Walkability_and_Safety_Index.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2c55e096-6d8c-4aaa-8485-eed67f61713e",
   "metadata": {},
   "source": [
    "## Import Libraries and Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "60bf164e-0a75-43bc-a369-9443fb085562",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option(\"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8d2fb1d0-7f2f-483b-b334-1942507e98f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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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>adm3_en</th>\n",
       "      <th>adm4_en</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm4_pcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH015518016</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Lomboy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518031</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Tapuac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518022</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Pantal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518024</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Barangay I (T. Bugallon)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH034919017</th>\n",
       "      <td>Palayan City</td>\n",
       "      <td>Imelda Valley</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  adm3_en                   adm4_en\n",
       "adm4_pcode                                         \n",
       "PH015518016  Dagupan City                    Lomboy\n",
       "PH015518031  Dagupan City                    Tapuac\n",
       "PH015518022  Dagupan City                    Pantal\n",
       "PH015518024  Dagupan City  Barangay I (T. Bugallon)\n",
       "PH034919017  Palayan City             Imelda Valley"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import barangay location dataset\n",
    "location = pd.read_csv(\"location.csv\")\n",
    "\n",
    "cities = ['Cagayan de Oro City', 'Dagupan City', 'Davao City', 'Iloilo City', 'Legazpi City', 'Mandaluyong City',\n",
    "          'Mandaue City', 'Muntinlupa City', 'Navotas City', 'Palayan City', 'Tacloban City', 'Zamboanga City']\n",
    "\n",
    "location.set_index(\"adm4_pcode\", inplace = True)\n",
    "location = location.loc[location['adm3_en'].isin(cities)]\n",
    "location = location[['adm3_en', 'adm4_en']]\n",
    "location.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd9884ad-023e-4606-848f-2f0bdcaae55e",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>o3</th>\n",
       "      <th>no2</th>\n",
       "      <th>so2</th>\n",
       "      <th>pm10</th>\n",
       "      <th>pm25</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm4_pcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH015518001</th>\n",
       "      <td>42.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>27.17</td>\n",
       "      <td>19.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518002</th>\n",
       "      <td>42.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>27.17</td>\n",
       "      <td>19.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518003</th>\n",
       "      <td>42.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>27.17</td>\n",
       "      <td>19.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518004</th>\n",
       "      <td>42.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>27.17</td>\n",
       "      <td>19.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518006</th>\n",
       "      <td>42.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>27.17</td>\n",
       "      <td>19.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                o3   no2   so2   pm10   pm25\n",
       "adm4_pcode                                  \n",
       "PH015518001  42.99  4.45  0.61  27.17  19.02\n",
       "PH015518002  42.99  4.45  0.61  27.17  19.02\n",
       "PH015518003  42.99  4.45  0.61  27.17  19.02\n",
       "PH015518004  42.99  4.45  0.61  27.17  19.02\n",
       "PH015518006  42.99  4.45  0.61  27.17  19.02"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import climate air quality dataset\n",
    "air_data = pd.read_csv(\"climate_air_quality.csv\")\n",
    "air_data.set_index(\"adm4_pcode\", inplace = True)\n",
    "air_data = air_data.loc[air_data['date'] == '2022-12-31']\n",
    "air_data.dropna()\n",
    "air_data = air_data[[\"o3\", \"no2\", \"so2\", \"pm10\", \"pm25\"]]\n",
    "air_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b82335e2-1d48-488e-aa88-031343aea47a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uuid</th>\n",
       "      <th>date</th>\n",
       "      <th>freq</th>\n",
       "      <th>tave</th>\n",
       "      <th>tmin</th>\n",
       "      <th>tmax</th>\n",
       "      <th>heat_index</th>\n",
       "      <th>pr</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>rh</th>\n",
       "      <th>solar_rad</th>\n",
       "      <th>uv_rad</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm4_pcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH015518001</th>\n",
       "      <td>CATMS001C88</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>D</td>\n",
       "      <td>27.7</td>\n",
       "      <td>24.92</td>\n",
       "      <td>30.96</td>\n",
       "      <td>29.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>69.18</td>\n",
       "      <td>208.36</td>\n",
       "      <td>24.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518002</th>\n",
       "      <td>CATMS003911</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>D</td>\n",
       "      <td>27.7</td>\n",
       "      <td>24.92</td>\n",
       "      <td>30.96</td>\n",
       "      <td>29.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>69.18</td>\n",
       "      <td>208.36</td>\n",
       "      <td>24.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518003</th>\n",
       "      <td>CATMS00559A</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>D</td>\n",
       "      <td>27.7</td>\n",
       "      <td>24.92</td>\n",
       "      <td>30.96</td>\n",
       "      <td>29.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>69.18</td>\n",
       "      <td>208.36</td>\n",
       "      <td>24.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518004</th>\n",
       "      <td>CATMS007223</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>D</td>\n",
       "      <td>27.7</td>\n",
       "      <td>24.92</td>\n",
       "      <td>30.96</td>\n",
       "      <td>29.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>69.18</td>\n",
       "      <td>208.36</td>\n",
       "      <td>24.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518006</th>\n",
       "      <td>CATMS008EAC</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>D</td>\n",
       "      <td>27.7</td>\n",
       "      <td>24.92</td>\n",
       "      <td>30.96</td>\n",
       "      <td>29.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>69.18</td>\n",
       "      <td>208.36</td>\n",
       "      <td>24.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    uuid        date freq  tave   tmin   tmax  heat_index  \\\n",
       "adm4_pcode                                                                  \n",
       "PH015518001  CATMS001C88  2022-12-31    D  27.7  24.92  30.96       29.55   \n",
       "PH015518002  CATMS003911  2022-12-31    D  27.7  24.92  30.96       29.55   \n",
       "PH015518003  CATMS00559A  2022-12-31    D  27.7  24.92  30.96       29.55   \n",
       "PH015518004  CATMS007223  2022-12-31    D  27.7  24.92  30.96       29.55   \n",
       "PH015518006  CATMS008EAC  2022-12-31    D  27.7  24.92  30.96       29.55   \n",
       "\n",
       "              pr  wind_speed     rh  solar_rad  uv_rad  \n",
       "adm4_pcode                                              \n",
       "PH015518001  0.0        0.64  69.18     208.36   24.18  \n",
       "PH015518002  0.0        0.64  69.18     208.36   24.18  \n",
       "PH015518003  0.0        0.64  69.18     208.36   24.18  \n",
       "PH015518004  0.0        0.64  69.18     208.36   24.18  \n",
       "PH015518006  0.0        0.64  69.18     208.36   24.18  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import climate atmosphere dataset\n",
    "heat_data = pd.read_csv(\"climate_atmosphere.csv\")\n",
    "heat_data.set_index(\"adm4_pcode\", inplace = True)\n",
    "heat_data = heat_data.loc[heat_data['date'] == '2022-12-31']\n",
    "heat_data.dropna()\n",
    "heat_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a51844a-0cb2-498c-8991-cf125a6451ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uuid</th>\n",
       "      <th>date</th>\n",
       "      <th>freq</th>\n",
       "      <th>avg_rad_min</th>\n",
       "      <th>avg_rad_max</th>\n",
       "      <th>avg_rad_mean</th>\n",
       "      <th>avg_rad_std</th>\n",
       "      <th>avg_rad_median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm4_pcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH015518016</th>\n",
       "      <td>NTLGT002256</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.371240</td>\n",
