc_imp_s   pc_bas_s  \\\n",
       "0        0.0       93.0   27.160494   27.160494  39.506173  11.728395   \n",
       "1        1.0      101.0   78.089888   69.662921   5.056180   0.000000   \n",
       "2        0.0       43.0   53.571429   48.214286   7.142857   0.000000   \n",
       "3        0.0       76.0  100.000000   87.591241  24.087591   2.919708   \n",
       "4       15.0       81.0  100.000000  100.000000  67.692308  18.461538   \n",
       "..       ...        ...         ...         ...        ...        ...   \n",
       "346     10.0      109.0   76.595745   73.404255  38.829787  16.489362   \n",
       "347    140.0        0.0   96.381579   65.131579  27.631579  22.368421   \n",
       "348      7.0       63.0   83.544304   83.544304  96.202532  11.392405   \n",
       "349      7.0       77.0   93.846154   93.846154  16.923077   9.230769   \n",
       "350     19.0       48.0   94.488189   94.488189  94.488189  22.834646   \n",
       "\n",
       "       pc_imp_h  \n",
       "0      0.000000  \n",
       "1      0.980392  \n",
       "2      0.000000  \n",
       "3      0.000000  \n",
       "4     15.625000  \n",
       "..          ...  \n",
       "346    8.403361  \n",
       "347  100.000000  \n",
       "348   10.000000  \n",
       "349    8.333333  \n",
       "350   28.358209  \n",
       "\n",
       "[351 rows x 38 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select variables of interest from household questionnaire\n",
    "\n",
    "# Aggregate data: access to water and sanitation (population counts)\n",
    "\n",
    "# Group by cluster n°\n",
    "\n",
    "# ------------------ WATER ------------------\n",
    "# water, improved/unimproved\n",
    "j_imp_wat = imp_wat_df[['hv001','hh_hab']].groupby(['hv001']).sum()\n",
    "j_imp_wat['v001'] = j_imp_wat.index\n",
    "j_imp_wat.index.name = 'index'\n",
    "j_imp_wat = j_imp_wat.rename(columns={'hh_hab':'pp_imp_w'})\n",
    "j_unimp_wat = unimp_wat_df[['hv001','hh_hab']].groupby(['hv001']).sum()\n",
    "j_unimp_wat['v001'] = j_unimp_wat.index\n",
    "j_unimp_wat.index.name = 'index'\n",
    "j_unimp_wat = j_unimp_wat.rename(columns={'hh_hab':'pp_unim_w'})\n",
    "\n",
    "# water, basic/not basic\n",
    "j_bas_wat = bas_wat_df[['hv001','hh_hab']].groupby(['hv001']).sum()\n",
    "j_bas_wat['v001'] = j_bas_wat.index\n",
    "j_bas_wat.index.name = 'index'\n",
    "j_bas_wat = j_bas_wat.rename(columns={'hh_hab':'pp_bas_w'})\n",
    "j_nobas_wat = nobas_wat_df[['hv001','hh_hab']].groupby(['hv001']).sum()\n",
    "j_nobas_wat['v001'] = j_nobas_wat.index\n",
    "j_nobas_wat.index.name = 'index'\n",
    "j_nobas_wat = j_nobas_wat.rename(columns={'hh_hab':'pp_noba_w'})\n",
    "\n",
    "# ------------------ SANITATION ------------------\n",
    "# sanitation, improved/unimproved\n",
    "j_imp_san = imp_san_df[['hv001','hh_hab']].groupby(['hv001']).sum()\n",
    "j_imp_san['v001'] = j_imp_san.index\n",
    "j_imp_san.index.name = 'index'\n",
    "j_imp_san = j_imp_san.rename(columns={'hh_hab':'pp_imp_s'})\n",
    "j_unimp_san = unimp_san_df[['hv001','hh_hab']].groupby(['hv001']).sum()\n",
    "j_unimp_san['v001'] = j_unimp_san.index\n",
    "j_unimp_san.index.name = 'index'\n",
    "j_unimp_san = j_unimp_san.rename(columns={'hh_hab':'pp_unim_s'})\n",
    "\n",
    "# sanitation, basic/not basic\n",
    "j_bas_san = bas_san_df[['hv001','hh_hab']].groupby(['hv001']).sum()\n",
    "j_bas_san['v001'] = j_bas_san.index\n",
    "j_bas_san.index.name = 'index'\n",
    "j_bas_san = j_bas_san.rename(columns={'hh_hab':'pp_bas_s'})\n",
    "j_nobas_san = nobas_san_df[['hv001','hh_hab']].groupby(['hv001']).sum()\n",
    "j_nobas_san['v001'] = j_nobas_san.index\n",
    "j_nobas_san.index.name = 'index'\n",
    "j_nobas_san = j_nobas_san.rename(columns={'hh_hab':'pp_noba_s'})\n",
    "\n",
    "# ------------------ HYGIENE ------------------\n",
    "# hygiene, safely managed\n",
    "j_imp_hyg = imp_hyg_df[['hv001','hh_hab']].groupby(['hv001']).sum()\n",
    "j_imp_hyg['v001'] = j_imp_hyg.index\n",
    "j_imp_hyg.index.name = 'index'\n",
    "j_imp_hyg = j_imp_hyg.rename(columns={'hh_hab':'pp_imp_h'})\n",
    "j_unimp_hyg = unimp_hyg_df[['hv001','hh_hab']].groupby(['hv001']).sum()\n",
    "j_unimp_hyg['v001'] = j_unimp_hyg.index\n",
    "j_unimp_hyg.index.name = 'index'\n",
    "j_unimp_hyg = j_unimp_hyg.rename(columns={'hh_hab':'pp_unim_h'})\n",
    "\n",
    "# Set key variable for join\n",
    "\n",
    "# Join\n",
    "gps_data_j2 = pd.merge(gps_data_j,j_imp_wat,on='v001',how='left')\n",
    "gps_data_j2 = pd.merge(gps_data_j2,j_unimp_wat,on='v001',how='left')\n",
    "gps_data_j2 = pd.merge(gps_data_j2,j_bas_wat,on='v001',how='left')\n",
    "gps_data_j2 = pd.merge(gps_data_j2,j_nobas_wat,on='v001',how='left')\n",
    "gps_data_j2 = pd.merge(gps_data_j2,j_imp_san,on='v001',how='left')\n",
    "gps_data_j2 = pd.merge(gps_data_j2,j_unimp_san,on='v001',how='left')\n",
    "gps_data_j2 = pd.merge(gps_data_j2,j_bas_san,on='v001',how='left')\n",
    "gps_data_j2 = pd.merge(gps_data_j2,j_nobas_san,on='v001',how='left')\n",
    "gps_data_j2 = pd.merge(gps_data_j2,j_imp_hyg,on='v001',how='left')\n",
    "gps_data_j2 = pd.merge(gps_data_j2,j_unimp_hyg,on='v001',how='left')\n",
    "\n",
    "# Replace NaN by 0s (population counts)\n",
    "col_list = ['pp_imp_w','pp_unim_w','pp_bas_w','pp_noba_w',\n",
    "            'pp_imp_s','pp_unim_s','pp_bas_s','pp_noba_s',\n",
    "            'pp_imp_h','pp_unim_h']\n",
    "for col in col_list:\n",
    "    gps_data_j2[col] = gps_data_j2[col].fillna(0)\n",
    "\n",
    "# Calculate % access to improved/basic wat. san. & safe hyg. (unweighted calculations)\n",
    "gps_data_j2['pc_imp_w'] = (gps_data_j2['pp_imp_w']/(gps_data_j2['pp_imp_w']+gps_data_j2['pp_unim_w']))*100\n",
    "gps_data_j2['pc_bas_w'] = (gps_data_j2['pp_bas_w']/(gps_data_j2['pp_bas_w']+gps_data_j2['pp_noba_w']))*100\n",
    "gps_data_j2['pc_imp_s'] = (gps_data_j2['pp_imp_s']/(gps_data_j2['pp_imp_s']+gps_data_j2['pp_unim_s']))*100\n",
    "gps_data_j2['pc_bas_s'] = (gps_data_j2['pp_bas_s']/(gps_data_j2['pp_bas_s']+gps_data_j2['pp_noba_s']))*100\n",
    "gps_data_j2['pc_imp_h'] = (gps_data_j2['pp_imp_h']/(gps_data_j2['pp_imp_h']+gps_data_j2['pp_unim_h']))*100\n",
    "\n",
    "# check results\n",
    "gps_data_j2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2e8ef412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subset OK\n",
      "subset OK\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>DHSID</th>\n",
       "      <th>DHSCC</th>\n",
       "      <th>DHSYEAR</th>\n",
       "      <th>v001</th>\n",
       "      <th>DHSREGCO</th>\n",
       "      <th>DHSREGNA</th>\n",
       "      <th>SOURCE</th>\n",
