ETL4_Data_Integrating.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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     "end_time": "2019-06-16T16:21:45.525260Z",
     "start_time": "2019-06-16T16:21:33.016984Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\magics\\pylab.py:160: UserWarning: pylab import has clobbered these variables: ['plt']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import zipfile\n",
    "import urllib\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import shapely\n",
    "from fiona.crs import from_epsg\n",
    "from shapely.geometry import Point, MultiPoint\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "from sklearn.ensemble  import RandomForestClassifier as rfc\n",
    "#from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "import pylab as pl\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "pl.rcParams['font.size'] = 20\n",
    "%pylab inline\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main Datasets: UberLyft & Ticket"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UberLyft & Ticket from 2015-01-01 to 2018-06-30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:45.879425Z",
     "start_time": "2019-06-16T16:21:45.527280Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(333768, 5)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>zone</th>\n",
       "      <th>pickup</th>\n",
       "      <th>dropoff</th>\n",
       "      <th>tickets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>48715.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  zone  pickup  dropoff  tickets\n",
       "0 2015-01-01   0.0    28.0  48715.0      0.0\n",
       "1 2015-01-01   1.0     1.0      0.0     10.0\n",
       "2 2015-01-01   3.0     9.0      0.0      0.0\n",
       "3 2015-01-01   4.0   411.0      0.0     43.0\n",
       "4 2015-01-01   6.0     2.0      0.0     22.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('output_final/data_final.csv')\n",
    "df[\"date\"] = pd.to_datetime(df[\"date\"],format='%Y-%m-%d')\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:45.891392Z",
     "start_time": "2019-06-16T16:21:45.880422Z"
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>zone</th>\n",
       "      <th>pickup</th>\n",
       "      <th>dropoff</th>\n",
       "      <th>tickets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>333763</th>\n",
       "      <td>2018-06-28</td>\n",
       "      <td>199.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333764</th>\n",
       "      <td>2018-06-29</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333765</th>\n",
       "      <td>2018-06-29</td>\n",
       "      <td>199.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333766</th>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>105.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333767</th>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>199.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date   zone  pickup  dropoff  tickets\n",
       "333763 2018-06-28  199.0     0.0      0.0     37.0\n",
       "333764 2018-06-29  105.0     0.0      0.0     72.0\n",
       "333765 2018-06-29  199.0     0.0      0.0     28.0\n",
       "333766 2018-06-30  105.0     0.0      0.0     14.0\n",
       "333767 2018-06-30  199.0     0.0      0.0     19.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ticket 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:46.104822Z",
     "start_time": "2019-06-16T16:21:45.894385Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(561439, 3)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>zone</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>08/13/2000</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>08/18/2000</td>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10/02/2000</td>\n",
       "      <td>141</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10/04/2000</td>\n",
       "      <td>165</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10/15/2000</td>\n",
       "      <td>248</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  zone  count\n",
       "0  08/13/2000    60      1\n",
       "1  08/18/2000   135      1\n",
       "2  10/02/2000   141      1\n",
       "3  10/04/2000   165      1\n",
       "4  10/15/2000   248      1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickets = pd.read_csv(\"output_tickets_zone_filterType/tickets_zone_filterType.csv\")\n",
    "print(tickets.shape)\n",
    "tickets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:46.118784Z",
     "start_time": "2019-06-16T16:21:46.107814Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(365,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-01-01', '2014-01-02', '2014-01-03', '2014-01-04',\n",
       "               '2014-01-05', '2014-01-06', '2014-01-07', '2014-01-08',\n",
       "               '2014-01-09', '2014-01-10',\n",
       "               ...\n",
       "               '2014-12-22', '2014-12-23', '2014-12-24', '2014-12-25',\n",
       "               '2014-12-26', '2014-12-27', '2014-12-28', '2014-12-29',\n",
       "               '2014-12-30', '2014-12-31'],\n",
       "              dtype='datetime64[ns]', length=365, freq='D')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates2014 = pd.date_range('01/01/2014', '12/31/2014', freq='D' )\n",
    "print(dates2014.shape)\n",
    "dates2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:47.377418Z",
     "start_time": "2019-06-16T16:21:46.120779Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(90315, 3)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>zone</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  zone  count\n",
       "0 2014-01-01     1      4\n",
       "1 2014-01-01     2     42\n",
       "2 2014-01-01     3      1\n",
       "3 2014-01-01     4     25\n",
       "4 2014-01-01     5     12"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickets[\"date\"] = pd.to_datetime(tickets[\"date\"],format='%m/%d/%Y')\n",
    "tickets = tickets.sort_values(by = 'date')\n",
    "\n",
    "ticket2014 = tickets[tickets['date'].isin(dates2014)]\n",
    "\n",
    "# data from different year may contains the row of the same date \n",
    "ticket2014 = ticket2014.groupby(['date','zone']).sum().reset_index()\n",
    "\n",
    "print(ticket2014.shape)\n",
    "ticket2014.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:47.389387Z",
     "start_time": "2019-06-16T16:21:47.378417Z"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>zone</th>\n",
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       "      <th>90310</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>258</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>90311</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>259</td>\n",
       "      <td>176</td>\n",
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       "    <tr>\n",
       "      <th>90312</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>260</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90313</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>261</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90314</th>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>262</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  zone  count\n",
       "90310 2014-12-31   258     16\n",
       "90311 2014-12-31   259    176\n",
       "90312 2014-12-31   260     31\n",
       "90313 2014-12-31   261      2\n",
       "90314 2014-12-31   262    249"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticket2014.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temporal Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:47.431275Z",
     "start_time": "2019-06-16T16:21:47.391381Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1277, 2)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup</th>\n",
       "      <th>tickets</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>48715.0</td>\n",
       "      <td>5375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>33877.0</td>\n",
       "      <td>47233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>50228.0</td>\n",
       "      <td>23933.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>34961.0</td>\n",
       "      <td>6651.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>36290.0</td>\n",
       "      <td>46178.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             pickup  tickets\n",
       "date                        \n",
       "2015-01-01  48715.0   5375.0\n",
       "2015-01-02  33877.0  47233.0\n",
       "2015-01-03  50228.0  23933.0\n",
       "2015-01-04  34961.0   6651.0\n",
       "2015-01-05  36290.0  46178.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.groupby('date').sum()[['pickup', 'tickets']]\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set required date range first\n",
    "**Avaliable from 01/01/2014 to 12/31/2018.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:47.437258Z",
     "start_time": "2019-06-16T16:21:47.432271Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1277"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_range = pd.date_range('01/01/2015', '06/30/2018', freq='D' )\n",
    "len(date_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weather data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:47.525026Z",
     "start_time": "2019-06-16T16:21:47.439253Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1277, 4)\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>WindSpeed</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>7.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>7.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>6.49</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>6.49</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>10.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            WindSpeed  Precipitation  Snow  Temperature\n",
       "2015-01-01       7.16           0.00   0.0         33.0\n",
       "2015-01-02       7.16           0.00   0.0         38.5\n",
       "2015-01-03       6.49           0.71   0.0         37.5\n",
       "2015-01-04       6.49           0.30   0.0         48.5\n",
       "2015-01-05      10.51           0.00   0.0         35.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nycweather = pd.read_csv('nycweather_2014_2018.csv')[['DATE', 'AWND', 'PRCP', 'SNOW', 'TMAX', 'TMIN']]\n",
    "nycweather['TAVG'] = nycweather[['TMAX', 'TMIN']].mean(axis=1)\n",
    "nycweather = nycweather[nycweather[nycweather['DATE']==str(date_range[0])[:10]].index[0] : nycweather[nycweather['DATE']==str(date_range[-1])[:10]].index[0]+1]\n",
    "nycweather.reset_index(drop=True, inplace=True)\n",
    "nycweather = nycweather.sort_values('DATE')\n",
    "nycweather.index = date_range\n",
    "nycweather.drop(['TMAX', 'TMIN', 'DATE'], axis=1, inplace=True)\n",
    "nycweather.columns = ['WindSpeed', 'Precipitation', 'Snow', 'Temperature']\n",
    "print(nycweather.shape)\n",
    "nycweather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:47.534036Z",
     "start_time": "2019-06-16T16:21:47.527024Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-06-26</th>\n",
       "      <td>3.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-27</th>\n",
       "      <td>3.58</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-28</th>\n",
       "      <td>2.91</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29</th>\n",
       "      <td>2.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>2.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            WindSpeed  Precipitation  Snow  Temperature\n",
       "2018-06-26       3.80           0.00   0.0         69.5\n",
       "2018-06-27       3.58           0.01   0.0         69.5\n",
       "2018-06-28       2.91           1.36   0.0         76.5\n",
       "2018-06-29       2.68           0.00   0.0         79.5\n",
       "2018-06-30       2.24           0.00   0.0         82.5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nycweather.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:48.066105Z",
     "start_time": "2019-06-16T16:21:47.534997Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,15))\n",
    "for i in range(nycweather.shape[1]):\n",
    "    ax = fig.add_subplot(4,2,i+1)\n",
    "    ax.plot(range(nycweather.shape[0]), nycweather[nycweather.columns[i]], '-', linewidth=0.8)\n",
    "    ax.set_ylabel(nycweather.columns[i], fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Holiday \n",
    "**Whether the day is holiday**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:48.135955Z",
     "start_time": "2019-06-16T16:21:48.068100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-01', '2015-01-19', '2015-02-16', '2015-05-25',\n",
       "               '2015-07-03', '2015-09-07', '2015-10-12', '2015-11-11',\n",
       "               '2015-11-26', '2015-12-25', '2016-01-01', '2016-01-18',\n",
       "               '2016-02-15', '2016-05-30', '2016-07-04', '2016-09-05',\n",
       "               '2016-10-10', '2016-11-11', '2016-11-24', '2016-12-26',\n",
       "               '2017-01-02', '2017-01-16', '2017-02-20', '2017-05-29',\n",
       "               '2017-07-04', '2017-09-04', '2017-10-09', '2017-11-10',\n",
       "               '2017-11-23', '2017-12-25', '2018-01-01', '2018-01-15',\n",
       "               '2018-02-19', '2018-05-28'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a list of holidays over this period\n",
    "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
    "calendar = USFederalHolidayCalendar()\n",
    "holidays_all = calendar.holidays(date_range[0], date_range[-1])\n",
    "holidays_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:48.323417Z",
     "start_time": "2019-06-16T16:21:48.136916Z"
    }
   },
   "outputs": [],
   "source": [
    "holidays = []\n",
    "for i in range(len(date_range)):\n",
    "    holidays.append(len(calendar.holidays(date_range[i-1], date_range[i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:48.336449Z",
     "start_time": "2019-06-16T16:21:48.324414Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1277, 1)\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>holidays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            holidays\n",
       "2015-01-01         0\n",
       "2015-01-02         1\n",
       "2015-01-03         0\n",
       "2015-01-04         0\n",
       "2015-01-05         0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holidays = pd.DataFrame(holidays)\n",
    "holidays.index = date_range\n",
    "holidays.columns = ['holidays']\n",
    "print(holidays.shape)\n",
    "holidays.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weekdays \n",
    "**Whether the day is weekday**<br>\n",
    "refer to https://cloud.tencent.com/developer/ask/176278"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:48.344390Z",
     "start_time": "2019-06-16T16:21:48.337380Z"
    }
   },
   "outputs": [],
   "source": [
    "holiday_date_list = holidays_all.date.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:48.475052Z",
     "start_time": "2019-06-16T16:21:48.345359Z"
    }
   },
   "outputs": [],
   "source": [
    "weekdays = []\n",
    "for i in range(len(date_range)):\n",
    "    weekdays.append(np.busday_count(date_range[i-1].date(), date_range[i].date(), holidays = holiday_date_list))\n",
    "weekdays[0] = weekdays[12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:48.492040Z",
     "start_time": "2019-06-16T16:21:48.479999Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1277, 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>weekdays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            weekdays\n",
       "2015-01-01         1\n",
       "2015-01-02         0\n",
       "2015-01-03         1\n",
       "2015-01-04         0\n",
       "2015-01-05         0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekdays = pd.DataFrame(weekdays)\n",
    "weekdays.index = date_range\n",
    "weekdays.columns = ['weekdays']\n",
    "print(weekdays.shape)\n",
    "weekdays.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Events data (Missing)\n",
    "**number & magnitude of events in the date**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:48.512946Z",
     "start_time": "2019-06-16T16:21:48.495957Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1277, 7)\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>WindSpeed</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>holidays</th>\n",
       "      <th>weekdays</th>\n",
       "      <th>pickup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>7.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>7.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33877.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>6.49</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>6.49</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34961.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>10.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36290.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            WindSpeed  Precipitation  Snow  Temperature  holidays  weekdays  \\\n",
       "2015-01-01       7.16           0.00   0.0         33.0         0         1   \n",
       "2015-01-02       7.16           0.00   0.0         38.5         1         0   \n",
       "2015-01-03       6.49           0.71   0.0         37.5         0         1   \n",
       "2015-01-04       6.49           0.30   0.0         48.5         0         0   \n",
       "2015-01-05      10.51           0.00   0.0         35.0         0         0   \n",
       "\n",
       "             pickup  \n",
       "2015-01-01  48715.0  \n",
       "2015-01-02  33877.0  \n",
       "2015-01-03  50228.0  \n",
       "2015-01-04  34961.0  \n",
       "2015-01-05  36290.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.merge(nycweather, holidays, left_index=True, right_index=True, how='left')\n",
    "X = pd.merge(X, weekdays, left_index=True, right_index=True, how='left')\n",
    "X = pd.merge(X, df[['pickup']], left_index=True, right_index=True, how='left')\n",
    "X = X.fillna(0)\n",
    "print(X.shape)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:48.523883Z",
     "start_time": "2019-06-16T16:21:48.513908Z"
    }
   },
   "outputs": [],
   "source": [
    "Y = df[['tickets']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:49.625936Z",
     "start_time": "2019-06-16T16:21:48.524880Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Tickets')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,15))\n",
    "for i in range(X.shape[1]):\n",
    "    ax = fig.add_subplot(3,3,i+1)\n",
    "    ax.plot(range(X.shape[0]), X[X.columns[i]], '-', linewidth=0.5)\n",
    "    ax.set_title(X.columns[i], fontsize=14)\n",
    "ax = fig.add_subplot(3,3,i+2)\n",
    "ax.plot(range(Y.shape[0]), Y, '-', linewidth=0.5, c='r')\n",
    "ax.set_title(\"Tickets\", fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:55:54.237193Z",
     "start_time": "2019-06-16T16:55:54.200292Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pd.merge(Y, X, left_index=True, right_index=True, how='inner').to_csv('output_final/temporal.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## standardize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:49.630952Z",
     "start_time": "2019-06-16T16:21:49.627928Z"
    }
   },
   "outputs": [],
   "source": [
    "def standardize(time_series):\n",
    "    return((time_series - mean(time_series)) / std(time_series))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:49.829425Z",
     "start_time": "2019-06-16T16:21:49.631917Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tickets</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>-1.938225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>1.858046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>-0.255125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>-1.822499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>1.762364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tickets\n",
       "date                \n",
       "2015-01-01 -1.938225\n",
       "2015-01-02  1.858046\n",
       "2015-01-03 -0.255125\n",
       "2015-01-04 -1.822499\n",
       "2015-01-05  1.762364"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = standardize(Y)\n",
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:49.844350Z",
     "start_time": "2019-06-16T16:21:49.830386Z"
    }
   },
   "outputs": [],
   "source": [
    "X = X.apply(standardize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:49.875266Z",
     "start_time": "2019-06-16T16:21:49.845347Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>holidays</th>\n",
       "      <th>weekdays</th>\n",
       "      <th>pickup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.277000e+03</td>\n",
       "      <td>1.277000e+03</td>\n",
       "      <td>1.277000e+03</td>\n",
       "      <td>1.277000e+03</td>\n",
       "      <td>1.277000e+03</td>\n",
       "      <td>1.277000e+03</td>\n",
       "      <td>1.277000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.535359e-16</td>\n",
       "      <td>-3.162875e-16</td>\n",
       "      <td>9.922021e-17</td>\n",
       "      <td>7.998474e-18</td>\n",
       "      <td>2.987256e-16</td>\n",
       "      <td>-1.486673e-16</td>\n",
       "      <td>1.092313e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000392e+00</td>\n",
       "      <td>1.000392e+00</td>\n",
       "      <td>1.000392e+00</td>\n",
       "      <td>1.000392e+00</td>\n",
       "      <td>1.000392e+00</td>\n",
       "      <td>1.000392e+00</td>\n",
       "      <td>1.000392e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.136430e+00</td>\n",
       "      <td>-3.680129e-01</td>\n",
       "      <td>-1.149385e-01</td>\n",
       "      <td>-2.764959e+00</td>\n",
       "      <td>-2.353123e-01</td>\n",
       "      <td>-1.486117e+00</td>\n",
       "      <td>-1.698927e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-6.809712e-01</td>\n",
       "      <td>-3.680129e-01</td>\n",
       "      <td>-1.149385e-01</td>\n",
       "      <td>-7.577164e-01</td>\n",
       "      <td>-2.353123e-01</td>\n",
       "      <td>-1.486117e+00</td>\n",
       "      <td>-8.175990e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.361904e-01</td>\n",
       "      <td>-3.680129e-01</td>\n",
       "      <td>-1.149385e-01</td>\n",
       "      <td>3.387209e-02</td>\n",
       "      <td>-2.353123e-01</td>\n",
       "      <td>6.728947e-01</td>\n",
       "      <td>-1.373594e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.020975e-01</td>\n",
       "      <td>-2.152126e-01</td>\n",
       "      <td>-1.149385e-01</td>\n",
       "      <td>9.102737e-01</td>\n",
       "      <td>-2.353123e-01</td>\n",
       "      <td>6.728947e-01</td>\n",
       "      <td>6.923560e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.319748e+00</td>\n",
       "      <td>8.891682e+00</td>\n",
       "      <td>2.607459e+01</td>\n",
       "      <td>1.843217e+00</td>\n",
       "      <td>4.249671e+00</td>\n",
       "      <td>6.728947e-01</td>\n",
       "      <td>2.715881e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          WindSpeed  Precipitation          Snow   Temperature      holidays  \\\n",
       "count  1.277000e+03   1.277000e+03  1.277000e+03  1.277000e+03  1.277000e+03   \n",
       "mean   1.535359e-16  -3.162875e-16  9.922021e-17  7.998474e-18  2.987256e-16   \n",
       "std    1.000392e+00   1.000392e+00  1.000392e+00  1.000392e+00  1.000392e+00   \n",
       "min   -2.136430e+00  -3.680129e-01 -1.149385e-01 -2.764959e+00 -2.353123e-01   \n",
       "25%   -6.809712e-01  -3.680129e-01 -1.149385e-01 -7.577164e-01 -2.353123e-01   \n",
       "50%   -1.361904e-01  -3.680129e-01 -1.149385e-01  3.387209e-02 -2.353123e-01   \n",
       "75%    5.020975e-01  -2.152126e-01 -1.149385e-01  9.102737e-01 -2.353123e-01   \n",
       "max    5.319748e+00   8.891682e+00  2.607459e+01  1.843217e+00  4.249671e+00   \n",
       "\n",
       "           weekdays        pickup  \n",
       "count  1.277000e+03  1.277000e+03  \n",
       "mean  -1.486673e-16  1.092313e-15  \n",
       "std    1.000392e+00  1.000392e+00  \n",
       "min   -1.486117e+00 -1.698927e+00  \n",
       "25%   -1.486117e+00 -8.175990e-01  \n",
       "50%    6.728947e-01 -1.373594e-01  \n",
       "75%    6.728947e-01  6.923560e-01  \n",
       "max    6.728947e-01  2.715881e+00  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## train & test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:49.892255Z",
     "start_time": "2019-06-16T16:21:49.877261Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1096\n",
      "181\n",
      "1096\n",
      "181\n"
     ]
    }
   ],
   "source": [
    "X_train = X[:'2017-12-31']\n",
    "X_test = X['2018-01-01':]\n",
    "print(len(X_train))\n",
    "print(len(X_test))\n",
    "\n",
    "Y_train = Y[:'2017-12-31']\n",
    "Y_test = Y['2018-01-01':]\n",
    "print(len(Y_train))\n",
    "print(len(Y_test))\n",
    "\n",
    "test = pd.merge(X_test, Y_test, left_index=True, right_index=True).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Taxi Zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:21:49.971021Z",
     "start_time": "2019-06-16T16:21:49.893218Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file in place, you can continue\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile(\"taxi_zones/taxi_zones.shp\"):    \n",
    "    !curl https://s3.amazonaws.com/nyc-tlc/misc/taxi_zones.zip > taxi_zones.zip\n",
    "    !mv taxi_zones.zip taxi_zones\n",
    "    !unzip -o taxi_zones/taxi_zones.zip -d taxi_zones\n",
    "else:\n",
    "    print(\"file in place, you can continue\")\n",
    "\n",
    "taxi_zones = gpd.read_file(\"taxi_zones/taxi_zones.shp\")\n",
    "# taxi_zones.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:43.459578Z",
     "start_time": "2019-06-16T16:21:49.972009Z"
    }
   },
   "outputs": [],
   "source": [
    "taxi_zones.crs = from_epsg(2263)\n",
    "taxi_zones = taxi_zones.to_crs(epsg=4326)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:43.497474Z",
     "start_time": "2019-06-16T16:22:43.460615Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 7)\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>OBJECTID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>zone</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>borough</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.116357</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>Newark Airport</td>\n",
       "      <td>1</td>\n",
       "      <td>EWR</td>\n",
       "      <td>POLYGON ((-74.18445450453645 40.69500424662989...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.084341</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.84792754846802 40.8713505269578,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-73.97177554799167 40.72582954148978...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.092146</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>POLYGON ((-74.17421887421997 40.56257630700625...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  Shape_Leng  Shape_Area                     zone  LocationID  \\\n",
       "0         1    0.116357    0.000782           Newark Airport           1   \n",
       "1         2    0.433470    0.004866              Jamaica Bay           2   \n",
       "2         3    0.084341    0.000314  Allerton/Pelham Gardens           3   \n",
       "3         4    0.043567    0.000112            Alphabet City           4   \n",
       "4         5    0.092146    0.000498            Arden Heights           5   \n",
       "\n",
       "         borough                                           geometry  \n",