       "      <td>2.145014</td>\n",
       "      <td>1.820992</td>\n",
       "      <td>0.239676</td>\n",
       "      <td>1.895808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518031</th>\n",
       "      <td>NTLGT002257</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.167201</td>\n",
       "      <td>9.034832</td>\n",
       "      <td>5.663995</td>\n",
       "      <td>2.687575</td>\n",
       "      <td>4.564902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518022</th>\n",
       "      <td>NTLGT002258</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.509837</td>\n",
       "      <td>9.022078</td>\n",
       "      <td>4.553129</td>\n",
       "      <td>2.248700</td>\n",
       "      <td>4.140003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518024</th>\n",
       "      <td>NTLGT002259</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>10.438459</td>\n",
       "      <td>15.016158</td>\n",
       "      <td>12.727308</td>\n",
       "      <td>2.288849</td>\n",
       "      <td>12.727308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH034919017</th>\n",
       "      <td>NTLGT00225A</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.589574</td>\n",
       "      <td>2.165237</td>\n",
       "      <td>1.054052</td>\n",
       "      <td>0.475109</td>\n",
       "      <td>0.924068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    uuid        date freq  avg_rad_min  avg_rad_max  \\\n",
       "adm4_pcode                                                            \n",
       "PH015518016  NTLGT002256  2022-01-01    Y     1.371240     2.145014   \n",
       "PH015518031  NTLGT002257  2022-01-01    Y     2.167201     9.034832   \n",
       "PH015518022  NTLGT002258  2022-01-01    Y     1.509837     9.022078   \n",
       "PH015518024  NTLGT002259  2022-01-01    Y    10.438459    15.016158   \n",
       "PH034919017  NTLGT00225A  2022-01-01    Y     0.589574     2.165237   \n",
       "\n",
       "             avg_rad_mean  avg_rad_std  avg_rad_median  \n",
       "adm4_pcode                                              \n",
       "PH015518016      1.820992     0.239676        1.895808  \n",
       "PH015518031      5.663995     2.687575        4.564902  \n",
       "PH015518022      4.553129     2.248700        4.140003  \n",
       "PH015518024     12.727308     2.288849       12.727308  \n",
       "PH034919017      1.054052     0.475109        0.924068  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import night-time lighting dataset\n",
    "light_data = pd.read_csv(\"nighttime_lights.csv\")\n",
    "light_data.set_index(\"adm4_pcode\", inplace = True)\n",
    "light_data = light_data.loc[light_data['date'] == '2022-01-01']\n",
    "light_data.dropna()\n",
    "light_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2a11f3a-5d61-4791-b6a9-ebb1909155a7",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atm_nearest</th>\n",
       "      <th>bank_nearest</th>\n",
       "      <th>college_nearest</th>\n",
       "      <th>community_centre_nearest</th>\n",
       "      <th>convenience_nearest</th>\n",
       "      <th>fire_station_nearest</th>\n",
       "      <th>kindergarten_nearest</th>\n",
       "      <th>market_place_nearest</th>\n",
       "      <th>park_nearest</th>\n",
       "      <th>police_nearest</th>\n",
       "      <th>school_nearest</th>\n",
       "      <th>shelter_nearest</th>\n",
       "      <th>supermarket_nearest</th>\n",
       "      <th>town_hall_nearest</th>\n",
       "      <th>avg_distance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm4_pcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH015518016</th>\n",
       "      <td>1350.961178</td>\n",
       "      <td>1044.693472</td>\n",
       "      <td>1526.219926</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1140.748886</td>\n",
       "      <td>2152.886539</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>3778.093535</td>\n",
       "      <td>1447.965309</td>\n",
       "      <td>3156.411541</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1131.480067</td>\n",
       "      <td>3152.435512</td>\n",
       "      <td>3563.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518031</th>\n",
       "      <td>525.205612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1294.675375</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>931.181252</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1928.834812</td>\n",
       "      <td>2514.551323</td>\n",
       "      <td>981.016720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>385.246173</td>\n",
       "      <td>977.276221</td>\n",
       "      <td>2824.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518022</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.102258</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2007.885364</td>\n",
       "      <td>102.676357</td>\n",
       "      <td>1242.966790</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1248.008152</td>\n",
       "      <td>2473.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518024</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>342.930960</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>84.278377</td>\n",
       "      <td>395.419002</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2066.877400</td>\n",
       "      <td>1666.029816</td>\n",
       "      <td>2706.934959</td>\n",
       "      <td>66.871015</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1785.634654</td>\n",
       "      <td>2794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH034919017</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1683.508479</td>\n",
       "      <td>5736.344670</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>4.358631</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6959.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              atm_nearest  bank_nearest  college_nearest  \\\n",
       "adm4_pcode                                                 \n",
       "PH015518016   1350.961178   1044.693472      1526.219926   \n",
       "PH015518031    525.205612      0.000000      1294.675375   \n",
       "PH015518022      0.000000      0.000000         0.000000   \n",
       "PH015518024      0.000000      0.000000       342.930960   \n",
       "PH034919017  10000.000000  10000.000000     10000.000000   \n",
       "\n",
       "             community_centre_nearest  convenience_nearest  \\\n",
       "adm4_pcode                                                   \n",
       "PH015518016              10000.000000          1140.748886   \n",
       "PH015518031              10000.000000             0.000000   \n",
       "PH015518022              10000.000000             0.000000   \n",
       "PH015518024              10000.000000            84.278377   \n",
       "PH034919017               1683.508479          5736.344670   \n",
       "\n",
       "             fire_station_nearest  kindergarten_nearest  market_place_nearest  \\\n",
       "adm4_pcode                                                                      \n",
       "PH015518016           2152.886539               10000.0           3778.093535   \n",
       "PH015518031            931.181252               10000.0           1928.834812   \n",
       "PH015518022             18.102258               10000.0           2007.885364   \n",
       "PH015518024            395.419002               10000.0           2066.877400   \n",
       "PH034919017          10000.000000               10000.0          10000.000000   \n",
       "\n",
       "             park_nearest  police_nearest  school_nearest  shelter_nearest  \\\n",
       "adm4_pcode                                                                   \n",
       "PH015518016   1447.965309     3156.411541        0.000000          10000.0   \n",
       "PH015518031   2514.551323      981.016720        0.000000          10000.0   \n",
       "PH015518022    102.676357     1242.966790        0.000000          10000.0   \n",
       "PH015518024   1666.029816     2706.934959       66.871015          10000.0   \n",
       "PH034919017  10000.000000        4.358631        0.000000          10000.0   \n",
       "\n",
       "             supermarket_nearest  town_hall_nearest  avg_distance  \n",
       "adm4_pcode                                                         \n",
       "PH015518016          1131.480067        3152.435512        3563.0  \n",
       "PH015518031           385.246173         977.276221        2824.0  \n",
       "PH015518022             0.000000        1248.008152        2473.0  \n",
       "PH015518024             0.000000        1785.634654        2794.0  \n",
       "PH034919017         10000.000000           0.000000        6959.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import amenities dataset\n",
    "amenity_data = pd.read_csv(\"osm_poi_amenity.csv\")\n",
    "amenity_data.set_index(\"adm4_pcode\", inplace = True)\n",
    "amenity_data = amenity_data.loc[amenity_data['date'] == '2022-01-01']\n",