       "      <th>URBAN_RURA</th>\n",
       "      <th>LATNUM</th>\n",
       "      <th>LONGNUM</th>\n",
       "      <th>...</th>\n",
       "      <th>wpop</th>\n",
       "      <th>pc_Wnoed</th>\n",
       "      <th>wcntBis_2</th>\n",
       "      <th>wcntBis_0</th>\n",
       "      <th>wcntBis_1</th>\n",
       "      <th>wcntBis_3</th>\n",
       "      <th>wcntBis_4</th>\n",
       "      <th>wcntBis_5</th>\n",
       "      <th>wpopBis</th>\n",
       "      <th>pc_WnoedBis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CI201200000001</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sud sans Abidjan</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>5.973453</td>\n",
       "      <td>-3.198473</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>72.413793</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>72.413793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CI201200000002</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.462503</td>\n",
       "      <td>-8.211979</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>74.074074</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>72.413793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CI201200000003</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>7.274597</td>\n",
       "      <td>-8.324563</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>62.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CI201200000004</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Nord-Est</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>7.731449</td>\n",
       "      <td>-3.059561</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>47.826087</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.0</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CI201200000005</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Centre-Nord</td>\n",
       "      <td>GPS</td>\n",
       "      <td>U</td>\n",
       "      <td>7.713830</td>\n",
       "      <td>-5.040708</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>65.384615</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>62.962963</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>CI201200000348</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Centre-Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.361619</td>\n",
       "      <td>-5.715785</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>54.838710</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>54.838710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>CI201200000349</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Centre</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.987534</td>\n",
       "      <td>-5.277727</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>59.259259</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.0</td>\n",
       "      <td>58.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>CI201200000350</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Centre-Est</td>\n",
       "      <td>GPS</td>\n",
       "      <td>U</td>\n",
       "      <td>7.136385</td>\n",
       "      <td>-3.200418</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>56.250000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>58.823529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>CI201200000351</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Centre-Ouest</td>\n",
       "      <td>GPS</td>\n",
       "      <td>R</td>\n",
       "      <td>6.658221</td>\n",
       "      <td>-6.512773</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>37.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>CI201200000352</td>\n",
       "      <td>CI</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Centre-Nord</td>\n",
       "      <td>GPS</td>\n",
       "      <td>U</td>\n",
       "      <td>7.695010</td>\n",
       "      <td>-5.034002</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37.931034</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.483871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              DHSID DHSCC  DHSYEAR   v001  DHSREGCO          DHSREGNA SOURCE  \\\n",
       "0    CI201200000001    CI   2012.0    1.0       9.0  Sud sans Abidjan    GPS   \n",
       "1    CI201200000002    CI   2012.0    2.0       8.0             Ouest    GPS   \n",
       "2    CI201200000003    CI   2012.0    3.0       8.0             Ouest    GPS   \n",
       "3    CI201200000004    CI   2012.0    4.0       6.0          Nord-Est    GPS   \n",
       "4    CI201200000005    CI   2012.0    5.0       3.0       Centre-Nord    GPS   \n",
       "..              ...   ...      ...    ...       ...               ...    ...   \n",
       "346  CI201200000348    CI   2012.0  348.0       4.0      Centre-Ouest    GPS   \n",
       "347  CI201200000349    CI   2012.0  349.0       1.0            Centre    GPS   \n",
       "348  CI201200000350    CI   2012.0  350.0       2.0        Centre-Est    GPS   \n",
       "349  CI201200000351    CI   2012.0  351.0       4.0      Centre-Ouest    GPS   \n",
       "350  CI201200000352    CI   2012.0  352.0       3.0       Centre-Nord    GPS   \n",
       "\n",
       "    URBAN_RURA    LATNUM   LONGNUM  ...  wpop   pc_Wnoed wcntBis_2 wcntBis_0  \\\n",
       "0            R  5.973453 -3.198473  ...  29.0  72.413793       3.0        21   \n",
       "1            R  6.462503 -8.211979  ...  27.0  74.074074       NaN        21   \n",
       "2            R  7.274597 -8.324563  ...  24.0  62.500000       1.0        15   \n",
       "3            R  7.731449 -3.059561  ...  23.0  47.826087       4.0        11   \n",
       "4            U  7.713830 -5.040708  ...  26.0  65.384615       3.0        17   \n",
       "..         ...       ...       ...  ...   ...        ...       ...       ...   \n",
       "346          R  6.361619 -5.715785  ...  31.0  54.838710       5.0        17   \n",
       "347          R  6.987534 -5.277727  ...  54.0  59.259259       3.0        33   \n",
       "348          U  7.136385 -3.200418  ...  16.0  56.250000       2.0        10   \n",
       "349          R  6.658221 -6.512773  ...  24.0  37.500000       9.0         9   \n",
       "350          U  7.695010 -5.034002  ...  29.0  37.931034       2.0        11   \n",
       "\n",
       "     wcntBis_1  wcntBis_3  wcntBis_4  wcntBis_5  wpopBis  pc_WnoedBis  \n",
       "0          4.0        1.0        NaN        NaN     29.0    72.413793  \n",
       "1          8.0        NaN        NaN        NaN     29.0    72.413793  \n",
       "2          7.0        1.0        NaN        NaN     24.0    62.500000  \n",
       "3          7.0        3.0        NaN        NaN     25.0    44.000000  \n",
       "4          NaN        6.0        1.0        NaN     27.0    62.962963  \n",
