quantifying_index.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "b7c3caea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "abd557a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/amsterdam-data.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "b5881e70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# selection of columns to consider for quantifying indices\n",
    "\n",
    "columns = [\n",
    "    'Wijk',\n",
    "    'Coding_3',\n",
    "    'Number_of_Inhabitants_5',\n",
    "    'Men_6',\n",
    "     'Women_7',\n",
    "    'k_0To15Years_8',\n",
    "    'k_15To25Years_9',\n",
    "    'k_25To45Year_10',\n",
    "    'k_45To65Year_11',\n",
    "    'k_65YearsOrOlder_12',\n",
    "    'WesternTotal_17',\n",
    "    'NonWesternTotal_18',\n",
    "    'BirthTotal_24',\n",
    "    'HouseholdTotal_28',\n",
    "    'Single-person_households_29',\n",
    "    'HouseholdsWithoutChildren_30',\n",
    "    'HouseholdsWithChildren_31',\n",
    "    'Education_levelLow_64',\n",
    "    'Level_of_education_Secondary_65',\n",
    "    'Education_levelHigh_66',\n",
    "    'Net_labor_force_participation_67',\n",
    "    'Percentage_of_Employees_68',\n",
    "    'Percentage_of_Self-Employed_69',\n",
    "    'AverageIncomePerIncome_Recipient_71',\n",
    "    'AverageIncomePerInhabitant_72',\n",
    "    'k_40LowestIncomePeople_73',\n",
    "    'k_20PersonsWithHighestIncome_74',\n",
    "    'AvgStandardizedIncomeOfHouseh_75',\n",
    "    'k_40Lowest_Income_Households_76',\n",
    "    'k_20HouseholdsWithHighestIncome_77',\n",
    "    'Low-Income_Households_78',\n",
    "    'DistanceToGeneral_Practitioner_106',\n",
    "    'DistanceToGroteSupermarkt_107',\n",
    "    'DistanceToKinderdagverblijf_108',\n",
    "    'DistanceToSchool_109',\n",
    "    'SchoolsWithin_3Km_110',\n",
    "    'LNG',\n",
    "    'LAT'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "09fb5b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ratios of different age/gender groups\n",
    "df['ratio_children'] = df.k_0To15Years_8 / df.Number_of_Inhabitants_5\n",
    "df['ratio_young'] = (df.k_15To25Years_9 + df.k_25To45Year_10) / df.Number_of_Inhabitants_5\n",
    "df['ratio_old'] = (df.k_45To65Year_11 + df.k_65YearsOrOlder_12) / df.Number_of_Inhabitants_5\n",
    "df['ratio_women'] = df.Women_7 / df.Number_of_Inhabitants_5\n",
    "\n",
    "# Ratios of people with migration background\n",
    "df['ratio_western_immigration'] = df.WesternTotal_17 / df.Number_of_Inhabitants_5\n",
    "df['ratio_nonwester_immigration'] = df.NonWesternTotal_18 /  df.Number_of_Inhabitants_5\n",
    "df['ratio_immigration'] = (df.WesternTotal_17 + df.NonWesternTotal_18) /  df.Number_of_Inhabitants_5\n",
    "\n",
    "# Ratio of households with children\n",
    "df['ratio_household_with_children'] = df.HouseholdsWithChildren_31 / df.HouseholdTotal_28\n",
    "\n",
    "# Ratio of people with low education level\n",
    "df['ratio_low_ed'] = df.Education_levelLow_64 / df.Number_of_Inhabitants_5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32fffbb6",
   "metadata": {},
   "source": [
    "# Isolation coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "024da033",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    99.000000\n",
       "mean      0.054850\n",
       "std       0.042625\n",
       "min       0.019920\n",
       "25%       0.027068\n",
       "50%       0.037736\n",
       "75%       0.067797\n",
       "max       0.277778\n",
       "Name: school_inverse, dtype: float64"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inverse of number of schools within 3km diameter\n",
    "df['school_inverse'] = 1 / df.SchoolsWithin_3Km_110\n",
    "df['school_inverse'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "dcd876ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    99.000000\n",
       "mean      2.013350\n",
       "std       2.875977\n",
       "min       0.401207\n",
       "25%       0.716796\n",
       "50%       1.228410\n",
       "75%       2.036608\n",
       "max      21.107778\n",
       "Name: isolation, dtype: float64"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Isolation coefficient based on - distance to GP/supermarket/kindergarten/schools and school_inverse\n",
    "df['isolation'] = (df.DistanceToGeneral_Practitioner_106 + df.DistanceToGroteSupermarkt_107) * \\\n",
    "    (df.DistanceToKinderdagverblijf_108 + df.DistanceToSchool_109) + df.school_inverse * 10\n",
    "\n",
    "df['isolation'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "378023b5",
