notebooks/01-march-and-311-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## M.A.R.C.H. incident analyses by location\n",
    "\n",
    "In this analysis, we isolate MARCH raids based on Census tract. Once we join the latitude and longitude points to their respective Census tracts, we'll be able to determine how many other businesses were targeted in the immediate neighborhood surrounding \"Friends and Lovers\" — its Census tract and all adjacent Census tracts.\n",
    "\n",
    "The data was downloaded here: https://github.com/gltd/march/tree/master/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gp\n",
    "from shapely.geometry import Point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2305"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "march_data_lat_long = pd.read_csv(\n",
    "    '../data/march_raids_with_lat.csv',\n",
    "    parse_dates=[\"inspection_date\"]\n",
    ")\n",
    "len(march_data_lat_long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                          2305\n",
       "mean     2014-09-28 09:12:53.101952\n",
       "min             2012-02-22 00:00:00\n",
       "25%             2013-05-03 00:00:00\n",
       "50%             2014-06-27 00:00:00\n",
       "75%             2016-02-27 00:00:00\n",
       "max             2017-07-21 00:00:00\n",
       "Name: inspection_date, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "march_data_lat_long[\"inspection_date\"].describe(datetime_is_numeric = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>borough_name</th>\n",
       "      <th>borough_abbrev</th>\n",
       "      <th>bin_number</th>\n",
       "      <th>block</th>\n",
       "      <th>lot</th>\n",
       "      <th>inspection_date</th>\n",
       "      <th>access_1</th>\n",
       "      <th>ecb_violation_number</th>\n",
       "      <th>dob_violation_number</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 NAGLE AVENUE</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>MN</td>\n",
       "      <td>1064121</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2015-07-18</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.930469</td>\n",
       "      <td>40.859105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10 AKRON STREET</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>SI</td>\n",
       "      <td>5104032</td>\n",
       "      <td>1546.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2013-04-12</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-74.158704</td>\n",
       "      <td>40.611921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10 WEST MT EDEN AVENUE</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>BX</td>\n",
       "      <td>2008329</td>\n",
       "      <td>2859.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2015-02-07</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.915216</td>\n",
       "      <td>40.844369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10-28 BEACH 20 STREET</td>\n",
       "      <td>Queens</td>\n",
       "      <td>QN</td>\n",
       "      <td>4298257</td>\n",
       "      <td>15561.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2013-04-19</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.752835</td>\n",
       "      <td>40.603038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10-28 BEACH 20 STREET</td>\n",
       "      <td>Queens</td>\n",
       "      <td>QN</td>\n",
       "      <td>4298257</td>\n",
       "      <td>15561.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2012-06-16</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.752835</td>\n",
       "      <td>40.603038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  address   borough_name borough_abbrev  bin_number    block  \\\n",
       "0          1 NAGLE AVENUE      Manhattan             MN     1064121   2171.0   \n",
       "1         10 AKRON STREET  Staten Island             SI     5104032   1546.0   \n",
       "2  10 WEST MT EDEN AVENUE          Bronx             BX     2008329   2859.0   \n",
       "3   10-28 BEACH 20 STREET         Queens             QN     4298257  15561.0   \n",
       "4   10-28 BEACH 20 STREET         Queens             QN     4298257  15561.0   \n",
       "\n",
       "    lot inspection_date                            access_1  \\\n",
       "0   1.0      2015-07-18  MARCH: NO ENFORCEMENT ACTION TAKEN   \n",
       "1   5.0      2013-04-12  MARCH: NO ENFORCEMENT ACTION TAKEN   \n",
       "2  44.0      2015-02-07  MARCH: NO ENFORCEMENT ACTION TAKEN   \n",
       "3  13.0      2013-04-19  MARCH: NO ENFORCEMENT ACTION TAKEN   \n",
       "4  13.0      2012-06-16  MARCH: NO ENFORCEMENT ACTION TAKEN   \n",
       "\n",
       "  ecb_violation_number dob_violation_number  longitude   latitude  \n",
       "0                  NaN                  NaN -73.930469  40.859105  \n",
       "1                  NaN                  NaN -74.158704  40.611921  \n",
       "2                  NaN                  NaN -73.915216  40.844369  \n",
       "3                  NaN                  NaN -73.752835  40.603038  \n",