       "..         ...        ...        ...        ...      ...          ...  \n",
       "346        6.0        3.0        NaN        NaN     31.0    54.838710  \n",
       "347       18.0        2.0        NaN        NaN     56.0    58.928571  \n",
       "348        3.0        1.0        1.0        NaN     17.0    58.823529  \n",
       "349        4.0        2.0        NaN        NaN     24.0    37.500000  \n",
       "350        8.0        8.0        1.0        1.0     31.0    35.483871  \n",
       "\n",
       "[351 rows x 54 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select variables of interest from women's questionnaire (excluding non-de jure residents)\n",
    "q_data_j = wpop_subset.copy()\n",
    "\n",
    "# Subset\n",
    "n_nedu_c = q_data_j[q_data_j['v149'].isin([0])& # 0: no education\n",
    "                    (~q_data_j['v139'].isin([97,99])) # 97: not de jure resident / 99: missing info\n",
    "                     ].n_count.sum()\n",
    "n_wpop_c = q_data_j.n_count.sum()\n",
    "# Check subset (women stats)\n",
    "if ((n_nedu==n_nedu_c) and (n_wpop==n_wpop_c)):\n",
    "    print(\"subset OK\")\n",
    "else:\n",
    "    print(\"incoherent values for subset\")\n",
    "\n",
    "# Aggregate data: educational attainment (women, unweighted calculation)\n",
    "\n",
    "gps_data_j3 = gps_data_j2.copy()\n",
    "\n",
    "# Set groups of educational attainment\n",
    "group_list = list(q_data_j['v149'].unique())\n",
    "\n",
    "# Join data aggregated by cluster (unweighted counts)\n",
    "for g in group_list:\n",
    "    if pd.notna(g):\n",
    "        df = q_data_j[q_data_j['v149']==g]\n",
    "        dfc = df[['n_count','v001']].groupby(['v001']).sum()\n",
    "        dfc = dfc.rename(columns={'n_count': 'wcnt_'+str(round(g))})\n",
    "        gps_data_j3 = pd.merge(gps_data_j3,dfc,on='v001',how='left')\n",
    "        # Calculate % of women with no education\n",
    "        if group_list.index(g)==(len(group_list)-1):\n",
    "            col_list = [col for col in gps_data_j3 if col.startswith('wcnt_')]\n",
    "            gps_data_j3['wpop'] = gps_data_j3[col_list].sum(axis=1)\n",
    "            # Prevalence of diarrhoea, by cluster\n",
    "            gps_data_j3['pc_Wnoed'] = (gps_data_j3['wcnt_0']/gps_data_j3['wpop'])*100\n",
    "            \n",
    "# ------------------------------------------------------------------------------------------\n",
    "# Select variables of interest from women's questionnaire (including non-de jure residents)\n",
    "q_data_j = wpop_subset_bis.copy()\n",
    "\n",
    "# Subset\n",
    "n_nedu_c = q_data_j[q_data_j['v149'].isin([0])].n_count.sum()\n",
    "n_wpop_c = q_data_j.n_count.sum()\n",
    "# Check subset (women stats)\n",
    "if ((n_nedu_bis==n_nedu_c) and (n_wpop_bis==n_wpop_c)):\n",
    "    print(\"subset OK\")\n",
    "else:\n",
    "    print(\"incoherent values for subset\")\n",
    "\n",
    "# Aggregate data: educational attainment (women, unweighted calculation)\n",
    "\n",
    "# Set groups of educational attainment\n",
    "group_list = list(q_data_j['v149'].unique())\n",
    "\n",
    "# Join data aggregated by cluster (unweighted counts)\n",
    "for g in group_list:\n",
    "    if pd.notna(g):\n",
    "        df = q_data_j[q_data_j['v149']==g]\n",
    "        dfc = df[['n_count','v001']].groupby(['v001']).sum()\n",
    "        dfc = dfc.rename(columns={'n_count': 'wcntBis_'+str(round(g))})\n",
    "        gps_data_j3 = pd.merge(gps_data_j3,dfc,on='v001',how='left')\n",
    "        # Calculate % of women with no education\n",
    "        if group_list.index(g)==(len(group_list)-1):\n",
    "            col_list = [col for col in gps_data_j3 if col.startswith('wcntBis_')]\n",
    "            gps_data_j3['wpopBis'] = gps_data_j3[col_list].sum(axis=1)\n",
    "            # Prevalence of diarrhoea, by cluster\n",
    "            gps_data_j3['pc_WnoedBis'] = (gps_data_j3['wcntBis_0']/gps_data_j3['wpopBis'])*100\n",
    "            \n",
    "# Check result\n",
    "gps_data_j3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cc561f8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SAVE to csv\n",
    "gps_data_j3.to_csv(path.join(dest_dir,\"DHSdata_aggr2join_buffer.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d184fc",
   "metadata": {},
   "source": [
    "### Generate buffer zones for ecological analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bfd93165",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate buffers from point coordinates\n",
    "\n",
    "# Set to metric system\n",
    "gps_data_j3 = gps_data_j3.to_crs(epsg=3857)\n",
    "\n",
    "# Add buffer column (distance will vary depending on situation)\n",
    "gps_data_j3['buffer'] = \"\"\n",
    "gps_data_j3['buffer'][gps_data_j3['URBAN_RURA']=='U'] = 2000\n",
    "gps_data_j3['buffer'][gps_data_j3['URBAN_RURA']=='R'] = 5000\n",
    "gps_data_j3['buffer'] = gps_data_j3['buffer'].astype('int')\n",
    "\n",
    "# Generate buffer & replace data geometry (from points to circles)\n",
    "gps_data_j_buffer = gps_data_j3.copy()\n",
    "gps_data_j_buffer['geometry'] = gps_data_j3.buffer(gps_data_j3['buffer'])\n",
    "\n",
    "# Exclude obs. with erroneous GPS location & set back to epsg:4326\n",
    "gps_data_j_buffer = gps_data_j_buffer[gps_data_j_buffer['LATNUM']!=0]\n",
    "gps_data_j_buffer = gps_data_j_buffer.reset_index(drop=True)\n",
    "gps_data_j_buffer = gps_data_j_buffer.to_crs(epsg=4326)\n",
    "\n",
    "# Merge buffers to define global clipping area for study\n",
    "gps_data_j_buffer2merge = gps_data_j_buffer[['DHSCC','geometry']]\n",
    "merged = gps_data_j_buffer2merge.dissolve(by='DHSCC', aggfunc='first')\n",
    "\n",
    "# Save to shp\n",
    "gps_data_j_buffer[['v001','geometry']].to_file(path.join(dest_dir,\"AGGR_DHS_buffer.shp\"))\n",
    "merged.to_file(path.join(dest_dir,\"AGGR_DHS_ClipArea.shp\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e52de8",
   "metadata": {},
   "source": [
    "# Check correlations between control variables and diarrhoea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9584acc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import spreg\n",
    "import statsmodels.api as sm\n",
    "\n",
    "from scipy import stats\n",
    "from pysal.lib import weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04af70c9",
   "metadata": {},
   "source": [
    "### improved / unimproved WASH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0b9f3fba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the data type(s) is/are:\n",
      "float64\n",
      "columns w/ nan: []\n",
      "total excluded obs.: 0\n",