   "metadata": {
    "scrolled": true
   },
   "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>Unnamed: 0</th>\n",
       "      <th>Wijk</th>\n",
       "      <th>Municipality_1</th>\n",
       "      <th>Coding_3</th>\n",
       "      <th>Change_in_layout_WijkenEnBuurten_4</th>\n",
       "      <th>Number_of_Inhabitants_5</th>\n",
       "      <th>Men_6</th>\n",
       "      <th>Women_7</th>\n",
       "      <th>k_0To15Years_8</th>\n",
       "      <th>k_15To25Years_9</th>\n",
       "      <th>...</th>\n",
       "      <th>socio</th>\n",
       "      <th>economic</th>\n",
       "      <th>avg_income_scaled</th>\n",
       "      <th>demography</th>\n",
       "      <th>economic_dis</th>\n",
       "      <th>economic_disadvantage</th>\n",
       "      <th>socio_disadvantage</th>\n",
       "      <th>demographic_demand</th>\n",
       "      <th>ratio_men</th>\n",
       "      <th>AverageIncomePerIncome_Recipient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Van Lennepbuurt</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>WK036319</td>\n",
       "      <td>1</td>\n",
       "      <td>6955</td>\n",
       "      <td>3435</td>\n",
       "      <td>3525</td>\n",
       "      <td>625</td>\n",
       "      <td>655</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055009</td>\n",
       "      <td>6.945900</td>\n",
       "      <td>0.160891</td>\n",
       "      <td>0.657331</td>\n",
       "      <td>6.945900</td>\n",
       "      <td>0.755960</td>\n",
       "      <td>0.055009</td>\n",
       "      <td>0.657331</td>\n",
       "      <td>0.493889</td>\n",
       "      <td>39.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Da Costabuurt</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>WK036317</td>\n",
       "      <td>1</td>\n",
       "      <td>4650</td>\n",
       "      <td>2320</td>\n",
       "      <td>2325</td>\n",
       "      <td>435</td>\n",
       "      <td>405</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010336</td>\n",
       "      <td>4.149476</td>\n",
       "      <td>0.277228</td>\n",
       "      <td>0.648789</td>\n",
       "      <td>4.149476</td>\n",
       "      <td>0.562867</td>\n",
       "      <td>0.010336</td>\n",
       "      <td>0.648789</td>\n",
       "      <td>0.498925</td>\n",
       "      <td>48.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>Erasmuspark</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>WK036338</td>\n",
       "      <td>1</td>\n",
       "      <td>5840</td>\n",
       "      <td>2765</td>\n",
       "      <td>3070</td>\n",
       "      <td>640</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042368</td>\n",
       "      <td>5.711066</td>\n",
       "      <td>0.193069</td>\n",
       "      <td>0.715521</td>\n",
       "      <td>5.711066</td>\n",
       "      <td>0.555445</td>\n",
       "      <td>0.042368</td>\n",
       "      <td>0.715521</td>\n",
       "      <td>0.473459</td>\n",
       "      <td>41.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>Nieuwe Pijp</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>WK036325</td>\n",
       "      <td>1</td>\n",
       "      <td>12375</td>\n",
       "      <td>5955</td>\n",
       "      <td>6425</td>\n",
       "      <td>1080</td>\n",
       "      <td>1230</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025407</td>\n",
       "      <td>4.354126</td>\n",
       "      <td>0.266089</td>\n",
       "      <td>0.662772</td>\n",
       "      <td>4.354126</td>\n",
       "      <td>0.616907</td>\n",
       "      <td>0.025407</td>\n",
       "      <td>0.662772</td>\n",
       "      <td>0.481212</td>\n",
       "      <td>47.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>Kinkerbuurt</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>WK036318</td>\n",
       "      <td>1</td>\n",
       "      <td>6735</td>\n",
       "      <td>3445</td>\n",
       "      <td>3295</td>\n",
       "      <td>720</td>\n",
       "      <td>625</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039511</td>\n",
       "      <td>4.581579</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.648414</td>\n",
       "      <td>4.581579</td>\n",
       "      <td>0.603084</td>\n",
       "      <td>0.039511</td>\n",
       "      <td>0.648414</td>\n",
       "      <td>0.511507</td>\n",
       "      <td>46.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58</td>\n",
       "      <td>Omval/Overamstel</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>WK036358</td>\n",