       "0            EWR  POLYGON ((-74.18445450453645 40.69500424662989...  \n",
       "1         Queens  (POLYGON ((-73.82337735819013 40.6389952954719...  \n",
       "2          Bronx  POLYGON ((-73.84792754846802 40.8713505269578,...  \n",
       "3      Manhattan  POLYGON ((-73.97177554799167 40.72582954148978...  \n",
       "4  Staten Island  POLYGON ((-74.17421887421997 40.56257630700625...  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(taxi_zones.shape)\n",
    "taxi_zones.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:43.735837Z",
     "start_time": "2019-06-16T16:22:43.498472Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc65757048>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taxi_zones.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACS Census Data\n",
    "Population & employment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Census_Tracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:43.744814Z",
     "start_time": "2019-06-16T16:22:43.736836Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file in place, you can continue\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile(\"data/Census_Tracts_2010.gz\"):\n",
    "    # https://www1.nyc.gov/assets/planning/download/zip/data-maps/open-data/nyct2010_19a.zip\n",
    "    url = 'https://data.cityofnewyork.us/api/geospatial/fxpq-c8ku?method=export&format=Shapefile'\n",
    "    urllib.request.urlretrieve(url, \"Census_Tracts_2010.gz\")\n",
    "    !mv Census_Tracts_2010.gz data\n",
    "    !unzip -o data/Census_Tracts_2010.gz -d data/Census_Tracts_2010\n",
    "else:\n",
    "    print(\"file in place, you can continue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:44.049157Z",
     "start_time": "2019-06-16T16:22:43.745810Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2166, 12)\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>boro_code</th>\n",
       "      <th>boro_ct201</th>\n",
       "      <th>boro_name</th>\n",
       "      <th>cdeligibil</th>\n",
       "      <th>ct2010</th>\n",
       "      <th>ctlabel</th>\n",
       "      <th>ntacode</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>puma</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>5000900</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>E</td>\n",
       "      <td>000900</td>\n",
       "      <td>9</td>\n",
       "      <td>SI22</td>\n",
       "      <td>West New Brighton-New Brighton-St. George</td>\n",
       "      <td>3903</td>\n",
       "      <td>2.497010e+06</td>\n",
       "      <td>7729.016794</td>\n",
       "      <td>POLYGON ((-74.07920577013245 40.64343078374567...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1009800</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>I</td>\n",
       "      <td>009800</td>\n",
       "      <td>98</td>\n",
       "      <td>MN19</td>\n",
       "      <td>Turtle Bay-East Midtown</td>\n",
       "      <td>3808</td>\n",
       "      <td>1.906016e+06</td>\n",
       "      <td>5534.199811</td>\n",
       "      <td>POLYGON ((-73.96432543478758 40.75638153099091...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1010000</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>I</td>\n",
       "      <td>010000</td>\n",
       "      <td>100</td>\n",
       "      <td>MN19</td>\n",
       "      <td>Turtle Bay-East Midtown</td>\n",
       "      <td>3808</td>\n",
       "      <td>1.860938e+06</td>\n",
       "      <td>5692.168737</td>\n",
       "      <td>POLYGON ((-73.96802436915851 40.75957814005282...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1010200</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>I</td>\n",
       "      <td>010200</td>\n",
       "      <td>102</td>\n",
       "      <td>MN17</td>\n",
       "      <td>Midtown-Midtown South</td>\n",
       "      <td>3807</td>\n",
       "      <td>1.860993e+06</td>\n",
       "      <td>5687.802439</td>\n",
       "      <td>POLYGON ((-73.97124277307127 40.76093641847906...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1010400</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>I</td>\n",
       "      <td>010400</td>\n",
       "      <td>104</td>\n",
       "      <td>MN17</td>\n",
       "      <td>Midtown-Midtown South</td>\n",
       "      <td>3807</td>\n",
       "      <td>1.864600e+06</td>\n",
       "      <td>5693.036367</td>\n",
       "      <td>POLYGON ((-73.97445730550224 40.76229308352487...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  boro_code boro_ct201      boro_name cdeligibil  ct2010 ctlabel ntacode  \\\n",
       "0         5    5000900  Staten Island          E  000900       9    SI22   \n",
       "1         1    1009800      Manhattan          I  009800      98    MN19   \n",
       "2         1    1010000      Manhattan          I  010000     100    MN19   \n",
       "3         1    1010200      Manhattan          I  010200     102    MN17   \n",
       "4         1    1010400      Manhattan          I  010400     104    MN17   \n",
       "\n",
       "                                     ntaname  puma    shape_area   shape_leng  \\\n",
       "0  West New Brighton-New Brighton-St. George  3903  2.497010e+06  7729.016794   \n",
       "1                    Turtle Bay-East Midtown  3808  1.906016e+06  5534.199811   \n",
       "2                    Turtle Bay-East Midtown  3808  1.860938e+06  5692.168737   \n",
       "3                      Midtown-Midtown South  3807  1.860993e+06  5687.802439   \n",
       "4                      Midtown-Midtown South  3807  1.864600e+06  5693.036367   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-74.07920577013245 40.64343078374567...  \n",
       "1  POLYGON ((-73.96432543478758 40.75638153099091...  \n",
       "2  POLYGON ((-73.96802436915851 40.75957814005282...  \n",
       "3  POLYGON ((-73.97124277307127 40.76093641847906...  \n",
       "4  POLYGON ((-73.97445730550224 40.76229308352487...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = ! ls data/Census_Tracts_2010 | grep .shp\n",
    "CensusTracts = gpd.GeoDataFrame.from_file('data/Census_Tracts_2010/' + filename[0])\n",
    "# CensusTracts = CensusTracts[CensusTracts['boro_name']=='Manhattan']\n",
    "print(CensusTracts.shape)\n",
    "CensusTracts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ACS Census Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:44.058134Z",
     "start_time": "2019-06-16T16:22:44.052150Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file in place, you can continue\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile(\"data/nyc_census_tracts.csv\"):\n",
    "    ! curl https://raw.githubusercontent.com/JunjieTsai/MLC2019_Project/master/data/nyc_census_tracts.csv > data/nyc_census_tracts.csv\n",
    "else:\n",
    "    print(\"file in place, you can continue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:44.124955Z",
     "start_time": "2019-06-16T16:22:44.061125Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2167, 37)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CensusTract</th>\n",
       "      <th>County</th>\n",
       "      <th>Borough</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Native</th>\n",
       "      <th>...</th>\n",
       "      <th>OtherTransp</th>\n",
       "      <th>WorkAtHome</th>\n",
       "      <th>MeanCommute</th>\n",
       "      <th>Employed</th>\n",
       "      <th>PrivateWork</th>\n",
       "      <th>PublicWork</th>\n",
       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>ct2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36005000100</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>7703</td>\n",
       "      <td>7133</td>\n",
       "      <td>570</td>\n",
       "      <td>29.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>60.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36005000200</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>5403</td>\n",
       "      <td>2659</td>\n",
       "      <td>2744</td>\n",
       "      <td>75.8</td>\n",
       "      <td>2.3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2308</td>\n",
       "      <td>80.8</td>\n",
       "      <td>16.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>000200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36005000400</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>5915</td>\n",
       "      <td>2896</td>\n",
       "      <td>3019</td>\n",
       "      <td>62.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2675</td>\n",
       "      <td>71.7</td>\n",
       "      <td>25.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>9.5</td>\n",
       "      <td>000400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36005001600</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>5879</td>\n",
       "      <td>2558</td>\n",
       "      <td>3321</td>\n",
       "      <td>65.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38.8</td>\n",
       "      <td>2120</td>\n",
       "      <td>75.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>001600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36005001900</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>2591</td>\n",
       "      <td>1206</td>\n",
       "      <td>1385</td>\n",
       "      <td>55.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>45.4</td>\n",
       "      <td>1083</td>\n",
       "      <td>76.8</td>\n",
       "      <td>15.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.2</td>\n",
       "      <td>001900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CensusTract County Borough  TotalPop   Men  Women  Hispanic  White  Black  \\\n",
       "0  36005000100  Bronx   Bronx      7703  7133    570      29.9    6.1   60.9   \n",
       "1  36005000200  Bronx   Bronx      5403  2659   2744      75.8    2.3   16.0   \n",
       "2  36005000400  Bronx   Bronx      5915  2896   3019      62.7    3.6   30.7   \n",
       "3  36005001600  Bronx   Bronx      5879  2558   3321      65.1    1.6   32.4   \n",
       "4  36005001900  Bronx   Bronx      2591  1206   1385      55.4    9.0   29.0   \n",
       "\n",
       "   Native   ...    OtherTransp  WorkAtHome  MeanCommute  Employed  \\\n",
       "0     0.2   ...            NaN         NaN          NaN         0   \n",
       "1     0.0   ...            0.0         0.0         43.0      2308   \n",
       "2     0.0   ...            0.5         2.1         45.0      2675   \n",
       "3     0.0   ...            1.6         1.7         38.8      2120   \n",
       "4     0.0   ...            2.4         6.2         45.4      1083   \n",
       "\n",
       "   PrivateWork  PublicWork  SelfEmployed  FamilyWork  Unemployment  ct2010  \n",
       "0          NaN         NaN           NaN         NaN           NaN  000100  \n",
       "1         80.8        16.2           2.9         0.0           7.7  000200  \n",
       "2         71.7        25.3           2.5         0.6           9.5  000400  \n",
       "3         75.0        21.3           3.8         0.0           8.7  001600  \n",
       "4         76.8        15.5           7.7         0.0          19.2  001900  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_tracts_data = pd.read_csv(\"data/nyc_census_tracts.csv\")\n",
    "# census_tracts_data = census_tracts_data[census_tracts_data['Borough']=='Manhattan']\n",
    "census_tracts_data['CensusTract'] = census_tracts_data['CensusTract'].astype(str)\n",
    "census_tracts_data['ct2010'] = census_tracts_data['CensusTract'].apply(lambda x: x[5:])\n",
    "print(census_tracts_data.shape)\n",
    "census_tracts_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:44.168837Z",
     "start_time": "2019-06-16T16:22:44.125951Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4671, 48)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>boro_code</th>\n",
       "      <th>boro_ct201</th>\n",
       "      <th>boro_name</th>\n",
       "      <th>cdeligibil</th>\n",
       "      <th>ct2010</th>\n",
       "      <th>ctlabel</th>\n",
       "      <th>ntacode</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>puma</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>...</th>\n",
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       "      <th>OtherTransp</th>\n",
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       "      <th>MeanCommute</th>\n",
       "      <th>Employed</th>\n",
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       "      <th>PublicWork</th>\n",
       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>5000900</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>E</td>\n",
       "      <td>000900</td>\n",
       "      <td>9</td>\n",
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       "      <td>West New Brighton-New Brighton-St. George</td>\n",
       "      <td>3903</td>\n",
       "      <td>2.497010e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>8.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>2632</td>\n",
       "      <td>86.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5000900</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>E</td>\n",
       "      <td>000900</td>\n",
       "      <td>9</td>\n",
       "      <td>SI22</td>\n",
       "      <td>West New Brighton-New Brighton-St. George</td>\n",
       "      <td>3903</td>\n",
       "      <td>2.497010e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>24.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>10.4</td>\n",
       "      <td>26.1</td>\n",
       "      <td>1120</td>\n",
       "      <td>89.3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.8</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>5000900</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>E</td>\n",
       "      <td>000900</td>\n",
       "      <td>9</td>\n",
       "      <td>SI22</td>\n",
       "      <td>West New Brighton-New Brighton-St. George</td>\n",
       "      <td>3903</td>\n",
       "      <td>2.497010e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>49.6</td>\n",
       "      <td>807</td>\n",
       "      <td>74.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3000900</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>I</td>\n",
       "      <td>000900</td>\n",
       "      <td>9</td>\n",
       "      <td>BK09</td>\n",
       "      <td>Brooklyn Heights-Cobble Hill</td>\n",
       "      <td>4004</td>\n",
       "      <td>1.737777e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>8.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>2632</td>\n",
       "      <td>86.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>3000900</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>I</td>\n",
       "      <td>000900</td>\n",
       "      <td>9</td>\n",
       "      <td>BK09</td>\n",
       "      <td>Brooklyn Heights-Cobble Hill</td>\n",
       "      <td>4004</td>\n",
       "      <td>1.737777e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>24.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>10.4</td>\n",
       "      <td>26.1</td>\n",
       "      <td>1120</td>\n",
       "      <td>89.3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  boro_code boro_ct201      boro_name cdeligibil  ct2010 ctlabel ntacode  \\\n",
       "0         5    5000900  Staten Island          E  000900       9    SI22   \n",
       "1         5    5000900  Staten Island          E  000900       9    SI22   \n",
       "2         5    5000900  Staten Island          E  000900       9    SI22   \n",
       "3         3    3000900       Brooklyn          I  000900       9    BK09   \n",
       "4         3    3000900       Brooklyn          I  000900       9    BK09   \n",
       "\n",
       "                                     ntaname  puma    shape_area  \\\n",
       "0  West New Brighton-New Brighton-St. George  3903  2.497010e+06   \n",
       "1  West New Brighton-New Brighton-St. George  3903  2.497010e+06   \n",
       "2  West New Brighton-New Brighton-St. George  3903  2.497010e+06   \n",
       "3               Brooklyn Heights-Cobble Hill  4004  1.737777e+06   \n",
       "4               Brooklyn Heights-Cobble Hill  4004  1.737777e+06   \n",
       "\n",
       "       ...       Walk OtherTransp WorkAtHome MeanCommute Employed  \\\n",
       "0      ...        8.9         3.8        7.0        30.4     2632   \n",
       "1      ...       24.1         3.3       10.4        26.1     1120   \n",
       "2      ...        5.9         0.7        4.0        49.6      807   \n",
       "3      ...        8.9         3.8        7.0        30.4     2632   \n",
       "4      ...       24.1         3.3       10.4        26.1     1120   \n",
       "\n",
       "   PrivateWork  PublicWork  SelfEmployed  FamilyWork  Unemployment  \n",
       "0         86.8         7.8           5.4         0.0           5.1  \n",
       "1         89.3         4.6           6.1         0.0           2.8  \n",
       "2         74.0        20.3           5.1         0.6          11.0  \n",
       "3         86.8         7.8           5.4         0.0           5.1  \n",
       "4         89.3         4.6           6.1         0.0           2.8  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge Census Tracts and ACS Census Data\n",
    "CensusTracts = pd.merge(CensusTracts, census_tracts_data, left_on='ct2010', right_on='ct2010', how='outer')\n",
    "print(CensusTracts.shape)\n",
    "CensusTracts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:45.486315Z",
     "start_time": "2019-06-16T16:22:44.170832Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc65359e80>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "CensusTracts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:46.148542Z",
     "start_time": "2019-06-16T16:22:45.488309Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\geopandas\\tools\\sjoin.py:44: UserWarning: CRS of frames being joined does not match!\n",
      "  warn('CRS of frames being joined does not match!')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9210, 49)\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>OBJECTID</th>\n",
       "      <th>zone</th>\n",
       "      <th>borough</th>\n",
       "      <th>geometry</th>\n",
       "      <th>boro_code</th>\n",
       "      <th>boro_ct201</th>\n",
       "      <th>boro_name</th>\n",
       "      <th>cdeligibil</th>\n",
       "      <th>ct2010</th>\n",
       "      <th>ctlabel</th>\n",
       "      <th>...</th>\n",
       "      <th>Walk</th>\n",
       "      <th>OtherTransp</th>\n",
       "      <th>WorkAtHome</th>\n",
       "      <th>MeanCommute</th>\n",
       "      <th>Employed</th>\n",
       "      <th>PrivateWork</th>\n",
       "      <th>PublicWork</th>\n",
       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Newark Airport</td>\n",
       "      <td>EWR</td>\n",
       "      <td>POLYGON ((-74.18445450453645 40.69500424662989...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>4</td>\n",
       "      <td>4107201</td>\n",
       "      <td>Queens</td>\n",
       "      <td>I</td>\n",
       "      <td>107201</td>\n",
       "      <td>1072.01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>42.0</td>\n",
       "      <td>933.0</td>\n",
       "      <td>64.2</td>\n",
       "      <td>33.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>4</td>\n",
       "      <td>4107202</td>\n",
       "      <td>Queens</td>\n",
       "      <td>I</td>\n",
       "      <td>107202</td>\n",
       "      <td>1072.02</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>4</td>\n",
       "      <td>4071600</td>\n",
       "      <td>Queens</td>\n",
       "      <td>I</td>\n",
       "      <td>071600</td>\n",
       "      <td>716</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>3</td>\n",
       "      <td>3070203</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>I</td>\n",
       "      <td>070203</td>\n",
       "      <td>702.03</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID            zone borough  \\\n",
       "0         1  Newark Airport     EWR   \n",
       "1         2     Jamaica Bay  Queens   \n",
       "1         2     Jamaica Bay  Queens   \n",
       "1         2     Jamaica Bay  Queens   \n",
       "1         2     Jamaica Bay  Queens   \n",
       "\n",
       "                                            geometry boro_code boro_ct201  \\\n",
       "0  POLYGON ((-74.18445450453645 40.69500424662989...       NaN        NaN   \n",
       "1  (POLYGON ((-73.82337735819013 40.6389952954719...         4    4107201   \n",
       "1  (POLYGON ((-73.82337735819013 40.6389952954719...         4    4107202   \n",
       "1  (POLYGON ((-73.82337735819013 40.6389952954719...         4    4071600   \n",
       "1  (POLYGON ((-73.82337735819013 40.6389952954719...         3    3070203   \n",
       "\n",
       "  boro_name cdeligibil  ct2010  ctlabel      ...      Walk OtherTransp  \\\n",
       "0       NaN        NaN     NaN      NaN      ...       NaN         NaN   \n",
       "1    Queens          I  107201  1072.01      ...       1.1         1.3   \n",
       "1    Queens          I  107202  1072.02      ...       NaN         NaN   \n",
       "1    Queens          I  071600      716      ...       NaN         NaN   \n",
       "1  Brooklyn          I  070203   702.03      ...       NaN         NaN   \n",
       "\n",
       "  WorkAtHome MeanCommute Employed PrivateWork  PublicWork  SelfEmployed  \\\n",
       "0        NaN         NaN      NaN         NaN         NaN           NaN   \n",
       "1        0.7        42.0    933.0        64.2        33.4           2.4   \n",
       "1        NaN         NaN      0.0         NaN         NaN           NaN   \n",
       "1        NaN         NaN      0.0         NaN         NaN           NaN   \n",
       "1        NaN         NaN      0.0         NaN         NaN           NaN   \n",
       "\n",
       "   FamilyWork  Unemployment  \n",
       "0         NaN           NaN  \n",
       "1         0.0          11.6  \n",
       "1         NaN           NaN  \n",
       "1         NaN           NaN  \n",
       "1         NaN           NaN  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sjoin Census Data to Taxi Zones\n",
    "df_spatial = gpd.sjoin(taxi_zones, CensusTracts, how='left')\n",
    "df_spatial.drop(['LocationID', 'Shape_Leng', 'Shape_Area', 'shape_area', 'shape_leng', 'index_right'], axis=1, inplace=True)\n",
    "print(df_spatial.shape)\n",
    "df_spatial.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:46.179463Z",
     "start_time": "2019-06-16T16:22:46.149539Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 34)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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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>OBJECTID</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>White</th>\n",
       "      <th>Black</th>\n",
       "      <th>Native</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Citizen</th>\n",
       "      <th>...</th>\n",
       "      <th>Walk</th>\n",
       "      <th>OtherTransp</th>\n",
       "      <th>WorkAtHome</th>\n",
       "      <th>MeanCommute</th>\n",
       "      <th>Employed</th>\n",
       "      <th>PrivateWork</th>\n",
       "      <th>PublicWork</th>\n",
       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>248.750000</td>\n",
       "      <td>279.250000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>94.400000</td>\n",
       "      <td>0.100000</td>\n",
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       "      <td>408.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>233.250000</td>\n",
       "      <td>64.200000</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.600000</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
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       "      <td>2762.852941</td>\n",
       "      <td>...</td>\n",
       "      <td>6.420588</td>\n",
       "      <td>1.708824</td>\n",
       "      <td>2.935294</td>\n",
       "      <td>43.608824</td>\n",
       "      <td>1709.000000</td>\n",
       "      <td>77.979412</td>\n",
       "      <td>17.741176</td>\n",
       "      <td>4.214706</td>\n",
       "      <td>0.070588</td>\n",
       "      <td>13.126471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4461.681818</td>\n",
       "      <td>2030.363636</td>\n",
       "      <td>2431.318182</td>\n",
       "      <td>37.200000</td>\n",
       "      <td>31.959091</td>\n",
       "      <td>13.486364</td>\n",
       "      <td>0.054545</td>\n",
       "      <td>13.813636</td>\n",
       "      <td>3268.227273</td>\n",
       "      <td>...</td>\n",
       "      <td>14.295455</td>\n",
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       "      <td>0.218182</td>\n",
       "      <td>8.936364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5654.111111</td>\n",
       "      <td>2756.111111</td>\n",
       "      <td>2898.000000</td>\n",
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       "      <td>64.955556</td>\n",
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       "      <td>0.088889</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>4058.222222</td>\n",
       "      <td>...</td>\n",
       "      <td>6.622222</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>7.622222</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>2531.333333</td>\n",
       "      <td>76.644444</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID     TotalPop          Men        Women   Hispanic      White  \\\n",
       "0         1          NaN          NaN          NaN        NaN        NaN   \n",
       "1         2   528.000000   248.750000   279.250000   4.900000  94.400000   \n",
       "2         3  4042.735294  1848.558824  2194.176471  24.332353  26.144118   \n",
       "3         4  4461.681818  2030.363636  2431.318182  37.200000  31.959091   \n",
       "4         5  5654.111111  2756.111111  2898.000000  14.744444  64.955556   \n",
       "\n",
       "       Black    Native      Asian      Citizen      ...            Walk  \\\n",
       "0        NaN       NaN        NaN          NaN      ...             NaN   \n",
       "1   0.100000  0.000000   0.300000   408.250000      ...        1.100000   \n",
       "2  38.247059  0.197059   8.635294  2762.852941      ...        6.420588   \n",
       "3  13.486364  0.054545  13.813636  3268.227273      ...       14.295455   \n",
       "4  11.033333  0.088889   7.833333  4058.222222      ...        6.622222   \n",
       "\n",
       "   OtherTransp  WorkAtHome  MeanCommute     Employed  PrivateWork  PublicWork  \\\n",
       "0          NaN         NaN          NaN          NaN          NaN         NaN   \n",
       "1     1.300000    0.700000    42.000000   233.250000    64.200000   33.400000   \n",
       "2     1.708824    2.935294    43.608824  1709.000000    77.979412   17.741176   \n",
       "3     4.063636    2.890909    38.695238  2248.227273    77.663636   15.427273   \n",
       "4     1.800000    7.622222    41.950000  2531.333333    76.644444   17.644444   \n",
       "\n",
       "   SelfEmployed  FamilyWork  Unemployment  \n",
       "0           NaN         NaN           NaN  \n",
       "1      2.400000    0.000000     11.600000  \n",
       "2      4.214706    0.070588     13.126471  \n",
       "3      6.690909    0.218182      8.936364  \n",
       "4      5.711111    0.000000      8.000000  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# groupby Taxi Zones\n",
    "# df_spatial.dropna(inplace=True)\n",
    "df_spatial = df_spatial.groupby('OBJECTID').mean()\n",
    "df_spatial = df_spatial.reset_index()\n",
    "print(df_spatial.shape)\n",
    "df_spatial.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:46.240296Z",
