Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "87d839c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import warnings\n",
    "from shapely.geometry import Polygon\n",
    "import sys\n",
    "import os\n",
    "import osmnx as ox\n",
    "city_geo = ox.geocoder.geocode_to_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bfe2a9ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresholds = [300, 600, 1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6550f610",
   "metadata": {},
   "outputs": [],
   "source": [
    "ls_in = ['Abidjan','Addis Ababa','Baghdad','Bangalore','Belgrade','Belo Horizonte','Bogota','Bologna','Cali','Canberra',\\\n",
    "         'Casablanca','Chandigarh','Chelyabinsk','Chengdu','Christchurch','Cochabamba','Copenhagen','Curitiba','Damascus',\\\n",
    "         'Detroit','Dhaka','Dongguan','Fortaleza','Ghent','Guatemala City','Gwangju','Hanoi','Indore','Johannesburg',\\\n",
    "         'Kampala','Karlsruhe','Kathmandu','Kharkiv','Kuala Lumpur','Kumamoto','Lagos','Leicester','Liverpool',\\\n",
    "         'Lubumbashi','Lviv','Manila','Memphis','Mexico City','Mombasa','Nanjing','New Delhi','Oran','Oslo','Perth',\\\n",
    "         'Philadelphia','Pune','Rennes','Rosario','Rostov','San Antonio','San Diego','Santo Domingo','Seattle','Seoul',\\\n",
    "         'Sevilla','Shijiazhuang','Shiraz','Skopje','Surabaya','Tallinn','Tashkent','Tel Aviv','Toronto','Turin',\\\n",
    "         'Utrecht','Utsunomiya','Washington DC','Yaounde','Yerevan','Zibo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ee43e9a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cities = pd.read_excel('cities.xlsx')\n",
    "cities_adj = cities[cities['City'].isin(ls_in)]\n",
    "cities_adj = cities_adj.sort_values('City').reset_index()\n",
    "\n",
    "ls_adj = ['Canberra','Karlsruhe','Oslo']\n",
    "\n",
    "cities_adj2 = cities[cities['City'].isin(ls_adj)]\n",
    "cities_adj2 = cities_adj2.sort_values('City').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "dde56538",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Abidjan            POINT (-4.00962 5.34699)\n",
       "Addis Ababa        POINT (38.78696 8.98080)\n",
       "Baghdad           POINT (44.37208 33.32650)\n",
       "Bangalore         POINT (77.59610 12.95079)\n",
       "Belgrade          POINT (20.42554 44.80930)\n",
       "                            ...            \n",
       "Utsunomiya       POINT (139.88657 36.59688)\n",
       "Washington DC    POINT (-77.01637 38.90483)\n",
       "Yaounde            POINT (11.50038 3.85368)\n",
       "Yerevan           POINT (44.50722 40.16141)\n",
       "Zibo             POINT (118.06205 36.80911)\n",
       "Length: 75, dtype: geometry"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "ser = gpd.GeoSeries(crs = 4326)\n",
    "for i in cities_adj['OSM_area']:\n",
    "    ser = pd.concat([ser, city_geo(i.split(', ')).dissolve().geometry.centroid])\n",
    "ser.index = ls_in\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "961b16c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ser.to_file('D:/Dumps/GEE-WP Scores/cities.gpkg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e27625a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"D:/Dumps/GEE-WP Scores/Gravity/100m grids/Grid_geoms/\"\n",
    "ls_file = os.listdir(path)\n",
    "ls_file = np.array(ls_file)[[i.rsplit('_')[1] == 'grav3' for i in ls_file]]\n",
    "ls_file = ls_file[[any([j in i for j in ls_in]) for i in ls_file]]\n",
    "\n",
    "ls_2 = ls_file[[any([j in i for j in ls_adj]) for i in ls_file]]\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "area = [city_geo(i.split(', ')).dissolve().geometry for i in cities_adj2['OSM_ineq_adj']]\n",
    "files = [gpd.read_file(path+i) for i in ls_2]\n",
    "scores = [files[i].overlay(gpd.GeoDataFrame(geometry = area[i], crs = 4326)) for i in range(len(files))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1354e9a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i[1].to_file('D:/Dumps/GEE-WP Scores/Gravity/100m grids/grid_geoms_adj/gridscore_grav3_'+cities_adj2['City'][i[0]]+'.gpkg')\\\n",
    " for i in enumerate(scores)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9db49771",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "path = \"D:/Dumps/GEE-WP Scores/Gravity/100m grids/Grid_geoms_adj/\"\n",