       "      <td>1</td>\n",
       "      <td>5250</td>\n",
       "      <td>2735</td>\n",
       "      <td>2515</td>\n",
       "      <td>420</td>\n",
       "      <td>790</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008470</td>\n",
       "      <td>2.952372</td>\n",
       "      <td>0.394802</td>\n",
       "      <td>0.584077</td>\n",
       "      <td>2.952372</td>\n",
       "      <td>0.436639</td>\n",
       "      <td>0.008470</td>\n",
       "      <td>0.584077</td>\n",
       "      <td>0.520952</td>\n",
       "      <td>58.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>80</td>\n",
       "      <td>Lutkemeer/Ookmeer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>WK036380</td>\n",
       "      <td>1</td>\n",
       "      <td>1190</td>\n",
       "      <td>660</td>\n",
       "      <td>530</td>\n",
       "      <td>175</td>\n",
       "      <td>160</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113693</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.221947</td>\n",
       "      <td>0.770378</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.708338</td>\n",
       "      <td>0.113693</td>\n",
       "      <td>0.770378</td>\n",
       "      <td>0.554622</td>\n",
       "      <td>44.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Bedrijventerrein Sloterdijk</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>WK036311</td>\n",
       "      <td>1</td>\n",
       "      <td>860</td>\n",
       "      <td>530</td>\n",
       "      <td>325</td>\n",
       "      <td>10</td>\n",
       "      <td>305</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.221947</td>\n",
       "      <td>0.391330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.912274</td>\n",
       "      <td>0.080449</td>\n",
       "      <td>0.391330</td>\n",
       "      <td>0.616279</td>\n",
       "      <td>44.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Westelijk Havengebied</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>WK036310</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.221947</td>\n",
       "      <td>0.497126</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.878832</td>\n",
       "      <td>0.016647</td>\n",
       "      <td>0.497126</td>\n",
       "      <td>0.586207</td>\n",
       "      <td>44.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>73</td>\n",
       "      <td>Waterland</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>WK036373</td>\n",
       "      <td>1</td>\n",
       "      <td>2415</td>\n",
       "      <td>1240</td>\n",
       "      <td>1175</td>\n",
       "      <td>460</td>\n",
       "      <td>250</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.221947</td>\n",
       "      <td>0.894503</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.586594</td>\n",
       "      <td>0.006901</td>\n",
       "      <td>0.894503</td>\n",
       "      <td>0.513458</td>\n",
       "      <td>44.233333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 148 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                         Wijk Municipality_1    Coding_3   \n",
       "19          19              Van Lennepbuurt      Amsterdam  WK036319    \\\n",
       "17          17                Da Costabuurt      Amsterdam  WK036317     \n",
       "38          38                  Erasmuspark      Amsterdam  WK036338     \n",
       "25          25                  Nieuwe Pijp      Amsterdam  WK036325     \n",
       "18          18                  Kinkerbuurt      Amsterdam  WK036318     \n",
       "..         ...                          ...            ...         ...   \n",
       "58          58             Omval/Overamstel      Amsterdam  WK036358     \n",
       "80          80            Lutkemeer/Ookmeer      Amsterdam  WK036380     \n",
       "11          11  Bedrijventerrein Sloterdijk      Amsterdam  WK036311     \n",
       "10          10        Westelijk Havengebied      Amsterdam  WK036310     \n",
       "73          73                    Waterland      Amsterdam  WK036373     \n",
       "\n",
       "    Change_in_layout_WijkenEnBuurten_4  Number_of_Inhabitants_5  Men_6   \n",
       "19                                   1                     6955   3435  \\\n",
       "17                                   1                     4650   2320   \n",
       "38                                   1                     5840   2765   \n",
       "25                                   1                    12375   5955   \n",