     "start_time": "2019-06-16T16:22:46.181454Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 40)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>zone</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>borough</th>\n",
       "      <th>geometry</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>...</th>\n",
       "      <th>Walk</th>\n",
       "      <th>OtherTransp</th>\n",
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       "      <th>SelfEmployed</th>\n",
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       "      <th>Unemployment</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
       "      <td>0.116357</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>Newark Airport</td>\n",
       "      <td>1</td>\n",
       "      <td>EWR</td>\n",
       "      <td>POLYGON ((-74.18445450453645 40.69500424662989...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>248.750000</td>\n",
       "      <td>279.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>233.250000</td>\n",
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       "      <td>2.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.084341</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.84792754846802 40.8713505269578,...</td>\n",
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       "      <td>1848.558824</td>\n",
       "      <td>2194.176471</td>\n",
       "      <td>...</td>\n",
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       "      <td>43.608824</td>\n",
       "      <td>1709.000000</td>\n",
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       "      <td>0.070588</td>\n",
       "      <td>13.126471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-73.97177554799167 40.72582954148978...</td>\n",
       "      <td>4461.681818</td>\n",
       "      <td>2030.363636</td>\n",
       "      <td>2431.318182</td>\n",
       "      <td>...</td>\n",
       "      <td>14.295455</td>\n",
       "      <td>4.063636</td>\n",
       "      <td>2.890909</td>\n",
       "      <td>38.695238</td>\n",
       "      <td>2248.227273</td>\n",
       "      <td>77.663636</td>\n",
       "      <td>15.427273</td>\n",
       "      <td>6.690909</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>8.936364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.092146</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>POLYGON ((-74.17421887421997 40.56257630700625...</td>\n",
       "      <td>5654.111111</td>\n",
       "      <td>2756.111111</td>\n",
       "      <td>2898.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.622222</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>7.622222</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>2531.333333</td>\n",
       "      <td>76.644444</td>\n",
       "      <td>17.644444</td>\n",
       "      <td>5.711111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  Shape_Leng  Shape_Area                     zone  LocationID  \\\n",
       "0         1    0.116357    0.000782           Newark Airport           1   \n",
       "1         2    0.433470    0.004866              Jamaica Bay           2   \n",
       "2         3    0.084341    0.000314  Allerton/Pelham Gardens           3   \n",
       "3         4    0.043567    0.000112            Alphabet City           4   \n",
       "4         5    0.092146    0.000498            Arden Heights           5   \n",
       "\n",
       "         borough                                           geometry  \\\n",
       "0            EWR  POLYGON ((-74.18445450453645 40.69500424662989...   \n",
       "1         Queens  (POLYGON ((-73.82337735819013 40.6389952954719...   \n",
       "2          Bronx  POLYGON ((-73.84792754846802 40.8713505269578,...   \n",
       "3      Manhattan  POLYGON ((-73.97177554799167 40.72582954148978...   \n",
       "4  Staten Island  POLYGON ((-74.17421887421997 40.56257630700625...   \n",
       "\n",
       "      TotalPop          Men        Women      ...            Walk  \\\n",
       "0          NaN          NaN          NaN      ...             NaN   \n",
       "1   528.000000   248.750000   279.250000      ...        1.100000   \n",
       "2  4042.735294  1848.558824  2194.176471      ...        6.420588   \n",
       "3  4461.681818  2030.363636  2431.318182      ...       14.295455   \n",
       "4  5654.111111  2756.111111  2898.000000      ...        6.622222   \n",
       "\n",
       "   OtherTransp  WorkAtHome  MeanCommute     Employed  PrivateWork  PublicWork  \\\n",
       "0          NaN         NaN          NaN          NaN          NaN         NaN   \n",
       "1     1.300000    0.700000    42.000000   233.250000    64.200000   33.400000   \n",
       "2     1.708824    2.935294    43.608824  1709.000000    77.979412   17.741176   \n",
       "3     4.063636    2.890909    38.695238  2248.227273    77.663636   15.427273   \n",
       "4     1.800000    7.622222    41.950000  2531.333333    76.644444   17.644444   \n",
       "\n",
       "   SelfEmployed  FamilyWork  Unemployment  \n",
       "0           NaN         NaN           NaN  \n",
       "1      2.400000    0.000000     11.600000  \n",
       "2      4.214706    0.070588     13.126471  \n",
       "3      6.690909    0.218182      8.936364  \n",
       "4      5.711111    0.000000      8.000000  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial = pd.merge(taxi_zones, df_spatial, on = 'OBJECTID', how='left')\n",
    "print(df_spatial.shape)\n",
    "df_spatial.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:46.247279Z",
     "start_time": "2019-06-16T16:22:46.241294Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['OBJECTID', 'Shape_Leng', 'Shape_Area', 'zone', 'LocationID', 'borough',\n",
       "       'geometry', 'TotalPop', 'Men', 'Women', 'Hispanic', 'White', 'Black',\n",
       "       'Native', 'Asian', 'Citizen', 'Income', 'IncomeErr', 'IncomePerCap',\n",
       "       'IncomePerCapErr', 'Poverty', 'ChildPoverty', 'Professional', 'Service',\n",
       "       'Office', 'Construction', 'Production', 'Drive', 'Carpool', 'Transit',\n",
       "       'Walk', 'OtherTransp', 'WorkAtHome', 'MeanCommute', 'Employed',\n",
       "       'PrivateWork', 'PublicWork', 'SelfEmployed', 'FamilyWork',\n",
       "       'Unemployment'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:46.260243Z",
     "start_time": "2019-06-16T16:22:46.249273Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file in place, you can continue\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile(\"data/crime.csv\"):\n",
    "    ! curl https://data.cityofnewyork.us/api/views/5uac-w243/rows.csv?accessType=DOWNLOAD > data/crime.csv\n",
    "else:\n",
    "    print(\"file in place, you can continue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:48.204363Z",
     "start_time": "2019-06-16T16:22:46.261240Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105069, 2)\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>LAW_CAT_CD</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>POINT (-73.93752515999995 40.76339148500005)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>POINT (-73.93752515999995 40.76339148500005)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FELONY</td>\n",
       "      <td>POINT (-73.84804910999998 40.83677644500005)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FELONY</td>\n",
       "      <td>POINT (-73.83091313699998 40.76670155600004)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MISDEMEANOR</td>\n",
       "      <td>POINT (-73.92237572199997 40.85214118700002)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    LAW_CAT_CD                                      geometry\n",
       "0  MISDEMEANOR  POINT (-73.93752515999995 40.76339148500005)\n",
       "1  MISDEMEANOR  POINT (-73.93752515999995 40.76339148500005)\n",
       "2       FELONY  POINT (-73.84804910999998 40.83677644500005)\n",
       "3       FELONY  POINT (-73.83091313699998 40.76670155600004)\n",
       "4  MISDEMEANOR  POINT (-73.92237572199997 40.85214118700002)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime = pd.read_csv(\"data/crime.csv\", usecols = ['BORO_NM', 'LAW_CAT_CD','Latitude', 'Longitude'])\n",
    "# crime = crime[crime['BORO_NM']=='MANHATTAN']\n",
    "crime['lonlat']=list(zip(crime.Longitude,crime.Latitude))\n",
    "crime['geometry']=crime[['lonlat']].applymap(lambda x:Point(x))\n",
    "crime = gpd.GeoDataFrame(crime[['LAW_CAT_CD', 'geometry']])\n",
    "print(crime.shape)\n",
    "crime.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:51.854046Z",
     "start_time": "2019-06-16T16:22:48.205346Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\geopandas\\tools\\sjoin.py:44: UserWarning: CRS of frames being joined does not match!\n",
      "  warn('CRS of frames being joined does not match!')\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:2048: RuntimeWarning: invalid value encountered in ? (vectorized)\n",
      "  outputs = ufunc(*inputs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105060, 42)\n",
      "Wall time: 3.64 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_spatial_crime = gpd.sjoin(df_spatial, crime, how='left')\n",
    "print(df_spatial_crime.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:51.874989Z",
     "start_time": "2019-06-16T16:22:51.855043Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105060, 2)\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>OBJECTID</th>\n",
       "      <th>LAW_CAT_CD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>FELONY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>MISDEMEANOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>FELONY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID   LAW_CAT_CD\n",
       "0         1          NaN\n",
       "1         2          NaN\n",
       "2         3       FELONY\n",
       "2         3  MISDEMEANOR\n",
       "2         3       FELONY"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial_crime = df_spatial_crime[['OBJECTID', 'LAW_CAT_CD']]\n",
    "print(df_spatial_crime.shape)\n",
    "df_spatial_crime.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:51.996664Z",
     "start_time": "2019-06-16T16:22:51.876984Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 43)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>zone</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>borough</th>\n",
       "      <th>geometry</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>...</th>\n",
       "      <th>MeanCommute</th>\n",
       "      <th>Employed</th>\n",
       "      <th>PrivateWork</th>\n",
       "      <th>PublicWork</th>\n",
       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>FELONY</th>\n",
       "      <th>VIOLATION</th>\n",
       "      <th>MISDEMEANOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.116357</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>Newark Airport</td>\n",
       "      <td>1</td>\n",
       "      <td>EWR</td>\n",
       "      <td>POLYGON ((-74.18445450453645 40.69500424662989...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>248.750000</td>\n",
       "      <td>279.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>233.250000</td>\n",
       "      <td>64.200000</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.084341</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.84792754846802 40.8713505269578,...</td>\n",
       "      <td>4042.735294</td>\n",
       "      <td>1848.558824</td>\n",
       "      <td>2194.176471</td>\n",
       "      <td>...</td>\n",
       "      <td>43.608824</td>\n",
       "      <td>1709.000000</td>\n",
       "      <td>77.979412</td>\n",
       "      <td>17.741176</td>\n",
       "      <td>4.214706</td>\n",
       "      <td>0.070588</td>\n",
       "      <td>13.126471</td>\n",
       "      <td>79.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-73.97177554799167 40.72582954148978...</td>\n",
       "      <td>4461.681818</td>\n",
       "      <td>2030.363636</td>\n",
       "      <td>2431.318182</td>\n",
       "      <td>...</td>\n",
       "      <td>38.695238</td>\n",
       "      <td>2248.227273</td>\n",
       "      <td>77.663636</td>\n",
       "      <td>15.427273</td>\n",
       "      <td>6.690909</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>8.936364</td>\n",
       "      <td>86.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.092146</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>POLYGON ((-74.17421887421997 40.56257630700625...</td>\n",
       "      <td>5654.111111</td>\n",
       "      <td>2756.111111</td>\n",
       "      <td>2898.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>2531.333333</td>\n",
       "      <td>76.644444</td>\n",
       "      <td>17.644444</td>\n",
       "      <td>5.711111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  Shape_Leng  Shape_Area                     zone  LocationID  \\\n",
       "0         1    0.116357    0.000782           Newark Airport           1   \n",
       "1         2    0.433470    0.004866              Jamaica Bay           2   \n",
       "2         3    0.084341    0.000314  Allerton/Pelham Gardens           3   \n",
       "3         4    0.043567    0.000112            Alphabet City           4   \n",
       "4         5    0.092146    0.000498            Arden Heights           5   \n",
       "\n",
       "         borough                                           geometry  \\\n",
       "0            EWR  POLYGON ((-74.18445450453645 40.69500424662989...   \n",
       "1         Queens  (POLYGON ((-73.82337735819013 40.6389952954719...   \n",
       "2          Bronx  POLYGON ((-73.84792754846802 40.8713505269578,...   \n",
       "3      Manhattan  POLYGON ((-73.97177554799167 40.72582954148978...   \n",
       "4  Staten Island  POLYGON ((-74.17421887421997 40.56257630700625...   \n",
       "\n",
       "      TotalPop          Men        Women     ...       MeanCommute  \\\n",
       "0          NaN          NaN          NaN     ...               NaN   \n",
       "1   528.000000   248.750000   279.250000     ...         42.000000   \n",
       "2  4042.735294  1848.558824  2194.176471     ...         43.608824   \n",
       "3  4461.681818  2030.363636  2431.318182     ...         38.695238   \n",
       "4  5654.111111  2756.111111  2898.000000     ...         41.950000   \n",
       "\n",
       "      Employed  PrivateWork  PublicWork  SelfEmployed  FamilyWork  \\\n",
       "0          NaN          NaN         NaN           NaN         NaN   \n",
       "1   233.250000    64.200000   33.400000      2.400000    0.000000   \n",
       "2  1709.000000    77.979412   17.741176      4.214706    0.070588   \n",
       "3  2248.227273    77.663636   15.427273      6.690909    0.218182   \n",
       "4  2531.333333    76.644444   17.644444      5.711111    0.000000   \n",
       "\n",
       "   Unemployment  FELONY  VIOLATION  MISDEMEANOR  \n",
       "0           NaN     NaN        NaN          NaN  \n",
       "1     11.600000     NaN        NaN          NaN  \n",
       "2     13.126471    79.0       36.0        173.0  \n",
       "3      8.936364    86.0       72.0        205.0  \n",
       "4      8.000000    29.0       18.0         42.0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for CrimeType in ['FELONY', 'VIOLATION', 'MISDEMEANOR']:\n",
    "    temp = df_spatial_crime[df_spatial_crime['LAW_CAT_CD'] == CrimeType]\n",
    "    temp = temp.groupby('OBJECTID').count()\n",
    "    temp.columns = [CrimeType]\n",
    "    df_spatial = pd.merge(df_spatial, temp, left_on='OBJECTID', right_index=True, how='left')\n",
    "print(df_spatial.shape)\n",
    "df_spatial.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transportation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:52.004643Z",
     "start_time": "2019-06-16T16:22:51.997662Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file in place, you can continue\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile(\"data/subway.gz\"):\n",
    "    url = 'https://data.cityofnewyork.us/api/geospatial/drex-xx56?method=export&format=Shapefile'\n",
    "    urllib.request.urlretrieve(url, \"subway.gz\")\n",
    "    !mv subway.gz data\n",
    "    !unzip -o data/subway.gz -d data/subway\n",
    "else:\n",
    "    print(\"file in place, you can continue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:52.202114Z",
     "start_time": "2019-06-16T16:22:52.006638Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1928, 5)\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>line</th>\n",
       "      <th>name</th>\n",
       "      <th>objectid</th>\n",
       "      <th>url</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Birchall Ave &amp; Sagamore St at NW corner</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (-73.86835600032798 40.84916900104506)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Birchall Ave &amp; Sagamore St at NE corner</td>\n",
       "      <td>1735.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (-73.86821300022677 40.84912800131844)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Morris Park Ave &amp; 180th St at NW corner</td>\n",
       "      <td>1736.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (-73.87349900050798 40.84122300105249)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Morris Park Ave &amp; 180th St at NW corner</td>\n",
       "      <td>1737.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (-73.87289199978331 40.84145300067447)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2-5</td>\n",
       "      <td>Boston Rd &amp; 178th St at SW corner</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>http://web.mta.info/nyct/service/</td>\n",
       "      <td>POINT (-73.87962300013866 40.84081500075867)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  line                                     name  objectid  \\\n",
       "0  2-5  Birchall Ave & Sagamore St at NW corner    1734.0   \n",
       "1  2-5  Birchall Ave & Sagamore St at NE corner    1735.0   \n",
       "2  2-5  Morris Park Ave & 180th St at NW corner    1736.0   \n",
       "3  2-5  Morris Park Ave & 180th St at NW corner    1737.0   \n",
       "4  2-5        Boston Rd & 178th St at SW corner    1738.0   \n",
       "\n",
       "                                 url  \\\n",
       "0  http://web.mta.info/nyct/service/   \n",
       "1  http://web.mta.info/nyct/service/   \n",
       "2  http://web.mta.info/nyct/service/   \n",
       "3  http://web.mta.info/nyct/service/   \n",
       "4  http://web.mta.info/nyct/service/   \n",
       "\n",
       "                                       geometry  \n",
       "0  POINT (-73.86835600032798 40.84916900104506)  \n",
       "1  POINT (-73.86821300022677 40.84912800131844)  \n",
       "2  POINT (-73.87349900050798 40.84122300105249)  \n",
       "3  POINT (-73.87289199978331 40.84145300067447)  \n",
       "4  POINT (-73.87962300013866 40.84081500075867)  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = ! ls data/subway | grep .shp\n",
    "subway = gpd.GeoDataFrame.from_file('data/subway/' + filename[0])\n",
    "print(subway.shape)\n",
    "subway.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:52.391143Z",
     "start_time": "2019-06-16T16:22:52.204109Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc710a63c8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subway.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:52.507839Z",
     "start_time": "2019-06-16T16:22:52.392115Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2016, 48)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\geopandas\\tools\\sjoin.py:44: UserWarning: CRS of frames being joined does not match!\n",
      "  warn('CRS of frames being joined does not match!')\n"
     ]
    }
   ],
   "source": [
    "df_spatial_subway = gpd.sjoin(df_spatial, subway, how='left')\n",
    "print(df_spatial_subway.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:52.523762Z",
     "start_time": "2019-06-16T16:22:52.508804Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 2)\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>OBJECTID</th>\n",
       "      <th>subway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  subway\n",
       "0         1       0\n",
       "1         2       0\n",
       "2         3       3\n",
       "3         4       0\n",
       "4         5       0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial_subway = df_spatial_subway.groupby('OBJECTID').count()['line'].reset_index()\n",
    "df_spatial_subway.columns=['OBJECTID', 'subway']\n",
    "print(df_spatial_subway.shape)\n",
    "df_spatial_subway.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:52.571669Z",
     "start_time": "2019-06-16T16:22:52.524807Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 44)\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>OBJECTID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>zone</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>borough</th>\n",
       "      <th>geometry</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>...</th>\n",
       "      <th>Employed</th>\n",
       "      <th>PrivateWork</th>\n",
       "      <th>PublicWork</th>\n",
       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>FELONY</th>\n",
       "      <th>VIOLATION</th>\n",
       "      <th>MISDEMEANOR</th>\n",
       "      <th>subway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.116357</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>Newark Airport</td>\n",
       "      <td>1</td>\n",
       "      <td>EWR</td>\n",
       "      <td>POLYGON ((-74.18445450453645 40.69500424662989...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>248.750000</td>\n",
       "      <td>279.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>233.250000</td>\n",
       "      <td>64.200000</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.084341</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.84792754846802 40.8713505269578,...</td>\n",
       "      <td>4042.735294</td>\n",
       "      <td>1848.558824</td>\n",
       "      <td>2194.176471</td>\n",
       "      <td>...</td>\n",
       "      <td>1709.000000</td>\n",
       "      <td>77.979412</td>\n",
       "      <td>17.741176</td>\n",
       "      <td>4.214706</td>\n",
       "      <td>0.070588</td>\n",
       "      <td>13.126471</td>\n",
       "      <td>79.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-73.97177554799167 40.72582954148978...</td>\n",
       "      <td>4461.681818</td>\n",
       "      <td>2030.363636</td>\n",
       "      <td>2431.318182</td>\n",
       "      <td>...</td>\n",
       "      <td>2248.227273</td>\n",
       "      <td>77.663636</td>\n",
       "      <td>15.427273</td>\n",
       "      <td>6.690909</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>8.936364</td>\n",
       "      <td>86.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.092146</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>POLYGON ((-74.17421887421997 40.56257630700625...</td>\n",
       "      <td>5654.111111</td>\n",
       "      <td>2756.111111</td>\n",
       "      <td>2898.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2531.333333</td>\n",
       "      <td>76.644444</td>\n",
       "      <td>17.644444</td>\n",
       "      <td>5.711111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  Shape_Leng  Shape_Area                     zone  LocationID  \\\n",
       "0         1    0.116357    0.000782           Newark Airport           1   \n",
       "1         2    0.433470    0.004866              Jamaica Bay           2   \n",
       "2         3    0.084341    0.000314  Allerton/Pelham Gardens           3   \n",
       "3         4    0.043567    0.000112            Alphabet City           4   \n",
       "4         5    0.092146    0.000498            Arden Heights           5   \n",
       "\n",
       "         borough                                           geometry  \\\n",
       "0            EWR  POLYGON ((-74.18445450453645 40.69500424662989...   \n",
       "1         Queens  (POLYGON ((-73.82337735819013 40.6389952954719...   \n",
       "2          Bronx  POLYGON ((-73.84792754846802 40.8713505269578,...   \n",
       "3      Manhattan  POLYGON ((-73.97177554799167 40.72582954148978...   \n",
       "4  Staten Island  POLYGON ((-74.17421887421997 40.56257630700625...   \n",
       "\n",
       "      TotalPop          Men        Women   ...       Employed  PrivateWork  \\\n",
       "0          NaN          NaN          NaN   ...            NaN          NaN   \n",
       "1   528.000000   248.750000   279.250000   ...     233.250000    64.200000   \n",
       "2  4042.735294  1848.558824  2194.176471   ...    1709.000000    77.979412   \n",
       "3  4461.681818  2030.363636  2431.318182   ...    2248.227273    77.663636   \n",
       "4  5654.111111  2756.111111  2898.000000   ...    2531.333333    76.644444   \n",
       "\n",
       "   PublicWork  SelfEmployed  FamilyWork  Unemployment  FELONY  VIOLATION  \\\n",
       "0         NaN           NaN         NaN           NaN     NaN        NaN   \n",
       "1   33.400000      2.400000    0.000000     11.600000     NaN        NaN   \n",
       "2   17.741176      4.214706    0.070588     13.126471    79.0       36.0   \n",
       "3   15.427273      6.690909    0.218182      8.936364    86.0       72.0   \n",
       "4   17.644444      5.711111    0.000000      8.000000    29.0       18.0   \n",
       "\n",
       "   MISDEMEANOR  subway  \n",
       "0          NaN       0  \n",
       "1          NaN       0  \n",
       "2        173.0       3  \n",
       "3        205.0       0  \n",
       "4         42.0       0  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial = pd.merge(df_spatial, df_spatial_subway, on='OBJECTID', how='left')\n",
    "# df_spatial.drop(['OBJECTID_y'], axis=1, inplace=True)\n",
    "print(df_spatial.shape)\n",
    "df_spatial.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### bus station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:52.579614Z",
     "start_time": "2019-06-16T16:22:52.572633Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file in place, you can continue\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile(\"data/bus.csv\"):\n",
    "    ! curl https://raw.githubusercontent.com/JunjieTsai/MLC2019_Project/master/data/bus_stop.csv > data/bus.csv\n",
    "else:\n",
    "    print(\"file in place, you can continue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:52.743176Z",
     "start_time": "2019-06-16T16:22:52.580613Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16231, 7)\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>Unnamed: 0</th>\n",
       "      <th>stop_id</th>\n",
       "      <th>stop_lat</th>\n",
       "      <th>stop_lon</th>\n",
       "      <th>stop_name</th>\n",
       "      <th>lonlat</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>300000</td>\n",
       "      <td>40.578350</td>\n",
       "      <td>-73.940029</td>\n",
       "      <td>ORIENTAL BL/MACKENZIE ST</td>\n",
       "      <td>(-73.94002900000001, 40.57835)</td>\n",
       "      <td>POINT (-73.94002900000002 40.57835)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>300002</td>\n",
       "      <td>40.578066</td>\n",
       "      <td>-73.943029</td>\n",
       "      <td>ORIENTAL BL/JAFFRAY ST</td>\n",
       "      <td>(-73.94302900000001, 40.578066)</td>\n",
       "      <td>POINT (-73.94302900000002 40.578066)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>300003</td>\n",
       "      <td>40.577909</td>\n",
       "      <td>-73.944643</td>\n",
       "      <td>ORIENTAL BL/HASTINGS ST</td>\n",
       "      <td>(-73.944643, 40.577909000000005)</td>\n",
       "      <td>POINT (-73.944643 40.57790900000001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>300004</td>\n",
