neighborhoods_info.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminary operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eda5dc77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import main libraries\n",
    "import json\n",
    "import requests\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the datasets\n",
    "response = requests.get(\"https://gitlab.com/drvicsana/cop-proyecto-2023/-/raw/main/project_data/incidentes2019.json\")\n",
    "incidents_db = json.loads(response.text)\n",
    "\n",
    "response = requests.get(\"https://gitlab.com/drvicsana/cop-proyecto-2023/-/raw/main/project_data/distancias_estaciones_barrios.json\")\n",
    "distances_stations_ntas_db = json.loads(response.text)\n",
    "\n",
    "response = requests.get(\"https://gitlab.com/drvicsana/cop-proyecto-2023/-/raw/main/project_data/distancias_estaciones.json\")\n",
    "distances_stations_db = json.loads(response.text)\n",
    "\n",
    "response = requests.get(\"https://gitlab.com/drvicsana/cop-proyecto-2023/-/raw/main/project_data/barrios.json\")\n",
    "ntas_db = json.loads(response.text)\n",
    "\n",
    "response = requests.get(\"https://gitlab.com/drvicsana/cop-proyecto-2023/-/raw/main/project_data/estaciones.json\")\n",
    "stations_db = json.loads(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define vehicle types\n",
    "vehicle_types = [\"engine\", \"ladder\", \"rescue\", \"squad\", \"hazardous\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60831e50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add population density to each neighborhood\n",
    "for neighbor in ntas_db:\n",
    "    population = neighbor[\"population\"]\n",
    "    \n",
    "    shape_area = neighbor[\"shape_area\"] / 10000000 # the shape area value was too big\n",
    "    \n",
    "    neighbor[\"population_density\"] = population / shape_area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of vehicles used in 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iterating all neighborhoods\n",
    "for neighbor in ntas_db:\n",
    "    # get the name of the neighborhood (nta)\n",
    "    nta = neighbor[\"nta\"]\n",
    "    # get population of the neighborhood\n",
    "    population = neighbor[\"population\"]\n",
    "    \n",
    "    # iterate all vehicle types\n",
    "    for vehicle_type in vehicle_types:\n",
    "        # set column name\n",
    "        vehicles_needed_first_shift = vehicle_type + \"_needed_first_shift\"\n",
    "        vehicles_needed_second_shift = vehicle_type + \"_needed_second_shift\"\n",
    "\n",
    "        # define counters for vehicle type needed per person, for each shift\n",
    "        neighbor[vehicles_needed_first_shift] = 0\n",
    "        neighbor[vehicles_needed_second_shift] = 0\n",
    "\n",
    "        # iterate all the incidents\n",
    "        for incident in incidents_db:\n",
    "            # get incident nta\n",
    "            incident_nta = incident[\"nta\"]\n",
    "            # check if the incident happened in the actual neighborhood\n",
    "            if incident_nta == nta:\n",
    "                # get the vehicles needed for the incident\n",
    "                vehicles_needed_for_incident = incident[\"units\"].count(vehicle_type)\n",
    "                # get incident duration\n",
    "                incident_duration = incident[\"incident_duration\"]\n",
    "                # get the shift\n",
    "                is_first_shift = incident[\"is_first_shift\"]\n",
    "                \n",
    "                # check if the vehicle was used in that incident\n",
    "                if vehicles_needed_for_incident != 0:\n",
    "                    # check the shift\n",
    "                    if is_first_shift:\n",
    "                        # sum how many vehicles of that type was used in this incident\n",
    "                        neighbor[vehicles_needed_first_shift] += vehicles_needed_for_incident\n",
    "\n",
    "                    else:\n",
    "                        # sum how many vehicles of that type was used in this incident\n",
    "                        neighbor[vehicles_needed_second_shift] += vehicles_needed_for_incident"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data check and scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 195 entries, QN08 to BK99\n",
      "Data columns (total 15 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   boro_name                      195 non-null    object \n",
      " 1   name                           195 non-null    object \n",
      " 2   shape_area                     195 non-null    float64\n",