      "min. values: [0.       0.044629]\n",
      "max. values: [1.       4.396831]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-66-5faa722657e3>:70: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print('min. values:',dbf_c.min().unique())\n",
      "<ipython-input-66-5faa722657e3>:71: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print('max. values:',dbf_c.max().unique())\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>dPrevU5</th>\n",
       "      <th>pc_imp_w</th>\n",
       "      <th>pc_imp_s</th>\n",
       "      <th>pc_Wnoed</th>\n",
       "      <th>wt</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.271605</td>\n",
       "      <td>0.395062</td>\n",
       "      <td>0.712144</td>\n",
       "      <td>1.803150</td>\n",
       "      <td>POINT (-356052.386 666169.653)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.414815</td>\n",
       "      <td>0.780899</td>\n",
       "      <td>0.050562</td>\n",
       "      <td>0.729469</td>\n",
       "      <td>0.997934</td>\n",
       "      <td>POINT (-914153.321 720932.789)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.161616</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.192455</td>\n",
       "      <td>POINT (-926686.114 811988.956)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.263374</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.240876</td>\n",
       "      <td>0.455577</td>\n",
       "      <td>0.470353</td>\n",
       "      <td>POINT (-340588.773 863284.824)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.374269</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.676923</td>\n",
       "      <td>0.638796</td>\n",
       "      <td>0.922173</td>\n",
       "      <td>POINT (-561129.048 861305.534)</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>0.237037</td>\n",
       "      <td>0.765957</td>\n",
       "      <td>0.388298</td>\n",
       "      <td>0.528752</td>\n",
       "      <td>1.532898</td>\n",
       "      <td>POINT (-636278.276 709631.737)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>0.289406</td>\n",
       "      <td>0.963816</td>\n",
       "      <td>0.276316</td>\n",
       "      <td>0.574879</td>\n",
       "      <td>0.537444</td>\n",
       "      <td>POINT (-587513.882 779784.103)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.835443</td>\n",
       "      <td>0.962025</td>\n",
       "      <td>0.543478</td>\n",
       "      <td>0.299906</td>\n",
       "      <td>POINT (-356268.902 796480.790)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>0.618357</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.169231</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>1.270933</td>\n",
       "      <td>POINT (-724998.574 742863.630)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>0.197531</td>\n",
       "      <td>0.944882</td>\n",
       "      <td>0.944882</td>\n",
       "      <td>0.352324</td>\n",
       "      <td>0.604654</td>\n",
       "      <td>POINT (-560382.539 859191.417)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      dPrevU5  pc_imp_w  pc_imp_s  pc_Wnoed        wt  \\\n",
       "0    0.074074  0.271605  0.395062  0.712144  1.803150   \n",
       "1    0.414815  0.780899  0.050562  0.729469  0.997934   \n",
       "2    0.161616  0.535714  0.071429  0.608696  0.192455   \n",
       "3    0.263374  1.000000  0.240876  0.455577  0.470353   \n",
       "4    0.374269  1.000000  0.676923  0.638796  0.922173   \n",
       "..        ...       ...       ...       ...       ...   \n",
       "346  0.237037  0.765957  0.388298  0.528752  1.532898   \n",
       "347  0.289406  0.963816  0.276316  0.574879  0.537444   \n",
       "348  0.484848  0.835443  0.962025  0.543478  0.299906   \n",
       "349  0.618357  0.938462  0.169231  0.347826  1.270933   \n",
       "350  0.197531  0.944882  0.944882  0.352324  0.604654   \n",
       "\n",
       "                           geometry  \n",
       "0    POINT (-356052.386 666169.653)  \n",
       "1    POINT (-914153.321 720932.789)  \n",
       "2    POINT (-926686.114 811988.956)  \n",
       "3    POINT (-340588.773 863284.824)  \n",
       "4    POINT (-561129.048 861305.534)  \n",
       "..                              ...  \n",
       "346  POINT (-636278.276 709631.737)  \n",
       "347  POINT (-587513.882 779784.103)  \n",
       "348  POINT (-356268.902 796480.790)  \n",
       "349  POINT (-724998.574 742863.630)  \n",
       "350  POINT (-560382.539 859191.417)  \n",
       "\n",
       "[351 rows x 6 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get data (copy of dataset)\n",
    "dbf_c = gps_data_j3.copy()\n",
    "\n",
    "# Select cols (keep only env. variables + ind. variable)\n",
    "#data_sel = 'include non-de jure'\n",
    "data_sel = 'exclude non-de jure'\n",
    "\n",
    "if data_sel == 'include non-de jure':\n",
    "    dbf_c = dbf_c[['dPrevU5bis',\n",
    "                   'pc_imp_w',\n",
    "                   'pc_imp_s',\n",
    "                   #'pc_imp_h',\n",
    "                   'pc_WnoedBis',\n",
    "                   'wt',\n",
    "                   'geometry']]\n",
    "else:\n",
    "    dbf_c = dbf_c[['dPrevU5',\n",
    "                   'pc_imp_w',\n",
    "                   'pc_imp_s',\n",
    "                   #'pc_imp_h',\n",
    "                   'pc_Wnoed',\n",
    "                   'wt',\n",
    "                   'geometry']]\n",
    "\n",
    "# Scale values (min/max) of all variables (except 'geometry')\n",
    "ind_cols = dbf_c.columns.difference(['wt','geometry']).to_list()\n",
    "for var in ind_cols:\n",
    "    dbf_c[var] = ((dbf_c[var] - dbf_c[var].min())/\n",
    "                  (dbf_c[var].max() - dbf_c[var].min()))\n",
    "\n",
    "# Check that all data types are numeric\n",
    "lst = []\n",
    "for col in ind_cols:\n",
    "    lst = lst+[str(dbf_c[col].dtype)]\n",
    "# function to get unique values\n",
    "def unique(list1):\n",
    "    # initialize a null list\n",
    "    unique_list = []\n",
    "    # traverse for all elements\n",
    "    for x in list1:\n",
    "        # check if exists in unique_list or not\n",
    "        if x not in unique_list:\n",
    "            unique_list.append(x)\n",
    "    # print list\n",
    "    for x in unique_list:\n",
    "        print(x)\n",
    "# driver code\n",
    "print(\"the data type(s) is/are:\")\n",
    "unique(lst)\n",
    "\n",
    "# Exclude NaNs:\n",
    "lst = []\n",
    "t = -1\n",
    "while True:\n",
    "    try:\n",
    "        t = dbf_c.isna().any().to_list().index(True, t + 1)\n",
    "        lst = lst+[t]\n",
    "    except ValueError:\n",
    "        break\n",
    "cols_wnan = []\n",
    "for i in lst:\n",