       "      <td>40.577718</td>\n",
       "      <td>-73.946200</td>\n",
       "      <td>ORIENTAL BL/FALMOUTH ST</td>\n",
       "      <td>(-73.9462, 40.577718)</td>\n",
       "      <td>POINT (-73.9462 40.577718)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>300006</td>\n",
       "      <td>40.577353</td>\n",
       "      <td>-73.949552</td>\n",
       "      <td>ORIENTAL BL/DOVER ST</td>\n",
       "      <td>(-73.949552, 40.577353)</td>\n",
       "      <td>POINT (-73.949552 40.577353)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  stop_id   stop_lat   stop_lon                 stop_name  \\\n",
       "0           0   300000  40.578350 -73.940029  ORIENTAL BL/MACKENZIE ST   \n",
       "1           1   300002  40.578066 -73.943029    ORIENTAL BL/JAFFRAY ST   \n",
       "2           2   300003  40.577909 -73.944643   ORIENTAL BL/HASTINGS ST   \n",
       "3           3   300004  40.577718 -73.946200   ORIENTAL BL/FALMOUTH ST   \n",
       "4           4   300006  40.577353 -73.949552      ORIENTAL BL/DOVER ST   \n",
       "\n",
       "                             lonlat                              geometry  \n",
       "0    (-73.94002900000001, 40.57835)   POINT (-73.94002900000002 40.57835)  \n",
       "1   (-73.94302900000001, 40.578066)  POINT (-73.94302900000002 40.578066)  \n",
       "2  (-73.944643, 40.577909000000005)  POINT (-73.944643 40.57790900000001)  \n",
       "3             (-73.9462, 40.577718)            POINT (-73.9462 40.577718)  \n",
       "4           (-73.949552, 40.577353)          POINT (-73.949552 40.577353)  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bus = pd.read_csv(\"data/bus.csv\")\n",
    "# https://anitagraser.com/2019/01/23/from-csv-to-geodataframe-in-two-lines/\n",
    "bus = gpd.GeoDataFrame(bus, geometry=[Point(xy) for xy in zip(bus.stop_lon, bus.stop_lat)])\n",
    "print(bus.shape)\n",
    "bus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:53.432363Z",
     "start_time": "2019-06-16T16:22:52.744209Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc65d2c710>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bus.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:54.129492Z",
     "start_time": "2019-06-16T16:22:53.433330Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\geopandas\\tools\\sjoin.py:44: UserWarning: CRS of frames being joined does not match!\n",
      "  warn('CRS of frames being joined does not match!')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16143, 51)\n"
     ]
    }
   ],
   "source": [
    "df_spatial_bus = gpd.sjoin(df_spatial, bus, how='left')\n",
    "print(df_spatial_bus.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:54.158415Z",
     "start_time": "2019-06-16T16:22:54.130489Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 2)\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>OBJECTID</th>\n",
       "      <th>bus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  bus\n",
       "0         1    0\n",
       "1         2    4\n",
       "2         3   53\n",
       "3         4   26\n",
       "4         5   38"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial_bus = df_spatial_bus.groupby('OBJECTID').count()['stop_id'].reset_index()\n",
    "df_spatial_bus.columns=['OBJECTID', 'bus']\n",
    "print(df_spatial_bus.shape)\n",
    "df_spatial_bus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:54.220724Z",
     "start_time": "2019-06-16T16:22:54.159861Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 45)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        text-align: right;\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>zone</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>borough</th>\n",
       "      <th>geometry</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>...</th>\n",
       "      <th>PrivateWork</th>\n",
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       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>FELONY</th>\n",
       "      <th>VIOLATION</th>\n",
       "      <th>MISDEMEANOR</th>\n",
       "      <th>subway</th>\n",
       "      <th>bus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.116357</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>Newark Airport</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
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       "      <td>528.000000</td>\n",
       "      <td>248.750000</td>\n",
       "      <td>279.250000</td>\n",
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       "      <td>4</td>\n",
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       "      <td>3</td>\n",
       "      <td>0.084341</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.84792754846802 40.8713505269578,...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>4.214706</td>\n",
       "      <td>0.070588</td>\n",
       "      <td>13.126471</td>\n",
       "      <td>79.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-73.97177554799167 40.72582954148978...</td>\n",
       "      <td>4461.681818</td>\n",
       "      <td>2030.363636</td>\n",
       "      <td>2431.318182</td>\n",
       "      <td>...</td>\n",
       "      <td>77.663636</td>\n",
       "      <td>15.427273</td>\n",
       "      <td>6.690909</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>8.936364</td>\n",
       "      <td>86.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.092146</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>POLYGON ((-74.17421887421997 40.56257630700625...</td>\n",
       "      <td>5654.111111</td>\n",
       "      <td>2756.111111</td>\n",
       "      <td>2898.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>76.644444</td>\n",
       "      <td>17.644444</td>\n",
       "      <td>5.711111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  Shape_Leng  Shape_Area                     zone  LocationID  \\\n",
       "0         1    0.116357    0.000782           Newark Airport           1   \n",
       "1         2    0.433470    0.004866              Jamaica Bay           2   \n",
       "2         3    0.084341    0.000314  Allerton/Pelham Gardens           3   \n",
       "3         4    0.043567    0.000112            Alphabet City           4   \n",
       "4         5    0.092146    0.000498            Arden Heights           5   \n",
       "\n",
       "         borough                                           geometry  \\\n",
       "0            EWR  POLYGON ((-74.18445450453645 40.69500424662989...   \n",
       "1         Queens  (POLYGON ((-73.82337735819013 40.6389952954719...   \n",
       "2          Bronx  POLYGON ((-73.84792754846802 40.8713505269578,...   \n",
       "3      Manhattan  POLYGON ((-73.97177554799167 40.72582954148978...   \n",
       "4  Staten Island  POLYGON ((-74.17421887421997 40.56257630700625...   \n",
       "\n",
       "      TotalPop          Men        Women ...   PrivateWork  PublicWork  \\\n",
       "0          NaN          NaN          NaN ...           NaN         NaN   \n",
       "1   528.000000   248.750000   279.250000 ...     64.200000   33.400000   \n",
       "2  4042.735294  1848.558824  2194.176471 ...     77.979412   17.741176   \n",
       "3  4461.681818  2030.363636  2431.318182 ...     77.663636   15.427273   \n",
       "4  5654.111111  2756.111111  2898.000000 ...     76.644444   17.644444   \n",
       "\n",
       "   SelfEmployed  FamilyWork  Unemployment  FELONY  VIOLATION  MISDEMEANOR  \\\n",
       "0           NaN         NaN           NaN     NaN        NaN          NaN   \n",
       "1      2.400000    0.000000     11.600000     NaN        NaN          NaN   \n",
       "2      4.214706    0.070588     13.126471    79.0       36.0        173.0   \n",
       "3      6.690909    0.218182      8.936364    86.0       72.0        205.0   \n",
       "4      5.711111    0.000000      8.000000    29.0       18.0         42.0   \n",
       "\n",
       "   subway  bus  \n",
       "0       0    0  \n",
       "1       0    4  \n",
       "2       3   53  \n",
       "3       0   26  \n",
       "4       0   38  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial = pd.merge(df_spatial, df_spatial_bus, on='OBJECTID', how='left')\n",
    "print(df_spatial.shape)\n",
    "df_spatial.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Education \n",
    "represented by SAT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SAT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:54.227671Z",
     "start_time": "2019-06-16T16:22:54.221686Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file in place, you can continue\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile(\"data/sat.csv\"):\n",
    "    ! curl https://data.cityofnewyork.us/api/views/f9bf-2cp4/rows.csv?accessType=DOWNLOAD > data/sat.csv\n",
    "else:\n",
    "    print(\"file in place, you can continue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:54.261615Z",
     "start_time": "2019-06-16T16:22:54.228667Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(478, 7)\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>DBN</th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>Num of SAT Test Takers</th>\n",
       "      <th>SAT Critical Reading Avg. Score</th>\n",
       "      <th>SAT Math Avg. Score</th>\n",
       "      <th>SAT Writing Avg. Score</th>\n",
       "      <th>SchoolDist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01M292</td>\n",
       "      <td>HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES</td>\n",
       "      <td>29</td>\n",
       "      <td>355</td>\n",
       "      <td>404</td>\n",
       "      <td>363</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01M448</td>\n",
       "      <td>UNIVERSITY NEIGHBORHOOD HIGH SCHOOL</td>\n",
       "      <td>91</td>\n",
       "      <td>383</td>\n",
       "      <td>423</td>\n",
       "      <td>366</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M450</td>\n",
       "      <td>EAST SIDE COMMUNITY SCHOOL</td>\n",
       "      <td>70</td>\n",
       "      <td>377</td>\n",
       "      <td>402</td>\n",
       "      <td>370</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01M458</td>\n",
       "      <td>FORSYTH SATELLITE ACADEMY</td>\n",
       "      <td>7</td>\n",
       "      <td>414</td>\n",
       "      <td>401</td>\n",
       "      <td>359</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01M509</td>\n",
       "      <td>MARTA VALLE HIGH SCHOOL</td>\n",
       "      <td>44</td>\n",
       "      <td>390</td>\n",
       "      <td>433</td>\n",
       "      <td>384</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DBN                                    SCHOOL NAME  \\\n",
       "0  01M292  HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES   \n",
       "1  01M448            UNIVERSITY NEIGHBORHOOD HIGH SCHOOL   \n",
       "2  01M450                     EAST SIDE COMMUNITY SCHOOL   \n",
       "3  01M458                      FORSYTH SATELLITE ACADEMY   \n",
       "4  01M509                        MARTA VALLE HIGH SCHOOL   \n",
       "\n",
       "  Num of SAT Test Takers SAT Critical Reading Avg. Score SAT Math Avg. Score  \\\n",
       "0                     29                             355                 404   \n",
       "1                     91                             383                 423   \n",
       "2                     70                             377                 402   \n",
       "3                      7                             414                 401   \n",
       "4                     44                             390                 433   \n",
       "\n",
       "  SAT Writing Avg. Score  SchoolDist  \n",
       "0                    363           1  \n",
       "1                    366           1  \n",
       "2                    370           1  \n",
       "3                    359           1  \n",
       "4                    384           1  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat = pd.read_csv(\"data/sat.csv\")\n",
    "sat['SchoolDist'] = sat.DBN.str[:2].astype('int')\n",
    "print(sat.shape)\n",
    "sat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  School distrct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:22:54.269558Z",
     "start_time": "2019-06-16T16:22:54.262577Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file in place, you can continue\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile(\"data/school_distrct.zip\"):\n",
    "    url = 'https://www1.nyc.gov/assets/planning/download/zip/data-maps/open-data/nysd_19a.zip'\n",
    "    urllib.request.urlretrieve(url, \"school_distrct.zip\")\n",
    "    ! mv school_distrct.zip data\n",
    "    ! unzip -o data/school_distrct.zip -d data/school_distrct\n",
    "else:\n",
    "    print(\"file in place, you can continue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:23:12.833109Z",
     "start_time": "2019-06-16T16:22:54.270556Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33, 4)\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>SchoolDist</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>35848.904605</td>\n",
       "      <td>4.676362e+07</td>\n",
       "      <td>POLYGON ((-73.93312005273502 40.69579940946588...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>37251.057847</td>\n",
       "      <td>5.189850e+07</td>\n",
       "      <td>POLYGON ((-73.911808518474 40.70344320989009, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>40317.452033</td>\n",
       "      <td>4.740069e+07</td>\n",
       "      <td>(POLYGON ((-73.9204450802963 40.665634537312, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29</td>\n",
       "      <td>135035.241651</td>\n",
       "      <td>4.201981e+08</td>\n",
       "      <td>POLYGON ((-73.73816280460379 40.72896636008991...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>125677.678898</td>\n",
       "      <td>4.247909e+08</td>\n",
       "      <td>POLYGON ((-73.74345129165096 40.7782494313074,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SchoolDist     Shape_Leng    Shape_Area  \\\n",
       "0          16   35848.904605  4.676362e+07   \n",
       "1          32   37251.057847  5.189850e+07   \n",
       "2          23   40317.452033  4.740069e+07   \n",
       "3          29  135035.241651  4.201981e+08   \n",
       "4          26  125677.678898  4.247909e+08   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-73.93312005273502 40.69579940946588...  \n",
       "1  POLYGON ((-73.911808518474 40.70344320989009, ...  \n",
       "2  (POLYGON ((-73.9204450802963 40.665634537312, ...  \n",
       "3  POLYGON ((-73.73816280460379 40.72896636008991...  \n",
       "4  POLYGON ((-73.74345129165096 40.7782494313074,...  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = ! ls data/school_distrct/nysd_19a | grep .shp\n",
    "school_distrct = gpd.GeoDataFrame.from_file('data/school_distrct/nysd_19a/' + filename[0])\n",
    "\n",
    "school_distrct.crs = from_epsg(2263)\n",
    "school_distrct = school_distrct.to_crs(epsg=4326)\n",
    "\n",
    "print(school_distrct.shape)\n",
    "school_distrct.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:23:13.025547Z",
     "start_time": "2019-06-16T16:23:12.834059Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc6a788f28>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "school_distrct.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:23:13.048524Z",
     "start_time": "2019-06-16T16:23:13.026546Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(496, 10)\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>SchoolDist</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>geometry</th>\n",
       "      <th>DBN</th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>Num of SAT Test Takers</th>\n",
       "      <th>SAT Critical Reading Avg. Score</th>\n",
       "      <th>SAT Math Avg. Score</th>\n",
       "      <th>SAT Writing Avg. Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>35848.904605</td>\n",
       "      <td>4.676362e+07</td>\n",
       "      <td>POLYGON ((-73.93312005273502 40.69579940946588...</td>\n",
       "      <td>16K393</td>\n",
       "      <td>FREDERICK DOUGLASS ACADEMY IV SECONDARY SCHOOL</td>\n",
       "      <td>20</td>\n",
       "      <td>355</td>\n",
       "      <td>355</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>35848.904605</td>\n",
       "      <td>4.676362e+07</td>\n",
       "      <td>POLYGON ((-73.93312005273502 40.69579940946588...</td>\n",
       "      <td>16K455</td>\n",
       "      <td>BOYS AND GIRLS HIGH SCHOOL</td>\n",
       "      <td>131</td>\n",
       "      <td>365</td>\n",
       "      <td>370</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>35848.904605</td>\n",
       "      <td>4.676362e+07</td>\n",
       "      <td>POLYGON ((-73.93312005273502 40.69579940946588...</td>\n",
       "      <td>16K498</td>\n",
       "      <td>BROOKLYN HIGH SCHOOL FOR LAW AND TECHNOLOGY</td>\n",
       "      <td>46</td>\n",
       "      <td>362</td>\n",
       "      <td>372</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>35848.904605</td>\n",
       "      <td>4.676362e+07</td>\n",
       "      <td>POLYGON ((-73.93312005273502 40.69579940946588...</td>\n",
       "      <td>16K594</td>\n",
       "      <td>GOTHAM PROFESSIONAL ARTS ACADEMY</td>\n",
       "      <td>36</td>\n",
       "      <td>370</td>\n",
       "      <td>372</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>35848.904605</td>\n",
       "      <td>4.676362e+07</td>\n",
       "      <td>POLYGON ((-73.93312005273502 40.69579940946588...</td>\n",
       "      <td>16K688</td>\n",
       "      <td>THE BROOKLYN ACADEMY OF GLOBAL FINANCE</td>\n",
       "      <td>s</td>\n",
       "      <td>s</td>\n",
       "      <td>s</td>\n",
       "      <td>s</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SchoolDist    Shape_Leng    Shape_Area  \\\n",
       "0          16  35848.904605  4.676362e+07   \n",
       "1          16  35848.904605  4.676362e+07   \n",
       "2          16  35848.904605  4.676362e+07   \n",
       "3          16  35848.904605  4.676362e+07   \n",
       "4          16  35848.904605  4.676362e+07   \n",
       "\n",
       "                                            geometry     DBN  \\\n",
       "0  POLYGON ((-73.93312005273502 40.69579940946588...  16K393   \n",
       "1  POLYGON ((-73.93312005273502 40.69579940946588...  16K455   \n",
       "2  POLYGON ((-73.93312005273502 40.69579940946588...  16K498   \n",
       "3  POLYGON ((-73.93312005273502 40.69579940946588...  16K594   \n",
       "4  POLYGON ((-73.93312005273502 40.69579940946588...  16K688   \n",
       "\n",
       "                                      SCHOOL NAME Num of SAT Test Takers  \\\n",
       "0  FREDERICK DOUGLASS ACADEMY IV SECONDARY SCHOOL                     20   \n",
       "1                      BOYS AND GIRLS HIGH SCHOOL                    131   \n",
       "2     BROOKLYN HIGH SCHOOL FOR LAW AND TECHNOLOGY                     46   \n",
       "3                GOTHAM PROFESSIONAL ARTS ACADEMY                     36   \n",
       "4          THE BROOKLYN ACADEMY OF GLOBAL FINANCE                      s   \n",
       "\n",
       "  SAT Critical Reading Avg. Score SAT Math Avg. Score SAT Writing Avg. Score  \n",
       "0                             355                 355                    358  \n",
       "1                             365                 370                    362  \n",
       "2                             362                 372                    358  \n",
       "3                             370                 372                    351  \n",
       "4                               s                   s                      s  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial_sat = pd.merge(school_distrct, sat, on = 'SchoolDist', how = 'left')\n",
    "print(df_spatial_sat.shape)\n",
    "df_spatial_sat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:23:13.053488Z",
     "start_time": "2019-06-16T16:23:13.049483Z"
    }
   },
   "outputs": [],
   "source": [
    "def deleteSATs(df):\n",
    "    return df.apply(lambda x: 0 if x=='s' else x).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:23:13.070426Z",
     "start_time": "2019-06-16T16:23:13.055469Z"
    }
   },
   "outputs": [],
   "source": [
    "df_spatial_sat['Num of SAT Test Takers'] = deleteSATs(df_spatial_sat['Num of SAT Test Takers'])\n",
    "df_spatial_sat['SAT Critical Reading Avg. Score'] = deleteSATs(df_spatial_sat['SAT Critical Reading Avg. Score'])\n",
    "df_spatial_sat['SAT Math Avg. Score'] = deleteSATs(df_spatial_sat['SAT Math Avg. Score'])\n",
    "df_spatial_sat['SAT Writing Avg. Score'] = deleteSATs(df_spatial_sat['SAT Writing Avg. Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:23:13.097354Z",
     "start_time": "2019-06-16T16:23:13.071424Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(444, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .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>SchoolDist</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>geometry</th>\n",
       "      <th>DBN</th>\n",
       "      <th>SCHOOL NAME</th>\n",
       "      <th>Num of SAT Test Takers</th>\n",
       "      <th>SAT Critical Reading Avg. Score</th>\n",
       "      <th>SAT Math Avg. Score</th>\n",
       "      <th>SAT Writing Avg. Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>35848.904605</td>\n",
       "      <td>4.676362e+07</td>\n",
       "      <td>POLYGON ((-73.93312005273502 40.69579940946588...</td>\n",
       "      <td>16K393</td>\n",
       "      <td>FREDERICK DOUGLASS ACADEMY IV SECONDARY SCHOOL</td>\n",
       "      <td>20</td>\n",
       "      <td>355</td>\n",
       "      <td>355</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>35848.904605</td>\n",
       "      <td>4.676362e+07</td>\n",
       "      <td>POLYGON ((-73.93312005273502 40.69579940946588...</td>\n",
       "      <td>16K455</td>\n",
       "      <td>BOYS AND GIRLS HIGH SCHOOL</td>\n",
       "      <td>131</td>\n",
       "      <td>365</td>\n",
       "      <td>370</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>35848.904605</td>\n",
       "      <td>4.676362e+07</td>\n",
       "      <td>POLYGON ((-73.93312005273502 40.69579940946588...</td>\n",
       "      <td>16K498</td>\n",
       "      <td>BROOKLYN HIGH SCHOOL FOR LAW AND TECHNOLOGY</td>\n",
       "      <td>46</td>\n",
       "      <td>362</td>\n",
       "      <td>372</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>35848.904605</td>\n",
       "      <td>4.676362e+07</td>\n",
       "      <td>POLYGON ((-73.93312005273502 40.69579940946588...</td>\n",
       "      <td>16K594</td>\n",
       "      <td>GOTHAM PROFESSIONAL ARTS ACADEMY</td>\n",
       "      <td>36</td>\n",
       "      <td>370</td>\n",
       "      <td>372</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32</td>\n",
       "      <td>37251.057847</td>\n",
       "      <td>5.189850e+07</td>\n",
       "      <td>POLYGON ((-73.911808518474 40.70344320989009, ...</td>\n",
       "      <td>32K403</td>\n",
       "      <td>ACADEMY FOR ENVIRONMENTAL LEADERSHIP</td>\n",
       "      <td>50</td>\n",
       "      <td>380</td>\n",
       "      <td>366</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SchoolDist    Shape_Leng    Shape_Area  \\\n",
       "0          16  35848.904605  4.676362e+07   \n",
       "1          16  35848.904605  4.676362e+07   \n",
       "2          16  35848.904605  4.676362e+07   \n",
       "3          16  35848.904605  4.676362e+07   \n",
       "5          32  37251.057847  5.189850e+07   \n",
       "\n",
       "                                            geometry     DBN  \\\n",
       "0  POLYGON ((-73.93312005273502 40.69579940946588...  16K393   \n",
       "1  POLYGON ((-73.93312005273502 40.69579940946588...  16K455   \n",
       "2  POLYGON ((-73.93312005273502 40.69579940946588...  16K498   \n",
       "3  POLYGON ((-73.93312005273502 40.69579940946588...  16K594   \n",
       "5  POLYGON ((-73.911808518474 40.70344320989009, ...  32K403   \n",
       "\n",
       "                                      SCHOOL NAME  Num of SAT Test Takers  \\\n",
       "0  FREDERICK DOUGLASS ACADEMY IV SECONDARY SCHOOL                      20   \n",
       "1                      BOYS AND GIRLS HIGH SCHOOL                     131   \n",
       "2     BROOKLYN HIGH SCHOOL FOR LAW AND TECHNOLOGY                      46   \n",
       "3                GOTHAM PROFESSIONAL ARTS ACADEMY                      36   \n",
       "5            ACADEMY FOR ENVIRONMENTAL LEADERSHIP                      50   \n",
       "\n",
       "   SAT Critical Reading Avg. Score  SAT Math Avg. Score  \\\n",
       "0                              355                  355   \n",
       "1                              365                  370   \n",
       "2                              362                  372   \n",
       "3                              370                  372   \n",
       "5                              380                  366   \n",
       "\n",
       "   SAT Writing Avg. Score  \n",
       "0                     358  \n",
       "1                     362  \n",
       "2                     358  \n",
       "3                     351  \n",
       "5                     352  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial_sat = df_spatial_sat[df_spatial_sat['Num of SAT Test Takers']>0]\n",
    "print(df_spatial_sat.shape)\n",
    "df_spatial_sat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:23:13.173153Z",
     "start_time": "2019-06-16T16:23:13.098388Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SchoolDist</th>\n",
       "      <th>Num of SAT Test Takers</th>\n",
       "      <th>SAT Critical Reading Avg. Score</th>\n",
       "      <th>SAT Math Avg. Score</th>\n",
       "      <th>SAT Writing Avg. Score</th>\n",
       "      <th>sat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>73.333333</td>\n",
       "      <td>423.777778</td>\n",
       "      <td>468.444444</td>\n",
       "      <td>413.555556</td>\n",
       "      <td>435.259259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>106.465517</td>\n",
       "      <td>424.068966</td>\n",
       "      <td>441.344828</td>\n",
       "      <td>419.448276</td>\n",
       "      <td>428.287356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>108.769231</td>\n",
       "      <td>424.846154</td>\n",
       "      <td>426.000000</td>\n",
       "      <td>417.076923</td>\n",
       "      <td>422.641026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>99.428571</td>\n",
       "      <td>393.142857</td>\n",
       "      <td>405.285714</td>\n",
       "      <td>392.714286</td>\n",
       "      <td>397.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>70.444444</td>\n",
       "      <td>407.333333</td>\n",
       "      <td>409.666667</td>\n",
       "      <td>401.222222</td>\n",
       "      <td>406.074074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SchoolDist  Num of SAT Test Takers  SAT Critical Reading Avg. Score  \\\n",
       "0           1               73.333333                       423.777778   \n",
       "1           2              106.465517                       424.068966   \n",
       "2           3              108.769231                       424.846154   \n",
       "3           4               99.428571                       393.142857   \n",
       "4           5               70.444444                       407.333333   \n",
       "\n",
       "   SAT Math Avg. Score  SAT Writing Avg. Score         sat  \n",
       "0           468.444444              413.555556  435.259259  \n",
       "1           441.344828              419.448276  428.287356  \n",