      " 3   population                     195 non-null    int64  \n",
      " 4   population_density             195 non-null    float64\n",
      " 5   engine_needed_first_shift      195 non-null    int64  \n",
      " 6   engine_needed_second_shift     195 non-null    int64  \n",
      " 7   ladder_needed_first_shift      195 non-null    int64  \n",
      " 8   ladder_needed_second_shift     195 non-null    int64  \n",
      " 9   rescue_needed_first_shift      195 non-null    int64  \n",
      " 10  rescue_needed_second_shift     195 non-null    int64  \n",
      " 11  squad_needed_first_shift       195 non-null    int64  \n",
      " 12  squad_needed_second_shift      195 non-null    int64  \n",
      " 13  hazardous_needed_first_shift   195 non-null    int64  \n",
      " 14  hazardous_needed_second_shift  195 non-null    int64  \n",
      "dtypes: float64(2), int64(11), object(2)\n",
      "memory usage: 24.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# get dataframe of the neighborhoods\n",
    "df_ntas = pd.DataFrame(ntas_db).set_index(\"nta\")\n",
    "df_ntas.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boro_name</th>\n",
       "      <th>name</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>engine_needed_first_shift</th>\n",
       "      <th>engine_needed_second_shift</th>\n",
       "      <th>ladder_needed_first_shift</th>\n",
       "      <th>ladder_needed_second_shift</th>\n",
       "      <th>rescue_needed_first_shift</th>\n",
       "      <th>rescue_needed_second_shift</th>\n",
       "      <th>squad_needed_first_shift</th>\n",
       "      <th>squad_needed_second_shift</th>\n",
       "      <th>hazardous_needed_first_shift</th>\n",
       "      <th>hazardous_needed_second_shift</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nta</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>QN08</th>\n",
       "      <td>Queens</td>\n",
       "      <td>St. Albans</td>\n",
       "      <td>0.223361</td>\n",
       "      <td>0.367078</td>\n",
       "      <td>0.109918</td>\n",
       "      <td>0.292654</td>\n",
       "      <td>0.339234</td>\n",
       "      <td>0.278632</td>\n",
       "      <td>0.342927</td>\n",
       "      <td>0.260234</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.236686</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.241176</td>\n",
       "      <td>0.346405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX28</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Van Cortlandt Village</td>\n",
       "      <td>0.062467</td>\n",
       "      <td>0.378462</td>\n",
       "      <td>0.341809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QN55</th>\n",
       "      <td>Queens</td>\n",
       "      <td>South Ozone Park</td>\n",
       "      <td>0.239059</td>\n",
       "      <td>0.573192</td>\n",
       "      <td>0.161128</td>\n",
       "      <td>0.487559</td>\n",
       "      <td>0.479621</td>\n",
       "      <td>0.464957</td>\n",
       "      <td>0.469801</td>\n",
       "      <td>0.479532</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.482353</td>\n",
       "      <td>0.379085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BK50</th>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Canarsie</td>\n",
       "      <td>0.237903</td>\n",
       "      <td>0.632227</td>\n",
       "      <td>0.178528</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX41</th>\n",
       "      <td>Bronx</td>\n",
       "      <td>Mount Hope</td>\n",
       "      <td>0.028422</td>\n",
       "      <td>0.391357</td>\n",
       "      <td>0.616431</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "     boro_name                   name  shape_area  population  \\\n",
       "nta                                                             \n",
       "QN08    Queens             St. Albans    0.223361    0.367078   \n",
       "BX28     Bronx  Van Cortlandt Village    0.062467    0.378462   \n",
       "QN55    Queens       South Ozone Park    0.239059    0.573192   \n",
       "BK50  Brooklyn               Canarsie    0.237903    0.632227   \n",
       "BX41     Bronx             Mount Hope    0.028422    0.391357   \n",
       "\n",
       "      population_density  engine_needed_first_shift  \\\n",
       "nta                                                   \n",
       "QN08            0.109918                   0.292654   \n",
       "BX28            0.341809                   0.000000   \n",
       "QN55            0.161128                   0.487559   \n",
       "BK50            0.178528                   0.000000   \n",
       "BX41            0.616431                   0.000000   \n",