    "    col = dbf_c.columns.to_list()[i]\n",
    "    cols_wnan = cols_wnan+[col]\n",
    "print(\"columns w/ nan:\",cols_wnan)\n",
    "for col in cols_wnan:\n",
    "    dbf_c = dbf_c[~dbf_c[col].isna()]\n",
    "print(\"total excluded obs.:\",gps_data_j3.shape[0]-dbf_c.shape[0])\n",
    "\n",
    "# Check min/max values:\n",
    "print('min. values:',dbf_c.min().unique())\n",
    "print('max. values:',dbf_c.max().unique())\n",
    "\n",
    "# Turn into points\n",
    "dbf_c['geometry'] = dbf_c.geometry.centroid\n",
    "\n",
    "# Check data\n",
    "dbf_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4895e87c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION\n",
      "----------\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES\n",
      "-----------------------------------------\n",
      "Data set            :     unknown\n",
      "Weights matrix      :     unknown\n",
      "Dependent Variable  :     dep_var                Number of Observations:         351\n",
      "Mean dependent var  :      0.3211                Number of Variables   :           4\n",
      "S.D. dependent var  :      0.2168                Degrees of Freedom    :         347\n",
      "R-squared           :      0.0062\n",
      "Adjusted R-squared  :     -0.0024\n",
      "Sum squared residual:      16.341                F-statistic           :      0.7253\n",
      "Sigma-square        :       0.047                Prob(F-statistic)     :      0.5374\n",
      "S.E. of regression  :       0.217                Log likelihood        :      40.226\n",
      "Sigma-square ML     :       0.047                Akaike info criterion :     -72.452\n",
      "S.E of regression ML:      0.2158                Schwarz criterion     :     -57.009\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT       0.3872642       0.0576948       6.7122866       0.0000000\n",
      "            pc_imp_w      -0.0001481       0.0521118      -0.0028417       0.9977343\n",
      "            pc_imp_s      -0.0516019       0.0437247      -1.1801555       0.2387468\n",
      "            pc_Wnoed      -0.0729681       0.0558322      -1.3069169       0.1921064\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER           11.173\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2          19.970           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3           5.763           0.1237\n",
      "Koenker-Bassett test              3           5.758           0.1240\n",
      "\n",
      "DIAGNOSTICS FOR SPATIAL DEPENDENCE\n",
      "TEST                           MI/DF       VALUE           PROB\n",
      "Moran's I (error)              0.0974         6.135           0.0000\n",
      "Lagrange Multiplier (lag)         1          29.388           0.0000\n",
      "Robust LM (lag)                   1           0.630           0.4272\n",
      "Lagrange Multiplier (error)       1          30.578           0.0000\n",
      "Robust LM (error)                 1           1.820           0.1773\n",
      "Lagrange Multiplier (SARMA)       2          31.208           0.0000\n",
      "\n",
      "================================ END OF REPORT =====================================\n"
     ]
    }
   ],
   "source": [
    "# OLS regression with pysal's spreg\n",
    "\n",
    "reg_var_list = sorted(dbf_c.columns.difference([dbf_c.columns[0],\n",
    "                                                'wt',\n",
    "                                                'geometry',]).to_list(),\n",
    "                      reverse=True)\n",
    "\n",
    "# set regression inputs\n",
    "y_var = dbf_c[dbf_c.columns[0]].values\n",
    "x_vars = dbf_c[reg_var_list].values\n",
    "weight = weights.KNN.from_dataframe(dbf_c,k=16)# k: 16 nearest neighbours\n",
    "\n",
    "# Run OLS regression with spatial diagnostics \n",
    "mols = spreg.OLS(y_var,x_vars,weight,\n",
    "                 name_x=reg_var_list,\n",
    "                 moran=True,#compute Moran’s I on the residuals (requires spat_diag=True)\n",
    "                 spat_diag=True)\n",
    "\n",
    "# See results\n",
    "print(mols.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d8cbfc22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION\n",
      "----------\n",
      "SUMMARY OF OUTPUT: MAXIMUM LIKELIHOOD SPATIAL LAG (METHOD = ORD)\n",
      "----------------------------------------------------------------\n",
      "Data set            :     unknown\n",
      "Weights matrix      :     unknown\n",
      "Dependent Variable  :     dep_var                Number of Observations:         351\n",
      "Mean dependent var  :      0.3211                Number of Variables   :           5\n",
      "S.D. dependent var  :      0.2168                Degrees of Freedom    :         346\n",
      "Pseudo R-squared    :      0.0701\n",
      "Spatial Pseudo R-squared:  0.0029\n",
      "Sigma-square ML     :       0.044                Log likelihood        :      48.564\n",
      "S.E of regression   :       0.209                Akaike info criterion :     -87.129\n",
      "                                                 Schwarz criterion     :     -67.825\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     z-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT       0.2140987       0.0657293       3.2572801       0.0011249\n",
      "            pc_imp_w       0.0184039       0.0502751       0.3660631       0.7143180\n",
      "            pc_imp_s      -0.0585299       0.0422244      -1.3861625       0.1656973\n",
      "            pc_Wnoed      -0.0550292       0.0538815      -1.0213003       0.3071122\n",
      "           W_dep_var       0.0288822       0.0064903       4.4500775       0.0000086\n",
      "------------------------------------------------------------------------------------\n",
      "================================ END OF REPORT =====================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vitor/miniconda3/envs/LS_Metrics/lib/python3.8/site-packages/scipy/optimize/_minimize.py:783: RuntimeWarning: Method 'bounded' does not support relative tolerance in x; defaulting to absolute tolerance.\n",
      "  warn(\"Method 'bounded' does not support relative tolerance in x; \"\n"
     ]
    }
   ],
   "source": [
    "# calculate ML Lag\n",
    "# See: https://spreg.readthedocs.io/en/latest/generated/spreg.ML_Lag.html\n",
    "# NOTE: method = \"ORD\" or \"LU\" works better for large samples\n",
    "# >> according to https://github.com/pysal/pysal/issues/587\n",
    "mlag = spreg.ML_Lag(y_var,x_vars,\n",
    "                    weight,\n",
    "                    method = \"ORD\",\n",
    "                    name_x=reg_var_list)\n",
    "\n",