       "2           426.000000              417.076923  422.641026  \n",
       "3           405.285714              392.714286  397.047619  \n",
       "4           409.666667              401.222222  406.074074  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial_sat = df_spatial_sat.groupby('SchoolDist').mean()\n",
    "df_spatial_sat = df_spatial_sat[['Num of SAT Test Takers',\n",
    "       'SAT Critical Reading Avg. Score', 'SAT Math Avg. Score',\n",
    "       'SAT Writing Avg. Score']]\n",
    "df_spatial_sat.reset_index(inplace=True)\n",
    "df_spatial_sat['sat'] = (df_spatial_sat['SAT Critical Reading Avg. Score']  + \\\n",
    "                        df_spatial_sat['SAT Math Avg. Score'] + \\\n",
    "                        df_spatial_sat['SAT Writing Avg. Score'])/3\n",
    "print(df_spatial_sat.shape)\n",
    "df_spatial_sat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:23:13.187148Z",
     "start_time": "2019-06-16T16:23:13.175148Z"
    }
   },
   "outputs": [],
   "source": [
    "df_spatial_sat = pd.merge(school_distrct, df_spatial_sat, on = 'SchoolDist', how = 'left')\n",
    "df_spatial_sat.drop(['Shape_Leng', 'Shape_Area', 'Num of SAT Test Takers', 'SAT Critical Reading Avg. Score', 'SAT Math Avg. Score', 'SAT Writing Avg. Score'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:23:13.458409Z",
     "start_time": "2019-06-16T16:23:13.189109Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(486, 48)\n"
     ]
    }
   ],
   "source": [
    "df_spatial_sat = gpd.sjoin(df_spatial, df_spatial_sat, how='left')\n",
    "print(df_spatial_sat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:23:13.633919Z",
     "start_time": "2019-06-16T16:23:13.459387Z"
    }
   },
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>zone</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>borough</th>\n",
       "      <th>geometry</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>...</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>FELONY</th>\n",
       "      <th>VIOLATION</th>\n",
       "      <th>MISDEMEANOR</th>\n",
       "      <th>subway</th>\n",
       "      <th>bus</th>\n",
       "      <th>index_right</th>\n",
       "      <th>SchoolDist</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
       "      <td>0.116357</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>Newark Airport</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
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       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>248.750000</td>\n",
       "      <td>279.250000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>11.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>367.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>248.750000</td>\n",
       "      <td>279.250000</td>\n",
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       "      <td>27.0</td>\n",
       "      <td>405.363636</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.084341</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
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       "      <td>1848.558824</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.070588</td>\n",
       "      <td>13.126471</td>\n",
       "      <td>79.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>29.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>385.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-73.97177554799167 40.72582954148978...</td>\n",
       "      <td>4461.681818</td>\n",
       "      <td>2030.363636</td>\n",
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       "      <td>86.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>428.287356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-73.97177554799167 40.72582954148978...</td>\n",
       "      <td>4461.681818</td>\n",
       "      <td>2030.363636</td>\n",
       "      <td>2431.318182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>8.936364</td>\n",
       "      <td>86.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>435.259259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.092146</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>POLYGON ((-74.17421887421997 40.56257630700625...</td>\n",
       "      <td>5654.111111</td>\n",
       "      <td>2756.111111</td>\n",
       "      <td>2898.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>14.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>453.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.150491</td>\n",
       "      <td>0.000606</td>\n",
       "      <td>Arrochar/Fort Wadsworth</td>\n",
       "      <td>6</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>POLYGON ((-74.06367464927494 40.6022063986771,...</td>\n",
       "      <td>4721.292683</td>\n",
       "      <td>2282.560976</td>\n",
       "      <td>2438.731707</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>7.615385</td>\n",
       "      <td>46.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>14.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>453.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.107417</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>Astoria</td>\n",
       "      <td>7</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.90413779719505 40.7675285876882,...</td>\n",
       "      <td>3846.102362</td>\n",
       "      <td>1851.574803</td>\n",
       "      <td>1994.527559</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129508</td>\n",
       "      <td>9.670732</td>\n",
       "      <td>218.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>14</td>\n",
       "      <td>104</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>446.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.027591</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>Astoria Park</td>\n",
       "      <td>8</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.92334184080988 40.77513718350537...</td>\n",
       "      <td>2774.166667</td>\n",
       "      <td>1434.333333</td>\n",
       "      <td>1339.833333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>6.020000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>446.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.099784</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>Auburndale</td>\n",
       "      <td>9</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.78502572762456 40.76104478957554...</td>\n",
       "      <td>2691.650000</td>\n",
       "      <td>1327.500000</td>\n",
       "      <td>1364.150000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.189474</td>\n",
       "      <td>6.952632</td>\n",
       "      <td>40.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>433.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.099784</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>Auburndale</td>\n",
       "      <td>9</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.78502572762456 40.76104478957554...</td>\n",
       "      <td>2691.650000</td>\n",
       "      <td>1327.500000</td>\n",
       "      <td>1364.150000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.189474</td>\n",
       "      <td>6.952632</td>\n",
       "      <td>40.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>459.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.099839</td>\n",
       "      <td>0.000436</td>\n",
       "      <td>Baisley Park</td>\n",
       "      <td>10</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.78326762579181 40.69000255270579...</td>\n",
       "      <td>3175.033333</td>\n",
       "      <td>1513.700000</td>\n",
       "      <td>1661.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111667</td>\n",
       "      <td>11.505000</td>\n",
       "      <td>173.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>405.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.099839</td>\n",
       "      <td>0.000436</td>\n",
       "      <td>Baisley Park</td>\n",
       "      <td>10</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.78326762579181 40.69000255270579...</td>\n",
       "      <td>3175.033333</td>\n",
       "      <td>1513.700000</td>\n",
       "      <td>1661.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111667</td>\n",
       "      <td>11.505000</td>\n",
       "      <td>173.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>445.575758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>0.079211</td>\n",
       "      <td>0.000265</td>\n",
       "      <td>Bath Beach</td>\n",
       "      <td>11</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>POLYGON ((-74.00109953530968 40.60304286103321...</td>\n",
       "      <td>3451.087719</td>\n",
       "      <td>1640.456140</td>\n",
       "      <td>1810.631579</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>9.961818</td>\n",
       "      <td>68.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>418.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.036661</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>Battery Park</td>\n",
       "      <td>12</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-74.01565901711844 40.70484134250074...</td>\n",
       "      <td>2877.555556</td>\n",
       "      <td>1402.666667</td>\n",
       "      <td>1474.888889</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>7.775000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>428.287356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0.050281</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>Battery Park City</td>\n",
       "      <td>13</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-74.01244254395085 40.71906593040101...</td>\n",
       "      <td>4648.416667</td>\n",
       "      <td>2339.416667</td>\n",
       "      <td>2309.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>6.533333</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>428.287356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.175214</td>\n",
       "      <td>0.001382</td>\n",
       "      <td>Bay Ridge</td>\n",
       "      <td>14</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>POLYGON ((-74.03407474632135 40.64432217496695...</td>\n",
       "      <td>4367.224299</td>\n",
       "      <td>2062.663551</td>\n",
       "      <td>2304.560748</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120192</td>\n",
       "      <td>8.006731</td>\n",
       "      <td>211.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>14</td>\n",
       "      <td>195</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>418.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0.144336</td>\n",
       "      <td>0.000925</td>\n",
       "      <td>Bay Terrace/Fort Totten</td>\n",
       "      <td>15</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.77740529323283 40.79660652207308...</td>\n",
       "      <td>3966.300000</td>\n",
       "      <td>1836.500000</td>\n",
       "      <td>2129.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>8.133333</td>\n",
       "      <td>42.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>433.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0.144336</td>\n",
       "      <td>0.000925</td>\n",
       "      <td>Bay Terrace/Fort Totten</td>\n",
       "      <td>15</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.77740529323283 40.79660652207308...</td>\n",
       "      <td>3966.300000</td>\n",
       "      <td>1836.500000</td>\n",
       "      <td>2129.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>8.133333</td>\n",
       "      <td>42.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>459.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>0.141292</td>\n",
       "      <td>0.000872</td>\n",
       "      <td>Bayside</td>\n",
       "      <td>16</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.76857442643772 40.77911370668763...</td>\n",
       "      <td>2922.208333</td>\n",
       "      <td>1383.583333</td>\n",
       "      <td>1538.625000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>7.237500</td>\n",
       "      <td>125.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0</td>\n",
       "      <td>139</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>433.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>0.141292</td>\n",
       "      <td>0.000872</td>\n",
       "      <td>Bayside</td>\n",
       "      <td>16</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.76857442643772 40.77911370668763...</td>\n",
       "      <td>2922.208333</td>\n",
       "      <td>1383.583333</td>\n",
       "      <td>1538.625000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.216667</td>\n",
       "      <td>7.237500</td>\n",
       "      <td>125.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>0</td>\n",
       "      <td>139</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>459.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0.093523</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>Bedford</td>\n",
       "      <td>17</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>POLYGON ((-73.94306549666851 40.70143269137623...</td>\n",
       "      <td>5267.108696</td>\n",
       "      <td>2572.728261</td>\n",
       "      <td>2694.380435</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102198</td>\n",
       "      <td>11.758242</td>\n",
       "      <td>376.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>15</td>\n",
       "      <td>97</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>409.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0.093523</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>Bedford</td>\n",
       "      <td>17</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>POLYGON ((-73.94306549666851 40.70143269137623...</td>\n",
       "      <td>5267.108696</td>\n",
       "      <td>2572.728261</td>\n",
       "      <td>2694.380435</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102198</td>\n",
       "      <td>11.758242</td>\n",
       "      <td>376.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>15</td>\n",
       "      <td>97</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>390.809524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0.093523</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>Bedford</td>\n",
       "      <td>17</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>POLYGON ((-73.94306549666851 40.70143269137623...</td>\n",
       "      <td>5267.108696</td>\n",
       "      <td>2572.728261</td>\n",
       "      <td>2694.380435</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102198</td>\n",
       "      <td>11.758242</td>\n",
       "      <td>376.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>15</td>\n",
       "      <td>97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>362.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>0.069800</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>Bedford Park</td>\n",
       "      <td>18</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.88514049610319 40.86639116468577...</td>\n",
       "      <td>4768.631579</td>\n",
       "      <td>2328.763158</td>\n",
       "      <td>2439.868421</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113158</td>\n",
       "      <td>13.357895</td>\n",
       "      <td>267.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>12</td>\n",
       "      <td>66</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>398.628205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0.101825</td>\n",
       "      <td>0.000547</td>\n",
       "      <td>Bellerose</td>\n",
       "      <td>19</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.72339732322405 40.75039734963143...</td>\n",
       "      <td>4232.666667</td>\n",
       "      <td>2033.333333</td>\n",
       "      <td>2199.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146667</td>\n",
       "      <td>6.280000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>389.740741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0.101825</td>\n",
       "      <td>0.000547</td>\n",
       "      <td>Bellerose</td>\n",
       "      <td>19</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.72339732322405 40.75039734963143...</td>\n",
       "      <td>4232.666667</td>\n",
       "      <td>2033.333333</td>\n",
       "      <td>2199.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146667</td>\n",
       "      <td>6.280000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>459.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>0.051440</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>Belmont</td>\n",
       "      <td>20</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.88386933956814 40.8642991793217,...</td>\n",
       "      <td>4529.080000</td>\n",
       "      <td>2181.320000</td>\n",
       "      <td>2347.760000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.136000</td>\n",
       "      <td>14.840000</td>\n",
       "      <td>84.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>398.628205</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>243</th>\n",
       "      <td>244</td>\n",
       "      <td>0.080569</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>Washington Heights South</td>\n",
       "      <td>244</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-73.94068965599914 40.85132371879379...</td>\n",
       "      <td>5326.084746</td>\n",
       "      <td>2619.355932</td>\n",
       "      <td>2706.728814</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114035</td>\n",
       "      <td>11.643860</td>\n",
       "      <td>252.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>29</td>\n",
       "      <td>103</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>384.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>245</td>\n",
       "      <td>0.095983</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>West Brighton</td>\n",
       "      <td>245</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>POLYGON ((-74.09788116552332 40.64036629396841...</td>\n",
       "      <td>3761.520833</td>\n",
       "      <td>1873.437500</td>\n",
       "      <td>1888.083333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>8.887500</td>\n",
       "      <td>39.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>14.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>453.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>246</td>\n",
       "      <td>0.069467</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>West Chelsea/Hudson Yards</td>\n",
       "      <td>246</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-74.00440121305829 40.76267962532284...</td>\n",
       "      <td>4161.071429</td>\n",
       "      <td>2197.285714</td>\n",
       "      <td>1963.785714</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.730769</td>\n",
       "      <td>62.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>428.287356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>247</td>\n",
       "      <td>0.092968</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>West Concourse</td>\n",
       "      <td>247</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.91222323132681 40.84236487604249...</td>\n",
       "      <td>4785.075000</td>\n",
       "      <td>2187.875000</td>\n",
       "      <td>2597.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>10.192500</td>\n",
       "      <td>291.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>30</td>\n",
       "      <td>44</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>374.952381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>247</td>\n",
       "      <td>0.092968</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>West Concourse</td>\n",
       "      <td>247</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.91222323132681 40.84236487604249...</td>\n",
       "      <td>4785.075000</td>\n",
       "      <td>2187.875000</td>\n",
       "      <td>2597.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>10.192500</td>\n",
       "      <td>291.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>30</td>\n",
       "      <td>44</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>377.422222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>248</td>\n",
       "      <td>0.056919</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>West Farms/Bronx River</td>\n",
       "      <td>248</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.86393889170346 40.84005285228221...</td>\n",
       "      <td>4507.060606</td>\n",
       "      <td>2207.909091</td>\n",
       "      <td>2299.151515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063636</td>\n",
       "      <td>11.921212</td>\n",
       "      <td>102.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>362.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>248</td>\n",
       "      <td>0.056919</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>West Farms/Bronx River</td>\n",
       "      <td>248</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.86393889170346 40.84005285228221...</td>\n",
       "      <td>4507.060606</td>\n",
       "      <td>2207.909091</td>\n",
       "      <td>2299.151515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063636</td>\n",
       "      <td>11.921212</td>\n",
       "      <td>102.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>380.177778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>248</td>\n",
       "      <td>0.056919</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>West Farms/Bronx River</td>\n",
       "      <td>248</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.86393889170346 40.84005285228221...</td>\n",
       "      <td>4507.060606</td>\n",
       "      <td>2207.909091</td>\n",
       "      <td>2299.151515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063636</td>\n",
       "      <td>11.921212</td>\n",
       "      <td>102.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>29.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>385.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>249</td>\n",
       "      <td>0.036384</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>West Village</td>\n",
       "      <td>249</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-74.00250787237373 40.72902464470071...</td>\n",
       "      <td>4529.702703</td>\n",
       "      <td>2205.513514</td>\n",
       "      <td>2324.189189</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059459</td>\n",
       "      <td>8.618919</td>\n",
       "      <td>161.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>428.287356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>250</td>\n",
       "      <td>0.079626</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>Westchester Village/Unionport</td>\n",
       "      <td>250</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.84553229974763 40.83918159375577...</td>\n",
       "      <td>4058.411765</td>\n",
       "      <td>1957.313725</td>\n",
       "      <td>2101.098039</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190196</td>\n",
       "      <td>10.803922</td>\n",
       "      <td>78.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>380.177778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>250</td>\n",
       "      <td>0.079626</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>Westchester Village/Unionport</td>\n",
       "      <td>250</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.84553229974763 40.83918159375577...</td>\n",
       "      <td>4058.411765</td>\n",
       "      <td>1957.313725</td>\n",
       "      <td>2101.098039</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190196</td>\n",
       "      <td>10.803922</td>\n",
       "      <td>78.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>29.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>385.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>251</td>\n",
       "      <td>0.137711</td>\n",
       "      <td>0.000626</td>\n",
       "      <td>Westerleigh</td>\n",
       "      <td>251</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>POLYGON ((-74.13107608658184 40.63115596499346...</td>\n",
       "      <td>3859.090909</td>\n",
       "      <td>1866.363636</td>\n",
       "      <td>1992.727273</td>\n",
       "      <td>...</td>\n",
       "      <td>0.194444</td>\n",
       "      <td>8.338889</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>14.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>453.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>252</td>\n",
       "      <td>0.158004</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>Whitestone</td>\n",
       "      <td>252</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.82050059436348 40.80101974798551...</td>\n",
       "      <td>4668.714286</td>\n",
       "      <td>2218.071429</td>\n",
       "      <td>2450.642857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>7.321429</td>\n",
       "      <td>76.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>433.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>253</td>\n",
       "      <td>0.036051</td>\n",
       "      <td>0.000078</td>\n",
       "      <td>Willets Point</td>\n",
       "      <td>253</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.83908494220884 40.76526518551812...</td>\n",
       "      <td>2235.333333</td>\n",
       "      <td>1014.000000</td>\n",
       "      <td>1221.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.550000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>433.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>254</td>\n",
       "      <td>0.085886</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>Williamsbridge/Olinville</td>\n",
       "      <td>254</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.85186704720081 40.87906715932692...</td>\n",
       "      <td>3267.981818</td>\n",
       "      <td>1518.454545</td>\n",
       "      <td>1749.527273</td>\n",
       "      <td>...</td>\n",
       "      <td>0.085455</td>\n",
       "      <td>12.809091</td>\n",
       "      <td>310.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>536.0</td>\n",
       "      <td>10</td>\n",
       "      <td>76</td>\n",
       "      <td>29.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>385.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>255</td>\n",
       "      <td>0.062384</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>Williamsburg (North Side)</td>\n",
       "      <td>255</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>POLYGON ((-73.96176213884964 40.72523705244288...</td>\n",
       "      <td>3376.869565</td>\n",
       "      <td>1622.304348</td>\n",
       "      <td>1754.565217</td>\n",
       "      <td>...</td>\n",
       "      <td>0.221739</td>\n",
       "      <td>6.552174</td>\n",
       "      <td>93.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>4</td>\n",
       "      <td>32</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>390.809524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>256</td>\n",
       "      <td>0.067915</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>Williamsburg (South Side)</td>\n",
       "      <td>256</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>POLYGON ((-73.95834350827948 40.71331455917075...</td>\n",
       "      <td>4021.050000</td>\n",
       "      <td>2004.150000</td>\n",
       "      <td>2016.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>6.665000</td>\n",
       "      <td>165.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>11</td>\n",
       "      <td>68</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>390.809524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>257</td>\n",