    "ls_file = os.listdir(path)\n",
    "ls_file = np.array(ls_file)[[i.rsplit('_')[1] == 'grav3' for i in ls_file]]\n",
    "ls_file = ls_file[[any([j in i for j in ls_in]) for i in ls_file]]\n",
    "\n",
    "#ls_2 = ls_file[[any([j in i for j in ls_adj]) for i in ls_file]]\n",
    "\n",
    "#warnings.filterwarnings('ignore')\n",
    "#area = [city_geo(i.split(', ')).dissolve().geometry for i in cities_adj2['OSM_ineq_adj']]\n",
    "#files = [gpd.read_file(path+i) for i in ls_2]\n",
    "#scores = [files[i].overlay(gpd.GeoDataFrame(geometry = area[i], crs = 4326)) for i in range(len(files))]\n",
    "\n",
    "print('preorocessing done')\n",
    "print('')\n",
    "\n",
    "cities_scores = list()\n",
    "for i in enumerate(ls_file):\n",
    "    city_score = gpd.read_file(path+i[1])\n",
    "    \n",
    "    #k = 0\n",
    "    #if i[1] in ls_adj:\n",
    "        #city_score2 = scores[k]\n",
    "        #k = k+1\n",
    "    #else:\n",
    "    #    city_score2 = city_score\n",
    "    \n",
    "    scores_df = city_score.iloc[range(0,len(city_score),3)]['geometry'].reset_index(drop = True)\n",
    "    for j in thresholds:\n",
    "        ls = list()\n",
    "        score = city_score['tr_'+str(j)].dropna().reset_index(drop =True)\n",
    "        for k in score:\n",
    "            if k == 0: \n",
    "                ls.append('0 no')\n",
    "            elif k < j: # one perfect score (no route cost to UGS); score is threshold - route (and entry) cost\n",
    "                ls.append('1 low')\n",
    "            elif k < j*2: # two perfect scores\n",
    "                ls.append('2 mediocre')\n",
    "            elif k < j*3: # three perfect scores\n",
    "                ls.append('3 sufficient')\n",
    "            elif k < j*4: # four perfect scores\n",
    "                ls.append('4 good')\n",
    "            else: # above four perfect scores (or 8 UGS on half the threshold distance)\n",
    "                ls.append('5 excellent')\n",
    "        print(i[1].rsplit('_')[2].rsplit('.')[0], j)\n",
    "        scores_df = pd.concat([scores_df, \n",
    "                               pd.Series(round(score, 1), name = 'Grav-Sc '+str(j)), \n",
    "                               pd.Series(ls, name = 'GravQnt '+str(j))], \n",
    "                              axis = 1)\n",
    "    cities_scores.append(scores_df)\n",
    "cities_scores[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8edd370f",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"D:/Dumps/GEE-WP Scores/E2SFCA/100m grids/Grid_geoms/\"\n",
    "ls_file = os.listdir(path)\n",
    "ls_file = np.array(ls_file)[[i.rsplit('.')[-1] == 'gpkg' for i in ls_file]]\n",
    "ls_file = ls_file[[any([j in i for j in ls_in]) for i in ls_file]]\n",
    "\n",
    "ls_2 = ls_file[[any([j in i for j in ls_adj]) for i in ls_file]]\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "area = [city_geo(i.split(', ')).dissolve().geometry for i in cities_adj2['OSM_ineq_adj']]\n",
    "files = [gpd.read_file(path+i) for i in ls_2]\n",
    "scores = [files[i].overlay(gpd.GeoDataFrame(geometry = area[i], crs = 4326)) for i in range(len(files))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bacff7b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "[i[1].to_file('D:/Dumps/GEE-WP Scores/Gravity/100m grids/grid_geoms_adj/'+cities_adj2['City'][i[0]]+'.gpkg')\\\n",
    " for i in enumerate(scores)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8f3a2359",
   "metadata": {},
   "outputs": [],
   "source": [
    "E2SCFA_class = list()\n",
    "path = \"D:/Dumps/GEE-WP Scores/E2SFCA/100m grids/grid_geoms_adj/\"\n",
    "ls_file = np.array(os.listdir(path))\n",
    "ls_file = pd.Series(ls_file)[pd.Series(ls_file).str.contains('gpkg')]\n",
    "ls_file = ls_file[[any([j in i for j in ls_in]) for i in ls_file]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "47f5bb50",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Abidjan 300\n",
      "Abidjan 600\n",
      "Abidjan 1000\n",
      "Addis Ababa 300\n",
      "Addis Ababa 600\n",
      "Addis Ababa 1000\n",
      "Baghdad 300\n",
      "Baghdad 600\n",
      "Baghdad 1000\n",