    "amenity_data = amenity_data[['atm_nearest',\n",
    "                             'bank_nearest',\n",
    "                             'college_nearest',\n",
    "                             'community_centre_nearest',\n",
    "                             'convenience_nearest',\n",
    "                             'fire_station_nearest',\n",
    "                             'kindergarten_nearest',\n",
    "                             'market_place_nearest',\n",
    "                             'park_nearest',\n",
    "                             'police_nearest',\n",
    "                             'school_nearest',\n",
    "                             'shelter_nearest',\n",
    "                             'supermarket_nearest',\n",
    "                             'town_hall_nearest',\n",
    "                            ]]\n",
    "amenity_data.dropna()\n",
    "amenity_data['avg_distance'] = round((amenity_data['atm_nearest'] + \\\n",
    "                                            amenity_data['bank_nearest'] + \\\n",
    "                                            amenity_data['college_nearest'] + \\\n",
    "                                            amenity_data['community_centre_nearest'] + \\\n",
    "                                            amenity_data['convenience_nearest'] + \\\n",
    "                                            amenity_data['fire_station_nearest'] + \\\n",
    "                                            amenity_data['kindergarten_nearest'] + \\\n",
    "                                            amenity_data['market_place_nearest'] + \\\n",
    "                                            amenity_data['park_nearest'] + \\\n",
    "                                            amenity_data['police_nearest'] + \\\n",
    "                                            amenity_data['school_nearest'] + \\\n",
    "                                            amenity_data['shelter_nearest'] + \\\n",
    "                                            amenity_data['supermarket_nearest'] + \\\n",
    "                                            amenity_data['town_hall_nearest']) / 14, 0)\n",
    "amenity_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2a40002c-6cb8-4653-89f6-37446b7d6960",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>uuid</th>\n",
       "      <th>date</th>\n",
       "      <th>freq</th>\n",
       "      <th>pop_count_total</th>\n",
       "      <th>pop_count_mean</th>\n",
       "      <th>pop_count_median</th>\n",
       "      <th>pop_count_stdev</th>\n",
       "      <th>pop_count_min</th>\n",
       "      <th>pop_count_max</th>\n",
       "      <th>pop_density_mean</th>\n",
       "      <th>pop_density_median</th>\n",
       "      <th>pop_density_stdev</th>\n",
       "      <th>pop_density_min</th>\n",
       "      <th>pop_density_max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm4_pcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH050506037</th>\n",
       "      <td>WDPOP0044AC</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>3198.0</td>\n",
       "      <td>55.137140</td>\n",
       "      <td>48.937912</td>\n",
       "      <td>17.289855</td>\n",
       "      <td>27.611353</td>\n",
       "      <td>119.356880</td>\n",
       "      <td>13548.703125</td>\n",
       "      <td>13548.703125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13548.703125</td>\n",
       "      <td>13548.703125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH050506019</th>\n",
       "      <td>WDPOP0044AD</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>355.0</td>\n",
       "      <td>39.409770</td>\n",
       "      <td>28.384478</td>\n",
       "      <td>24.658198</td>\n",
       "      <td>17.391975</td>\n",
       "      <td>86.987427</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>PH063022118</th>\n",
       "      <td>WDPOP0044AE</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>153.644381</td>\n",
       "      <td>26.973276</td>\n",
       "      <td>288.041872</td>\n",
       "      <td>9.792673</td>\n",
       "      <td>1129.023682</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>PH063022015</th>\n",
       "      <td>WDPOP0044AF</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>785.0</td>\n",
       "      <td>52.350012</td>\n",
       "      <td>52.382965</td>\n",
       "      <td>11.724451</td>\n",
       "      <td>31.649677</td>\n",
       "      <td>76.538689</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>PH063022110</th>\n",
       "      <td>WDPOP0044B0</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>9718.0</td>\n",
       "      <td>323.917448</td>\n",
       "      <td>268.267914</td>\n",
       "      <td>200.023861</td>\n",
       "      <td>10.259146</td>\n",
       "      <td>680.106323</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",
       "</div>"
      ],
      "text/plain": [
       "                    uuid        date freq  pop_count_total  pop_count_mean  \\\n",
       "adm4_pcode                                                                   \n",
       "PH050506037  WDPOP0044AC  2020-01-01    Y           3198.0       55.137140   \n",
       "PH050506019  WDPOP0044AD  2020-01-01    Y            355.0       39.409770   \n",
       "PH063022118  WDPOP0044AE  2020-01-01    Y           1997.0      153.644381   \n",
       "PH063022015  WDPOP0044AF  2020-01-01    Y            785.0       52.350012   \n",
       "PH063022110  WDPOP0044B0  2020-01-01    Y           9718.0      323.917448   \n",
       "\n",
       "             pop_count_median  pop_count_stdev  pop_count_min  pop_count_max  \\\n",
       "adm4_pcode                                                                     \n",
       "PH050506037         48.937912        17.289855      27.611353     119.356880   \n",
       "PH050506019         28.384478        24.658198      17.391975      86.987427   \n",
       "PH063022118         26.973276       288.041872       9.792673    1129.023682   \n",
       "PH063022015         52.382965        11.724451      31.649677      76.538689   \n",
       "PH063022110        268.267914       200.023861      10.259146     680.106323   \n",
       "\n",
       "             pop_density_mean  pop_density_median  pop_density_stdev  \\\n",
       "adm4_pcode                                                             \n",
       "PH050506037      13548.703125        13548.703125                0.0   \n",
       "PH050506019               NaN                 NaN                NaN   \n",
       "PH063022118               NaN                 NaN                NaN   \n",
       "PH063022015               NaN                 NaN                NaN   \n",
       "PH063022110               NaN                 NaN                NaN   \n",
       "\n",
       "             pop_density_min  pop_density_max  \n",
       "adm4_pcode                                     \n",
       "PH050506037     13548.703125     13548.703125  \n",
       "PH050506019              NaN              NaN  \n",
       "PH063022118              NaN              NaN  \n",
       "PH063022015              NaN              NaN  \n",
       "PH063022110              NaN              NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import world population dataset\n",
    "pop_data = pd.read_csv(\"worldpop_population.csv\")\n",
    "pop_data.set_index(\"adm4_pcode\", inplace = True)\n",
    "pop_data = pop_data.loc[pop_data['date'] == '2020-01-01']\n",
    "pop_data['pop_count_total'] = round(pop_data['pop_count_total'])\n",
    "pop_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "384bd5e0-0a96-4b68-952f-f351141e2212",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>uuid</th>\n",
       "      <th>date</th>\n",
       "      <th>freq</th>\n",
       "      <th>brgy_total_area</th>\n",
       "      <th>brgy_distance_to_coast</th>\n",
       "      <th>brgy_is_coastal</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm4_pcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH050506053</th>\n",
       "      <td>BGEOG000000</td>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>S</td>\n",
       "      <td>0.8428</td>\n",
       "      <td>990.730626</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((123.735986255 13.189494837, 123.7402...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH050506056</th>\n",
       "      <td>BGEOG000001</td>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>S</td>\n",
       "      <td>7.0818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>POLYGON ((123.758319446 13.0930309230001, 123....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH050506072</th>\n",
       "      <td>BGEOG000002</td>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>S</td>\n",
       "      <td>0.0335</td>\n",
       "      <td>2560.801915</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((123.734030311 13.138708508, 123.7340...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH050506020</th>\n",
       "      <td>BGEOG000003</td>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>S</td>\n",