       "\n",
       "      engine_needed_second_shift  ladder_needed_first_shift  \\\n",
       "nta                                                           \n",
       "QN08                    0.339234                   0.278632   \n",
       "BX28                    0.000000                   0.000000   \n",
       "QN55                    0.479621                   0.464957   \n",
       "BK50                    0.000000                   0.000000   \n",
       "BX41                    0.000000                   0.000000   \n",
       "\n",
       "      ladder_needed_second_shift  rescue_needed_first_shift  \\\n",
       "nta                                                           \n",
       "QN08                    0.342927                   0.260234   \n",
       "BX28                    0.000000                   0.000000   \n",
       "QN55                    0.469801                   0.479532   \n",
       "BK50                    0.000000                   0.000000   \n",
       "BX41                    0.000000                   0.000000   \n",
       "\n",
       "      rescue_needed_second_shift  squad_needed_first_shift  \\\n",
       "nta                                                          \n",
       "QN08                    0.333333                  0.236686   \n",
       "BX28                    0.000000                  0.000000   \n",
       "QN55                    0.900000                  0.538462   \n",
       "BK50                    0.000000                  0.000000   \n",
       "BX41                    0.000000                  0.000000   \n",
       "\n",
       "      squad_needed_second_shift  hazardous_needed_first_shift  \\\n",
       "nta                                                             \n",
       "QN08                       0.52                      0.241176   \n",
       "BX28                       0.00                      0.000000   \n",
       "QN55                       0.48                      0.482353   \n",
       "BK50                       0.00                      0.000000   \n",
       "BX41                       0.00                      0.000000   \n",
       "\n",
       "      hazardous_needed_second_shift  \n",
       "nta                                  \n",
       "QN08                       0.346405  \n",
       "BX28                       0.000000  \n",
       "QN55                       0.379085  \n",
       "BK50                       0.000000  \n",
       "BX41                       0.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define Min-Max Scaler\n",
    "MM_scaler = MinMaxScaler()\n",
    "\n",
    "# scale the data\n",
    "df_ntas.loc[:, \"shape_area\":\"hazardous_needed_second_shift\"] = MM_scaler.fit_transform(df_ntas.loc[:, \"shape_area\":\"hazardous_needed_second_shift\"])\n",
    "df_ntas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>42 South Street</th>\n",
       "      <th>49 Beekman Street</th>\n",
       "      <th>100 Duane Street</th>\n",
       "      <th>14 N. Moore Street</th>\n",
       "      <th>75 Canal Street</th>\n",
       "      <th>25 Pitt Street</th>\n",
       "      <th>222 East 2 Street</th>\n",
       "      <th>340 East 14 Street</th>\n",
       "      <th>253 Lafayette Street</th>\n",
       "      <th>42 Great Jones Street</th>\n",
       "      <th>...</th>\n",
       "      <th>256 Hylan Blvd.</th>\n",
       "      <th>278 McClean Avenue</th>\n",
       "      <th>1850 Clove Road</th>\n",
       "      <th>1592 Richmond Road</th>\n",
       "      <th>256 Nelson Ave.</th>\n",
       "      <th>345 Annadale Road</th>\n",
       "      <th>1560 Drumgoole Road West</th>\n",
       "      <th>7219 Amboy Road</th>\n",
       "      <th>1100 Rossville Ave</th>\n",
       "      <th>305 Front Street</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>QN08</th>\n",
       "      <td>2123.18</td>\n",
       "      <td>2130.20</td>\n",
       "      <td>2104.71</td>\n",
       "      <td>2073.11</td>\n",
       "      <td>1896.78</td>\n",
       "      <td>1881.48</td>\n",
       "      <td>1925.06</td>\n",
       "      <td>1876.57</td>\n",
       "      <td>2030.47</td>\n",
       "      <td>1974.10</td>\n",
       "      <td>...</td>\n",
       "      <td>2449.03</td>\n",
       "      <td>2393.86</td>\n",
       "      <td>2396.74</td>\n",
       "      <td>2617.64</td>\n",
       "      <td>3444.35</td>\n",
       "      <td>3471.11</td>\n",
       "      <td>3761.95</td>\n",
       "      <td>3933.27</td>\n",
       "      <td>3559.47</td>\n",
       "      <td>2631.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX28</th>\n",