       "      <td>0.058669</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>Windsor Terrace</td>\n",
       "      <td>257</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>POLYGON ((-73.97984405598619 40.66073568810627...</td>\n",
       "      <td>3851.687500</td>\n",
       "      <td>1809.500000</td>\n",
       "      <td>2042.187500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.117241</td>\n",
       "      <td>8.175862</td>\n",
       "      <td>27.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7</td>\n",
       "      <td>37</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>389.606061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>257</td>\n",
       "      <td>0.058669</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>Windsor Terrace</td>\n",
       "      <td>257</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>POLYGON ((-73.97984405598619 40.66073568810627...</td>\n",
       "      <td>3851.687500</td>\n",
       "      <td>1809.500000</td>\n",
       "      <td>2042.187500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.117241</td>\n",
       "      <td>8.175862</td>\n",
       "      <td>27.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7</td>\n",
       "      <td>37</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>382.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>258</td>\n",
       "      <td>0.089013</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>Woodhaven</td>\n",
       "      <td>258</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.8450433445435 40.68932720442396,...</td>\n",
       "      <td>3995.342857</td>\n",
       "      <td>1937.600000</td>\n",
       "      <td>2057.742857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.152941</td>\n",
       "      <td>8.594118</td>\n",
       "      <td>87.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>8</td>\n",
       "      <td>86</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>414.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>258</td>\n",
       "      <td>0.089013</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>Woodhaven</td>\n",
       "      <td>258</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.8450433445435 40.68932720442396,...</td>\n",
       "      <td>3995.342857</td>\n",
       "      <td>1937.600000</td>\n",
       "      <td>2057.742857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.152941</td>\n",
       "      <td>8.594118</td>\n",
       "      <td>87.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>8</td>\n",
       "      <td>86</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>367.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>258</td>\n",
       "      <td>0.089013</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>Woodhaven</td>\n",
       "      <td>258</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.8450433445435 40.68932720442396,...</td>\n",
       "      <td>3995.342857</td>\n",
       "      <td>1937.600000</td>\n",
       "      <td>2057.742857</td>\n",
       "      <td>...</td>\n",
       "      <td>0.152941</td>\n",
       "      <td>8.594118</td>\n",
       "      <td>87.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>8</td>\n",
       "      <td>86</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>405.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>259</td>\n",
       "      <td>0.126750</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>Woodlawn/Wakefield</td>\n",
       "      <td>259</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.85107257276964 40.91037982057809...</td>\n",
       "      <td>2952.236842</td>\n",
       "      <td>1397.605263</td>\n",
       "      <td>1554.631579</td>\n",
       "      <td>...</td>\n",
       "      <td>0.147368</td>\n",
       "      <td>9.986842</td>\n",
       "      <td>140.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>398.628205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>259</td>\n",
       "      <td>0.126750</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>Woodlawn/Wakefield</td>\n",
       "      <td>259</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.85107257276964 40.91037982057809...</td>\n",
       "      <td>2952.236842</td>\n",
       "      <td>1397.605263</td>\n",
       "      <td>1554.631579</td>\n",
       "      <td>...</td>\n",
       "      <td>0.147368</td>\n",
       "      <td>9.986842</td>\n",
       "      <td>140.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>5</td>\n",
       "      <td>72</td>\n",
       "      <td>29.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>385.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>260</td>\n",
       "      <td>0.133514</td>\n",
       "      <td>0.000422</td>\n",
       "      <td>Woodside</td>\n",
       "      <td>260</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.90175515202327 40.760783744451, ...</td>\n",
       "      <td>4384.247423</td>\n",
       "      <td>2097.979381</td>\n",
       "      <td>2286.268041</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082222</td>\n",
       "      <td>10.110000</td>\n",
       "      <td>101.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>446.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>260</td>\n",
       "      <td>0.133514</td>\n",
       "      <td>0.000422</td>\n",
       "      <td>Woodside</td>\n",
       "      <td>260</td>\n",
       "      <td>Queens</td>\n",
       "      <td>POLYGON ((-73.90175515202327 40.760783744451, ...</td>\n",
       "      <td>4384.247423</td>\n",
       "      <td>2097.979381</td>\n",
       "      <td>2286.268041</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082222</td>\n",
       "      <td>10.110000</td>\n",
       "      <td>101.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>414.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>261</td>\n",
       "      <td>0.027120</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>World Trade Center</td>\n",
       "      <td>261</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-74.01332755938394 40.70503904262129...</td>\n",
       "      <td>3656.333333</td>\n",
       "      <td>1826.777778</td>\n",
       "      <td>1829.555556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.925000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>33</td>\n",
       "      <td>39</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>428.287356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>262</td>\n",
       "      <td>0.049064</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>Yorkville East</td>\n",
       "      <td>262</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>(POLYGON ((-73.94383399984505 40.7828673613000...</td>\n",
       "      <td>5202.407407</td>\n",
       "      <td>2337.555556</td>\n",
       "      <td>2864.851852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>6.888000</td>\n",
       "      <td>74.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>428.287356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>262</td>\n",
       "      <td>0.049064</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>Yorkville East</td>\n",
       "      <td>262</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>(POLYGON ((-73.94383399984505 40.7828673613000...</td>\n",
       "      <td>5202.407407</td>\n",
       "      <td>2337.555556</td>\n",
       "      <td>2864.851852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>6.888000</td>\n",
       "      <td>74.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>397.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>263</td>\n",
       "      <td>0.037017</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>Yorkville West</td>\n",
       "      <td>263</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-73.95218765492481 40.77302811508169...</td>\n",
       "      <td>4927.458333</td>\n",
       "      <td>2263.250000</td>\n",
       "      <td>2664.208333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.195455</td>\n",
       "      <td>6.086364</td>\n",
       "      <td>92.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>428.287356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>486 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     OBJECTID  Shape_Leng  Shape_Area                           zone  \\\n",
       "0           1    0.116357    0.000782                 Newark Airport   \n",
       "1           2    0.433470    0.004866                    Jamaica Bay   \n",
       "1           2    0.433470    0.004866                    Jamaica Bay   \n",
       "1           2    0.433470    0.004866                    Jamaica Bay   \n",
       "2           3    0.084341    0.000314        Allerton/Pelham Gardens   \n",
       "3           4    0.043567    0.000112                  Alphabet City   \n",
       "3           4    0.043567    0.000112                  Alphabet City   \n",
       "4           5    0.092146    0.000498                  Arden Heights   \n",
       "5           6    0.150491    0.000606        Arrochar/Fort Wadsworth   \n",
       "6           7    0.107417    0.000390                        Astoria   \n",
       "7           8    0.027591    0.000027                   Astoria Park   \n",
       "8           9    0.099784    0.000338                     Auburndale   \n",
       "8           9    0.099784    0.000338                     Auburndale   \n",
       "9          10    0.099839    0.000436                   Baisley Park   \n",
       "9          10    0.099839    0.000436                   Baisley Park   \n",
       "10         11    0.079211    0.000265                     Bath Beach   \n",
       "11         12    0.036661    0.000042                   Battery Park   \n",
       "12         13    0.050281    0.000149              Battery Park City   \n",
       "13         14    0.175214    0.001382                      Bay Ridge   \n",
       "14         15    0.144336    0.000925        Bay Terrace/Fort Totten   \n",
       "14         15    0.144336    0.000925        Bay Terrace/Fort Totten   \n",
       "15         16    0.141292    0.000872                        Bayside   \n",
       "15         16    0.141292    0.000872                        Bayside   \n",
       "16         17    0.093523    0.000323                        Bedford   \n",
       "16         17    0.093523    0.000323                        Bedford   \n",
       "16         17    0.093523    0.000323                        Bedford   \n",
       "17         18    0.069800    0.000149                   Bedford Park   \n",
       "18         19    0.101825    0.000547                      Bellerose   \n",
       "18         19    0.101825    0.000547                      Bellerose   \n",
       "19         20    0.051440    0.000135                        Belmont   \n",
       "..        ...         ...         ...                            ...   \n",
       "243       244    0.080569    0.000360       Washington Heights South   \n",
       "244       245    0.095983    0.000466                  West Brighton   \n",
       "245       246    0.069467    0.000281      West Chelsea/Hudson Yards   \n",
       "246       247    0.092968    0.000206                 West Concourse   \n",
       "246       247    0.092968    0.000206                 West Concourse   \n",
       "247       248    0.056919    0.000150         West Farms/Bronx River   \n",
       "247       248    0.056919    0.000150         West Farms/Bronx River   \n",
       "247       248    0.056919    0.000150         West Farms/Bronx River   \n",
       "248       249    0.036384    0.000072                   West Village   \n",
       "249       250    0.079626    0.000241  Westchester Village/Unionport   \n",
       "249       250    0.079626    0.000241  Westchester Village/Unionport   \n",
       "250       251    0.137711    0.000626                    Westerleigh   \n",
       "251       252    0.158004    0.001025                     Whitestone   \n",
       "252       253    0.036051    0.000078                  Willets Point   \n",
       "253       254    0.085886    0.000360       Williamsbridge/Olinville   \n",
       "254       255    0.062384    0.000172      Williamsburg (North Side)   \n",
       "255       256    0.067915    0.000169      Williamsburg (South Side)   \n",
       "256       257    0.058669    0.000139                Windsor Terrace   \n",
       "256       257    0.058669    0.000139                Windsor Terrace   \n",
       "257       258    0.089013    0.000366                      Woodhaven   \n",
       "257       258    0.089013    0.000366                      Woodhaven   \n",
       "257       258    0.089013    0.000366                      Woodhaven   \n",
       "258       259    0.126750    0.000395             Woodlawn/Wakefield   \n",
       "258       259    0.126750    0.000395             Woodlawn/Wakefield   \n",
       "259       260    0.133514    0.000422                       Woodside   \n",
       "259       260    0.133514    0.000422                       Woodside   \n",
       "260       261    0.027120    0.000034             World Trade Center   \n",
       "261       262    0.049064    0.000122                 Yorkville East   \n",
       "261       262    0.049064    0.000122                 Yorkville East   \n",
       "262       263    0.037017    0.000066                 Yorkville West   \n",
       "\n",
       "     LocationID        borough  \\\n",
       "0             1            EWR   \n",
       "1             2         Queens   \n",
       "1             2         Queens   \n",
       "1             2         Queens   \n",
       "2             3          Bronx   \n",
       "3             4      Manhattan   \n",
       "3             4      Manhattan   \n",
       "4             5  Staten Island   \n",
       "5             6  Staten Island   \n",
       "6             7         Queens   \n",
       "7             8         Queens   \n",
       "8             9         Queens   \n",
       "8             9         Queens   \n",
       "9            10         Queens   \n",
       "9            10         Queens   \n",
       "10           11       Brooklyn   \n",
       "11           12      Manhattan   \n",
       "12           13      Manhattan   \n",
       "13           14       Brooklyn   \n",
       "14           15         Queens   \n",
       "14           15         Queens   \n",
       "15           16         Queens   \n",
       "15           16         Queens   \n",
       "16           17       Brooklyn   \n",
       "16           17       Brooklyn   \n",
       "16           17       Brooklyn   \n",
       "17           18          Bronx   \n",
       "18           19         Queens   \n",
       "18           19         Queens   \n",
       "19           20          Bronx   \n",
       "..          ...            ...   \n",
       "243         244      Manhattan   \n",
       "244         245  Staten Island   \n",
       "245         246      Manhattan   \n",
       "246         247          Bronx   \n",
       "246         247          Bronx   \n",
       "247         248          Bronx   \n",
       "247         248          Bronx   \n",
       "247         248          Bronx   \n",
       "248         249      Manhattan   \n",
       "249         250          Bronx   \n",
       "249         250          Bronx   \n",
       "250         251  Staten Island   \n",
       "251         252         Queens   \n",
       "252         253         Queens   \n",
       "253         254          Bronx   \n",
       "254         255       Brooklyn   \n",
       "255         256       Brooklyn   \n",
       "256         257       Brooklyn   \n",
       "256         257       Brooklyn   \n",
       "257         258         Queens   \n",
       "257         258         Queens   \n",
       "257         258         Queens   \n",
       "258         259          Bronx   \n",
       "258         259          Bronx   \n",
       "259         260         Queens   \n",
       "259         260         Queens   \n",
       "260         261      Manhattan   \n",
       "261         262      Manhattan   \n",
       "261         262      Manhattan   \n",
       "262         263      Manhattan   \n",
       "\n",
       "                                              geometry     TotalPop  \\\n",
       "0    POLYGON ((-74.18445450453645 40.69500424662989...          NaN   \n",
       "1    (POLYGON ((-73.82337735819013 40.6389952954719...   528.000000   \n",
       "1    (POLYGON ((-73.82337735819013 40.6389952954719...   528.000000   \n",
       "1    (POLYGON ((-73.82337735819013 40.6389952954719...   528.000000   \n",
       "2    POLYGON ((-73.84792754846802 40.8713505269578,...  4042.735294   \n",
       "3    POLYGON ((-73.97177554799167 40.72582954148978...  4461.681818   \n",
       "3    POLYGON ((-73.97177554799167 40.72582954148978...  4461.681818   \n",
       "4    POLYGON ((-74.17421887421997 40.56257630700625...  5654.111111   \n",
       "5    POLYGON ((-74.06367464927494 40.6022063986771,...  4721.292683   \n",
       "6    POLYGON ((-73.90413779719505 40.7675285876882,...  3846.102362   \n",
       "7    POLYGON ((-73.92334184080988 40.77513718350537...  2774.166667   \n",
       "8    POLYGON ((-73.78502572762456 40.76104478957554...  2691.650000   \n",
       "8    POLYGON ((-73.78502572762456 40.76104478957554...  2691.650000   \n",
       "9    POLYGON ((-73.78326762579181 40.69000255270579...  3175.033333   \n",
       "9    POLYGON ((-73.78326762579181 40.69000255270579...  3175.033333   \n",
       "10   POLYGON ((-74.00109953530968 40.60304286103321...  3451.087719   \n",
       "11   POLYGON ((-74.01565901711844 40.70484134250074...  2877.555556   \n",
       "12   POLYGON ((-74.01244254395085 40.71906593040101...  4648.416667   \n",
       "13   POLYGON ((-74.03407474632135 40.64432217496695...  4367.224299   \n",
       "14   POLYGON ((-73.77740529323283 40.79660652207308...  3966.300000   \n",
       "14   POLYGON ((-73.77740529323283 40.79660652207308...  3966.300000   \n",
       "15   POLYGON ((-73.76857442643772 40.77911370668763...  2922.208333   \n",
       "15   POLYGON ((-73.76857442643772 40.77911370668763...  2922.208333   \n",
       "16   POLYGON ((-73.94306549666851 40.70143269137623...  5267.108696   \n",
       "16   POLYGON ((-73.94306549666851 40.70143269137623...  5267.108696   \n",
       "16   POLYGON ((-73.94306549666851 40.70143269137623...  5267.108696   \n",
       "17   POLYGON ((-73.88514049610319 40.86639116468577...  4768.631579   \n",
       "18   POLYGON ((-73.72339732322405 40.75039734963143...  4232.666667   \n",
       "18   POLYGON ((-73.72339732322405 40.75039734963143...  4232.666667   \n",
       "19   POLYGON ((-73.88386933956814 40.8642991793217,...  4529.080000   \n",
       "..                                                 ...          ...   \n",
       "243  POLYGON ((-73.94068965599914 40.85132371879379...  5326.084746   \n",
       "244  POLYGON ((-74.09788116552332 40.64036629396841...  3761.520833   \n",
       "245  POLYGON ((-74.00440121305829 40.76267962532284...  4161.071429   \n",
       "246  POLYGON ((-73.91222323132681 40.84236487604249...  4785.075000   \n",
       "246  POLYGON ((-73.91222323132681 40.84236487604249...  4785.075000   \n",
       "247  POLYGON ((-73.86393889170346 40.84005285228221...  4507.060606   \n",
       "247  POLYGON ((-73.86393889170346 40.84005285228221...  4507.060606   \n",
       "247  POLYGON ((-73.86393889170346 40.84005285228221...  4507.060606   \n",
       "248  POLYGON ((-74.00250787237373 40.72902464470071...  4529.702703   \n",
       "249  POLYGON ((-73.84553229974763 40.83918159375577...  4058.411765   \n",
       "249  POLYGON ((-73.84553229974763 40.83918159375577...  4058.411765   \n",
       "250  POLYGON ((-74.13107608658184 40.63115596499346...  3859.090909   \n",
       "251  POLYGON ((-73.82050059436348 40.80101974798551...  4668.714286   \n",
       "252  POLYGON ((-73.83908494220884 40.76526518551812...  2235.333333   \n",
       "253  POLYGON ((-73.85186704720081 40.87906715932692...  3267.981818   \n",
       "254  POLYGON ((-73.96176213884964 40.72523705244288...  3376.869565   \n",
       "255  POLYGON ((-73.95834350827948 40.71331455917075...  4021.050000   \n",
       "256  POLYGON ((-73.97984405598619 40.66073568810627...  3851.687500   \n",
       "256  POLYGON ((-73.97984405598619 40.66073568810627...  3851.687500   \n",
       "257  POLYGON ((-73.8450433445435 40.68932720442396,...  3995.342857   \n",
       "257  POLYGON ((-73.8450433445435 40.68932720442396,...  3995.342857   \n",
       "257  POLYGON ((-73.8450433445435 40.68932720442396,...  3995.342857   \n",
       "258  POLYGON ((-73.85107257276964 40.91037982057809...  2952.236842   \n",
       "258  POLYGON ((-73.85107257276964 40.91037982057809...  2952.236842   \n",
       "259  POLYGON ((-73.90175515202327 40.760783744451, ...  4384.247423   \n",
       "259  POLYGON ((-73.90175515202327 40.760783744451, ...  4384.247423   \n",
       "260  POLYGON ((-74.01332755938394 40.70503904262129...  3656.333333   \n",
       "261  (POLYGON ((-73.94383399984505 40.7828673613000...  5202.407407   \n",
       "261  (POLYGON ((-73.94383399984505 40.7828673613000...  5202.407407   \n",
       "262  POLYGON ((-73.95218765492481 40.77302811508169...  4927.458333   \n",
       "\n",
       "             Men        Women     ...      FamilyWork  Unemployment  FELONY  \\\n",
       "0            NaN          NaN     ...             NaN           NaN     NaN   \n",
       "1     248.750000   279.250000     ...        0.000000     11.600000     NaN   \n",
       "1     248.750000   279.250000     ...        0.000000     11.600000     NaN   \n",
       "1     248.750000   279.250000     ...        0.000000     11.600000     NaN   \n",
       "2    1848.558824  2194.176471     ...        0.070588     13.126471    79.0   \n",
       "3    2030.363636  2431.318182     ...        0.218182      8.936364    86.0   \n",
       "3    2030.363636  2431.318182     ...        0.218182      8.936364    86.0   \n",
       "4    2756.111111  2898.000000     ...        0.000000      8.000000    29.0   \n",
       "5    2282.560976  2438.731707     ...        0.102564      7.615385    46.0   \n",
       "6    1851.574803  1994.527559     ...        0.129508      9.670732   218.0   \n",
       "7    1434.333333  1339.833333     ...        0.200000      6.020000     3.0   \n",
       "8    1327.500000  1364.150000     ...        0.189474      6.952632    40.0   \n",
       "8    1327.500000  1364.150000     ...        0.189474      6.952632    40.0   \n",
       "9    1513.700000  1661.333333     ...        0.111667     11.505000   173.0   \n",
       "9    1513.700000  1661.333333     ...        0.111667     11.505000   173.0   \n",
       "10   1640.456140  1810.631579     ...        0.120000      9.961818    68.0   \n",
       "11   1402.666667  1474.888889     ...        0.075000      7.775000     4.0   \n",
       "12   2339.416667  2309.000000     ...        0.025000      6.533333    31.0   \n",
       "13   2062.663551  2304.560748     ...        0.120192      8.006731   211.0   \n",
       "14   1836.500000  2129.800000     ...        0.222222      8.133333    42.0   \n",
       "14   1836.500000  2129.800000     ...        0.222222      8.133333    42.0   \n",
       "15   1383.583333  1538.625000     ...        0.216667      7.237500   125.0   \n",
       "15   1383.583333  1538.625000     ...        0.216667      7.237500   125.0   \n",
       "16   2572.728261  2694.380435     ...        0.102198     11.758242   376.0   \n",
       "16   2572.728261  2694.380435     ...        0.102198     11.758242   376.0   \n",
       "16   2572.728261  2694.380435     ...        0.102198     11.758242   376.0   \n",
       "17   2328.763158  2439.868421     ...        0.113158     13.357895   267.0   \n",
       "18   2033.333333  2199.333333     ...        0.146667      6.280000    45.0   \n",
       "18   2033.333333  2199.333333     ...        0.146667      6.280000    45.0   \n",
       "19   2181.320000  2347.760000     ...        0.136000     14.840000    84.0   \n",
       "..           ...          ...     ...             ...           ...     ...   \n",
       "243  2619.355932  2706.728814     ...        0.114035     11.643860   252.0   \n",
       "244  1873.437500  1888.083333     ...        0.166667      8.887500    39.0   \n",
       "245  2197.285714  1963.785714     ...        0.000000      7.730769    62.0   \n",
       "246  2187.875000  2597.200000     ...        0.140000     10.192500   291.0   \n",
       "246  2187.875000  2597.200000     ...        0.140000     10.192500   291.0   \n",
       "247  2207.909091  2299.151515     ...        0.063636     11.921212   102.0   \n",
       "247  2207.909091  2299.151515     ...        0.063636     11.921212   102.0   \n",
       "247  2207.909091  2299.151515     ...        0.063636     11.921212   102.0   \n",
       "248  2205.513514  2324.189189     ...        0.059459      8.618919   161.0   \n",
       "249  1957.313725  2101.098039     ...        0.190196     10.803922    78.0   \n",
       "249  1957.313725  2101.098039     ...        0.190196     10.803922    78.0   \n",
       "250  1866.363636  1992.727273     ...        0.194444      8.338889    33.0   \n",
       "251  2218.071429  2450.642857     ...        0.071429      7.321429    76.0   \n",
       "252  1014.000000  1221.333333     ...        0.000000      8.550000     6.0   \n",
       "253  1518.454545  1749.527273     ...        0.085455     12.809091   310.0   \n",
       "254  1622.304348  1754.565217     ...        0.221739      6.552174    93.0   \n",
       "255  2004.150000  2016.900000     ...        0.190000      6.665000   165.0   \n",
       "256  1809.500000  2042.187500     ...        0.117241      8.175862    27.0   \n",
       "256  1809.500000  2042.187500     ...        0.117241      8.175862    27.0   \n",
       "257  1937.600000  2057.742857     ...        0.152941      8.594118    87.0   \n",
       "257  1937.600000  2057.742857     ...        0.152941      8.594118    87.0   \n",
       "257  1937.600000  2057.742857     ...        0.152941      8.594118    87.0   \n",
       "258  1397.605263  1554.631579     ...        0.147368      9.986842   140.0   \n",
       "258  1397.605263  1554.631579     ...        0.147368      9.986842   140.0   \n",
       "259  2097.979381  2286.268041     ...        0.082222     10.110000   101.0   \n",
       "259  2097.979381  2286.268041     ...        0.082222     10.110000   101.0   \n",
       "260  1826.777778  1829.555556     ...        0.000000      6.925000    60.0   \n",
       "261  2337.555556  2864.851852     ...        0.128000      6.888000    74.0   \n",
       "261  2337.555556  2864.851852     ...        0.128000      6.888000    74.0   \n",
       "262  2263.250000  2664.208333     ...        0.195455      6.086364    92.0   \n",
       "\n",
       "     VIOLATION  MISDEMEANOR  subway  bus  index_right  SchoolDist         sat  \n",
       "0          NaN          NaN       0    0          NaN         NaN         NaN  \n",
       "1          NaN          NaN       0    4          9.0        18.0  374.428571  \n",
       "1          NaN          NaN       0    4          7.0        19.0  367.944444  \n",
       "1          NaN          NaN       0    4         28.0        27.0  405.363636  \n",
       "2         36.0        173.0       3   53         29.0        11.0  385.980392  \n",
       "3         72.0        205.0       0   26         31.0         2.0  428.287356  \n",
       "3         72.0        205.0       0   26         17.0         1.0  435.259259  \n",
       "4         18.0         42.0       0   38         14.0        31.0  453.055556  \n",