       "      <td>0.0621</td>\n",
       "      <td>379.361723</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((123.754103911 13.1415778380001, 123....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH050506051</th>\n",
       "      <td>BGEOG000004</td>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>S</td>\n",
       "      <td>1.4442</td>\n",
       "      <td>1398.701072</td>\n",
       "      <td>False</td>\n",
       "      <td>POLYGON ((123.740796218 13.16167992, 123.74169...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    uuid        date freq  brgy_total_area  \\\n",
       "adm4_pcode                                                   \n",
       "PH050506053  BGEOG000000  2003-01-01    S           0.8428   \n",
       "PH050506056  BGEOG000001  2003-01-01    S           7.0818   \n",
       "PH050506072  BGEOG000002  2003-01-01    S           0.0335   \n",
       "PH050506020  BGEOG000003  2003-01-01    S           0.0621   \n",
       "PH050506051  BGEOG000004  2003-01-01    S           1.4442   \n",
       "\n",
       "             brgy_distance_to_coast  brgy_is_coastal  \\\n",
       "adm4_pcode                                             \n",
       "PH050506053              990.730626            False   \n",
       "PH050506056                0.000000             True   \n",
       "PH050506072             2560.801915            False   \n",
       "PH050506020              379.361723            False   \n",
       "PH050506051             1398.701072            False   \n",
       "\n",
       "                                                      geometry  \n",
       "adm4_pcode                                                      \n",
       "PH050506053  POLYGON ((123.735986255 13.189494837, 123.7402...  \n",
       "PH050506056  POLYGON ((123.758319446 13.0930309230001, 123....  \n",
       "PH050506072  POLYGON ((123.734030311 13.138708508, 123.7340...  \n",
       "PH050506020  POLYGON ((123.754103911 13.1415778380001, 123....  \n",
       "PH050506051  POLYGON ((123.740796218 13.16167992, 123.74169...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import barangay geography dataset\n",
    "land_data = pd.read_csv(\"brgy_geography.csv\")\n",
    "land_data.set_index(\"adm4_pcode\", inplace = True)\n",
    "land_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "175de10e-d4d5-43ac-8686-7bcf03328f26",
   "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>pop_count_total</th>\n",
       "      <th>brgy_total_area</th>\n",
       "      <th>population_density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm4_pcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH050506037</th>\n",
       "      <td>3198.0</td>\n",
       "      <td>0.4738</td>\n",
       "      <td>6749.683411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH050506019</th>\n",
       "      <td>355.0</td>\n",
       "      <td>0.0763</td>\n",
       "      <td>4652.686763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH063022118</th>\n",
       "      <td>1997.0</td>\n",
       "      <td>0.1259</td>\n",
       "      <td>15861.795075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH063022015</th>\n",
       "      <td>785.0</td>\n",
       "      <td>0.1261</td>\n",
       "      <td>6225.218081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH063022110</th>\n",
       "      <td>9718.0</td>\n",
       "      <td>0.2428</td>\n",
       "      <td>40024.711697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             pop_count_total  brgy_total_area  population_density\n",
       "adm4_pcode                                                       \n",
       "PH050506037           3198.0           0.4738         6749.683411\n",
       "PH050506019            355.0           0.0763         4652.686763\n",
       "PH063022118           1997.0           0.1259        15861.795075\n",
       "PH063022015            785.0           0.1261         6225.218081\n",
       "PH063022110           9718.0           0.2428        40024.711697"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge world population and barangay geography dataframes\n",
    "density_data = pd.merge(pop_data, land_data, on=\"adm4_pcode\", how=\"inner\")\n",
    "density_data = density_data[['pop_count_total', 'brgy_total_area']]\n",
    "\n",
    "# Calculate the population density (population / square kilometer)\n",
    "density_data['population_density'] = (density_data['pop_count_total'] / density_data['brgy_total_area'])\n",
    "density_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1454525c-d8dd-493c-bdd2-881efb3f8d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging all dataframe based on location (adm4_pcode)\n",
    "indicators = pd.merge(location, air_data, on=\"adm4_pcode\", how=\"inner\")\n",
    "indicators = pd.merge(indicators, heat_data, on=\"adm4_pcode\", how=\"inner\")\n",
    "indicators = pd.merge(indicators, light_data, on=\"adm4_pcode\", how=\"inner\")\n",
    "indicators = pd.merge(indicators, amenity_data, on=\"adm4_pcode\", how=\"inner\")\n",
    "indicators = pd.merge(indicators, density_data, on=\"adm4_pcode\", how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "91113ca7-22c4-48b8-8d96-deb06127c0a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>adm3_en</th>\n",
       "      <th>adm4_en</th>\n",
       "      <th>o3</th>\n",
       "      <th>no2</th>\n",
       "      <th>so2</th>\n",
       "      <th>pm10</th>\n",
       "      <th>pm25</th>\n",
       "      <th>uuid_x</th>\n",
       "      <th>date_x</th>\n",
       "      <th>freq_x</th>\n",
       "      <th>tave</th>\n",
       "      <th>tmin</th>\n",
       "      <th>tmax</th>\n",
       "      <th>heat_index</th>\n",
       "      <th>pr</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>rh</th>\n",
       "      <th>solar_rad</th>\n",
       "      <th>uv_rad</th>\n",
       "      <th>uuid_y</th>\n",
       "      <th>date_y</th>\n",
       "      <th>freq_y</th>\n",
       "      <th>avg_rad_min</th>\n",
       "      <th>avg_rad_max</th>\n",
       "      <th>avg_rad_mean</th>\n",
       "      <th>avg_rad_std</th>\n",
       "      <th>avg_rad_median</th>\n",
       "      <th>atm_nearest</th>\n",
       "      <th>bank_nearest</th>\n",
       "      <th>college_nearest</th>\n",
       "      <th>community_centre_nearest</th>\n",
       "      <th>convenience_nearest</th>\n",
       "      <th>fire_station_nearest</th>\n",
       "      <th>kindergarten_nearest</th>\n",
       "      <th>market_place_nearest</th>\n",
       "      <th>park_nearest</th>\n",
       "      <th>police_nearest</th>\n",
       "      <th>school_nearest</th>\n",
       "      <th>shelter_nearest</th>\n",
       "      <th>supermarket_nearest</th>\n",
       "      <th>town_hall_nearest</th>\n",
       "      <th>avg_distance</th>\n",
       "      <th>pop_count_total</th>\n",
       "      <th>brgy_total_area</th>\n",
       "      <th>population_density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm4_pcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH015518016</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Lomboy</td>\n",
       "      <td>42.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>27.17</td>\n",
       "      <td>19.02</td>\n",
       "      <td>CATMS01AC06</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>D</td>\n",
       "      <td>27.70</td>\n",
       "      <td>24.92</td>\n",
       "      <td>30.96</td>\n",
       "      <td>29.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>69.18</td>\n",
       "      <td>208.36</td>\n",
       "      <td>24.18</td>\n",
       "      <td>NTLGT002256</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.371240</td>\n",
       "      <td>2.145014</td>\n",
       "      <td>1.820992</td>\n",
       "      <td>0.239676</td>\n",
       "      <td>1.895808</td>\n",
       "      <td>1350.961178</td>\n",
       "      <td>1044.693472</td>\n",
       "      <td>1526.219926</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1140.748886</td>\n",
       "      <td>2152.886539</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>3778.093535</td>\n",
       "      <td>1447.965309</td>\n",
       "      <td>3156.411541</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1131.480067</td>\n",
       "      <td>3152.435512</td>\n",
       "      <td>3563.0</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>1.0216</td>\n",
       "      <td>1025.841817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518031</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Tapuac</td>\n",
       "      <td>42.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>27.17</td>\n",
       "      <td>19.02</td>\n",
       "      <td>CATMS03580D</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>D</td>\n",
       "      <td>27.70</td>\n",
       "      <td>24.92</td>\n",
       "      <td>30.96</td>\n",
       "      <td>29.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>69.18</td>\n",
       "      <td>208.36</td>\n",
       "      <td>24.18</td>\n",