       "18                                   1                     6735   3445   \n",
       "..                                 ...                      ...    ...   \n",
       "58                                   1                     5250   2735   \n",
       "80                                   1                     1190    660   \n",
       "11                                   1                      860    530   \n",
       "10                                   1                      145     85   \n",
       "73                                   1                     2415   1240   \n",
       "\n",
       "    Women_7  k_0To15Years_8  k_15To25Years_9  ...     socio  economic   \n",
       "19     3525             625              655  ...  0.055009  6.945900  \\\n",
       "17     2325             435              405  ...  0.010336  4.149476   \n",
       "38     3070             640              640  ...  0.042368  5.711066   \n",
       "25     6425            1080             1230  ...  0.025407  4.354126   \n",
       "18     3295             720              625  ...  0.039511  4.581579   \n",
       "..      ...             ...              ...  ...       ...       ...   \n",
       "58     2515             420              790  ...  0.008470  2.952372   \n",
       "80      530             175              160  ...  0.113693       NaN   \n",
       "11      325              10              305  ...  0.080449       NaN   \n",
       "10       60               5               55  ...  0.016647       NaN   \n",
       "73     1175             460              250  ...  0.006901       NaN   \n",
       "\n",
       "    avg_income_scaled  demography  economic_dis  economic_disadvantage   \n",
       "19           0.160891    0.657331      6.945900               0.755960  \\\n",
       "17           0.277228    0.648789      4.149476               0.562867   \n",
       "38           0.193069    0.715521      5.711066               0.555445   \n",
       "25           0.266089    0.662772      4.354126               0.616907   \n",
       "18           0.250000    0.648414      4.581579               0.603084   \n",
       "..                ...         ...           ...                    ...   \n",
       "58           0.394802    0.584077      2.952372               0.436639   \n",
       "80           0.221947    0.770378           NaN               0.708338   \n",
       "11           0.221947    0.391330           NaN               0.912274   \n",
       "10           0.221947    0.497126           NaN               0.878832   \n",
       "73           0.221947    0.894503           NaN               0.586594   \n",
       "\n",
       "    socio_disadvantage  demographic_demand  ratio_men   \n",
       "19            0.055009            0.657331   0.493889  \\\n",
       "17            0.010336            0.648789   0.498925   \n",
       "38            0.042368            0.715521   0.473459   \n",
       "25            0.025407            0.662772   0.481212   \n",
       "18            0.039511            0.648414   0.511507   \n",
       "..                 ...                 ...        ...   \n",
       "58            0.008470            0.584077   0.520952   \n",
       "80            0.113693            0.770378   0.554622   \n",
       "11            0.080449            0.391330   0.616279   \n",
       "10            0.016647            0.497126   0.586207   \n",
       "73            0.006901            0.894503   0.513458   \n",
       "\n",
       "    AverageIncomePerIncome_Recipient  \n",
       "19                         39.300000  \n",
       "17                         48.700000  \n",
       "38                         41.900000  \n",
       "25                         47.800000  \n",
       "18                         46.500000  \n",
       "..                               ...  \n",
       "58                         58.200000  \n",
       "80                         44.233333  \n",
       "11                         44.233333  \n",
       "10                         44.233333  \n",
       "73                         44.233333  \n",
       "\n",
       "[99 rows x 148 columns]"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorted by isolation\n",
    "df.sort_values(by=['isolation'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d2aaaa",
   "metadata": {},
   "source": [
    "# Economic disadvantage coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "fffaa2ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low-Income_Households_78               0\n",