    "print(mlag.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d0ede568",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_vars: ['pc_imp_w', 'pc_imp_s', 'pc_Wnoed']\n",
      "                            WLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.011\n",
      "Model:                            WLS   Adj. R-squared:                  0.003\n",
      "Method:                 Least Squares   F-statistic:                     1.303\n",
      "Date:                Sun, 03 Apr 2022   Prob (F-statistic):              0.273\n",
      "Time:                        18:39:02   Log-Likelihood:                -21.306\n",
      "No. Observations:                 351   AIC:                             50.61\n",
      "Df Residuals:                     347   BIC:                             66.06\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.3840      0.057      6.768      0.000       0.272       0.496\n",
      "x1             0.0230      0.056      0.409      0.683      -0.088       0.134\n",
      "x2            -0.0440      0.047     -0.933      0.351      -0.137       0.049\n",
      "x3            -0.1105      0.058     -1.916      0.056      -0.224       0.003\n",
      "==============================================================================\n",
      "Omnibus:                       44.500   Durbin-Watson:                   2.157\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               75.177\n",
      "Skew:                           0.757   Prob(JB):                     4.74e-17\n",
      "Kurtosis:                       4.687   Cond. No.                         10.1\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Weighted Least Squares\n",
    "Y = y_var.copy()\n",
    "X = x_vars.copy()\n",
    "print(\"x_vars:\",reg_var_list)\n",
    "X = sm.add_constant(X)\n",
    "wls_model = sm.WLS(Y,X, weights=list(dbf_c.wt))\n",
    "results = wls_model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e88cee1",
   "metadata": {},
   "source": [
    "### Basic / non-basic WASH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2d4b7b61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the data type(s) is/are:\n",
      "float64\n",
      "columns w/ nan: []\n",
      "total excluded obs.: 0\n",
      "min. values: [0.       0.044629]\n",
      "max. values: [1.       4.396831]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-70-9f7363959f01>:70: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print('min. values:',dbf_c.min().unique())\n",
      "<ipython-input-70-9f7363959f01>:71: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print('max. values:',dbf_c.max().unique())\n"
     ]
    },
    {
     "data": {
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       "        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>dPrevU5</th>\n",
       "      <th>pc_bas_w</th>\n",
       "      <th>pc_bas_s</th>\n",
       "      <th>pc_Wnoed</th>\n",
       "      <th>wt</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.271605</td>\n",
       "      <td>0.117284</td>\n",
       "      <td>0.712144</td>\n",
       "      <td>1.803150</td>\n",
       "      <td>POINT (-356052.386 666169.653)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.414815</td>\n",
       "      <td>0.696629</td>\n",
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       "      <td>0.997934</td>\n",
       "      <td>POINT (-914153.321 720932.789)</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.161616</td>\n",
       "      <td>0.482143</td>\n",
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       "      <td>0.608696</td>\n",
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       "      <td>POINT (-926686.114 811988.956)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.263374</td>\n",
       "      <td>0.875912</td>\n",
       "      <td>0.029197</td>\n",
       "      <td>0.455577</td>\n",
       "      <td>0.470353</td>\n",
       "      <td>POINT (-340588.773 863284.824)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.374269</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.184615</td>\n",
       "      <td>0.638796</td>\n",
       "      <td>0.922173</td>\n",
       "      <td>POINT (-561129.048 861305.534)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>346</th>\n",
       "      <td>0.237037</td>\n",
       "      <td>0.734043</td>\n",
       "      <td>0.164894</td>\n",
       "      <td>0.528752</td>\n",
       "      <td>1.532898</td>\n",
       "      <td>POINT (-636278.276 709631.737)</td>\n",
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       "      <th>347</th>\n",
       "      <td>0.289406</td>\n",
       "      <td>0.651316</td>\n",
       "      <td>0.223684</td>\n",
       "      <td>0.574879</td>\n",
       "      <td>0.537444</td>\n",
       "      <td>POINT (-587513.882 779784.103)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>0.484848</td>\n",
       "      <td>0.835443</td>\n",
       "      <td>0.113924</td>\n",
       "      <td>0.543478</td>\n",
       "      <td>0.299906</td>\n",
       "      <td>POINT (-356268.902 796480.790)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>0.618357</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.092308</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>1.270933</td>\n",
       "      <td>POINT (-724998.574 742863.630)</td>\n",
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       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>0.197531</td>\n",
       "      <td>0.944882</td>\n",
       "      <td>0.228346</td>\n",
       "      <td>0.352324</td>\n",
       "      <td>0.604654</td>\n",
       "      <td>POINT (-560382.539 859191.417)</td>\n",
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       "<p>351 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      dPrevU5  pc_bas_w  pc_bas_s  pc_Wnoed        wt  \\\n",
       "0    0.074074  0.271605  0.117284  0.712144  1.803150   \n",
       "1    0.414815  0.696629  0.000000  0.729469  0.997934   \n",
       "2    0.161616  0.482143  0.000000  0.608696  0.192455   \n",
       "3    0.263374  0.875912  0.029197  0.455577  0.470353   \n",
       "4    0.374269  1.000000  0.184615  0.638796  0.922173   \n",
       "..        ...       ...       ...       ...       ...   \n",
       "346  0.237037  0.734043  0.164894  0.528752  1.532898   \n",
       "347  0.289406  0.651316  0.223684  0.574879  0.537444   \n",
       "348  0.484848  0.835443  0.113924  0.543478  0.299906   \n",
       "349  0.618357  0.938462  0.092308  0.347826  1.270933   \n",
       "350  0.197531  0.944882  0.228346  0.352324  0.604654   \n",
       "\n",
       "                           geometry  \n",
       "0    POINT (-356052.386 666169.653)  \n",