      "Bangalore 300\n",
      "Bangalore 600\n",
      "Bangalore 1000\n",
      "Belgrade 300\n",
      "Belgrade 600\n",
      "Belgrade 1000\n",
      "Belo Horizonte 300\n",
      "Belo Horizonte 600\n",
      "Belo Horizonte 1000\n",
      "Bogota 300\n",
      "Bogota 600\n",
      "Bogota 1000\n",
      "Bologna 300\n",
      "Bologna 600\n",
      "Bologna 1000\n",
      "Cali 300\n",
      "Cali 600\n",
      "Cali 1000\n",
      "Canberra 300\n",
      "Canberra 600\n",
      "Canberra 1000\n",
      "Casablanca 300\n",
      "Casablanca 600\n",
      "Casablanca 1000\n",
      "Chandigarh 300\n",
      "Chandigarh 600\n",
      "Chandigarh 1000\n",
      "Chelyabinsk 300\n",
      "Chelyabinsk 600\n",
      "Chelyabinsk 1000\n",
      "Chengdu 300\n",
      "Chengdu 600\n",
      "Chengdu 1000\n",
      "Christchurch 300\n",
      "Christchurch 600\n",
      "Christchurch 1000\n",
      "Cochabamba 300\n",
      "Cochabamba 600\n",
      "Cochabamba 1000\n",
      "Copenhagen 300\n",
      "Copenhagen 600\n",
      "Copenhagen 1000\n",
      "Curitiba 300\n",
      "Curitiba 600\n",
      "Curitiba 1000\n",
      "Damascus 300\n",
      "Damascus 600\n",
      "Damascus 1000\n",
      "Detroit 300\n",
      "Detroit 600\n",
      "Detroit 1000\n",
      "Dhaka 300\n",
      "Dhaka 600\n",
      "Dhaka 1000\n",
      "Dongguan 300\n",
      "Dongguan 600\n",
      "Dongguan 1000\n",
      "Fortaleza 300\n",
      "Fortaleza 600\n",
      "Fortaleza 1000\n",
      "Ghent 300\n",
      "Ghent 600\n",
      "Ghent 1000\n",
      "gridscore_grav3_Canberra 300\n",
      "gridscore_grav3_Canberra 600\n",
      "gridscore_grav3_Canberra 1000\n",
      "gridscore_grav3_Karlsruhe 300\n",
      "gridscore_grav3_Karlsruhe 600\n",
      "gridscore_grav3_Karlsruhe 1000\n",
      "gridscore_grav3_Oslo 300\n",
      "gridscore_grav3_Oslo 600\n",
      "gridscore_grav3_Oslo 1000\n",
      "Guatemala City 300\n",
      "Guatemala City 600\n",
      "Guatemala City 1000\n",
      "Gwangju 300\n",
      "Gwangju 600\n",
      "Gwangju 1000\n",
      "Hanoi 300\n",
      "Hanoi 600\n",
      "Hanoi 1000\n",
      "Indore 300\n",
      "Indore 600\n",
      "Indore 1000\n",
      "Johannesburg 300\n",
      "Johannesburg 600\n",
      "Johannesburg 1000\n",
      "Kampala 300\n",
      "Kampala 600\n",
      "Kampala 1000\n",
      "Karlsruhe 300\n",
      "Karlsruhe 600\n",
      "Karlsruhe 1000\n",
      "Kathmandu 300\n",
      "Kathmandu 600\n",
      "Kathmandu 1000\n",
      "Kharkiv 300\n",
      "Kharkiv 600\n",
      "Kharkiv 1000\n",
      "Kuala Lumpur 300\n",
      "Kuala Lumpur 600\n",
      "Kuala Lumpur 1000\n",
      "Kumamoto 300\n",
      "Kumamoto 600\n",
      "Kumamoto 1000\n",
      "Lagos 300\n",
      "Lagos 600\n",
      "Lagos 1000\n",
      "Leicester 300\n",
      "Leicester 600\n",
      "Leicester 1000\n",
      "Liverpool 300\n",
      "Liverpool 600\n",
      "Liverpool 1000\n",
      "Lubumbashi 300\n",
      "Lubumbashi 600\n",
      "Lubumbashi 1000\n",
      "Lviv 300\n",
      "Lviv 600\n",
      "Lviv 1000\n",
      "Manila 300\n",
      "Manila 600\n",
      "Manila 1000\n",
      "Memphis 300\n",
      "Memphis 600\n",
      "Memphis 1000\n",
      "Mexico City 300\n",
      "Mexico City 600\n",
      "Mexico City 1000\n",
      "Mombasa 300\n",
      "Mombasa 600\n",
      "Mombasa 1000\n",
      "Nanjing 300\n",
      "Nanjing 600\n",
      "Nanjing 1000\n",
      "New Delhi 300\n",
      "New Delhi 600\n",
      "New Delhi 1000\n",
      "Oran 300\n",
      "Oran 600\n",
      "Oran 1000\n",
      "Oslo 300\n",
      "Oslo 600\n",
      "Oslo 1000\n",
      "Perth 300\n",
      "Perth 600\n",
      "Perth 1000\n",
      "Philadelphia 300\n",
      "Philadelphia 600\n",
      "Philadelphia 1000\n",
      "Pune 300\n",
      "Pune 600\n",
      "Pune 1000\n",
      "Rennes 300\n",
      "Rennes 600\n",
      "Rennes 1000\n",
      "Rosario 300\n",
      "Rosario 600\n",
      "Rosario 1000\n",
      "Rostov 300\n",
      "Rostov 600\n",
      "Rostov 1000\n",
      "San Antonio 300\n",
      "San Antonio 600\n",
      "San Antonio 1000\n",
      "San Diego 300\n",
      "San Diego 600\n",
      "San Diego 1000\n",
      "Santo Domingo 300\n",
      "Santo Domingo 600\n",