       "      <td>1894.39</td>\n",
       "      <td>1874.63</td>\n",
       "      <td>1935.64</td>\n",
       "      <td>2020.57</td>\n",
       "      <td>1809.95</td>\n",
       "      <td>1751.58</td>\n",
       "      <td>1728.54</td>\n",
       "      <td>1601.29</td>\n",
       "      <td>1843.17</td>\n",
       "      <td>1762.19</td>\n",
       "      <td>...</td>\n",
       "      <td>3168.88</td>\n",
       "      <td>3133.14</td>\n",
       "      <td>3051.15</td>\n",
       "      <td>3274.89</td>\n",
       "      <td>3596.90</td>\n",
       "      <td>3272.39</td>\n",
       "      <td>3250.70</td>\n",
       "      <td>3276.83</td>\n",
       "      <td>3037.71</td>\n",
       "      <td>3351.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QN55</th>\n",
       "      <td>1872.67</td>\n",
       "      <td>1776.72</td>\n",
       "      <td>1811.35</td>\n",
       "      <td>1821.87</td>\n",
       "      <td>1646.27</td>\n",
       "      <td>1630.97</td>\n",
       "      <td>1674.55</td>\n",
       "      <td>1626.81</td>\n",
       "      <td>1779.96</td>\n",
       "      <td>1724.34</td>\n",
       "      <td>...</td>\n",
       "      <td>2113.36</td>\n",
       "      <td>2058.18</td>\n",
       "      <td>2061.06</td>\n",
       "      <td>2281.96</td>\n",
       "      <td>3108.67</td>\n",
       "      <td>3135.43</td>\n",
       "      <td>3426.27</td>\n",
       "      <td>3597.59</td>\n",
       "      <td>3223.79</td>\n",
       "      <td>2295.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BK50</th>\n",
       "      <td>1532.61</td>\n",
       "      <td>1587.44</td>\n",
       "      <td>1614.04</td>\n",
       "      <td>1630.87</td>\n",
       "      <td>1548.42</td>\n",
       "      <td>1533.11</td>\n",
       "      <td>1576.70</td>\n",
       "      <td>1662.97</td>\n",
       "      <td>1682.11</td>\n",
       "      <td>1704.08</td>\n",
       "      <td>...</td>\n",
       "      <td>1726.71</td>\n",
       "      <td>1671.54</td>\n",
       "      <td>1674.41</td>\n",
       "      <td>1895.32</td>\n",
       "      <td>2722.02</td>\n",
       "      <td>2748.79</td>\n",
       "      <td>3039.63</td>\n",
       "      <td>3210.95</td>\n",
       "      <td>2837.15</td>\n",
       "      <td>1909.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX41</th>\n",
       "      <td>1774.19</td>\n",
       "      <td>1754.43</td>\n",
       "      <td>1815.43</td>\n",
       "      <td>1900.37</td>\n",
       "      <td>1689.74</td>\n",
       "      <td>1631.38</td>\n",
       "      <td>1608.33</td>\n",
       "      <td>1481.08</td>\n",
       "      <td>1722.97</td>\n",
       "      <td>1641.99</td>\n",
       "      <td>...</td>\n",
       "      <td>3048.68</td>\n",
       "      <td>3012.94</td>\n",
       "      <td>2893.91</td>\n",
       "      <td>3117.65</td>\n",
       "      <td>3439.67</td>\n",
       "      <td>3115.16</td>\n",
       "      <td>3093.46</td>\n",
       "      <td>3119.59</td>\n",
       "      <td>2880.48</td>\n",
       "      <td>3201.09</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>BK93</th>\n",
       "      <td>1571.05</td>\n",
       "      <td>1625.88</td>\n",
       "      <td>1652.48</td>\n",
       "      <td>1669.31</td>\n",
       "      <td>1586.86</td>\n",
       "      <td>1571.56</td>\n",
       "      <td>1615.15</td>\n",
       "      <td>1701.41</td>\n",
       "      <td>1720.55</td>\n",
       "      <td>1742.52</td>\n",
       "      <td>...</td>\n",
       "      <td>1753.73</td>\n",
       "      <td>1705.62</td>\n",
       "      <td>1706.59</td>\n",
       "      <td>1922.34</td>\n",
       "      <td>2756.10</td>\n",
       "      <td>2775.81</td>\n",
       "      <td>3066.65</td>\n",
       "      <td>3243.13</td>\n",
       "      <td>2869.33</td>\n",
       "      <td>1936.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BK73</th>\n",
       "      <td>723.32</td>\n",
       "      <td>730.33</td>\n",
       "      <td>704.84</td>\n",
       "      <td>673.24</td>\n",
       "      <td>496.91</td>\n",
       "      <td>481.61</td>\n",
       "      <td>525.20</td>\n",
       "      <td>611.46</td>\n",
       "      <td>630.60</td>\n",
       "      <td>652.57</td>\n",
       "      <td>...</td>\n",
       "      <td>1791.46</td>\n",
       "      <td>1750.57</td>\n",
       "      <td>1744.32</td>\n",
       "      <td>1960.06</td>\n",
       "      <td>2799.28</td>\n",
       "      <td>2813.53</td>\n",
       "      <td>2988.50</td>\n",
       "      <td>3097.89</td>\n",
       "      <td>2775.52</td>\n",
       "      <td>1973.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN28</th>\n",
