GINI Analysis UGSA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "86fd83b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "from pysal.explore import inequality\n",
    "from inequality.gini import Gini_Spatial\n",
    "from pysal.lib import weights\n",
    "import warnings\n",
    "from shapely.geometry import Polygon\n",
    "import osmnx as ox\n",
    "city_geo = ox.geocoder.geocode_to_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f825908e",
   "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": 70,
   "id": "86f7f93a",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(ls_in)\n",
    "\n",
    "ls_adj = ['Canberra','Karlsruhe','Oslo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b818547f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cities = pd.read_excel('cities.xlsx')\n",
    "cities_adj = cities[cities['City'].isin(ls_adj)]\n",
    "cities_adj = cities_adj.sort_values('City').reset_index()\n",
    "\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": 86,
   "id": "e03fe025",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canberra\n",
      "Karlsruhe\n",
      "Oslo\n",
      "CPU times: total: 1min 34s\n",
      "Wall time: 1min 34s\n"
     ]
    }
   ],
   "source": [
    "%%time\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_adj]) for i in ls_file]]\n",
    "ls_file = np.array(ls_file)[[any([j in i for j in ls_adj]) for i in ls_file]]\n",
    "\n",
    "#ls_sub = np.array(ls_file)[ls_bool]\n",
    "#ls_out = np.array(ls_file)[~np.array(ls_bool)]\n",
    "\n",
    "#area = [city_geo(i.split(', ')).dissolve().geometry for i in cities_adj2['OSM_ineq_adj']]\n",
    "\n",
    "#j = 0\n",
    "#k = 0\n",
    "city_score = []\n",
    "for i in range(len(ls_file)):\n",
    "    #if ls_bool[i]:\n",
    "    file = gpd.read_file(path+ls_file[i])\n",
    "    \n",
    "    minx = file.bounds.minx\n",
    "    maxx = file.bounds.maxx\n",
    "    miny = file.bounds.miny\n",
    "    maxy = file.bounds.maxy\n",
    "\n",
    "    # Some geometries result in a multipolygon when overlaying (like i.e. 0.05 meters), coords error.\n",
    "    # Therefore recreate the polygon.\n",
    "    Poly = []\n",
    "    for k in range(len(file)):\n",
    "        Poly.append(Polygon([(minx[k],maxy[k]),(maxx[k],maxy[k]),(maxx[k],miny[k]),(minx[k],miny[k])]))\n",
    "    file.geometry = Poly\n",
    "    \n",
    "    city_score.append(file)\n",
    "    \n",
    "    print(cities_adj['City'][i])\n",
    "        \n",
    "   #     print(ls_sub[j].split('_')[-1].split('.')[0])\n",
    "    #    j = j+1\n",
    "    #else:\n",
    "    #    city_score.append(gpd.read_file(path+ls_out[k]))\n",
    "    #    \n",
    "    #    print(ls_out[k].split('_')[-1].split('.')[0])\n",
    "    #    k = k+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "95b4f33b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Polygon    109716\n",
       "dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_score[2].geom_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8c6a4a7a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canberra\n",
      "gini 300 0.7573\n",
      "spatial gini 300 0.7151\n",
      "gini 600 0.6745\n",
      "spatial gini 600 0.6475\n",
      "gini 1000 0.5713\n",
      "spatial gini 1000 0.5598\n",
      " \n",
      "Karlsruhe\n",
      "gini 300 0.7592\n",
      "spatial gini 300 0.6877\n",
      "gini 600 0.6219\n",
      "spatial gini 600 0.5784\n",
      "gini 1000 0.4976\n",
      "spatial gini 1000 0.482\n",
      " \n",
      "Oslo\n",
      "gini 300 0.8751\n",
      "spatial gini 300 0.8769\n",
      "gini 600 0.7218\n",
      "spatial gini 600 0.7298\n",
      "gini 1000 0.5839\n",
      "spatial gini 1000 0.5954\n",
      " \n",
      "CPU times: total: 3min 34s\n",
      "Wall time: 3min 35s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "warnings.filterwarnings('ignore')\n",
    "Gcities_gini = []\n",