       "      <td>NTLGT002257</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.167201</td>\n",
       "      <td>9.034832</td>\n",
       "      <td>5.663995</td>\n",
       "      <td>2.687575</td>\n",
       "      <td>4.564902</td>\n",
       "      <td>525.205612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1294.675375</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>931.181252</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1928.834812</td>\n",
       "      <td>2514.551323</td>\n",
       "      <td>981.016720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>385.246173</td>\n",
       "      <td>977.276221</td>\n",
       "      <td>2824.0</td>\n",
       "      <td>8001.0</td>\n",
       "      <td>1.0440</td>\n",
       "      <td>7663.793103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518022</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Pantal</td>\n",
       "      <td>42.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>27.17</td>\n",
       "      <td>19.02</td>\n",
       "      <td>CATMS02573C</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>D</td>\n",
       "      <td>27.70</td>\n",
       "      <td>24.92</td>\n",
       "      <td>30.96</td>\n",
       "      <td>29.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>69.18</td>\n",
       "      <td>208.36</td>\n",
       "      <td>24.18</td>\n",
       "      <td>NTLGT002258</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.509837</td>\n",
       "      <td>9.022078</td>\n",
       "      <td>4.553129</td>\n",
       "      <td>2.248700</td>\n",
       "      <td>4.140003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.102258</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2007.885364</td>\n",
       "      <td>102.676357</td>\n",
       "      <td>1242.966790</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1248.008152</td>\n",
       "      <td>2473.0</td>\n",
       "      <td>31224.0</td>\n",
       "      <td>3.2625</td>\n",
       "      <td>9570.574713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518024</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Barangay I (T. Bugallon)</td>\n",
       "      <td>42.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>27.17</td>\n",
       "      <td>19.02</td>\n",
       "      <td>CATMS02904E</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>D</td>\n",
       "      <td>27.70</td>\n",
       "      <td>24.92</td>\n",
       "      <td>30.96</td>\n",
       "      <td>29.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>69.18</td>\n",
       "      <td>208.36</td>\n",
       "      <td>24.18</td>\n",
       "      <td>NTLGT002259</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>10.438459</td>\n",
       "      <td>15.016158</td>\n",
       "      <td>12.727308</td>\n",
       "      <td>2.288849</td>\n",
       "      <td>12.727308</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>342.930960</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>84.278377</td>\n",
       "      <td>395.419002</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2066.877400</td>\n",
       "      <td>1666.029816</td>\n",
       "      <td>2706.934959</td>\n",
       "      <td>66.871015</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1785.634654</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>0.1814</td>\n",
       "      <td>12144.432194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH034919017</th>\n",
       "      <td>Palayan City</td>\n",
       "      <td>Imelda Valley</td>\n",
       "      <td>42.85</td>\n",
       "      <td>4.47</td>\n",
       "      <td>0.17</td>\n",
       "      <td>31.61</td>\n",
       "      <td>22.02</td>\n",
       "      <td>CATMS05209D</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>D</td>\n",
       "      <td>25.51</td>\n",
       "      <td>21.96</td>\n",
       "      <td>29.61</td>\n",
       "      <td>26.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>77.92</td>\n",
       "      <td>182.33</td>\n",
       "      <td>21.61</td>\n",
       "      <td>NTLGT00225A</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.589574</td>\n",
       "      <td>2.165237</td>\n",
       "      <td>1.054052</td>\n",
       "      <td>0.475109</td>\n",
       "      <td>0.924068</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>1683.508479</td>\n",
       "      <td>5736.344670</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>4.358631</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6959.0</td>\n",
       "      <td>4607.0</td>\n",
       "      <td>6.3369</td>\n",
       "      <td>727.011630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  adm3_en                   adm4_en     o3   no2   so2   pm10  \\\n",
       "adm4_pcode                                                                      \n",
       "PH015518016  Dagupan City                    Lomboy  42.99  4.45  0.61  27.17   \n",
       "PH015518031  Dagupan City                    Tapuac  42.99  4.45  0.61  27.17   \n",
       "PH015518022  Dagupan City                    Pantal  42.99  4.45  0.61  27.17   \n",
       "PH015518024  Dagupan City  Barangay I (T. Bugallon)  42.99  4.45  0.61  27.17   \n",
       "PH034919017  Palayan City             Imelda Valley  42.85  4.47  0.17  31.61   \n",
       "\n",
       "              pm25       uuid_x      date_x freq_x   tave   tmin   tmax  \\\n",
       "adm4_pcode                                                                \n",
       "PH015518016  19.02  CATMS01AC06  2022-12-31      D  27.70  24.92  30.96   \n",
       "PH015518031  19.02  CATMS03580D  2022-12-31      D  27.70  24.92  30.96   \n",
       "PH015518022  19.02  CATMS02573C  2022-12-31      D  27.70  24.92  30.96   \n",
       "PH015518024  19.02  CATMS02904E  2022-12-31      D  27.70  24.92  30.96   \n",
       "PH034919017  22.02  CATMS05209D  2022-12-31      D  25.51  21.96  29.61   \n",
       "\n",
       "             heat_index   pr  wind_speed     rh  solar_rad  uv_rad  \\\n",
       "adm4_pcode                                                           \n",
       "PH015518016       29.55  0.0        0.64  69.18     208.36   24.18   \n",
       "PH015518031       29.55  0.0        0.64  69.18     208.36   24.18   \n",
       "PH015518022       29.55  0.0        0.64  69.18     208.36   24.18   \n",
       "PH015518024       29.55  0.0        0.64  69.18     208.36   24.18   \n",
       "PH034919017       26.97  0.0        1.10  77.92     182.33   21.61   \n",
       "\n",
       "                  uuid_y      date_y freq_y  avg_rad_min  avg_rad_max  \\\n",
       "adm4_pcode                                                              \n",
       "PH015518016  NTLGT002256  2022-01-01      Y     1.371240     2.145014   \n",
       "PH015518031  NTLGT002257  2022-01-01      Y     2.167201     9.034832   \n",
       "PH015518022  NTLGT002258  2022-01-01      Y     1.509837     9.022078   \n",
       "PH015518024  NTLGT002259  2022-01-01      Y    10.438459    15.016158   \n",
       "PH034919017  NTLGT00225A  2022-01-01      Y     0.589574     2.165237   \n",
       "\n",
       "             avg_rad_mean  avg_rad_std  avg_rad_median   atm_nearest  \\\n",
       "adm4_pcode                                                             \n",
       "PH015518016      1.820992     0.239676        1.895808   1350.961178   \n",
       "PH015518031      5.663995     2.687575        4.564902    525.205612   \n",
       "PH015518022      4.553129     2.248700        4.140003      0.000000   \n",
       "PH015518024     12.727308     2.288849       12.727308      0.000000   \n",
       "PH034919017      1.054052     0.475109        0.924068  10000.000000   \n",
       "\n",
       "             bank_nearest  college_nearest  community_centre_nearest  \\\n",
       "adm4_pcode                                                             \n",
       "PH015518016   1044.693472      1526.219926              10000.000000   \n",
       "PH015518031      0.000000      1294.675375              10000.000000   \n",
       "PH015518022      0.000000         0.000000              10000.000000   \n",
       "PH015518024      0.000000       342.930960              10000.000000   \n",
       "PH034919017  10000.000000     10000.000000               1683.508479   \n",
       "\n",
       "             convenience_nearest  fire_station_nearest  kindergarten_nearest  \\\n",
       "adm4_pcode                                                                     \n",
       "PH015518016          1140.748886           2152.886539               10000.0   \n",
       "PH015518031             0.000000            931.181252               10000.0   \n",
       "PH015518022             0.000000             18.102258               10000.0   \n",
       "PH015518024            84.278377            395.419002               10000.0   \n",
       "PH034919017          5736.344670          10000.000000               10000.0   \n",
       "\n",
       "             market_place_nearest  park_nearest  police_nearest  \\\n",
       "adm4_pcode                                                        \n",