      "Santo Domingo 1000\n",
      "Seattle 300\n",
      "Seattle 600\n",
      "Seattle 1000\n",
      "Seoul 300\n",
      "Seoul 600\n",
      "Seoul 1000\n",
      "Sevilla 300\n",
      "Sevilla 600\n",
      "Sevilla 1000\n",
      "Shijiazhuang 300\n",
      "Shijiazhuang 600\n",
      "Shijiazhuang 1000\n",
      "Shiraz 300\n",
      "Shiraz 600\n",
      "Shiraz 1000\n",
      "Skopje 300\n",
      "Skopje 600\n",
      "Skopje 1000\n",
      "Surabaya 300\n",
      "Surabaya 600\n",
      "Surabaya 1000\n",
      "Tallinn 300\n",
      "Tallinn 600\n",
      "Tallinn 1000\n",
      "Tashkent 300\n",
      "Tashkent 600\n",
      "Tashkent 1000\n",
      "Tel Aviv 300\n",
      "Tel Aviv 600\n",
      "Tel Aviv 1000\n",
      "Toronto 300\n",
      "Toronto 600\n",
      "Toronto 1000\n",
      "Turin 300\n",
      "Turin 600\n",
      "Turin 1000\n",
      "Utrecht 300\n",
      "Utrecht 600\n",
      "Utrecht 1000\n",
      "Utsunomiya 300\n",
      "Utsunomiya 600\n",
      "Utsunomiya 1000\n",
      "Washington DC 300\n",
      "Washington DC 600\n",
      "Washington DC 1000\n",
      "Yaounde 300\n",
      "Yaounde 600\n",
      "Yaounde 1000\n",
      "Yerevan 300\n",
      "Yerevan 600\n",
      "Yerevan 1000\n",
      "Zibo 300\n",
      "Zibo 600\n",
      "Zibo 1000\n",
      "CPU times: total: 18min 13s\n",
      "Wall time: 18min 41s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>E2SCFA-Sc 300</th>\n",
       "      <th>E2SCFA-class 300</th>\n",
       "      <th>E2SCFA-Sc 600</th>\n",
       "      <th>E2SCFA-class 600</th>\n",
       "      <th>E2SCFA-Sc 1000</th>\n",
       "      <th>E2SCFA-class 1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0 no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0 no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0 no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0 no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0 no</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>62388</th>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>35.08</td>\n",
       "      <td>4 good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62389</th>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0 no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62390</th>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0 no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62391</th>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0 no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62392</th>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 no</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0 no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62393 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       population  E2SCFA-Sc 300 E2SCFA-class 300  E2SCFA-Sc 600  \\\n",
       "0               1            0.0             0 no            0.0   \n",
       "1               1            0.0             0 no            0.0   \n",
       "2               1            0.0             0 no            0.0   \n",
       "3              32            0.0             0 no            0.0   \n",
       "4              31            0.0             0 no            0.0   \n",
       "...           ...            ...              ...            ...   \n",
       "62388          14            0.0             0 no            0.0   \n",
       "62389          16            0.0             0 no            0.0   \n",
       "62390          16            0.0             0 no            0.0   \n",
       "62391          17            0.0             0 no            0.0   \n",
       "62392          18            0.0             0 no            0.0   \n",
       "\n",
       "      E2SCFA-class 600  E2SCFA-Sc 1000 E2SCFA-class 1000  \n",
       "0                 0 no            0.00              0 no  \n",
       "1                 0 no            0.00              0 no  \n",
       "2                 0 no            0.00              0 no  \n",
       "3                 0 no            0.00              0 no  \n",
       "4                 0 no            0.00              0 no  \n",
       "...                ...             ...               ...  \n",
       "62388             0 no           35.08            4 good  \n",