       "4                  NaN                  NaN -73.752835  40.603038  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "march_data_lat_long.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chart what MARCH raids looked like over time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11cc013a0>]"
      ]
     },
     "execution_count": 5,
     "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": [
    "plt.plot(\n",
    "    march_data_lat_long.set_index(\n",
    "        \"inspection_date\"\n",
    "   )[\"address\"].resample(\n",
    "        \"MS\"\n",
    "    ).count(\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge MARCH data with Census tracts\n",
    "This part of the analysis:\n",
    "* turns  data into geodataframe\n",
    "* merges MARCH data with Census tract data\n",
    "* produces a count per Census tract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "march_data_lat_long[\"geometry\"]=( \n",
    "    march_data_lat_long.apply(lambda z: Point(z[\"longitude\"], z[\"latitude\"]), axis = 1)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "march_data_lat_long_geodf = gp.GeoDataFrame(march_data_lat_long, crs = \"EPSG:4269\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Geographic 2D CRS: EPSG:4269>\n",
       "Name: NAD83\n",
       "Axis Info [ellipsoidal]:\n",
       "- Lat[north]: Geodetic latitude (degree)\n",
       "- Lon[east]: Geodetic longitude (degree)\n",
       "Area of Use:\n",
       "- name: North America - NAD83\n",
       "- bounds: (167.65, 14.92, -47.74, 86.46)\n",
       "Datum: North American Datum 1983\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "march_data_lat_long_geodf.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>borough_name</th>\n",
       "      <th>borough_abbrev</th>\n",
       "      <th>bin_number</th>\n",
       "      <th>block</th>\n",
       "      <th>lot</th>\n",
       "      <th>inspection_date</th>\n",
       "      <th>access_1</th>\n",
       "      <th>ecb_violation_number</th>\n",
       "      <th>dob_violation_number</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 NAGLE AVENUE</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>MN</td>\n",
       "      <td>1064121</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2015-07-18</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.930469</td>\n",
       "      <td>40.859105</td>\n",
       "      <td>POINT (-73.93047 40.85911)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10 AKRON STREET</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>SI</td>\n",
       "      <td>5104032</td>\n",
       "      <td>1546.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2013-04-12</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-74.158704</td>\n",
       "      <td>40.611921</td>\n",
       "      <td>POINT (-74.15870 40.61192)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10 WEST MT EDEN AVENUE</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>BX</td>\n",
       "      <td>2008329</td>\n",
       "      <td>2859.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2015-02-07</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.915216</td>\n",
       "      <td>40.844369</td>\n",
       "      <td>POINT (-73.91522 40.84437)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10-28 BEACH 20 STREET</td>\n",
       "      <td>Queens</td>\n",
       "      <td>QN</td>\n",
       "      <td>4298257</td>\n",
       "      <td>15561.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2013-04-19</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.752835</td>\n",
       "      <td>40.603038</td>\n",
       "      <td>POINT (-73.75284 40.60304)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10-28 BEACH 20 STREET</td>\n",
       "      <td>Queens</td>\n",
       "      <td>QN</td>\n",
       "      <td>4298257</td>\n",
       "      <td>15561.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2012-06-16</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.752835</td>\n",
       "      <td>40.603038</td>\n",
       "      <td>POINT (-73.75284 40.60304)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  address   borough_name borough_abbrev  bin_number    block  \\\n",
       "0          1 NAGLE AVENUE      Manhattan             MN     1064121   2171.0   \n",
       "1         10 AKRON STREET  Staten Island             SI     5104032   1546.0   \n",
       "2  10 WEST MT EDEN AVENUE          Bronx             BX     2008329   2859.0   \n",
       "3   10-28 BEACH 20 STREET         Queens             QN     4298257  15561.0   \n",
       "4   10-28 BEACH 20 STREET         Queens             QN     4298257  15561.0   \n",
       "\n",
       "    lot inspection_date                            access_1  \\\n",
       "0   1.0      2015-07-18  MARCH: NO ENFORCEMENT ACTION TAKEN   \n",
       "1   5.0      2013-04-12  MARCH: NO ENFORCEMENT ACTION TAKEN   \n",
       "2  44.0      2015-02-07  MARCH: NO ENFORCEMENT ACTION TAKEN   \n",
       "3  13.0      2013-04-19  MARCH: NO ENFORCEMENT ACTION TAKEN   \n",
       "4  13.0      2012-06-16  MARCH: NO ENFORCEMENT ACTION TAKEN   \n",
       "\n",