       "k_40LowestIncomePeople_73              0\n",
       "Net_labor_force_participation_67       0\n",
       "AverageIncomePerIncome_Recipient_71    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing values \n",
    "\n",
    "cols = ['Low-Income_Households_78', 'k_40LowestIncomePeople_73', 'Net_labor_force_participation_67', 'AverageIncomePerIncome_Recipient_71']\n",
    "df[cols].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "c90b405d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputate the missing values with mean of the corresponding column\n",
    "for c in cols:\n",
    "    df[c] = df[c].fillna(df[c].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "80038c41",
   "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>Low-Income_Households_78</th>\n",
       "      <th>k_40LowestIncomePeople_73</th>\n",
       "      <th>Net_labor_force_participation_67</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.281443</td>\n",
       "      <td>38.761224</td>\n",
       "      <td>68.326531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.825967</td>\n",
       "      <td>8.452407</td>\n",
       "      <td>6.226631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.200000</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.350000</td>\n",
       "      <td>33.250000</td>\n",
       "      <td>64.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.300000</td>\n",
       "      <td>37.400000</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.100000</td>\n",
       "      <td>45.050000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.400000</td>\n",
       "      <td>66.600000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Low-Income_Households_78  k_40LowestIncomePeople_73   \n",
       "count                 99.000000                  99.000000  \\\n",
       "mean                  11.281443                  38.761224   \n",
       "std                    3.825967                   8.452407   \n",
       "min                    2.200000                  18.800000   \n",
       "25%                    8.350000                  33.250000   \n",
       "50%                   11.300000                  37.400000   \n",
       "75%                   14.100000                  45.050000   \n",
       "max                   20.400000                  66.600000   \n",
       "\n",
       "       Net_labor_force_participation_67  \n",
       "count                         99.000000  \n",
       "mean                          68.326531  \n",
       "std                            6.226631  \n",
       "min                           39.000000  \n",
       "25%                           64.500000  \n",
       "50%                           68.000000  \n",
       "75%                           73.000000  \n",
       "max                           81.000000  "
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[cols].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "4503ca80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Economic disadvantage coefficient based on - \n",
    "# percentages of low-income households / people with lowest 40% income \n",
    "# inverse of employment rate / inverse of income per recipient\n",
    "\n",
    "df['economic_disadvantage'] = \\\n",
    "    (df['Low-Income_Households_78']/100) + (df['k_40LowestIncomePeople_73']/100) + \\\n",
    "    (1/df['Net_labor_force_participation_67']*10) + (1/df['AverageIncomePerIncome_Recipient_71'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "b1ae5193",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    99.000000\n",
       "mean      0.672752\n",
       "std       0.131991\n",
       "min       0.380193\n",
       "25%       0.570669\n",
       "50%       0.654176\n",
       "75%       0.772239\n",
       "max       0.964666\n",
       "Name: economic_disadvantage, dtype: float64"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['economic_disadvantage'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18690229",
   "metadata": {},
   "source": [
    "# Social disadvantage coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "04395ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    99.000000\n",
       "mean      0.061595\n",
       "std       0.054628\n",
       "min       0.002331\n",
       "25%       0.019508\n",
       "50%       0.042008\n",
       "75%       0.099825\n",
       "max       0.213252\n",
       "Name: socio_disadvantage, dtype: float64"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Social disadvantage coefficient based on - \n",