       "1    POINT (-914153.321 720932.789)  \n",
       "2    POINT (-926686.114 811988.956)  \n",
       "3    POINT (-340588.773 863284.824)  \n",
       "4    POINT (-561129.048 861305.534)  \n",
       "..                              ...  \n",
       "346  POINT (-636278.276 709631.737)  \n",
       "347  POINT (-587513.882 779784.103)  \n",
       "348  POINT (-356268.902 796480.790)  \n",
       "349  POINT (-724998.574 742863.630)  \n",
       "350  POINT (-560382.539 859191.417)  \n",
       "\n",
       "[351 rows x 6 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get data (copy of dataset)\n",
    "dbf_c = gps_data_j3.copy()\n",
    "\n",
    "# Select cols (keep only env. variables + ind. variable)\n",
    "#data_sel = 'include non-de jure'\n",
    "data_sel = 'exclude non-de jure'\n",
    "\n",
    "if data_sel == 'include non-de jure':\n",
    "    dbf_c = dbf_c[['dPrevU5bis',\n",
    "                   'pc_bas_w',\n",
    "                   'pc_bas_s',\n",
    "                   #'pc_imp_h',\n",
    "                   'pc_WnoedBis',\n",
    "                   'wt',\n",
    "                   'geometry']]\n",
    "else:\n",
    "    dbf_c = dbf_c[['dPrevU5',\n",
    "                   'pc_bas_w',\n",
    "                   'pc_bas_s',\n",
    "                   #'pc_imp_h',\n",
    "                   'pc_Wnoed',\n",
    "                   'wt',\n",
    "                   'geometry']]\n",
    "\n",
    "# Scale values (min/max) of all variables (except 'geometry')\n",
    "ind_cols = dbf_c.columns.difference(['wt','geometry']).to_list()\n",
    "for var in ind_cols:\n",
    "    dbf_c[var] = ((dbf_c[var] - dbf_c[var].min())/\n",
    "                  (dbf_c[var].max() - dbf_c[var].min()))\n",
    "\n",
    "# Check that all data types are numeric\n",
    "lst = []\n",
    "for col in ind_cols:\n",
    "    lst = lst+[str(dbf_c[col].dtype)]\n",
    "# function to get unique values\n",
    "def unique(list1):\n",
    "    # initialize a null list\n",
    "    unique_list = []\n",
    "    # traverse for all elements\n",
    "    for x in list1:\n",
    "        # check if exists in unique_list or not\n",
    "        if x not in unique_list:\n",
    "            unique_list.append(x)\n",
    "    # print list\n",
    "    for x in unique_list:\n",
    "        print(x)\n",
    "# driver code\n",
    "print(\"the data type(s) is/are:\")\n",
    "unique(lst)\n",
    "\n",
    "# Exclude NaNs:\n",
    "lst = []\n",
    "t = -1\n",
    "while True:\n",
    "    try:\n",
    "        t = dbf_c.isna().any().to_list().index(True, t + 1)\n",
    "        lst = lst+[t]\n",
    "    except ValueError:\n",
    "        break\n",
    "cols_wnan = []\n",
    "for i in lst:\n",
    "    col = dbf_c.columns.to_list()[i]\n",
    "    cols_wnan = cols_wnan+[col]\n",
    "print(\"columns w/ nan:\",cols_wnan)\n",
    "for col in cols_wnan:\n",
    "    dbf_c = dbf_c[~dbf_c[col].isna()]\n",
    "print(\"total excluded obs.:\",gps_data_j3.shape[0]-dbf_c.shape[0])\n",
    "\n",
    "# Check min/max values:\n",
    "print('min. values:',dbf_c.min().unique())\n",
    "print('max. values:',dbf_c.max().unique())\n",
    "\n",
    "# Turn into points\n",
    "dbf_c['geometry'] = dbf_c.geometry.centroid\n",
    "\n",
    "# Check data\n",
    "dbf_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d4dde21b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION\n",
      "----------\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES\n",
      "-----------------------------------------\n",
      "Data set            :     unknown\n",
      "Weights matrix      :     unknown\n",
      "Dependent Variable  :     dep_var                Number of Observations:         351\n",
      "Mean dependent var  :      0.3211                Number of Variables   :           4\n",
      "S.D. dependent var  :      0.2168                Degrees of Freedom    :         347\n",
      "R-squared           :      0.0171\n",
      "Adjusted R-squared  :      0.0086\n",
      "Sum squared residual:      16.163                F-statistic           :      2.0120\n",
      "Sigma-square        :       0.047                Prob(F-statistic)     :       0.112\n",
      "S.E. of regression  :       0.216                Log likelihood        :      42.156\n",
      "Sigma-square ML     :       0.046                Akaike info criterion :     -76.311\n",
      "S.E of regression ML:      0.2146                Schwarz criterion     :     -60.868\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT       0.4043844       0.0588467       6.8718303       0.0000000\n",
      "            pc_bas_s      -0.1515722       0.0647939      -2.3392975       0.0198868\n",
      "            pc_bas_w       0.0229150       0.0500444       0.4578933       0.6473157\n",
      "            pc_Wnoed      -0.1190764       0.0609930      -1.9522950       0.0517070\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER           11.203\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2          16.809           0.0002\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                3           9.033           0.0289\n",
      "Koenker-Bassett test              3           9.623           0.0221\n",
      "\n",
      "DIAGNOSTICS FOR SPATIAL DEPENDENCE\n",
      "TEST                           MI/DF       VALUE           PROB\n",
      "Moran's I (error)              0.0982         6.140           0.0000\n",
      "Lagrange Multiplier (lag)         1          28.893           0.0000\n",
      "Robust LM (lag)                   1           0.891           0.3451\n",
      "Lagrange Multiplier (error)       1          31.091           0.0000\n",
      "Robust LM (error)                 1           3.089           0.0788\n",
      "Lagrange Multiplier (SARMA)       2          31.982           0.0000\n",
      "\n",
      "================================ END OF REPORT =====================================\n"
     ]
    }
   ],
   "source": [
    "# OLS regression with pysal's spreg\n",
    "\n",
    "reg_var_list = sorted(dbf_c.columns.difference([dbf_c.columns[0],\n",
    "                                                'wt',\n",
    "                                                'geometry',]).to_list(),\n",
    "                      key=str.lower,\n",
    "                      reverse=False)\n",
    "\n",
    "# set regression inputs\n",
    "y_var = dbf_c[dbf_c.columns[0]].values\n",
    "x_vars = dbf_c[reg_var_list].values\n",
    "weight = weights.KNN.from_dataframe(dbf_c,k=16)# k: 16 nearest neighbours\n",
    "\n",
    "# Run OLS regression with spatial diagnostics \n",
    "mols = spreg.OLS(y_var,x_vars,weight,\n",
    "                 name_x=reg_var_list,\n",