       "62389             0 no            0.00              0 no  \n",
       "62390             0 no            0.00              0 no  \n",
       "62391             0 no            0.00              0 no  \n",
       "62392             0 no            0.00              0 no  \n",
       "\n",
       "[62393 rows x 7 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "E2SCFA_class = list()\n",
    "path = \"D:/Dumps/GEE-WP Scores/E2SFCA/100m grids/grid_geoms_adj/\"\n",
    "ls_file = np.array(os.listdir(path))\n",
    "ls_file = pd.Series(ls_file)[pd.Series(ls_file).str.contains('gpkg')]\n",
    "ls_file = ls_file[[any([j in i for j in ls_in]) for i in ls_file]]\n",
    "\n",
    "#ls_2 = ls_file[[any([j in i for j in ls_adj]) for i in ls_file]]\n",
    "\n",
    "#warnings.filterwarnings('ignore')\n",
    "#area = [city_geo(i.split(', ')).dissolve().geometry for i in cities_adj2['OSM_ineq_adj']]\n",
    "#files = [gpd.read_file(path+i) for i in ls_2]\n",
    "#scores = [files[i].overlay(gpd.GeoDataFrame(geometry = area[i], crs = 4326)) for i in range(len(files))]\n",
    "\n",
    "for i in ls_file:\n",
    "    city_score = gpd.read_file(path+i)\n",
    "    \n",
    "    #k = 0\n",
    "    #if i[1] in ls_adj:\n",
    "    #    city_score2 = scores[k]\n",
    "    #    k = k+1\n",
    "    #else:\n",
    "    #    city_score2 = city_score\n",
    "    \n",
    "    score_df = city_score[['population']]\n",
    "    for j in thresholds:\n",
    "        ls = list()\n",
    "        score = city_score['Sc-norm '+str(j)]\n",
    "        for k in score:\n",
    "            if k == 0: # m2 of UGS per person and weighted with Gaussian (threshold capped) distance decay\n",
    "                ls.append('0 no')\n",
    "            elif k < 9: # m2 per person required by the WHO\n",
    "                ls.append('1 low')\n",
    "            elif k < 18: # m2 per person twice required by the WHO\n",
    "                ls.append('2 mediocre')\n",
    "            elif k < 30: # m2 per person preferred by the WHO\n",
    "                ls.append('3 sufficient')\n",
    "            elif k < 100: # over 100m2 per person, the additive value of m2 per person becomes almost obsolete\n",
    "                ls.append('4 good') # below the 100m2 is still a good score\n",
    "            else:\n",
    "                ls.append('5 excellent') # above it will be excellent\n",
    "        print(i.rsplit('.')[0], j)\n",
    "        score_df = pd.concat([score_df, \n",
    "                              round(pd.Series(score, name = 'E2SCFA-Sc '+str(j)),2), \n",
    "                              pd.Series(ls, name = 'E2SCFA-class '+str(j))], \n",
    "                             axis = 1)\n",
    "    E2SCFA_class.append(score_df)\n",
    "E2SCFA_class[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bf93c584",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Abidjan\n",
      "Addis Ababa\n",
      "Baghdad\n",
      "Bangalore\n",
      "Belgrade\n",
      "Belo Horizonte\n",
      "Bogota\n",
      "Bologna\n",
      "Cali\n",
      "Canberra\n",
      "Casablanca\n",
      "Chandigarh\n",
      "Chelyabinsk\n",
      "Chengdu\n",
      "Christchurch\n",
      "Cochabamba\n",
      "Copenhagen\n",
      "Curitiba\n",
      "Damascus\n",
      "Detroit\n",
      "Dhaka\n",
      "Dongguan\n",
      "Fortaleza\n",
      "Ghent\n",
      "Guatemala City\n",
      "Gwangju\n",
      "Hanoi\n",
      "Indore\n",
      "Johannesburg\n",
      "Kampala\n",
      "Karlsruhe\n",
      "Kathmandu\n",
      "Kharkiv\n",
      "Kuala Lumpur\n",
      "Kumamoto\n",
      "Lagos\n",
      "Leicester\n",
      "Liverpool\n",
      "Lubumbashi\n",
      "Lviv\n",
      "Manila\n",
      "Memphis\n",
      "Mexico City\n",
      "Mombasa\n",
      "Nanjing\n",
      "New Delhi\n",
      "Oran\n",
      "Oslo\n",
      "Perth\n",
      "Philadelphia\n",
      "Pune\n",
      "Rennes\n",
      "Rosario\n",
      "Rostov\n",
      "San Antonio\n",
      "San Diego\n",
      "Santo Domingo\n",
      "Seattle\n",
      "Seoul\n",
      "Sevilla\n",
      "Shijiazhuang\n",
      "Shiraz\n",
      "Skopje\n",
      "Surabaya\n",
      "Tallinn\n",
      "Tashkent\n",
      "Tel Aviv\n",
      "Toronto\n",