    "Gcities_spatialgini = []\n",
    "for city in enumerate(city_score):\n",
    "    Gtr_gini = []\n",
    "    Gtr_spatialgini = []\n",
    "    print(ls_file[city[0]].split('.')[0].split('_')[-1])\n",
    "    for i in city[1][['tr_300','tr_600','tr_1000']]:\n",
    "        float_df = city[1][city[1][i].isna() == False][['geometry','population',i]]\n",
    "\n",
    "        gini = pd.DataFrame(float_df[i].value_counts()).sort_index()\n",
    "        gini['pop'] = float_df.groupby(i)['population'].sum()\n",
    "        gini = gini.reset_index()\n",
    "        gini['frac_pop'] = gini['pop'] / gini['pop'].sum()\n",
    "        gini['richer_frac'] = 1-gini['frac_pop'].cumsum()\n",
    "        gini['ugs'] = (gini['index'] * gini['pop']) \n",
    "        gini['frac_ugs'] = gini.ugs/gini.ugs.sum()\n",
    "        gini_score = 1-(gini.frac_ugs*(gini.frac_pop+2*gini.richer_frac)).sum()\n",
    "        print('gini',i.rsplit('_')[-1],round(gini_score,4))\n",
    "        Gtr_gini.append(gini_score)\n",
    "        \n",
    "        wq = weights.Queen.from_dataframe(float_df)\n",
    "        wq.transform = \"B\"\n",
    "        spatial_gini = Gini_Spatial(float_df[i], wq).g\n",
    "        print('spatial gini',i.rsplit('_')[-1],round(spatial_gini,4))\n",
    "        Gtr_spatialgini.append(spatial_gini)\n",
    "    Gcities_gini.append(Gtr_gini)\n",
    "    Gcities_spatialgini.append(Gtr_spatialgini)\n",
    "    print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c7273a05",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gini G-300</th>\n",
       "      <th>Gini G-600</th>\n",
       "      <th>Gini G-1000</th>\n",
       "      <th>S-gini G-300</th>\n",
       "      <th>S-gini G-600</th>\n",
       "      <th>S-gini G-1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canberra</th>\n",
       "      <td>0.757310</td>\n",
       "      <td>0.674484</td>\n",
       "      <td>0.571259</td>\n",
       "      <td>0.715117</td>\n",
       "      <td>0.647454</td>\n",
       "      <td>0.559838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karlsruhe</th>\n",
       "      <td>0.759177</td>\n",
       "      <td>0.621880</td>\n",
       "      <td>0.497628</td>\n",
       "      <td>0.687741</td>\n",
       "      <td>0.578448</td>\n",
       "      <td>0.482046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oslo</th>\n",
       "      <td>0.875084</td>\n",
       "      <td>0.721834</td>\n",
       "      <td>0.583930</td>\n",
       "      <td>0.876949</td>\n",
       "      <td>0.729761</td>\n",
       "      <td>0.595352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gini G-300  Gini G-600  Gini G-1000  S-gini G-300  S-gini G-600  \\\n",
       "Canberra     0.757310    0.674484     0.571259      0.715117      0.647454   \n",
       "Karlsruhe    0.759177    0.621880     0.497628      0.687741      0.578448   \n",
       "Oslo         0.875084    0.721834     0.583930      0.876949      0.729761   \n",
       "\n",
       "           S-gini G-1000  \n",
       "Canberra        0.559838  \n",
       "Karlsruhe       0.482046  \n",
       "Oslo            0.595352  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gini_df = pd.DataFrame(Gcities_gini, columns = ['Gini G-300','Gini G-600','Gini G-1000'])\n",
    "spatialgini_df = pd.DataFrame(Gcities_spatialgini, columns = ['S-gini G-300','S-gini G-600','S-gini G-1000'])\n",
    "comb_gini = pd.concat([gini_df, spatialgini_df], axis = 1)\n",
    "comb_gini.index = [i.split('_')[-1].split('.')[0] for i in ls_file]\n",
    "comb_gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e75d3127",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gini G-300</th>\n",
       "      <th>Gini G-600</th>\n",
       "      <th>Gini G-1000</th>\n",
       "      <th>S-gini G-300</th>\n",
       "      <th>S-gini G-600</th>\n",
       "      <th>S-gini G-1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Karlsruhe</th>\n",