       "      <td>446.34</td>\n",
       "      <td>463.45</td>\n",
       "      <td>452.35</td>\n",
       "      <td>432.20</td>\n",
       "      <td>240.24</td>\n",
       "      <td>101.92</td>\n",
       "      <td>243.76</td>\n",
       "      <td>387.74</td>\n",
       "      <td>406.88</td>\n",
       "      <td>428.85</td>\n",
       "      <td>...</td>\n",
       "      <td>1774.52</td>\n",
       "      <td>1738.78</td>\n",
       "      <td>1727.38</td>\n",
       "      <td>1943.12</td>\n",
       "      <td>2778.55</td>\n",
       "      <td>2619.51</td>\n",
       "      <td>2747.46</td>\n",
       "      <td>2856.85</td>\n",
       "      <td>2534.48</td>\n",
       "      <td>1956.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BK40</th>\n",
       "      <td>657.93</td>\n",
       "      <td>767.28</td>\n",
       "      <td>739.37</td>\n",
       "      <td>756.19</td>\n",
       "      <td>906.25</td>\n",
       "      <td>1014.20</td>\n",
       "      <td>1085.85</td>\n",
       "      <td>1121.00</td>\n",
       "      <td>902.06</td>\n",
       "      <td>975.73</td>\n",
       "      <td>...</td>\n",
       "      <td>1058.62</td>\n",
       "      <td>1017.73</td>\n",
       "      <td>1011.48</td>\n",
       "      <td>1227.22</td>\n",
       "      <td>2066.44</td>\n",
       "      <td>2080.69</td>\n",
       "      <td>2371.53</td>\n",
       "      <td>2548.01</td>\n",
       "      <td>2174.21</td>\n",
       "      <td>1240.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BK99</th>\n",
       "      <td>1512.50</td>\n",
       "      <td>1567.33</td>\n",
       "      <td>1593.94</td>\n",
       "      <td>1610.76</td>\n",
       "      <td>1601.23</td>\n",
       "      <td>1648.58</td>\n",
       "      <td>1692.17</td>\n",
       "      <td>1778.44</td>\n",
       "      <td>1689.70</td>\n",
       "      <td>1728.94</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.66</td>\n",
       "      <td>1405.49</td>\n",
       "      <td>1408.36</td>\n",
       "      <td>1629.27</td>\n",
       "      <td>2455.97</td>\n",
       "      <td>2482.74</td>\n",
       "      <td>2773.57</td>\n",
       "      <td>2944.89</td>\n",
       "      <td>2571.09</td>\n",
       "      <td>1642.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 219 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      42 South Street  49 Beekman Street  100 Duane Street  \\\n",
       "QN08          2123.18            2130.20           2104.71   \n",
       "BX28          1894.39            1874.63           1935.64   \n",
       "QN55          1872.67            1776.72           1811.35   \n",
       "BK50          1532.61            1587.44           1614.04   \n",
       "BX41          1774.19            1754.43           1815.43   \n",
       "...               ...                ...               ...   \n",
       "BK93          1571.05            1625.88           1652.48   \n",
       "BK73           723.32             730.33            704.84   \n",
       "MN28           446.34             463.45            452.35   \n",
       "BK40           657.93             767.28            739.37   \n",
       "BK99          1512.50            1567.33           1593.94   \n",
       "\n",
       "      14 N. Moore Street  75 Canal Street  25 Pitt Street  222 East 2 Street  \\\n",
       "QN08             2073.11          1896.78         1881.48            1925.06   \n",
       "BX28             2020.57          1809.95         1751.58            1728.54   \n",
       "QN55             1821.87          1646.27         1630.97            1674.55   \n",
       "BK50             1630.87          1548.42         1533.11            1576.70   \n",
       "BX41             1900.37          1689.74         1631.38            1608.33   \n",
       "...                  ...              ...             ...                ...   \n",
       "BK93             1669.31          1586.86         1571.56            1615.15   \n",
       "BK73              673.24           496.91          481.61             525.20   \n",
       "MN28              432.20           240.24          101.92             243.76   \n",
       "BK40              756.19           906.25         1014.20            1085.85   \n",
       "BK99             1610.76          1601.23         1648.58            1692.17   \n",
       "\n",
       "      340 East 14 Street  253 Lafayette Street  42 Great Jones Street  ...  \\\n",
       "QN08             1876.57               2030.47                1974.10  ...   \n",
       "BX28             1601.29               1843.17                1762.19  ...   \n",