      "Turin\n",
      "Utrecht\n",
      "Utsunomiya\n",
      "Washington DC\n",
      "Yaounde\n",
      "Yerevan\n",
      "Zibo\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[1;32m<timed exec>:4\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "scores = list()\n",
    "write_path = 'D:/Dumps/GEE-WP Scores/city_summary/'\n",
    "for i in range(len(ls_file)):\n",
    "    score = pd.concat([E2SCFA_class[i], cities_scores[i]], axis = 1)\n",
    "    \n",
    "    score = score[['geometry','population',\n",
    "                   'E2SCFA-class 300','E2SCFA-class 600','E2SCFA-class 1000',\n",
    "                   'GravQnt 300','GravQnt 600','GravQnt 1000',\n",
    "                   'E2SCFA-Sc 300','E2SCFA-Sc 600','E2SCFA-Sc 1000',\n",
    "                   'Grav-Sc 300','Grav-Sc 600','Grav-Sc 1000']]\n",
    "        \n",
    "    # Adding categories without grid geoms attached to ensure that each category is in a plotted map\n",
    "    ls2 = pd.DataFrame(list([\n",
    "                [Polygon(),'5 excellent','5 excellent','5 excellent','5 excellent','5 excellent','5 excellent'],\n",
    "                [Polygon(),'4 good','4 good','4 good','4 good','4 good','4 good'],\n",
    "                [Polygon(),'3 sufficient','3 sufficient','3 sufficient','3 sufficient','3 sufficient','3 sufficient'],\n",
    "                [Polygon(),'2 mediocre','2 mediocre','2 mediocre','2 mediocre','2 mediocre','2 mediocre'],\n",
    "                [Polygon(),'1 low','1 low','1 low','1 low','1 low','1 low'],\n",
    "                [Polygon(),'0 no','0 no','0 no','0 no','0 no','0 no']]))\n",
    "    \n",
    "    ls2.columns = ['geometry',\n",
    "                   'E2SCFA-class 300','E2SCFA-class 600','E2SCFA-class 1000',\n",
    "                   'GravQnt 300','GravQnt 600','GravQnt 1000']\n",
    "    \n",
    "    score = pd.concat([score, pd.DataFrame(ls2)])\n",
    "    \n",
    "    score = gpd.GeoDataFrame(score, geometry = 'geometry', crs = 4326)\n",
    "    score.to_file(write_path+cities_adj['City'][i]+'.gpkg')\n",
    "    score.iloc[:,score.columns != 'geometry'].to_csv(write_path+cities_adj['City'][i]+'.csv')\n",
    "    print(cities_adj['City'][i])\n",
    "    scores.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2e6e27f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d5bdd722",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers or slices, not str",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[45], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m [[\u001b[38;5;28mprint\u001b[39m(city[\u001b[38;5;241m1\u001b[39m], \n\u001b[0;32m      2\u001b[0m         scores[cities_adj[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCity\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mE2SCFA-class \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(t))[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mE2SCFA-class \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(t)]\u001b[38;5;241m.\u001b[39mcount()) \n\u001b[0;32m      3\u001b[0m   \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m thresholds] \n\u001b[0;32m      4\u001b[0m  \u001b[38;5;28;01mfor\u001b[39;00m city \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(cities)]\n\u001b[0;32m      5\u001b[0m sys\u001b[38;5;241m.\u001b[39mstdout\u001b[38;5;241m.\u001b[39mflush()\n",
      "Cell \u001b[1;32mIn[45], line 1\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[1;32m----> 1\u001b[0m [[\u001b[38;5;28mprint\u001b[39m(city[\u001b[38;5;241m1\u001b[39m], \n\u001b[0;32m      2\u001b[0m         scores[cities_adj[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCity\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mE2SCFA-class \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(t))[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mE2SCFA-class \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(t)]\u001b[38;5;241m.\u001b[39mcount()) \n\u001b[0;32m      3\u001b[0m   \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m thresholds] \n\u001b[0;32m      4\u001b[0m  \u001b[38;5;28;01mfor\u001b[39;00m city \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(cities)]\n\u001b[0;32m      5\u001b[0m sys\u001b[38;5;241m.\u001b[39mstdout\u001b[38;5;241m.\u001b[39mflush()\n",