    "# ratio of people with low education level and ratio of people with non-western migration background\n",
    "\n",
    "df['socio_disadvantage'] = df.ratio_low_ed * df.ratio_nonwester_immigration\n",
    "df['socio_disadvantage'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fcb5c31",
   "metadata": {},
   "source": [
    "# Demographic demand coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "0912331d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    99.000000\n",
       "mean      0.741639\n",
       "std       0.127348\n",
       "min       0.391330\n",
       "25%       0.662874\n",
       "50%       0.740852\n",
       "75%       0.817307\n",
       "max       1.100000\n",
       "Name: demographic_demand, dtype: float64"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Demographic demand coefficient based on ratio of households with children and ratio of women\n",
    "\n",
    "df['demographic_demand'] = df['ratio_household_with_children'] + df['ratio_women']\n",
    "df['demographic_demand'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8962f57",
   "metadata": {},
   "source": [
    "# Correlation of the coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "478d8be1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap of correlation of the coefficient\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "fig = sns.heatmap(df[['economic_disadvantage', 'socio_disadvantage', 'isolation', 'demographic_demand', \n",
    "               ]].corr(), vmin=-1, vmax=1, annot=True).get_figure()\n",
    "\n",
    "plt.xticks(rotation=20) \n",
    "plt.yticks(rotation=0) \n",
    "\n",
    "fig.savefig('heatmap.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "559f0409",
   "metadata": {},
   "source": [
    "# Calculating quantifying indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "9b8b65bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols =['economic_disadvantage', 'socio_disadvantage', 'isolation', 'demographic_demand', 'ratio_women']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "783e682d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99 entries, 0 to 98\n",
      "Data columns (total 5 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   economic_disadvantage  99 non-null     float64\n",
      " 1   socio_disadvantage     99 non-null     float64\n",
      " 2   isolation              99 non-null     float64\n",
      " 3   demographic_demand     99 non-null     float64\n",
      " 4   ratio_women            99 non-null     float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 4.0 KB\n"
     ]
    }
   ],
   "source": [
    "df[['economic_disadvantage', 'socio_disadvantage', 'isolation', 'demographic_demand', 'ratio_women']].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "e221b7b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df[['Wijk', 'LAT', 'LNG']+cols].copy()\n",
    "\n",
    "\n",
    "# MinMax scale each coefficients so that they are in the same scale\n",
    "# to ensure no one coefficient contributes too much or too less\n",
    "for c in cols:\n",
    "     data[c] = (data[c] - min(data[c])) / (max(data[c] - min(data[c])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "3f14be6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the quantifying index by summing the coefficients\n",
    "def metric(df, cols=cols):\n",
    "    df['metric'] = df[cols].sum(axis=1)\n",
    "    return df\n",
    "\n",
    "data = metric(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "84c46dad",
   "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>Wijk</th>\n",
       "      <th>metric</th>\n",
       "      <th>economic_disadvantage</th>\n",
       "      <th>socio_disadvantage</th>\n",
       "      <th>isolation</th>\n",
       "      <th>demographic_demand</th>\n",
       "      <th>ratio_women</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Geuzenveld</td>\n",
       "      <td>3.346258</td>\n",
       "      <td>0.834228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.031099</td>\n",
       "      <td>0.716581</td>\n",
       "      <td>0.764351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Holendrecht</td>\n",
       "      <td>3.187890</td>\n",
       "      <td>0.776717</td>\n",
       "      <td>0.730628</td>\n",
       "      <td>0.134363</td>\n",
       "      <td>0.657177</td>\n",
       "      <td>0.889006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>H-buurt</td>\n",
       "      <td>3.150210</td>\n",
       "      <td>0.911529</td>\n",