    "                 moran=True,#compute Moran’s I on the residuals (requires spat_diag=True)\n",
    "                 spat_diag=True)\n",
    "\n",
    "# See results\n",
    "print(mols.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1be0e63c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION\n",
      "----------\n",
      "SUMMARY OF OUTPUT: MAXIMUM LIKELIHOOD SPATIAL LAG (METHOD = ORD)\n",
      "----------------------------------------------------------------\n",
      "Data set            :     unknown\n",
      "Weights matrix      :     unknown\n",
      "Dependent Variable  :     dep_var                Number of Observations:         351\n",
      "Mean dependent var  :      0.3211                Number of Variables   :           5\n",
      "S.D. dependent var  :      0.2168                Degrees of Freedom    :         346\n",
      "Pseudo R-squared    :      0.0779\n",
      "Spatial Pseudo R-squared:  0.0119\n",
      "Sigma-square ML     :       0.043                Log likelihood        :      50.371\n",
      "S.E of regression   :       0.208                Akaike info criterion :     -90.743\n",
      "                                                 Schwarz criterion     :     -71.439\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     z-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT       0.2297447       0.0670724       3.4253250       0.0006141\n",
      "            pc_bas_s      -0.1484603       0.0625988      -2.3716149       0.0177105\n",
      "            pc_bas_w       0.0363475       0.0483064       0.7524367       0.4517885\n",
      "            pc_Wnoed      -0.0940385       0.0589280      -1.5958211       0.1105287\n",
      "           W_dep_var       0.0285768       0.0065060       4.3923601       0.0000112\n",
      "------------------------------------------------------------------------------------\n",
      "================================ END OF REPORT =====================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vitor/miniconda3/envs/LS_Metrics/lib/python3.8/site-packages/scipy/optimize/_minimize.py:783: RuntimeWarning: Method 'bounded' does not support relative tolerance in x; defaulting to absolute tolerance.\n",
      "  warn(\"Method 'bounded' does not support relative tolerance in x; \"\n"
     ]
    }
   ],
   "source": [
    "# calculate ML Lag\n",
    "# See: https://spreg.readthedocs.io/en/latest/generated/spreg.ML_Lag.html\n",
    "# NOTE: method = \"ORD\" or \"LU\" works better for large samples\n",
    "# >> according to https://github.com/pysal/pysal/issues/587\n",
    "mlag = spreg.ML_Lag(y_var,x_vars,\n",
    "                    weight,\n",
    "                    method = \"ORD\",\n",
    "                    name_x=reg_var_list)\n",
    "\n",
    "print(mlag.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e7f99ede",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_vars: ['pc_bas_s', 'pc_bas_w', 'pc_Wnoed']\n",
      "                            WLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.042\n",
      "Model:                            WLS   Adj. R-squared:                  0.034\n",
      "Method:                 Least Squares   F-statistic:                     5.071\n",
      "Date:                Sun, 03 Apr 2022   Prob (F-statistic):            0.00189\n",
      "Time:                        18:40:26   Log-Likelihood:                -15.742\n",
      "No. Observations:                 351   AIC:                             39.48\n",
      "Df Residuals:                     347   BIC:                             54.93\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.4459      0.058      7.669      0.000       0.332       0.560\n",
      "x1            -0.2246      0.065     -3.475      0.001      -0.352      -0.097\n",
      "x2             0.0583      0.051      1.139      0.256      -0.042       0.159\n",
      "x3            -0.2191      0.064     -3.415      0.001      -0.345      -0.093\n",
      "==============================================================================\n",
      "Omnibus:                       33.090   Durbin-Watson:                   2.180\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               47.777\n",
      "Skew:                           0.647   Prob(JB):                     4.22e-11\n",
      "Kurtosis:                       4.262   Cond. No.                         11.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Weighted Least Squares\n",
    "Y = y_var.copy()\n",
    "X = x_vars.copy()\n",
    "print(\"x_vars:\",reg_var_list)\n",
    "X = sm.add_constant(X)\n",
    "wls_model = sm.WLS(Y,X, weights=list(dbf_c.wt))\n",
    "results = wls_model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15dc0483",
   "metadata": {},
   "source": [
    "# Plot Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ee64ee10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sperman's rho: SpearmanrResult(correlation=-0.06272909609777945, pvalue=0.2964358800637946)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 861.84x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Basic sanitation VS Diarrhoea\n",
    "\n",
    "# Get complete dataset\n",
    "data_g = dbf_c.copy()\n",
    "\n",
    "x=\"pc_bas_s\"\n",
    "xthreshold = 0\n",
    "y=\"dPrevU5\"\n",
    "\n",
    "g = sns.lmplot(\n",
    "    data=data_g[data_g[x]>xthreshold],\n",
    "    x=x, y=y,\n",
    "    #hue=\"b_lev\",\n",
    "    height=9,\n",
    "    aspect=1.33)\n",
    "\n",
    "print(\"Sperman's rho:\", stats.spearmanr(data_g[y][data_g[x]>xthreshold],data_g[x][data_g[x]>xthreshold]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "2685daa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sperman's rho: SpearmanrResult(correlation=-0.04249374057042117, pvalue=0.4280696506427839)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 861.84x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Women's edu. VS Diarrhoea\n",
    "\n",
    "# Get complete dataset\n",
    "data_g = dbf_c.copy()\n",
    "\n",
    "x=\"pc_Wnoed\"\n",
    "xthreshold = 0\n",
    "y=\"dPrevU5\"\n",
    "\n",
    "g = sns.lmplot(\n",
    "    data=data_g[data_g[x]>xthreshold],\n",
    "    x=x, y=y,\n",
    "    #hue=\"b_lev\",\n",
    "    height=9,\n",
    "    aspect=1.33)\n",
    "\n",
    "print(\"Sperman's rho:\", stats.spearmanr(data_g[y][data_g[x]>xthreshold],data_g[x][data_g[x]>xthreshold]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93262b32",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "904bbe4d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "LS_Metrics",
   "language": "python",
   "name": "ls_metrics"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}