       "PH015518016           3778.093535   1447.965309     3156.411541   \n",
       "PH015518031           1928.834812   2514.551323      981.016720   \n",
       "PH015518022           2007.885364    102.676357     1242.966790   \n",
       "PH015518024           2066.877400   1666.029816     2706.934959   \n",
       "PH034919017          10000.000000  10000.000000        4.358631   \n",
       "\n",
       "             school_nearest  shelter_nearest  supermarket_nearest  \\\n",
       "adm4_pcode                                                          \n",
       "PH015518016        0.000000          10000.0          1131.480067   \n",
       "PH015518031        0.000000          10000.0           385.246173   \n",
       "PH015518022        0.000000          10000.0             0.000000   \n",
       "PH015518024       66.871015          10000.0             0.000000   \n",
       "PH034919017        0.000000          10000.0         10000.000000   \n",
       "\n",
       "             town_hall_nearest  avg_distance  pop_count_total  \\\n",
       "adm4_pcode                                                      \n",
       "PH015518016        3152.435512        3563.0           1048.0   \n",
       "PH015518031         977.276221        2824.0           8001.0   \n",
       "PH015518022        1248.008152        2473.0          31224.0   \n",
       "PH015518024        1785.634654        2794.0           2203.0   \n",
       "PH034919017           0.000000        6959.0           4607.0   \n",
       "\n",
       "             brgy_total_area  population_density  \n",
       "adm4_pcode                                        \n",
       "PH015518016           1.0216         1025.841817  \n",
       "PH015518031           1.0440         7663.793103  \n",
       "PH015518022           3.2625         9570.574713  \n",
       "PH015518024           0.1814        12144.432194  \n",
       "PH034919017           6.3369          727.011630  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicators.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "959b0a57-106a-4548-a726-882fcb53d7c6",
   "metadata": {},
   "source": [
    "## Calculate the Scores of each Indicator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee1c145-b859-45bf-a466-50b135c5e3f4",
   "metadata": {},
   "source": [
    "### Air Quality Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7f2f966c-ddf0-4aec-b4c7-ba35cbc93610",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of conditions\n",
    "o3_conditions = [indicators[\"o3\"] <= 50,\n",
    "                (indicators[\"o3\"] > 50) & (indicators[\"o3\"] <= 100),\n",
    "                (indicators[\"o3\"] > 100) & (indicators[\"o3\"] <= 130),\n",
    "                (indicators[\"o3\"] > 130) & (indicators[\"o3\"] <= 240),\n",
    "                 indicators[\"o3\"] > 240]\n",
    "\n",
    "no2_conditions = [indicators[\"no2\"] <= 40,\n",
    "                 (indicators[\"no2\"] > 40) & (indicators[\"no2\"] <= 90),\n",
    "                 (indicators[\"no2\"] > 90) & (indicators[\"no2\"] <= 120),\n",
    "                 (indicators[\"no2\"] > 120) & (indicators[\"no2\"] <= 230),\n",
    "                  indicators[\"no2\"] > 230]\n",
    "\n",
    "so2_conditions = [indicators[\"so2\"] <= 100,\n",
    "                 (indicators[\"so2\"] > 100) & (indicators[\"so2\"] <= 200),\n",
    "                 (indicators[\"so2\"] > 200) & (indicators[\"so2\"] <= 350),\n",
    "                 (indicators[\"so2\"] > 350) & (indicators[\"so2\"] <= 500),\n",
    "                  indicators[\"so2\"] > 500]\n",
    "\n",
    "pm10_conditions = [indicators[\"pm10\"] <= 20,\n",
    "                  (indicators[\"pm10\"] > 20) & (indicators[\"pm10\"] <= 40),\n",
    "                  (indicators[\"pm10\"] > 40) & (indicators[\"pm10\"] <= 50),\n",
    "                  (indicators[\"pm10\"] > 50) & (indicators[\"pm10\"] <= 100),\n",
    "                   indicators[\"pm10\"] > 100]\n",
    "\n",
    "pm25_conditions = [indicators[\"pm25\"] <= 10,\n",
    "                  (indicators[\"pm25\"] > 10) & (indicators[\"pm25\"] <= 20),\n",
    "                  (indicators[\"pm25\"] > 20) & (indicators[\"pm25\"] <= 25),\n",
    "                  (indicators[\"pm25\"] > 25) & (indicators[\"pm25\"] <= 50),\n",
    "                   indicators[\"pm25\"] > 50]\n",
    "\n",
    "# List of values to return\n",
    "score  = [95, 90, 85, 80, 75]\n",
    "\n",
    "# Create new columns in the DF based on the conditions\n",
    "indicators[\"o3_score\"] = np.select(o3_conditions, score, 85)\n",
    "indicators[\"no2_score\"] = np.select(no2_conditions, score, 85)\n",
    "indicators[\"so2_score\"] = np.select(so2_conditions, score, 85)\n",
    "indicators[\"pm10_score\"] = np.select(pm10_conditions, score, 85)\n",
    "indicators[\"pm25_score\"] = np.select(pm25_conditions, score, 85)\n",
    "\n",
    "# Create final metric column\n",
    "indicators[\"air_score\"] = round((indicators[\"o3_score\"] + \\\n",
    "                                 indicators[\"no2_score\"] + \\\n",
    "                                 indicators[\"so2_score\"] + \\\n",
    "                                 indicators[\"pm10_score\"] + \\\n",
    "                                 indicators[\"pm25_score\"]) / 5, 0).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66b5443b-6c5a-46af-bf9d-516f9b62f973",
   "metadata": {},
   "source": [
    "### Heat Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "501c8e45-9803-44e7-87be-068a8786cf84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of conditions\n",
    "heat_conditions = [indicators[\"heat_index\"] <= 27,\n",
    "                  (indicators[\"heat_index\"] > 27) & (indicators[\"heat_index\"] <= 32),\n",
    "                  (indicators[\"heat_index\"] > 32) & (indicators[\"heat_index\"] <= 41),\n",
    "                  (indicators[\"heat_index\"] > 41) & (indicators[\"heat_index\"] <= 51),\n",
    "                   indicators[\"heat_index\"] > 51]\n",
    "\n",
    "# List of values to return\n",
    "score = [95, 90, 85, 80, 75]\n",
    "\n",
    "# Create final metric column\n",
    "indicators[\"heat_score\"] = np.select(heat_conditions, score, 85)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71910e01-67da-4cad-8e55-ce7d6408a4e6",
   "metadata": {},
   "source": [
    "### Night Light Score\n",
    "\n",
    "**Very Low Lighting** (0–5 nW/cm²/sr) : This indicates minimal or almost no artificial lighting at night.\n",
    "\n",
    "**Low Lighting** (5–10 nW/cm²/sr) : Some artificial lighting, but insufficient for good visibility and safety at night.\n",
    "\n",
    "**Moderate Lighting** (10–20 nW/cm²/sr) : Adequate lighting, may cover key areas, but not comprehensive.\n",
    "\n",
    "**Good Lighting** (20–40 nW/cm²/sr) : Well-lit areas suitable for pedestrian activities and safety.\n",
    "\n",
    "**Very Good Lighting** (40+ nW/cm²/sr) : Excellent lighting, creating a bright and safe environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e067d0f-70d6-427d-91a9-fd22c79a3ecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of conditions\n",
    "light_conditions = [indicators['avg_rad_mean'] > 40,\n",
    "                   (indicators['avg_rad_mean'] > 20) & (indicators['avg_rad_mean'] <= 40),\n",
    "                   (indicators['avg_rad_mean'] > 10) & (indicators['avg_rad_mean'] <= 20),\n",
    "                   (indicators['avg_rad_mean'] > 5) & (indicators['avg_rad_mean'] <= 10),\n",
    "                    indicators['avg_rad_mean'] <= 5]\n",
    "\n",
    "# List of values to return\n",
    "score = [95, 90, 85, 80, 75]\n",
    "\n",
    "# Create final metric column\n",
    "indicators['light_score'] = np.select(light_conditions, score, 85)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e22b98-ecc2-4189-a1a4-474b8076971f",
   "metadata": {},
   "source": [
    "### Amenities Distance Score\n",
    "\n",
    "**Very Close** (0–500 meters) : All essential amenities are within a 5–10 minute walk, making the barangay highly accessible and convenient.\n",
    "Suitable for urban centers with compact infrastructure.\n",
    "\n",
    "**Close** (500 meters – 1 kilometer) : Amenities are within a 10–15 minute walk or a short commute. Still considered highly accessible, though residents may rely slightly more on public transport or short vehicle trips.\n",
    "\n",
    "**Moderate Distance** (1–2 kilometers) : Amenities are reachable by a 15–30 minute walk or a short drive. This range is typical for suburban areas,\n",
    "where accessibility is good but may require transportation for some.\n",
    "\n",
    "**Far** (2–5 kilometers) : Residents need to travel 30 minutes or more by foot or a longer vehicle trip to reach amenities. This is typical for less densely populated areas, or rural barangays with fewer services nearby.\n",
    "\n",
    "**Very Far** (5+ kilometers) : Amenities are quite far, making it difficult for residents to access essential services without a car or significant travel time. This often applies to rural areas or places where infrastructure is less developed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f0189e4-aede-43e9-aeba-72d2f37989ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of conditions\n",