      "Cell \u001b[1;32mIn[45], line 2\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m      1\u001b[0m [[\u001b[38;5;28mprint\u001b[39m(city[\u001b[38;5;241m1\u001b[39m], \n\u001b[1;32m----> 2\u001b[0m         scores[cities_adj[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCity\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mE2SCFA-class \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(t))[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mE2SCFA-class \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(t)]\u001b[38;5;241m.\u001b[39mcount()) \n\u001b[0;32m      3\u001b[0m   \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m thresholds] \n\u001b[0;32m      4\u001b[0m  \u001b[38;5;28;01mfor\u001b[39;00m city \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(cities)]\n\u001b[0;32m      5\u001b[0m sys\u001b[38;5;241m.\u001b[39mstdout\u001b[38;5;241m.\u001b[39mflush()\n",
      "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not str"
     ]
    }
   ],
   "source": [
    "[[print(city[1], \n",
    "        scores[cities_adj['City'][0]].groupby('E2SCFA-class '+str(t))['E2SCFA-class '+str(t)].count()) \n",
    "  for t in thresholds] \n",
    " for city in enumerate(cities)]\n",
    "sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ccd3d734",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "summ_df = pd.DataFrame([i.groupby('E2SCFA-class 300')['population'].sum() / i['population'].sum() for i in scores])\n",
    "summ_df.index = cities_adj['City']\n",
    "summ_df.to_csv(write_path+'E2SFCA_300_popweighaccess.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1a45171c",
   "metadata": {},
   "outputs": [],
   "source": [
    "summ_df = pd.DataFrame([i.groupby('GravQnt 300')['population'].sum() / i['population'].sum() for i in scores])\n",
    "summ_df.index = cities_adj['City']\n",
    "summ_df.to_csv(write_path+'Gravity_300_popweighaccess.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b9f3cb44",
   "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>GravQnt 300</th>\n",
       "      <th>0 no</th>\n",
       "      <th>1 low</th>\n",
       "      <th>2 mediocre</th>\n",
       "      <th>3 sufficient</th>\n",
       "      <th>4 good</th>\n",
       "      <th>5 excellent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</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>Addis Ababa</th>\n",
       "      <td>0.867830</td>\n",
       "      <td>0.081889</td>\n",
       "      <td>0.048946</td>\n",
       "      <td>0.001174</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuala Lumpur</th>\n",
       "      <td>0.964355</td>\n",
       "      <td>0.015030</td>\n",
       "      <td>0.019675</td>\n",
       "      <td>0.000848</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lubumbashi</th>\n",
       "      <td>0.620006</td>\n",
       "      <td>0.000883</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santo Domingo</th>\n",
       "      <td>0.879935</td>\n",
       "      <td>0.084828</td>\n",
       "      <td>0.033939</td>\n",
       "      <td>0.001239</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shijiazhuang</th>\n",
       "      <td>0.964942</td>\n",
       "      <td>0.028548</td>\n",
       "      <td>0.006510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgrade</th>\n",
       "      <td>0.646852</td>\n",
       "      <td>0.173439</td>\n",
       "      <td>0.006092</td>\n",
       "      <td>0.007533</td>\n",
       "      <td>0.006325</td>\n",
       "      <td>0.159759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christchurch</th>\n",
       "      <td>0.568520</td>\n",
       "      <td>0.254499</td>\n",
       "      <td>0.003501</td>\n",
       "      <td>0.001354</td>\n",
       "      <td>0.002634</td>\n",
       "      <td>0.169491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chandigarh</th>\n",
       "      <td>0.511777</td>\n",
       "      <td>0.252170</td>\n",
       "      <td>0.038292</td>\n",
       "      <td>0.003146</td>\n",
       "      <td>0.002002</td>\n",
       "      <td>0.192614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tallinn</th>\n",
       "      <td>0.550658</td>\n",
       "      <td>0.191035</td>\n",
       "      <td>0.008468</td>\n",