       "      <td>0.759177</td>\n",
       "      <td>0.621880</td>\n",
       "      <td>0.497628</td>\n",
       "      <td>0.687741</td>\n",
       "      <td>0.578448</td>\n",
       "      <td>0.482046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canberra</th>\n",
       "      <td>0.757310</td>\n",
       "      <td>0.674484</td>\n",
       "      <td>0.571259</td>\n",
       "      <td>0.715117</td>\n",
       "      <td>0.647454</td>\n",
       "      <td>0.559838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oslo</th>\n",
       "      <td>0.875084</td>\n",
       "      <td>0.721834</td>\n",
       "      <td>0.583930</td>\n",
       "      <td>0.876949</td>\n",
       "      <td>0.729761</td>\n",
       "      <td>0.595352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gini G-300  Gini G-600  Gini G-1000  S-gini G-300  S-gini G-600  \\\n",
       "Karlsruhe    0.759177    0.621880     0.497628      0.687741      0.578448   \n",
       "Canberra     0.757310    0.674484     0.571259      0.715117      0.647454   \n",
       "Oslo         0.875084    0.721834     0.583930      0.876949      0.729761   \n",
       "\n",
       "           S-gini G-1000  \n",
       "Karlsruhe       0.482046  \n",
       "Canberra        0.559838  \n",
       "Oslo            0.595352  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_gini2 = comb_gini\n",
    "comb_gini.sort_values(by='S-gini G-300')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "77214cf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_gini.to_csv('D:/Dumps/GEE-WP Scores/Gravity/gini_scores_2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b1bbf18f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canberra\n",
      "Karlsruhe\n",
      "Oslo\n",
      "CPU times: total: 25.6 s\n",
      "Wall time: 26.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "path = \"D:/Dumps/GEE-WP Scores/E2SFCA/100m grids/Grid_geoms_adj/\"\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_adj]) for i in ls_file]]\n",
    "ls_file = np.array(ls_file)[[any([j in i for j in ls_adj]) for i in ls_file]]\n",
    "\n",
    "#ls_sub = np.array(ls_file)[ls_bool]\n",
    "#ls_out = np.array(ls_file)[~np.array(ls_bool)]\n",
    "\n",
    "#area = [city_geo(i.split(', ')).dissolve().geometry for i in cities_adj2['OSM_ineq_adj']]\n",
    "\n",
    "#j = 0\n",
    "#k = 0\n",
    "city_score = []\n",
    "for i in range(len(ls_file)):\n",
    "    #if ls_bool[i]:\n",
    "    #if ls_bool[i]:\n",
    "    file = gpd.read_file(path+ls_file[i])\n",
    "    \n",
    "    minx = file.bounds.minx\n",
    "    maxx = file.bounds.maxx\n",
    "    miny = file.bounds.miny\n",
    "    maxy = file.bounds.maxy\n",
    "\n",
    "        # Some geometries result in a multipolygon when dissolving (like i.e. 0.05 meters), coords error.\n",
    "        # Therefore recreate the polygon.\n",
    "    Poly = []\n",
    "    for k in range(len(file)):\n",
    "        Poly.append(Polygon([(minx[k],maxy[k]),(maxx[k],maxy[k]),(maxx[k],miny[k]),(minx[k],miny[k])]))\n",
    "    file.geometry = Poly\n",
    "    \n",
    "    city_score.append(file)\n",
    "    \n",
    "    print(cities_adj['City'][i])\n",
    "   #     print(ls_sub[j].split('_')[-1].split('.')[0])\n",
    "    #    j = j+1\n",
    "    #else:\n",
    "    #    city_score.append(gpd.read_file(path+ls_out[k]))\n",
    "    #    \n",
    "    #    print(ls_out[k].split('_')[-1].split('.')[0])\n",
    "    #    k = k+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "87710b3d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canberra\n",
      "gini 300 0.8477\n",
      "spatial gini 300 0.8216\n",
      "gini 600 0.7592\n",
      "spatial gini 600 0.7394\n",
      "gini 1000 0.688\n",
      "spatial gini 1000 0.6739\n",
      " \n",
      "Karlsruhe\n",
      "gini 300 0.6954\n",
      "spatial gini 300 0.6679\n",
      "gini 600 0.5952\n",