    "distance_conditions = [indicators['avg_distance'] <= 500,\n",
    "                      (indicators['avg_distance'] > 500) & (indicators['avg_distance'] <= 1000),\n",
    "                      (indicators['avg_distance'] > 1000) & (indicators['avg_distance'] <= 2000),\n",
    "                      (indicators['avg_distance'] > 2000) & (indicators['avg_distance'] <= 5000),\n",
    "                       indicators['avg_distance'] > 5000]\n",
    "\n",
    "# List of values to return\n",
    "score = [95, 90, 85, 80, 75]\n",
    "\n",
    "# Create final metric column\n",
    "indicators['distance_score'] = np.select(distance_conditions, score, 85)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd84cfb4-0148-416e-bbcc-62c9506ac4c0",
   "metadata": {},
   "source": [
    "### Population Density Score\n",
    "\n",
    "**Very Low Density** (Less than 2,000 people/km²) : This is unusually low for an urban area, potentially indicating underdevelopment, abandoned areas, or a lack of vibrancy.\n",
    "\n",
    "**Low Density** (2,000–5,000 people/km²) : This is a lower-end urban density, often seen in peripheral urban barangays or those transitioning from suburban to urban.\n",
    "\n",
    "**Moderate Density** (5,000–10,000 people/km²) : This is a healthy density for urban areas, supporting efficient public transportation and services, without being overcrowded.\n",
    "\n",
    "**High Density** (10,000–20,000 people/km²) : A common range in highly developed urban centers, where density is necessary to support a vibrant economy but can lead to challenges like traffic congestion and housing pressure.\n",
    "\n",
    "**Very High Density** (More than 20,000 people/km²) : This represents very crowded conditions, often seen in central parts of cities like Manila, where land is scarce and housing and services are highly concentrated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2820c234-c45c-4c3c-8166-fd8ec7b551d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of conditions\n",
    "density_conditions = [indicators['population_density'] <= 2000,\n",
    "                     (indicators['population_density'] > 2000) & (indicators['population_density'] <= 5000),\n",
    "                     (indicators['population_density'] > 5000) & (indicators['population_density'] <= 10000),\n",
    "                     (indicators['population_density'] > 10000) & (indicators['population_density'] <= 20000),\n",
    "                      indicators['population_density'] > 20000]\n",
    "\n",
    "# List of values to return\n",
    "score = [95, 90, 85, 80, 75]\n",
    "\n",
    "# Create final metric column\n",
    "indicators['density_score'] = np.select(density_conditions, score, 85)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "520a2259-53b5-42ef-a027-c2c87a946320",
   "metadata": {},
   "source": [
    "### Raw Values of Indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d5568b07-c31a-4307-b7c4-7a82d55ab4d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>o3</th>\n",
       "      <th>no2</th>\n",
       "      <th>so2</th>\n",
       "      <th>pm10</th>\n",
       "      <th>pm25</th>\n",
       "      <th>heat_index</th>\n",
       "      <th>avg_rad_mean</th>\n",
       "      <th>avg_distance</th>\n",
       "      <th>population_density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm4_pcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH015518016</th>\n",
       "      <td>42.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>27.17</td>\n",
       "      <td>19.02</td>\n",
       "      <td>29.55</td>\n",
       "      <td>1.820992</td>\n",
       "      <td>3563.0</td>\n",
       "      <td>1025.841817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518031</th>\n",
       "      <td>42.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>27.17</td>\n",
       "      <td>19.02</td>\n",
       "      <td>29.55</td>\n",
       "      <td>5.663995</td>\n",
       "      <td>2824.0</td>\n",
       "      <td>7663.793103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518022</th>\n",
       "      <td>42.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>27.17</td>\n",
       "      <td>19.02</td>\n",
       "      <td>29.55</td>\n",
       "      <td>4.553129</td>\n",
       "      <td>2473.0</td>\n",
       "      <td>9570.574713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518024</th>\n",
       "      <td>42.99</td>\n",
       "      <td>4.45</td>\n",
       "      <td>0.61</td>\n",
       "      <td>27.17</td>\n",
       "      <td>19.02</td>\n",
       "      <td>29.55</td>\n",
       "      <td>12.727308</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>12144.432194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH034919017</th>\n",
       "      <td>42.85</td>\n",
       "      <td>4.47</td>\n",
       "      <td>0.17</td>\n",
       "      <td>31.61</td>\n",
       "      <td>22.02</td>\n",
       "      <td>26.97</td>\n",
       "      <td>1.054052</td>\n",
       "      <td>6959.0</td>\n",
       "      <td>727.011630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                o3   no2   so2   pm10   pm25  heat_index  avg_rad_mean  \\\n",
       "adm4_pcode                                                               \n",
       "PH015518016  42.99  4.45  0.61  27.17  19.02       29.55      1.820992   \n",
       "PH015518031  42.99  4.45  0.61  27.17  19.02       29.55      5.663995   \n",
       "PH015518022  42.99  4.45  0.61  27.17  19.02       29.55      4.553129   \n",
       "PH015518024  42.99  4.45  0.61  27.17  19.02       29.55     12.727308   \n",
       "PH034919017  42.85  4.47  0.17  31.61  22.02       26.97      1.054052   \n",
       "\n",
       "             avg_distance  population_density  \n",
       "adm4_pcode                                     \n",
       "PH015518016        3563.0         1025.841817  \n",
       "PH015518031        2824.0         7663.793103  \n",
       "PH015518022        2473.0         9570.574713  \n",
       "PH015518024        2794.0        12144.432194  \n",
       "PH034919017        6959.0          727.011630  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select all the raw indicator columns\n",
    "indicators_raw = indicators[['o3', 'no2', 'so2', 'pm10', 'pm25', 'heat_index', 'avg_rad_mean', 'avg_distance', 'population_density']]\n",
    "indicators_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6b4eefb-30ad-44be-90aa-60a28644bac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the correlation matrix\n",
    "raw_corr = sns.heatmap(indicators_raw.corr(numeric_only=True), cmap=\"YlGnBu\", annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e22eefa0-d5f2-4343-a799-a4ffb97d6f9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save as CSV\n",
    "indicators_raw.to_csv('indicators_raw.csv', index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f2ee3b-a5b0-4cb4-87c1-38c86b42d299",
   "metadata": {},
   "source": [
    "### Scores of Indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "37788d5a-d91e-410d-a141-31f43813c443",
   "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>air_score</th>\n",
       "      <th>heat_score</th>\n",
       "      <th>light_score</th>\n",
       "      <th>distance_score</th>\n",
       "      <th>density_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm4_pcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH015518016</th>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518031</th>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518022</th>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518024</th>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH034919017</th>\n",
       "      <td>92</td>\n",
       "      <td>95</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             air_score  heat_score  light_score  distance_score  density_score\n",
       "adm4_pcode                                                                    \n",
       "PH015518016         93          90           75              80             95\n",
       "PH015518031         93          90           80              80             85\n",
       "PH015518022         93          90           75              80             85\n",
       "PH015518024         93          90           85              80             80\n",
       "PH034919017         92          95           75              75             95"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select all the score columns\n",
    "indicators_score = indicators[['air_score', 'heat_score', 'light_score', 'distance_score', 'density_score']]\n",
    "indicators_score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ac71df86-6d96-45f4-96de-eb1c6a817181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the correlation matrix\n",