       "5         55.0         61.0       1   90         14.0        31.0  453.055556  \n",
       "6         85.0        421.0      14  104         25.0        30.0  446.416667  \n",
       "7          NaN          2.0       0    0         25.0        30.0  446.416667  \n",
       "8         13.0         46.0       0   87         22.0        25.0  433.212121  \n",
       "8         13.0         46.0       0   87          4.0        26.0  459.200000  \n",
       "9         71.0        204.0       0   71         28.0        27.0  405.363636  \n",
       "9         71.0        204.0       0   71         23.0        28.0  445.575758  \n",
       "10        45.0        152.0       0   45         10.0        20.0  418.400000  \n",
       "11         NaN          5.0       1    9         31.0         2.0  428.287356  \n",
       "12        13.0         36.0       0   19         31.0         2.0  428.287356  \n",
       "13        89.0        425.0      14  195         10.0        20.0  418.400000  \n",
       "14        21.0         20.0       0  109         22.0        25.0  433.212121  \n",
       "14        21.0         20.0       0  109          4.0        26.0  459.200000  \n",
       "15        37.0        121.0       0  139         22.0        25.0  433.212121  \n",
       "15        37.0        121.0       0  139          4.0        26.0  459.200000  \n",
       "16       172.0        499.0      15   97          8.0        13.0  409.666667  \n",
       "16       172.0        499.0      15   97         18.0        14.0  390.809524  \n",
       "16       172.0        499.0      15   97          0.0        16.0  362.500000  \n",
       "17       100.0        516.0      12   66         20.0        10.0  398.628205  \n",
       "18        22.0         71.0       0   81          3.0        29.0  389.740741  \n",
       "18        22.0         71.0       0   81          4.0        26.0  459.200000  \n",
       "19        62.0        201.0       0   33         20.0        10.0  398.628205  \n",
       "..         ...          ...     ...  ...          ...         ...         ...  \n",
       "243      129.0        553.0      29  103         13.0         6.0  384.000000  \n",
       "244       38.0         90.0       0   99         14.0        31.0  453.055556  \n",
       "245       26.0         81.0       2   33         31.0         2.0  428.287356  \n",
       "246      148.0        575.0      30   44         16.0         9.0  374.952381  \n",
       "246      148.0        575.0      30   44         15.0         7.0  377.422222  \n",
       "247       88.0        211.0       4   36         21.0        12.0  362.555556  \n",
       "247       88.0        211.0       4   36         30.0         8.0  380.177778  \n",
       "247       88.0        211.0       4   36         29.0        11.0  385.980392  \n",
       "248       36.0        216.0      19   28         31.0         2.0  428.287356  \n",
       "249       62.0        133.0       4   36         30.0         8.0  380.177778  \n",
       "249       62.0        133.0       4   36         29.0        11.0  385.980392  \n",
       "250       34.0         75.0       0  121         14.0        31.0  453.055556  \n",
       "251       32.0        131.0       0  108         22.0        25.0  433.212121  \n",
       "252        NaN          4.0       0    2         22.0        25.0  433.212121  \n",
       "253      177.0        536.0      10   76         29.0        11.0  385.980392  \n",
       "254       36.0        149.0       4   32         18.0        14.0  390.809524  \n",
       "255       82.0        185.0      11   68         18.0        14.0  390.809524  \n",
       "256       18.0         40.0       7   37          5.0        15.0  389.606061  \n",
       "256       18.0         40.0       7   37          6.0        17.0  382.296296  \n",
       "257       78.0        204.0       8   86         24.0        24.0  414.000000  \n",
       "257       78.0        204.0       8   86          7.0        19.0  367.944444  \n",
       "257       78.0        204.0       8   86         28.0        27.0  405.363636  \n",
       "258       78.0        264.0       5   72         20.0        10.0  398.628205  \n",
       "258       78.0        264.0       5   72         29.0        11.0  385.980392  \n",
       "259       60.0        166.0      11   72         25.0        30.0  446.416667  \n",
       "259       60.0        166.0      11   72         24.0        24.0  414.000000  \n",
       "260       23.0        158.0      33   39         31.0         2.0  428.287356  \n",
       "261       25.0        114.0       0   26         31.0         2.0  428.287356  \n",
       "261       25.0        114.0       0   26         19.0         4.0  397.047619  \n",
       "262       38.0        181.0       8   26         31.0         2.0  428.287356  \n",
       "\n",
       "[486 rows x 48 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial_sat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:23:13.649947Z",
     "start_time": "2019-06-16T16:23:13.634953Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>sat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>382.578884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>385.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>431.773308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>453.055556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID         sat\n",
       "0         1         NaN\n",
       "1         2  382.578884\n",
       "2         3  385.980392\n",
       "3         4  431.773308\n",
       "4         5  453.055556"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial_sat = df_spatial_sat.groupby('OBJECTID').mean()['sat'].reset_index()\n",
    "df_spatial_sat.columns=['OBJECTID', 'sat']\n",
    "print(df_spatial_sat.shape)\n",
    "df_spatial_sat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:23:13.696780Z",
     "start_time": "2019-06-16T16:23:13.650909Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 46)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>zone</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>borough</th>\n",
       "      <th>geometry</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>...</th>\n",
       "      <th>PublicWork</th>\n",
       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>FELONY</th>\n",
       "      <th>VIOLATION</th>\n",
       "      <th>MISDEMEANOR</th>\n",
       "      <th>subway</th>\n",
       "      <th>bus</th>\n",
       "      <th>sat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.116357</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>Newark Airport</td>\n",
       "      <td>1</td>\n",
       "      <td>EWR</td>\n",
       "      <td>POLYGON ((-74.18445450453645 40.69500424662989...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>248.750000</td>\n",
       "      <td>279.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>382.578884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.084341</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.84792754846802 40.8713505269578,...</td>\n",
       "      <td>4042.735294</td>\n",
       "      <td>1848.558824</td>\n",
       "      <td>2194.176471</td>\n",
       "      <td>...</td>\n",
       "      <td>17.741176</td>\n",
       "      <td>4.214706</td>\n",
       "      <td>0.070588</td>\n",
       "      <td>13.126471</td>\n",
       "      <td>79.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>385.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-73.97177554799167 40.72582954148978...</td>\n",
       "      <td>4461.681818</td>\n",
       "      <td>2030.363636</td>\n",
       "      <td>2431.318182</td>\n",
       "      <td>...</td>\n",
       "      <td>15.427273</td>\n",
       "      <td>6.690909</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>8.936364</td>\n",
       "      <td>86.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>431.773308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.092146</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>POLYGON ((-74.17421887421997 40.56257630700625...</td>\n",
       "      <td>5654.111111</td>\n",
       "      <td>2756.111111</td>\n",
       "      <td>2898.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.644444</td>\n",
       "      <td>5.711111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>453.055556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  Shape_Leng  Shape_Area                     zone  LocationID  \\\n",
       "0         1    0.116357    0.000782           Newark Airport           1   \n",
       "1         2    0.433470    0.004866              Jamaica Bay           2   \n",
       "2         3    0.084341    0.000314  Allerton/Pelham Gardens           3   \n",
       "3         4    0.043567    0.000112            Alphabet City           4   \n",
       "4         5    0.092146    0.000498            Arden Heights           5   \n",
       "\n",
       "         borough                                           geometry  \\\n",
       "0            EWR  POLYGON ((-74.18445450453645 40.69500424662989...   \n",
       "1         Queens  (POLYGON ((-73.82337735819013 40.6389952954719...   \n",
       "2          Bronx  POLYGON ((-73.84792754846802 40.8713505269578,...   \n",
       "3      Manhattan  POLYGON ((-73.97177554799167 40.72582954148978...   \n",
       "4  Staten Island  POLYGON ((-74.17421887421997 40.56257630700625...   \n",
       "\n",
       "      TotalPop          Men        Women     ...      PublicWork  \\\n",
       "0          NaN          NaN          NaN     ...             NaN   \n",
       "1   528.000000   248.750000   279.250000     ...       33.400000   \n",
       "2  4042.735294  1848.558824  2194.176471     ...       17.741176   \n",
       "3  4461.681818  2030.363636  2431.318182     ...       15.427273   \n",
       "4  5654.111111  2756.111111  2898.000000     ...       17.644444   \n",
       "\n",
       "   SelfEmployed  FamilyWork  Unemployment  FELONY  VIOLATION  MISDEMEANOR  \\\n",
       "0           NaN         NaN           NaN     NaN        NaN          NaN   \n",
       "1      2.400000    0.000000     11.600000     NaN        NaN          NaN   \n",
       "2      4.214706    0.070588     13.126471    79.0       36.0        173.0   \n",
       "3      6.690909    0.218182      8.936364    86.0       72.0        205.0   \n",
       "4      5.711111    0.000000      8.000000    29.0       18.0         42.0   \n",
       "\n",
       "   subway  bus         sat  \n",
       "0       0    0         NaN  \n",
       "1       0    4  382.578884  \n",
       "2       3   53  385.980392  \n",
       "3       0   26  431.773308  \n",
       "4       0   38  453.055556  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial = pd.merge(df_spatial, df_spatial_sat, on='OBJECTID', how='left')\n",
    "print(df_spatial.shape)\n",
    "df_spatial.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BBL (Borough-Block-Lot)\n",
    "Unused"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:23:13.704731Z",
     "start_time": "2019-06-16T16:23:13.697784Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file in place, you can continue\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile(\"data/BBL.gz\"):\n",
    "    url = 'https://data.cityofnewyork.us/api/geospatial/nqwf-w8eh?method=export&format=Shapefile'\n",
    "    urllib.request.urlretrieve(url, \"BBL.gz\")\n",
    "    !mv BBL.gz data\n",
    "    !unzip -o data/BBL.gz -d data/BBL\n",
    "else:\n",
    "    print(\"file in place, you can continue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:24:23.718795Z",
     "start_time": "2019-06-16T16:23:13.705767Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(45582, 17)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>base_bbl</th>\n",
       "      <th>bin</th>\n",
       "      <th>cnstrct_yr</th>\n",
       "      <th>doitt_id</th>\n",
       "      <th>feat_code</th>\n",
       "      <th>geomsource</th>\n",
       "      <th>groundelev</th>\n",
       "      <th>heightroof</th>\n",
       "      <th>date_lstmo</th>\n",
       "      <th>time_lstmo</th>\n",
       "      <th>lststatype</th>\n",
       "      <th>mpluto_bbl</th>\n",
       "      <th>name</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_len</th>\n",
       "      <th>geometry</th>\n",
       "      <th>borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1008870001</td>\n",
       "      <td>1018457.0</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>584238.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>37.0</td>\n",
       "      <td>155.490000</td>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>00:00:00.000</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1008870001</td>\n",
       "      <td>None</td>\n",
       "      <td>4594.256246</td>\n",
       "      <td>347.419155</td>\n",
       "      <td>POLYGON ((-73.98236677504677 40.7452378978967,...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1013490035</td>\n",
       "      <td>1039988.0</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>66240.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>52.0</td>\n",
       "      <td>167.214468</td>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>00:00:00.000</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1013490035</td>\n",
       "      <td>None</td>\n",
       "      <td>4652.202721</td>\n",
       "      <td>302.585551</td>\n",
       "      <td>POLYGON ((-73.96425191767109 40.75860443341254...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1021210037</td>\n",
       "      <td>1062896.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>708881.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>154.0</td>\n",
       "      <td>59.722628</td>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>00:00:00.000</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1021210037</td>\n",
       "      <td>None</td>\n",
       "      <td>2245.290397</td>\n",
       "      <td>237.134669</td>\n",
       "      <td>POLYGON ((-73.93870129348369 40.83782255082231...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1017730020</td>\n",
       "      <td>1087605.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>869430.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>18.0</td>\n",
       "      <td>47.625370</td>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>00:00:00.000</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1017730020</td>\n",
       "      <td>None</td>\n",
       "      <td>63339.404067</td>\n",
       "      <td>1433.365195</td>\n",
       "      <td>POLYGON ((-73.93631082260336 40.80331094790955...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1005040036</td>\n",
       "      <td>1007814.0</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>263088.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>Photogramm</td>\n",
       "      <td>19.0</td>\n",
       "      <td>74.860000</td>\n",
       "      <td>2017-08-22</td>\n",
       "      <td>00:00:00.000</td>\n",
       "      <td>Constructed</td>\n",
       "      <td>1005040036</td>\n",
       "      <td>None</td>\n",
       "      <td>4988.414472</td>\n",
       "      <td>289.193571</td>\n",
       "      <td>POLYGON ((-74.00326692427507 40.72554497463773...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     base_bbl        bin  cnstrct_yr  doitt_id  feat_code  geomsource  \\\n",
       "0  1008870001  1018457.0      1935.0  584238.0     2100.0  Photogramm   \n",
       "1  1013490035  1039988.0      1931.0   66240.0     2100.0  Photogramm   \n",
       "2  1021210037  1062896.0      1910.0  708881.0     2100.0  Photogramm   \n",
       "3  1017730020  1087605.0      1998.0  869430.0     2100.0  Photogramm   \n",
       "4  1005040036  1007814.0      1920.0  263088.0     2100.0  Photogramm   \n",
       "\n",
       "   groundelev  heightroof  date_lstmo    time_lstmo   lststatype  mpluto_bbl  \\\n",
       "0        37.0  155.490000  2017-08-22  00:00:00.000  Constructed  1008870001   \n",
       "1        52.0  167.214468  2017-08-22  00:00:00.000  Constructed  1013490035   \n",
       "2       154.0   59.722628  2017-08-22  00:00:00.000  Constructed  1021210037   \n",
       "3        18.0   47.625370  2017-08-22  00:00:00.000  Constructed  1017730020   \n",
       "4        19.0   74.860000  2017-08-22  00:00:00.000  Constructed  1005040036   \n",
       "\n",
       "   name    shape_area    shape_len  \\\n",
       "0  None   4594.256246   347.419155   \n",
       "1  None   4652.202721   302.585551   \n",
       "2  None   2245.290397   237.134669   \n",
       "3  None  63339.404067  1433.365195   \n",
       "4  None   4988.414472   289.193571   \n",
       "\n",
       "                                            geometry borough  \n",
       "0  POLYGON ((-73.98236677504677 40.7452378978967,...       1  \n",
       "1  POLYGON ((-73.96425191767109 40.75860443341254...       1  \n",
       "2  POLYGON ((-73.93870129348369 40.83782255082231...       1  \n",
       "3  POLYGON ((-73.93631082260336 40.80331094790955...       1  \n",
       "4  POLYGON ((-74.00326692427507 40.72554497463773...       1  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = ! ls data/BBL | grep .shp\n",
    "BBL = gpd.GeoDataFrame.from_file('data/BBL/' + filename[1])\n",
    "BBL['borough'] = BBL['base_bbl'].apply(lambda x: x[0])\n",
    "BBL = BBL[BBL['borough'] == '1'].reset_index(drop=True)\n",
    "print(BBL.shape)\n",
    "BBL.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Park\n",
    "Unused"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:24:23.728791Z",
     "start_time": "2019-06-16T16:24:23.719756Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file in place, you can continue\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile(\"data/parks.gz\"):\n",
    "    url = 'https://data.cityofnewyork.us/api/geospatial/k2ya-ucmv?method=export&format=Shapefile'\n",
    "    urllib.request.urlretrieve(url, \"parks.gz\")\n",
    "    !mv parks.gz data\n",
    "    !unzip -o data/parks.gz -d data/parks\n",
    "else:\n",
    "    print(\"file in place, you can continue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:24:24.073569Z",
     "start_time": "2019-06-16T16:24:23.729730Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(384, 36)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acquisitio</th>\n",
       "      <th>acres</th>\n",
       "      <th>address</th>\n",
       "      <th>borough</th>\n",
       "      <th>class</th>\n",
       "      <th>commission</th>\n",
       "      <th>communityb</th>\n",
       "      <th>councildis</th>\n",
       "      <th>department</th>\n",
       "      <th>eapply</th>\n",
       "      <th>...</th>\n",
       "      <th>precinct</th>\n",
       "      <th>retired</th>\n",
       "      <th>signname</th>\n",
       "      <th>subcategor</th>\n",
       "      <th>typecatego</th>\n",
       "      <th>url</th>\n",
       "      <th>us_congres</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19440801000000.00000</td>\n",
       "      <td>0.991</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>PLGD</td>\n",
       "      <td>20100106000000.00000</td>\n",
       "      <td>111</td>\n",
       "      <td>9</td>\n",
       "      <td>M-11</td>\n",
       "      <td>Abraham Lincoln Playground</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>False</td>\n",
       "      <td>Abraham Lincoln Playground</td>\n",
       "      <td>Neighborhood Plgd</td>\n",
       "      <td>Community Park</td>\n",
       "      <td>http://www.nycgovparks.org/parks/M193/</td>\n",
       "      <td>13</td>\n",
       "      <td>No</td>\n",
       "      <td>10037</td>\n",
       "      <td>POLYGON ((-73.93682693707335 40.81184552631012...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20021120000000.00000</td>\n",
       "      <td>0.110</td>\n",
       "      <td>335 EAST 108 STREET</td>\n",
       "      <td>M</td>\n",
       "      <td>PARK</td>\n",
       "      <td>20100106000000.00000</td>\n",
       "      <td>111</td>\n",
       "      <td>8</td>\n",
       "      <td>M-11</td>\n",
       "      <td>Humacao Community Garden</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>Humacao Community Garden</td>\n",
       "      <td>Greenthumb</td>\n",
       "      <td>Garden</td>\n",
       "      <td>http://www.nycgovparks.org/parks/M344/</td>\n",
       "      <td>13</td>\n",
       "      <td>No</td>\n",
       "      <td>10029</td>\n",
       "      <td>POLYGON ((-73.9393568469474 40.79144347341266,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19060110000000.00000</td>\n",
       "      <td>0.340</td>\n",
       "      <td>None</td>\n",
       "      <td>M</td>\n",
       "      <td>PARK</td>\n",
       "      <td>20100106000000.00000</td>\n",
       "      <td>109</td>\n",
       "      <td>7</td>\n",
       "      <td>M-09</td>\n",
       "      <td>Montefiore SquarePark</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>Montefiore Square</td>\n",
       "      <td>Sitting Area/Triangle/Mall</td>\n",
       "      <td>Triangle/Plaza</td>\n",
       "      <td>http://www.nycgovparks.org/parks/M055/</td>\n",
       "      <td>13</td>\n",
       "      <td>No</td>\n",
       "      <td>10031</td>\n",
       "      <td>POLYGON ((-73.953952321884 40.82122637435634, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19971219000000.00000</td>\n",
       "      <td>0.118</td>\n",
       "      <td>128 WEST 139 STREET</td>\n",
       "      <td>M</td>\n",
       "      <td>PLGD</td>\n",
       "      <td>20100106000000.00000</td>\n",
       "      <td>110</td>\n",
       "      <td>9</td>\n",
       "      <td>M-10</td>\n",
       "      <td>Abyssinian Tot Lot</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>False</td>\n",
       "      <td>Abyssinian Tot Lot</td>\n",
       "      <td>Neighborhood Plgd</td>\n",
       "      <td>Playground</td>\n",
       "      <td>http://www.nycgovparks.org/parks/M303/</td>\n",
       "      <td>13</td>\n",
       "      <td>No</td>\n",
       "      <td>10030</td>\n",
       "      <td>POLYGON ((-73.9406885465869 40.81726604067758,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19061010000000.00000</td>\n",
       "      <td>3.900</td>\n",
       "      <td>294 10 Av</td>\n",
       "      <td>M</td>\n",
       "      <td>PARK</td>\n",
       "      <td>20100106000000.00000</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>M-04</td>\n",
       "      <td>Chelsea Park</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>Chelsea Park</td>\n",
       "      <td>Neighborhood Plgd</td>\n",
       "      <td>Playground</td>\n",
       "      <td>http://www.nycgovparks.org/parks/M011/</td>\n",
       "      <td>10</td>\n",
       "      <td>No</td>\n",
       "      <td>10001</td>\n",
       "      <td>POLYGON ((-74.00177474199378 40.75069828559118...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              acquisitio  acres              address borough class  \\\n",
       "0   19440801000000.00000  0.991                 None       M  PLGD   \n",
       "13  20021120000000.00000  0.110  335 EAST 108 STREET       M  PARK   \n",
       "15  19060110000000.00000  0.340                 None       M  PARK   \n",
       "18  19971219000000.00000  0.118  128 WEST 139 STREET       M  PLGD   \n",
       "27  19061010000000.00000  3.900            294 10 Av       M  PARK   \n",
       "\n",
       "              commission communityb councildis department  \\\n",
       "0   20100106000000.00000        111          9       M-11   \n",
       "13  20100106000000.00000        111          8       M-11   \n",
       "15  20100106000000.00000        109          7       M-09   \n",
       "18  20100106000000.00000        110          9       M-10   \n",
       "27  20100106000000.00000        104          3       M-04   \n",
       "\n",
       "                        eapply  \\\n",
       "0   Abraham Lincoln Playground   \n",
       "13    Humacao Community Garden   \n",
       "15       Montefiore SquarePark   \n",
       "18          Abyssinian Tot Lot   \n",
       "27                Chelsea Park   \n",
       "\n",
       "                          ...                          precinct retired  \\\n",
       "0                         ...                                25   False   \n",
       "13                        ...                                23   False   \n",
       "15                        ...                                30   False   \n",
       "18                        ...                                32   False   \n",
       "27                        ...                                10   False   \n",
       "\n",
       "                      signname                  subcategor      typecatego  \\\n",
       "0   Abraham Lincoln Playground           Neighborhood Plgd  Community Park   \n",
       "13    Humacao Community Garden                  Greenthumb          Garden   \n",
       "15           Montefiore Square  Sitting Area/Triangle/Mall  Triangle/Plaza   \n",
       "18          Abyssinian Tot Lot           Neighborhood Plgd      Playground   \n",
       "27                Chelsea Park           Neighborhood Plgd      Playground   \n",
       "\n",
       "                                       url us_congres waterfront zipcode  \\\n",
       "0   http://www.nycgovparks.org/parks/M193/         13         No   10037   \n",
       "13  http://www.nycgovparks.org/parks/M344/         13         No   10029   \n",
       "15  http://www.nycgovparks.org/parks/M055/         13         No   10031   \n",
       "18  http://www.nycgovparks.org/parks/M303/         13         No   10030   \n",
       "27  http://www.nycgovparks.org/parks/M011/         10         No   10001   \n",
       "\n",
       "                                             geometry  \n",
       "0   POLYGON ((-73.93682693707335 40.81184552631012...  \n",
       "13  POLYGON ((-73.9393568469474 40.79144347341266,...  \n",
       "15  POLYGON ((-73.953952321884 40.82122637435634, ...  \n",
       "18  POLYGON ((-73.9406885465869 40.81726604067758,...  \n",
       "27  POLYGON ((-74.00177474199378 40.75069828559118...  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = ! ls data/parks | grep .shp\n",
    "parks = gpd.GeoDataFrame.from_file('data/parks/' + filename[0])\n",
    "# parks = parks[parks['borough'] == 'M']\n",
    "print(parks.shape)\n",
    "parks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:24:24.358811Z",
     "start_time": "2019-06-16T16:24:24.075591Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc70d79198>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "parks.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open Parking and Camera Violations\n",
    "* Initial dataset loaded 05/14/2016.  \n",
    "https://data.cityofnewyork.us/City-Government/Open-Parking-and-Camera-Violations/nc67-uf89"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parking Meters GPS Coordinates and Status\n",
    "Count number  \n",
    "https://data.cityofnewyork.us/Transportation/Parking-Meters-GPS-Coordinates-and-Status/5jsj-cq4s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:24:36.381754Z",
     "start_time": "2019-06-16T16:24:36.374746Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file in place, you can continue\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile(\"data/meters.gz\"):\n",
    "    url = 'https://data.cityofnewyork.us/api/geospatial/5jsj-cq4s?method=export&format=Shapefile'\n",
    "    urllib.request.urlretrieve(url, \"meters.gz\")\n",
    "    !mv meters.gz data\n",
    "    !unzip -o data/meters.gz -d data/meters\n",
    "else:\n",