       "      <td>0.013782</td>\n",
       "      <td>0.028532</td>\n",
       "      <td>0.207526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canberra</th>\n",
       "      <td>0.363544</td>\n",
       "      <td>0.322177</td>\n",
       "      <td>0.008795</td>\n",
       "      <td>0.021862</td>\n",
       "      <td>0.039734</td>\n",
       "      <td>0.243888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "GravQnt 300        0 no     1 low  2 mediocre  3 sufficient    4 good  \\\n",
       "City                                                                    \n",
       "Addis Ababa    0.867830  0.081889    0.048946      0.001174  0.000161   \n",
       "Kuala Lumpur   0.964355  0.015030    0.019675      0.000848  0.000092   \n",
       "Lubumbashi     0.620006  0.000883    0.000274      0.000194  0.000000   \n",
       "Santo Domingo  0.879935  0.084828    0.033939      0.001239  0.000058   \n",
       "Shijiazhuang   0.964942  0.028548    0.006510      0.000000  0.000000   \n",
       "...                 ...       ...         ...           ...       ...   \n",
       "Belgrade       0.646852  0.173439    0.006092      0.007533  0.006325   \n",
       "Christchurch   0.568520  0.254499    0.003501      0.001354  0.002634   \n",
       "Chandigarh     0.511777  0.252170    0.038292      0.003146  0.002002   \n",
       "Tallinn        0.550658  0.191035    0.008468      0.013782  0.028532   \n",
       "Canberra       0.363544  0.322177    0.008795      0.021862  0.039734   \n",
       "\n",
       "GravQnt 300    5 excellent  \n",
       "City                        \n",
       "Addis Ababa       0.000000  \n",
       "Kuala Lumpur      0.000000  \n",
       "Lubumbashi        0.000000  \n",
       "Santo Domingo     0.000000  \n",
       "Shijiazhuang      0.000000  \n",
       "...                    ...  \n",
       "Belgrade          0.159759  \n",
       "Christchurch      0.169491  \n",
       "Chandigarh        0.192614  \n",
       "Tallinn           0.207526  \n",
       "Canberra          0.243888  \n",
       "\n",
       "[75 rows x 6 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summ_df.sort_values('5 excellent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c92b86a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = [files[i].overlay(gpd.GeoDataFrame(geometry = area[i], crs = 4326)) for i in range(len(files))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62b78aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc[0].to_file('D:/Dumps/GEE-WP Scores/Gravity/100m grids/grid_geoms_adj/gridscore_grav3_Canberra.gpkg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc72c09d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc[1].to_file('D:/Dumps/GEE-WP Scores/Gravity/100m grids/grid_geoms_adj/gridscore_grav3_Karlsruhe.gpkg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "780c2507",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc[2].to_file('D:/Dumps/GEE-WP Scores/Gravity/100m grids/grid_geoms_adj/gridscore_grav3_Oslo.gpkg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecac440b",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"D:/Dumps/GEE-WP Scores/Gravity/100m grids/Grid_geoms/\"\n",
    "ls_file = os.listdir(path)\n",
    "ls_file = np.array(ls_file)[[i.rsplit('_')[1] == 'grav3' for i in ls_file]]\n",
    "ls_file = ls_file[[any([j in i for j in ls_in]) for i in ls_file]]\n",
    "\n",
    "ls_2 = ls_file[[any([j in i for j in ls_adj]) for i in ls_file]]\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "area = [city_geo(i.split(', ')).dissolve().geometry for i in cities_adj2['OSM_ineq_adj']]\n",
    "files = [gpd.read_file(path+i) for i in ls_2]\n",
    "scores = [files[i].overlay(gpd.GeoDataFrame(geometry = area[i], crs = 4326)) for i in range(len(files))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7fb027a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc[0].to_file('D:/Dumps/GEE-WP Scores/Gravity/100m grids/grid_geoms_adj/Canberra.gpkg')\n",
    "sc[1].to_file('D:/Dumps/GEE-WP Scores/Gravity/100m grids/grid_geoms_adj/Karlsruhe.gpkg')\n",
    "sc[2].to_file('D:/Dumps/GEE-WP Scores/Gravity/100m grids/grid_geoms_adj/Oslo.gpkg')"
   ]
  }
 ],
 "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}