      "spatial gini 600 0.6071\n",
      "gini 1000 0.5057\n",
      "spatial gini 1000 0.5483\n",
      " \n",
      "Oslo\n",
      "gini 300 0.8702\n",
      "spatial gini 300 0.8864\n",
      "gini 600 0.7699\n",
      "spatial gini 600 0.8053\n",
      "gini 1000 0.6819\n",
      "spatial gini 1000 0.7316\n",
      " \n",
      "CPU times: total: 3min 46s\n",
      "Wall time: 3min 46s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# For loop city/GINI\n",
    "# First non-spatial GINI for the 50 cities.\n",
    "\n",
    "# Queen contignuity\n",
    "# Spatial GINI - value cell itself\n",
    "# Weight is values of surrounding cells\n",
    "# Near and far, with weights of 1 for near and 0 for far.\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "cities_gini = []\n",
    "cities_spatialgini = []\n",
    "for city in enumerate(city_score):\n",
    "    tr_gini = []\n",
    "    tr_spatialgini = []\n",
    "    print(ls_file[city[0]].split('.')[0])\n",
    "    for i in city[1].columns[city[1].columns.str.contains('Sc-access')]:\n",
    "        float_df = city[1][['geometry','population',i]]\n",
    "\n",
    "        gini = pd.DataFrame(float_df[i].value_counts()).sort_index()\n",
    "        gini['pop'] = float_df.groupby(i)['population'].sum()\n",
    "        gini = gini.reset_index()\n",
    "        gini['frac_pop'] = gini['pop'] / gini['pop'].sum()\n",
    "        gini['richer_frac'] = 1-gini['frac_pop'].cumsum()\n",
    "        gini['ugs'] = (gini['index'] * gini['pop']) \n",
    "        gini['frac_ugs'] = gini.ugs/gini.ugs.sum()\n",
    "        gini_score = 1-(gini.frac_ugs*(gini.frac_pop+2*gini.richer_frac)).sum()\n",
    "        print('gini',i.rsplit(' ')[-1],round(gini_score,4))\n",
    "        tr_gini.append(gini_score)\n",
    "        \n",
    "        wq = weights.Queen.from_dataframe(float_df)\n",
    "        wq.transform = \"B\"\n",
    "        spatial_gini = Gini_Spatial(float_df[i], wq).g\n",
    "        print('spatial gini',i.rsplit(' ')[-1],round(spatial_gini,4))\n",
    "        tr_spatialgini.append(spatial_gini)\n",
    "    cities_gini.append(tr_gini)\n",
    "    cities_spatialgini.append(tr_spatialgini)\n",
    "    print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "53d7d01c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Gini E-300</th>\n",
       "      <th>Gini E-600</th>\n",
       "      <th>Gini E-1000</th>\n",
       "      <th>S-gini E-300</th>\n",
       "      <th>S-gini E-600</th>\n",
       "      <th>S-gini E-1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canberra</th>\n",
       "      <td>0.847729</td>\n",
       "      <td>0.759195</td>\n",
       "      <td>0.687979</td>\n",
       "      <td>0.821566</td>\n",
       "      <td>0.739383</td>\n",
       "      <td>0.673909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karlsruhe</th>\n",
       "      <td>0.695399</td>\n",
       "      <td>0.595181</td>\n",
       "      <td>0.505731</td>\n",
       "      <td>0.667862</td>\n",
       "      <td>0.607083</td>\n",
       "      <td>0.548317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oslo</th>\n",
       "      <td>0.870242</td>\n",
       "      <td>0.769926</td>\n",
       "      <td>0.681926</td>\n",
       "      <td>0.886350</td>\n",
       "      <td>0.805276</td>\n",
       "      <td>0.731582</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gini E-300  Gini E-600  Gini E-1000  S-gini E-300  S-gini E-600  \\\n",
       "Canberra     0.847729    0.759195     0.687979      0.821566      0.739383   \n",
       "Karlsruhe    0.695399    0.595181     0.505731      0.667862      0.607083   \n",
       "Oslo         0.870242    0.769926     0.681926      0.886350      0.805276   \n",
       "\n",
       "           S-gini E-1000  \n",
       "Canberra        0.673909  \n",
       "Karlsruhe       0.548317  \n",