    "score_corr = sns.heatmap(indicators_score.corr(numeric_only=True), cmap=\"YlGnBu\", annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c8935273-fba8-4b5d-b820-de090f97dca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save as CSV\n",
    "indicators_score.to_csv('indicators_score.csv', index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6713600-f399-4e23-bbcb-940b8e51fb51",
   "metadata": {},
   "source": [
    "## Calculate the Initial Walkability and Safety Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a1978d4f-ba02-4040-8a56-7f07a10a2107",
   "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>adm3_en</th>\n",
       "      <th>adm4_en</th>\n",
       "      <th>air_score</th>\n",
       "      <th>heat_score</th>\n",
       "      <th>light_score</th>\n",
       "      <th>distance_score</th>\n",
       "      <th>density_score</th>\n",
       "      <th>WSI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm4_pcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH015518016</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Lomboy</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "      <td>95</td>\n",
       "      <td>86.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518031</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Tapuac</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "      <td>85.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518022</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Pantal</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "      <td>84.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518024</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Barangay I (T. Bugallon)</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>85.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH034919017</th>\n",
       "      <td>Palayan City</td>\n",
       "      <td>Imelda Valley</td>\n",
       "      <td>92</td>\n",
       "      <td>95</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "      <td>86.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  adm3_en                   adm4_en  air_score  heat_score  \\\n",
       "adm4_pcode                                                                   \n",
       "PH015518016  Dagupan City                    Lomboy         93          90   \n",
       "PH015518031  Dagupan City                    Tapuac         93          90   \n",
       "PH015518022  Dagupan City                    Pantal         93          90   \n",
       "PH015518024  Dagupan City  Barangay I (T. Bugallon)         93          90   \n",
       "PH034919017  Palayan City             Imelda Valley         92          95   \n",
       "\n",
       "             light_score  distance_score  density_score   WSI  \n",
       "adm4_pcode                                                     \n",
       "PH015518016           75              80             95  86.6  \n",
       "PH015518031           80              80             85  85.6  \n",
       "PH015518022           75              80             85  84.6  \n",
       "PH015518024           85              80             80  85.6  \n",
       "PH034919017           75              75             95  86.4  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the Initial Walkability and Safety Index\n",
    "WSI = indicators[['adm3_en', 'adm4_en', 'air_score', 'heat_score', 'light_score', 'distance_score', 'density_score']].copy()\n",
    "WSI['WSI'] = round((WSI[\"air_score\"] + \\\n",
    "                    WSI[\"heat_score\"] + \\\n",
    "                    WSI['light_score'] + \\\n",
    "                    WSI['distance_score'] + \\\n",
    "                    WSI['density_score']) / 5, 1)\n",
    "WSI.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7b7aed1f-d76e-4072-b20a-8864f9af4eae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save as CSV\n",
    "WSI.to_csv('index_initial.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "28704fe4-30b0-44fe-ac42-e81508650148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the correlation matrix\n",
    "wsi_corr = sns.heatmap(WSI.corr(numeric_only=True), cmap=\"YlGnBu\", annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ed27d7-8c17-4fe0-b5ad-ae755c308a1b",
   "metadata": {},
   "source": [
    "## Calculate the Weighted Walkability and Safety Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "16fc0216-274d-435d-ac1a-70e9993665e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Air Quality: 0.1825\n",
      "Heat: 0.002\n",
      "Night-time Light: 0.3353\n",
      "Distance to Amenities: 0.4542\n",
      "Population Density: 0.026\n",
      "Total: 1.0\n"
     ]
    }
   ],
   "source": [
    "air = 0.45**2\n",
    "heat = 0.047**2\n",
    "light = 0.61**2\n",
    "distance = 0.71**2\n",
    "density = 0.17**2\n",
    "total = air + heat + light + distance + density\n",
    "\n",
    "p_air = round(air/total, 4)\n",
    "p_heat = round(heat/total, 4)\n",
    "p_light = round(light/total, 4)\n",
    "p_distance = round(distance/total, 4)\n",
    "p_density = round(density/total, 4)\n",
    "p_total = p_air + p_heat + p_light + p_distance + p_density\n",
    "\n",
    "print(f\"Air Quality: {p_air}\")\n",
    "print(f\"Heat: {p_heat}\")\n",
    "print(f\"Night-time Light: {p_light}\")\n",
    "print(f\"Distance to Amenities: {p_distance}\")\n",
    "print(f\"Population Density: {p_density}\")\n",
    "print(f\"Total: {p_total}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fe167827-4dcc-401b-a5af-1dae089910a3",
   "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>adm3_en</th>\n",
       "      <th>adm4_en</th>\n",
       "      <th>air_score</th>\n",
       "      <th>heat_score</th>\n",
       "      <th>light_score</th>\n",
       "      <th>distance_score</th>\n",
       "      <th>density_score</th>\n",
       "      <th>WSI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adm4_pcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PH015518016</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Lomboy</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "      <td>95</td>\n",
       "      <td>81.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518031</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Tapuac</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "      <td>82.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518022</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Pantal</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "      <td>80.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH015518024</th>\n",
       "      <td>Dagupan City</td>\n",
       "      <td>Barangay I (T. Bugallon)</td>\n",
       "      <td>93</td>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>84.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PH034919017</th>\n",
       "      <td>Palayan City</td>\n",
       "      <td>Imelda Valley</td>\n",
       "      <td>92</td>\n",
       "      <td>95</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "      <td>78.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  adm3_en                   adm4_en  air_score  heat_score  \\\n",
       "adm4_pcode                                                                   \n",
       "PH015518016  Dagupan City                    Lomboy         93          90   \n",
       "PH015518031  Dagupan City                    Tapuac         93          90   \n",
       "PH015518022  Dagupan City                    Pantal         93          90   \n",
       "PH015518024  Dagupan City  Barangay I (T. Bugallon)         93          90   \n",
       "PH034919017  Palayan City             Imelda Valley         92          95   \n",
       "\n",
       "             light_score  distance_score  density_score   WSI  \n",
       "adm4_pcode                                                     \n",
       "PH015518016           75              80             95  81.1  \n",
       "PH015518031           80              80             85  82.5  \n",
       "PH015518022           75              80             85  80.8  \n",
       "PH015518024           85              80             80  84.1  \n",
       "PH034919017           75              75             95  78.7  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WSI_weighted = WSI.copy()\n",
    "WSI_weighted['WSI'] = round(((p_air * WSI[\"air_score\"]) + \\\n",
    "                             (p_heat * WSI[\"heat_score\"]) + \\\n",
    "                             (p_light * WSI['light_score']) + \\\n",
    "                             (p_distance * WSI['distance_score']) + \\\n",
    "                             (p_density * WSI['density_score'])), 1)\n",
    "WSI_weighted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2516f957-6c61-4e9b-9dbb-317a544ae0e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save as CSV\n",
    "WSI_weighted.to_csv('index_weighted.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2b3eb3f0-d5cc-41b8-b1a0-653a92faaa6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the correlation matrix\n",
    "weighted_corr = sns.heatmap(WSI_weighted.corr(numeric_only=True), cmap=\"YlGnBu\", annot=True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}