       "QN55             1626.81               1779.96                1724.34  ...   \n",
       "BK50             1662.97               1682.11                1704.08  ...   \n",
       "BX41             1481.08               1722.97                1641.99  ...   \n",
       "...                  ...                   ...                    ...  ...   \n",
       "BK93             1701.41               1720.55                1742.52  ...   \n",
       "BK73              611.46                630.60                 652.57  ...   \n",
       "MN28              387.74                406.88                 428.85  ...   \n",
       "BK40             1121.00                902.06                 975.73  ...   \n",
       "BK99             1778.44               1689.70                1728.94  ...   \n",
       "\n",
       "      256 Hylan Blvd.  278 McClean Avenue  1850 Clove Road  \\\n",
       "QN08          2449.03             2393.86          2396.74   \n",
       "BX28          3168.88             3133.14          3051.15   \n",
       "QN55          2113.36             2058.18          2061.06   \n",
       "BK50          1726.71             1671.54          1674.41   \n",
       "BX41          3048.68             3012.94          2893.91   \n",
       "...               ...                 ...              ...   \n",
       "BK93          1753.73             1705.62          1706.59   \n",
       "BK73          1791.46             1750.57          1744.32   \n",
       "MN28          1774.52             1738.78          1727.38   \n",
       "BK40          1058.62             1017.73          1011.48   \n",
       "BK99          1460.66             1405.49          1408.36   \n",
       "\n",
       "      1592 Richmond Road  256 Nelson Ave.  345 Annadale Road  \\\n",
       "QN08             2617.64          3444.35            3471.11   \n",
       "BX28             3274.89          3596.90            3272.39   \n",
       "QN55             2281.96          3108.67            3135.43   \n",
       "BK50             1895.32          2722.02            2748.79   \n",
       "BX41             3117.65          3439.67            3115.16   \n",
       "...                  ...              ...                ...   \n",
       "BK93             1922.34          2756.10            2775.81   \n",
       "BK73             1960.06          2799.28            2813.53   \n",
       "MN28             1943.12          2778.55            2619.51   \n",
       "BK40             1227.22          2066.44            2080.69   \n",
       "BK99             1629.27          2455.97            2482.74   \n",
       "\n",
       "      1560 Drumgoole Road West  7219 Amboy Road  1100 Rossville Ave  \\\n",
       "QN08                   3761.95          3933.27             3559.47   \n",
       "BX28                   3250.70          3276.83             3037.71   \n",
       "QN55                   3426.27          3597.59             3223.79   \n",
       "BK50                   3039.63          3210.95             2837.15   \n",
       "BX41                   3093.46          3119.59             2880.48   \n",
       "...                        ...              ...                 ...   \n",
       "BK93                   3066.65          3243.13             2869.33   \n",
       "BK73                   2988.50          3097.89             2775.52   \n",
       "MN28                   2747.46          2856.85             2534.48   \n",
       "BK40                   2371.53          2548.01             2174.21   \n",
       "BK99                   2773.57          2944.89             2571.09   \n",
       "\n",
       "      305 Front Street  \n",
       "QN08           2631.33  \n",
       "BX28           3351.18  \n",
       "QN55           2295.65  \n",
       "BK50           1909.01  \n",
       "BX41           3201.09  \n",
       "...                ...  \n",
       "BK93           1936.03  \n",
       "BK73           1973.75  \n",
       "MN28           1956.81  \n",
       "BK40           1240.91  \n",
       "BK99           1642.95  \n",
       "\n",
       "[195 rows x 219 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the dataframe of the distances between stations and neighborhoods\n",
    "df_distances_station_nta = pd.DataFrame(distances_stations_ntas_db)\n",
    "df_distances_station_nta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export files\n",
    "df_ntas.to_csv(\"dataset/neighborhoods_info_standardized.csv\")\n",
    "df_distances_station_nta.to_csv(\"dataset/distance_stations_neighborhoods.csv\")"
   ]
  }
 ],
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