    "    print(\"file in place, you can continue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:24:37.622410Z",
     "start_time": "2019-06-16T16:24:36.993041Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14763, 6)\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>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>meter_no</th>\n",
       "      <th>meter_type</th>\n",
       "      <th>status</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.677012</td>\n",
       "      <td>-73.865170</td>\n",
       "      <td>3773004</td>\n",
       "      <td>Strada</td>\n",
       "      <td>Active</td>\n",
       "      <td>POINT (-73.86517020266234 40.6770120488885)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.761239</td>\n",
       "      <td>-73.925041</td>\n",
       "      <td>4953003</td>\n",
       "      <td>Strada</td>\n",
       "      <td>Active</td>\n",
       "      <td>POINT (-73.9250406819429 40.76123920493658)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.727927</td>\n",
       "      <td>-73.854794</td>\n",
       "      <td>4173077</td>\n",
       "      <td>Strada</td>\n",
       "      <td>Active</td>\n",
       "      <td>POINT (-73.85479441192734 40.72792668002486)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.730597</td>\n",
       "      <td>-73.860763</td>\n",
       "      <td>4173097</td>\n",
       "      <td>Strada</td>\n",
       "      <td>Active</td>\n",
       "      <td>POINT (-73.86076269886161 40.73059678692016)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.685706</td>\n",
       "      <td>-73.825778</td>\n",
       "      <td>4293047</td>\n",
       "      <td>Strada</td>\n",
       "      <td>Active</td>\n",
       "      <td>POINT (-73.82577820994788 40.68570620241398)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat       long meter_no meter_type  status  \\\n",
       "0  40.677012 -73.865170  3773004     Strada  Active   \n",
       "1  40.761239 -73.925041  4953003     Strada  Active   \n",
       "2  40.727927 -73.854794  4173077     Strada  Active   \n",
       "3  40.730597 -73.860763  4173097     Strada  Active   \n",
       "4  40.685706 -73.825778  4293047     Strada  Active   \n",
       "\n",
       "                                       geometry  \n",
       "0   POINT (-73.86517020266234 40.6770120488885)  \n",
       "1   POINT (-73.9250406819429 40.76123920493658)  \n",
       "2  POINT (-73.85479441192734 40.72792668002486)  \n",
       "3  POINT (-73.86076269886161 40.73059678692016)  \n",
       "4  POINT (-73.82577820994788 40.68570620241398)  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = ! ls data/meters | grep .shp\n",
    "meters = gpd.GeoDataFrame.from_file('data/meters/' + filename[0])\n",
    "print(meters.shape)\n",
    "meters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:24:38.528189Z",
     "start_time": "2019-06-16T16:24:37.853606Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc65d48f28>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "meters.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:27:41.843054Z",
     "start_time": "2019-06-16T16:27:41.190800Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\geopandas\\tools\\sjoin.py:44: UserWarning: CRS of frames being joined does not match!\n",
      "  warn('CRS of frames being joined does not match!')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14804, 13)\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>OBJECTID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>zone</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>borough</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>meter_no</th>\n",
       "      <th>meter_type</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.116357</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>Newark Airport</td>\n",
       "      <td>1</td>\n",
       "      <td>EWR</td>\n",
       "      <td>POLYGON ((-74.18445450453645 40.69500424662989...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.084341</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.84792754846802 40.8713505269578,...</td>\n",
       "      <td>1904.0</td>\n",
       "      <td>40.870638</td>\n",
       "      <td>-73.846756</td>\n",
       "      <td>2293030</td>\n",
       "      <td>Strada</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.084341</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.84792754846802 40.8713505269578,...</td>\n",
       "      <td>1068.0</td>\n",
       "      <td>40.870905</td>\n",
       "      <td>-73.847271</td>\n",
       "      <td>2293029</td>\n",
       "      <td>Strada</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.084341</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.84792754846802 40.8713505269578,...</td>\n",
       "      <td>2180.0</td>\n",
       "      <td>40.871363</td>\n",
       "      <td>-73.848150</td>\n",
       "      <td>2293028</td>\n",
       "      <td>Strada</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  Shape_Leng  Shape_Area                     zone  LocationID  \\\n",
       "0         1    0.116357    0.000782           Newark Airport           1   \n",
       "1         2    0.433470    0.004866              Jamaica Bay           2   \n",
       "2         3    0.084341    0.000314  Allerton/Pelham Gardens           3   \n",
       "2         3    0.084341    0.000314  Allerton/Pelham Gardens           3   \n",
       "2         3    0.084341    0.000314  Allerton/Pelham Gardens           3   \n",
       "\n",
       "  borough                                           geometry  index_right  \\\n",
       "0     EWR  POLYGON ((-74.18445450453645 40.69500424662989...          NaN   \n",
       "1  Queens  (POLYGON ((-73.82337735819013 40.6389952954719...          NaN   \n",
       "2   Bronx  POLYGON ((-73.84792754846802 40.8713505269578,...       1904.0   \n",
       "2   Bronx  POLYGON ((-73.84792754846802 40.8713505269578,...       1068.0   \n",
       "2   Bronx  POLYGON ((-73.84792754846802 40.8713505269578,...       2180.0   \n",
       "\n",
       "         lat       long meter_no meter_type  status  \n",
       "0        NaN        NaN      NaN        NaN     NaN  \n",
       "1        NaN        NaN      NaN        NaN     NaN  \n",
       "2  40.870638 -73.846756  2293030     Strada  Active  \n",
       "2  40.870905 -73.847271  2293029     Strada  Active  \n",
       "2  40.871363 -73.848150  2293028     Strada  Active  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial_meters = gpd.sjoin(taxi_zones, meters, how='left')\n",
    "print(df_spatial_meters.shape)\n",
    "df_spatial_meters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:27:42.179965Z",
     "start_time": "2019-06-16T16:27:42.162010Z"
    }
   },
   "outputs": [],
   "source": [
    "df_spatial_meters = df_spatial_meters.groupby('OBJECTID').count()[['meter_no']].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:28:13.173622Z",
     "start_time": "2019-06-16T16:28:13.165670Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 2)\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>OBJECTID</th>\n",
       "      <th>meter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  meter\n",
       "0         1      0\n",
       "1         2      0\n",
       "2         3     16\n",
       "3         4      3\n",
       "4         5      0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial_meters.columns = ['OBJECTID', 'meter']\n",
    "print(df_spatial_meters.shape)\n",
    "df_spatial_meters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:29:02.655796Z",
     "start_time": "2019-06-16T16:29:02.607927Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 47)\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>OBJECTID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>zone</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>borough</th>\n",
       "      <th>geometry</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>...</th>\n",
       "      <th>SelfEmployed</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>FELONY</th>\n",
       "      <th>VIOLATION</th>\n",
       "      <th>MISDEMEANOR</th>\n",
       "      <th>subway</th>\n",
       "      <th>bus</th>\n",
       "      <th>sat</th>\n",
       "      <th>meter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.116357</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>Newark Airport</td>\n",
       "      <td>1</td>\n",
       "      <td>EWR</td>\n",
       "      <td>POLYGON ((-74.18445450453645 40.69500424662989...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>248.750000</td>\n",
       "      <td>279.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>382.578884</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.084341</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.84792754846802 40.8713505269578,...</td>\n",
       "      <td>4042.735294</td>\n",
       "      <td>1848.558824</td>\n",
       "      <td>2194.176471</td>\n",
       "      <td>...</td>\n",
       "      <td>4.214706</td>\n",
       "      <td>0.070588</td>\n",
       "      <td>13.126471</td>\n",
       "      <td>79.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>385.980392</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-73.97177554799167 40.72582954148978...</td>\n",
       "      <td>4461.681818</td>\n",
       "      <td>2030.363636</td>\n",
       "      <td>2431.318182</td>\n",
       "      <td>...</td>\n",
       "      <td>6.690909</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>8.936364</td>\n",
       "      <td>86.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>431.773308</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.092146</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>POLYGON ((-74.17421887421997 40.56257630700625...</td>\n",
       "      <td>5654.111111</td>\n",
       "      <td>2756.111111</td>\n",
       "      <td>2898.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.711111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>453.055556</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  Shape_Leng  Shape_Area                     zone  LocationID  \\\n",
       "0         1    0.116357    0.000782           Newark Airport           1   \n",
       "1         2    0.433470    0.004866              Jamaica Bay           2   \n",
       "2         3    0.084341    0.000314  Allerton/Pelham Gardens           3   \n",
       "3         4    0.043567    0.000112            Alphabet City           4   \n",
       "4         5    0.092146    0.000498            Arden Heights           5   \n",
       "\n",
       "         borough                                           geometry  \\\n",
       "0            EWR  POLYGON ((-74.18445450453645 40.69500424662989...   \n",
       "1         Queens  (POLYGON ((-73.82337735819013 40.6389952954719...   \n",
       "2          Bronx  POLYGON ((-73.84792754846802 40.8713505269578,...   \n",
       "3      Manhattan  POLYGON ((-73.97177554799167 40.72582954148978...   \n",
       "4  Staten Island  POLYGON ((-74.17421887421997 40.56257630700625...   \n",
       "\n",
       "      TotalPop          Men        Women  ...    SelfEmployed  FamilyWork  \\\n",
       "0          NaN          NaN          NaN  ...             NaN         NaN   \n",
       "1   528.000000   248.750000   279.250000  ...        2.400000    0.000000   \n",
       "2  4042.735294  1848.558824  2194.176471  ...        4.214706    0.070588   \n",
       "3  4461.681818  2030.363636  2431.318182  ...        6.690909    0.218182   \n",
       "4  5654.111111  2756.111111  2898.000000  ...        5.711111    0.000000   \n",
       "\n",
       "   Unemployment  FELONY  VIOLATION  MISDEMEANOR  subway  bus         sat  \\\n",
       "0           NaN     NaN        NaN          NaN       0    0         NaN   \n",
       "1     11.600000     NaN        NaN          NaN       0    4  382.578884   \n",
       "2     13.126471    79.0       36.0        173.0       3   53  385.980392   \n",
       "3      8.936364    86.0       72.0        205.0       0   26  431.773308   \n",
       "4      8.000000    29.0       18.0         42.0       0   38  453.055556   \n",
       "\n",
       "   meter  \n",
       "0      0  \n",
       "1      0  \n",
       "2     16  \n",
       "3      3  \n",
       "4      0  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial = pd.merge(df_spatial, df_spatial_meters, on='OBJECTID', how = 'left')\n",
    "print(df_spatial.shape)\n",
    "df_spatial.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parking Lot\n",
    "Count number & area  \n",
    "https://data.cityofnewyork.us/City-Government/Parking-Lot/h7zy-iq3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:30:05.051604Z",
     "start_time": "2019-06-16T16:30:05.045652Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file in place, you can continue\n"
     ]
    }
   ],
   "source": [
    "if not os.path.isfile(\"data/parkinglot.gz\"):\n",
    "    url = 'https://data.cityofnewyork.us/api/geospatial/h7zy-iq3d?method=export&format=Shapefile'\n",
    "    urllib.request.urlretrieve(url, \"parkinglot.gz\")\n",
    "    !mv parkinglot.gz data\n",
    "    !unzip -o data/parkinglot.gz -d data/parkinglot\n",
    "else:\n",
    "    print(\"file in place, you can continue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:30:06.847196Z",
     "start_time": "2019-06-16T16:30:05.520744Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20714, 7)\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>feat_code</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>source_id</th>\n",
       "      <th>status</th>\n",
       "      <th>sub_code</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>32676.073685</td>\n",
       "      <td>778.318836</td>\n",
       "      <td>2.150000e+10</td>\n",
       "      <td>Unchanged</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>POLYGON ((-73.97910559725612 40.69049642121562...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>108080.622252</td>\n",
       "      <td>3737.772791</td>\n",
       "      <td>2.150000e+10</td>\n",
       "      <td>Updated</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>POLYGON ((-73.97943374194773 40.69316130194368...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>5407.860486</td>\n",
       "      <td>442.772495</td>\n",
       "      <td>2.150000e+10</td>\n",
       "      <td>Unchanged</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>POLYGON ((-73.98323992493719 40.70281275758538...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>4027.729151</td>\n",
       "      <td>284.623893</td>\n",
       "      <td>2.150000e+10</td>\n",
       "      <td>Unchanged</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>POLYGON ((-73.98189157977659 40.70310347358521...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>122903.007235</td>\n",
       "      <td>1567.687419</td>\n",
       "      <td>2.150000e+10</td>\n",
       "      <td>Unchanged</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>POLYGON ((-73.98594989444811 40.70232449447619...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feat_code     shape_area   shape_leng     source_id     status  sub_code  \\\n",
       "0     5000.0   32676.073685   778.318836  2.150000e+10  Unchanged  500000.0   \n",
       "1     5000.0  108080.622252  3737.772791  2.150000e+10    Updated  500000.0   \n",
       "2     5000.0    5407.860486   442.772495  2.150000e+10  Unchanged  500000.0   \n",
       "3     5000.0    4027.729151   284.623893  2.150000e+10  Unchanged  500000.0   \n",
       "4     5000.0  122903.007235  1567.687419  2.150000e+10  Unchanged  500000.0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-73.97910559725612 40.69049642121562...  \n",
       "1  POLYGON ((-73.97943374194773 40.69316130194368...  \n",
       "2  POLYGON ((-73.98323992493719 40.70281275758538...  \n",
       "3  POLYGON ((-73.98189157977659 40.70310347358521...  \n",
       "4  POLYGON ((-73.98594989444811 40.70232449447619...  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = ! ls data/parkinglot | grep .shp\n",
    "parkinglot = gpd.GeoDataFrame.from_file('data/parkinglot/' + filename[0])\n",
    "print(parkinglot.shape)\n",
    "parkinglot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:30:11.674284Z",
     "start_time": "2019-06-16T16:30:06.849190Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc10cd69e8>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "parkinglot.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:46:08.638524Z",
     "start_time": "2019-06-16T16:46:07.089502Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\geopandas\\tools\\sjoin.py:44: UserWarning: CRS of frames being joined does not match!\n",
      "  warn('CRS of frames being joined does not match!')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20735, 14)\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>OBJECTID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>zone</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>borough</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>feat_code</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>source_id</th>\n",
       "      <th>status</th>\n",
       "      <th>sub_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.116357</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>Newark Airport</td>\n",
       "      <td>1</td>\n",
       "      <td>EWR</td>\n",
       "      <td>POLYGON ((-74.18445450453645 40.69500424662989...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>900.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>48612.053493</td>\n",
       "      <td>1348.232494</td>\n",
       "      <td>2.500000e+09</td>\n",
       "      <td>Unchanged</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>949.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2211.579943</td>\n",
       "      <td>189.694616</td>\n",
       "      <td>2.500000e+09</td>\n",
       "      <td>Unchanged</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>952.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4297.587625</td>\n",
       "      <td>427.117125</td>\n",
       "      <td>2.500000e+09</td>\n",
       "      <td>Updated</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>944.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>2746.482252</td>\n",
       "      <td>310.007537</td>\n",
       "      <td>2.500000e+09</td>\n",
       "      <td>Unchanged</td>\n",
       "      <td>500000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  Shape_Leng  Shape_Area            zone  LocationID borough  \\\n",
       "0         1    0.116357    0.000782  Newark Airport           1     EWR   \n",
       "1         2    0.433470    0.004866     Jamaica Bay           2  Queens   \n",
       "1         2    0.433470    0.004866     Jamaica Bay           2  Queens   \n",
       "1         2    0.433470    0.004866     Jamaica Bay           2  Queens   \n",
       "1         2    0.433470    0.004866     Jamaica Bay           2  Queens   \n",
       "\n",
       "                                            geometry  index_right  feat_code  \\\n",
       "0  POLYGON ((-74.18445450453645 40.69500424662989...          NaN        NaN   \n",
       "1  (POLYGON ((-73.82337735819013 40.6389952954719...        900.0     5000.0   \n",
       "1  (POLYGON ((-73.82337735819013 40.6389952954719...        949.0     5000.0   \n",
       "1  (POLYGON ((-73.82337735819013 40.6389952954719...        952.0     5000.0   \n",
       "1  (POLYGON ((-73.82337735819013 40.6389952954719...        944.0     5000.0   \n",
       "\n",
       "     shape_area   shape_leng     source_id     status  sub_code  \n",
       "0           NaN          NaN           NaN        NaN       NaN  \n",
       "1  48612.053493  1348.232494  2.500000e+09  Unchanged  500000.0  \n",
       "1   2211.579943   189.694616  2.500000e+09  Unchanged  500000.0  \n",
       "1   4297.587625   427.117125  2.500000e+09    Updated  500000.0  \n",
       "1   2746.482252   310.007537  2.500000e+09  Unchanged  500000.0  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial_parkinglot = gpd.sjoin(taxi_zones, parkinglot, how='left')\n",
    "print(df_spatial_parkinglot.shape)\n",
    "df_spatial_parkinglot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:46:18.042216Z",
     "start_time": "2019-06-16T16:46:18.032207Z"
    }
   },
   "outputs": [],
   "source": [
    "df_spatial_parkinglot = df_spatial_parkinglot.groupby('OBJECTID').sum()[['shape_area']].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:46:30.116527Z",
     "start_time": "2019-06-16T16:46:30.108547Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>parkinglot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.143448e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.170854e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.891444e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.937056e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID    parkinglot\n",
       "0         1  0.000000e+00\n",
       "1         2  1.143448e+05\n",
       "2         3  1.170854e+06\n",
       "3         4  1.891444e+05\n",
       "4         5  3.937056e+05"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial_parkinglot.columns = ['OBJECTID', 'parkinglot']\n",
    "print(df_spatial_parkinglot.shape)\n",
    "df_spatial_parkinglot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:46:43.669176Z",
     "start_time": "2019-06-16T16:46:43.621279Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 48)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>zone</th>\n",
       "      <th>LocationID</th>\n",
       "      <th>borough</th>\n",
       "      <th>geometry</th>\n",
       "      <th>TotalPop</th>\n",
       "      <th>Men</th>\n",
       "      <th>Women</th>\n",
       "      <th>...</th>\n",
       "      <th>FamilyWork</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>FELONY</th>\n",
       "      <th>VIOLATION</th>\n",
       "      <th>MISDEMEANOR</th>\n",
       "      <th>subway</th>\n",
       "      <th>bus</th>\n",
       "      <th>sat</th>\n",
       "      <th>meter</th>\n",
       "      <th>parkinglot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.116357</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>Newark Airport</td>\n",
       "      <td>1</td>\n",
       "      <td>EWR</td>\n",
       "      <td>POLYGON ((-74.18445450453645 40.69500424662989...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.433470</td>\n",
       "      <td>0.004866</td>\n",
       "      <td>Jamaica Bay</td>\n",
       "      <td>2</td>\n",
       "      <td>Queens</td>\n",
       "      <td>(POLYGON ((-73.82337735819013 40.6389952954719...</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>248.750000</td>\n",
       "      <td>279.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>382.578884</td>\n",
       "      <td>0</td>\n",
       "      <td>1.143448e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.084341</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>Allerton/Pelham Gardens</td>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>POLYGON ((-73.84792754846802 40.8713505269578,...</td>\n",
       "      <td>4042.735294</td>\n",
       "      <td>1848.558824</td>\n",
       "      <td>2194.176471</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070588</td>\n",
       "      <td>13.126471</td>\n",
       "      <td>79.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>385.980392</td>\n",
       "      <td>16</td>\n",
       "      <td>1.170854e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.043567</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>POLYGON ((-73.97177554799167 40.72582954148978...</td>\n",
       "      <td>4461.681818</td>\n",
       "      <td>2030.363636</td>\n",
       "      <td>2431.318182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218182</td>\n",
       "      <td>8.936364</td>\n",
       "      <td>86.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>431.773308</td>\n",
       "      <td>3</td>\n",
       "      <td>1.891444e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.092146</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>5</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>POLYGON ((-74.17421887421997 40.56257630700625...</td>\n",
       "      <td>5654.111111</td>\n",
       "      <td>2756.111111</td>\n",
       "      <td>2898.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>453.055556</td>\n",
       "      <td>0</td>\n",
       "      <td>3.937056e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID  Shape_Leng  Shape_Area                     zone  LocationID  \\\n",
       "0         1    0.116357    0.000782           Newark Airport           1   \n",
       "1         2    0.433470    0.004866              Jamaica Bay           2   \n",
       "2         3    0.084341    0.000314  Allerton/Pelham Gardens           3   \n",
       "3         4    0.043567    0.000112            Alphabet City           4   \n",
       "4         5    0.092146    0.000498            Arden Heights           5   \n",
       "\n",
       "         borough                                           geometry  \\\n",
       "0            EWR  POLYGON ((-74.18445450453645 40.69500424662989...   \n",
       "1         Queens  (POLYGON ((-73.82337735819013 40.6389952954719...   \n",
       "2          Bronx  POLYGON ((-73.84792754846802 40.8713505269578,...   \n",
       "3      Manhattan  POLYGON ((-73.97177554799167 40.72582954148978...   \n",
       "4  Staten Island  POLYGON ((-74.17421887421997 40.56257630700625...   \n",
       "\n",
       "      TotalPop          Men        Women      ...       FamilyWork  \\\n",
       "0          NaN          NaN          NaN      ...              NaN   \n",
       "1   528.000000   248.750000   279.250000      ...         0.000000   \n",
       "2  4042.735294  1848.558824  2194.176471      ...         0.070588   \n",
       "3  4461.681818  2030.363636  2431.318182      ...         0.218182   \n",
       "4  5654.111111  2756.111111  2898.000000      ...         0.000000   \n",
       "\n",
       "   Unemployment  FELONY  VIOLATION  MISDEMEANOR  subway  bus         sat  \\\n",
       "0           NaN     NaN        NaN          NaN       0    0         NaN   \n",
       "1     11.600000     NaN        NaN          NaN       0    4  382.578884   \n",
       "2     13.126471    79.0       36.0        173.0       3   53  385.980392   \n",
       "3      8.936364    86.0       72.0        205.0       0   26  431.773308   \n",
       "4      8.000000    29.0       18.0         42.0       0   38  453.055556   \n",
       "\n",
       "   meter    parkinglot  \n",
       "0      0  0.000000e+00  \n",
       "1      0  1.143448e+05  \n",
       "2     16  1.170854e+06  \n",
       "3      3  1.891444e+05  \n",
       "4      0  3.937056e+05  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spatial = pd.merge(df_spatial, df_spatial_parkinglot, on='OBJECTID', how = 'left')\n",
    "print(df_spatial.shape)\n",
    "df_spatial.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parking Regulation Locations and Signs\n",
    "https://data.cityofnewyork.us/Transportation/Parking-Regulation-Locations-and-Signs/xswq-wnv9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meter parking price (Potential)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Garage parking price (Potential)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Google POI (Potential)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-16T16:56:03.255595Z",
     "start_time": "2019-06-16T16:56:03.222714Z"
    }
   },
   "outputs": [],
   "source": [
    "df_spatial.drop(['geometry'], axis=1).to_csv('output_final/spatial.csv')"
   ]
  }
 ],
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