       "      <td>0.929263</td>\n",
       "      <td>0.077678</td>\n",
       "      <td>0.485697</td>\n",
       "      <td>0.746042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Osdorp-Midden</td>\n",
       "      <td>3.131694</td>\n",
       "      <td>0.748648</td>\n",
       "      <td>0.856971</td>\n",
       "      <td>0.029376</td>\n",
       "      <td>0.672145</td>\n",
       "      <td>0.824553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Slotermeer-Noordoost</td>\n",
       "      <td>3.121461</td>\n",
       "      <td>0.912840</td>\n",
       "      <td>0.895908</td>\n",
       "      <td>0.024328</td>\n",
       "      <td>0.582051</td>\n",
       "      <td>0.706333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Zuidas</td>\n",
       "      <td>1.236245</td>\n",
       "      <td>0.131028</td>\n",
       "      <td>0.125587</td>\n",
       "      <td>0.054500</td>\n",
       "      <td>0.300747</td>\n",
       "      <td>0.624383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Burgwallen-Nieuwe Zijde</td>\n",
       "      <td>1.222770</td>\n",
       "      <td>0.409791</td>\n",
       "      <td>0.072838</td>\n",
       "      <td>0.043235</td>\n",
       "      <td>0.167038</td>\n",
       "      <td>0.529867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grachtengordel-Zuid</td>\n",
       "      <td>1.152271</td>\n",
       "      <td>0.237038</td>\n",
       "      <td>0.028508</td>\n",
       "      <td>0.019306</td>\n",
       "      <td>0.283315</td>\n",
       "      <td>0.584104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Sloterdijk-West</td>\n",
       "      <td>1.077911</td>\n",
       "      <td>0.201671</td>\n",
       "      <td>0.032697</td>\n",
       "      <td>0.084988</td>\n",
       "      <td>0.258484</td>\n",
       "      <td>0.500071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Noordelijke IJ-oevers-Oost</td>\n",
       "      <td>0.780906</td>\n",
       "      <td>0.625304</td>\n",
       "      <td>0.091358</td>\n",
       "      <td>0.060400</td>\n",
       "      <td>0.003844</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Wijk    metric  economic_disadvantage   \n",
       "78                  Geuzenveld  3.346258               0.834228  \\\n",
       "96                 Holendrecht  3.187890               0.776717   \n",
       "93                     H-buurt  3.150210               0.911529   \n",
       "82               Osdorp-Midden  3.131694               0.748648   \n",
       "76        Slotermeer-Noordoost  3.121461               0.912840   \n",
       "..                         ...       ...                    ...   \n",
       "23                      Zuidas  1.236245               0.131028   \n",
       "1      Burgwallen-Nieuwe Zijde  1.222770               0.409791   \n",
       "3          Grachtengordel-Zuid  1.152271               0.237038   \n",
       "36             Sloterdijk-West  1.077911               0.201671   \n",
       "72  Noordelijke IJ-oevers-Oost  0.780906               0.625304   \n",
       "\n",
       "    socio_disadvantage  isolation  demographic_demand  ratio_women  \n",
       "78            1.000000   0.031099            0.716581     0.764351  \n",
       "96            0.730628   0.134363            0.657177     0.889006  \n",
       "93            0.929263   0.077678            0.485697     0.746042  \n",
       "82            0.856971   0.029376            0.672145     0.824553  \n",
       "76            0.895908   0.024328            0.582051     0.706333  \n",
       "..                 ...        ...                 ...          ...  \n",
       "23            0.125587   0.054500            0.300747     0.624383  \n",
       "1             0.072838   0.043235            0.167038     0.529867  \n",
       "3             0.028508   0.019306            0.283315     0.584104  \n",
       "36            0.032697   0.084988            0.258484     0.500071  \n",
       "72            0.091358   0.060400            0.003844     0.000000  \n",
       "\n",
       "[99 rows x 7 columns]"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the Neighborhoods by descending order of final 'metric' or quantifying index\n",
    "\n",
    "data.sort_values(by=\"metric\", ascending=False)[['Wijk', 'metric']+cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "fe0ec14c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the final results as a csv file\n",
    "data.to_csv('./data/data.csv', sep=';')"
   ]
  }
 ],
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