       "Oslo            0.731582  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gini_df = pd.DataFrame(cities_gini, columns = ['Gini E-300','Gini E-600','Gini E-1000'])\n",
    "spatialgini_df = pd.DataFrame(cities_spatialgini, columns = ['S-gini E-300','S-gini E-600','S-gini E-1000'])\n",
    "Gcomb_gini = pd.concat([gini_df, spatialgini_df], axis = 1)\n",
    "Gcomb_gini.index = [i.split('_')[-1].split('.')[0] for i in ls_file]\n",
    "Gcomb_gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "68c2bc78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Gini G-300</th>\n",
       "      <th>Gini G-600</th>\n",
       "      <th>Gini G-1000</th>\n",
       "      <th>S-gini G-300</th>\n",
       "      <th>S-gini G-600</th>\n",
       "      <th>S-gini G-1000</th>\n",
       "      <th>Gini E-300</th>\n",
       "      <th>Gini E-600</th>\n",
       "      <th>Gini E-1000</th>\n",
       "      <th>S-gini E-300</th>\n",
       "      <th>S-gini E-600</th>\n",
       "      <th>S-gini E-1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Canberra</th>\n",
       "      <td>0.757310</td>\n",
       "      <td>0.674484</td>\n",
       "      <td>0.571259</td>\n",
       "      <td>0.715117</td>\n",
       "      <td>0.647454</td>\n",
       "      <td>0.559838</td>\n",
       "      <td>0.847729</td>\n",
       "      <td>0.759195</td>\n",
       "      <td>0.687979</td>\n",
       "      <td>0.821566</td>\n",
       "      <td>0.739383</td>\n",
       "      <td>0.673909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karlsruhe</th>\n",
       "      <td>0.759177</td>\n",
       "      <td>0.621880</td>\n",
       "      <td>0.497628</td>\n",
       "      <td>0.687741</td>\n",
       "      <td>0.578448</td>\n",
       "      <td>0.482046</td>\n",
       "      <td>0.695399</td>\n",
       "      <td>0.595181</td>\n",
       "      <td>0.505731</td>\n",
       "      <td>0.667862</td>\n",
       "      <td>0.607083</td>\n",
       "      <td>0.548317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oslo</th>\n",
       "      <td>0.875084</td>\n",
       "      <td>0.721834</td>\n",
       "      <td>0.583930</td>\n",
       "      <td>0.876949</td>\n",
       "      <td>0.729761</td>\n",
       "      <td>0.595352</td>\n",
       "      <td>0.870242</td>\n",
       "      <td>0.769926</td>\n",
       "      <td>0.681926</td>\n",
       "      <td>0.886350</td>\n",
       "      <td>0.805276</td>\n",
       "      <td>0.731582</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gini G-300  Gini G-600  Gini G-1000  S-gini G-300  S-gini G-600  \\\n",
       "Canberra     0.757310    0.674484     0.571259      0.715117      0.647454   \n",
       "Karlsruhe    0.759177    0.621880     0.497628      0.687741      0.578448   \n",
       "Oslo         0.875084    0.721834     0.583930      0.876949      0.729761   \n",
       "\n",
       "           S-gini G-1000  Gini E-300  Gini E-600  Gini E-1000  S-gini E-300  \\\n",
       "Canberra        0.559838    0.847729    0.759195     0.687979      0.821566   \n",
       "Karlsruhe       0.482046    0.695399    0.595181     0.505731      0.667862   \n",
       "Oslo            0.595352    0.870242    0.769926     0.681926      0.886350   \n",
       "\n",
       "           S-gini E-600  S-gini E-1000  \n",
       "Canberra       0.739383       0.673909  \n",
       "Karlsruhe      0.607083       0.548317  \n",
       "Oslo           0.805276       0.731582  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gini_comb = pd.concat([comb_gini, Gcomb_gini], axis = 1)\n",
    "gini_comb.to_csv('D:/Dumps/GEE-WP Scores/gini_all_2.csv')\n",
    "gini_comb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "27a451a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Gcomb_gini.to_csv('D:/Dumps/GEE-WP Scores/E2SFCA/gini_scores_2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f128cad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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