       "  ecb_violation_number dob_violation_number  longitude   latitude  \\\n",
       "0                  NaN                  NaN -73.930469  40.859105   \n",
       "1                  NaN                  NaN -74.158704  40.611921   \n",
       "2                  NaN                  NaN -73.915216  40.844369   \n",
       "3                  NaN                  NaN -73.752835  40.603038   \n",
       "4                  NaN                  NaN -73.752835  40.603038   \n",
       "\n",
       "                     geometry  \n",
       "0  POINT (-73.93047 40.85911)  \n",
       "1  POINT (-74.15870 40.61192)  \n",
       "2  POINT (-73.91522 40.84437)  \n",
       "3  POINT (-73.75284 40.60304)  \n",
       "4  POINT (-73.75284 40.60304)  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "march_data_lat_long_geodf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the polygon data of New York City Census tracts\n",
    "- read in the NY Census tracts\n",
    "- filter it down to NYC Census tracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>TRACTCE</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>NAMELSAD</th>\n",
       "      <th>MTFCC</th>\n",
       "      <th>FUNCSTAT</th>\n",
       "      <th>ALAND</th>\n",
       "      <th>AWATER</th>\n",
       "      <th>INTPTLAT</th>\n",
       "      <th>INTPTLON</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>081</td>\n",
       "      <td>044800</td>\n",
       "      <td>36081044800</td>\n",
       "      <td>448</td>\n",
       "      <td>Census Tract 448</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>208002</td>\n",
       "      <td>0</td>\n",
       "      <td>+40.7110219</td>\n",
       "      <td>-073.8026344</td>\n",
       "      <td>POLYGON ((-73.80646 40.71206, -73.80556 40.712...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>081</td>\n",
       "      <td>045800</td>\n",
       "      <td>36081045800</td>\n",
       "      <td>458</td>\n",
       "      <td>Census Tract 458</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>245281</td>\n",
       "      <td>0</td>\n",
       "      <td>+40.7152626</td>\n",
       "      <td>-073.7909261</td>\n",
       "      <td>POLYGON ((-73.79364 40.71382, -73.79362 40.713...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>081</td>\n",
       "      <td>046200</td>\n",
       "      <td>36081046200</td>\n",
       "      <td>462</td>\n",
       "      <td>Census Tract 462</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>249611</td>\n",
       "      <td>0</td>\n",
       "      <td>+40.7098547</td>\n",
       "      <td>-073.7879749</td>\n",
       "      <td>POLYGON ((-73.79203 40.71107, -73.79101 40.711...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>081</td>\n",
       "      <td>046300</td>\n",
       "      <td>36081046300</td>\n",
       "      <td>463</td>\n",
       "      <td>Census Tract 463</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>159415</td>\n",
       "      <td>0</td>\n",
       "      <td>+40.7440007</td>\n",
       "      <td>-073.8710900</td>\n",
       "      <td>POLYGON ((-73.87468 40.74335, -73.87423 40.743...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>081</td>\n",
       "      <td>046400</td>\n",
       "      <td>36081046400</td>\n",
       "      <td>464</td>\n",
       "      <td>Census Tract 464</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>228767</td>\n",
       "      <td>0</td>\n",
       "      <td>+40.7168637</td>\n",
       "      <td>-073.7869958</td>\n",
       "      <td>POLYGON ((-73.79187 40.71379, -73.79085 40.714...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP TRACTCE        GEOID NAME          NAMELSAD  MTFCC  \\\n",
       "0      36      081  044800  36081044800  448  Census Tract 448  G5020   \n",
       "1      36      081  045800  36081045800  458  Census Tract 458  G5020   \n",
       "2      36      081  046200  36081046200  462  Census Tract 462  G5020   \n",
       "3      36      081  046300  36081046300  463  Census Tract 463  G5020   \n",
       "4      36      081  046400  36081046400  464  Census Tract 464  G5020   \n",
       "\n",
       "  FUNCSTAT   ALAND  AWATER     INTPTLAT      INTPTLON  \\\n",
       "0        S  208002       0  +40.7110219  -073.8026344   \n",
       "1        S  245281       0  +40.7152626  -073.7909261   \n",
       "2        S  249611       0  +40.7098547  -073.7879749   \n",
       "3        S  159415       0  +40.7440007  -073.8710900   \n",
       "4        S  228767       0  +40.7168637  -073.7869958   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-73.80646 40.71206, -73.80556 40.712...  \n",
       "1  POLYGON ((-73.79364 40.71382, -73.79362 40.713...  \n",
       "2  POLYGON ((-73.79203 40.71107, -73.79101 40.711...  \n",
       "3  POLYGON ((-73.87468 40.74335, -73.87423 40.743...  \n",
       "4  POLYGON ((-73.79187 40.71379, -73.79085 40.714...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_state_tracts = (\n",
    "    gp.read_file(\n",
    "        \"../data/censusTracts/states/tl_2019_36_NY_tract/tl_2019_36_tract.shp\"\n",
    "    )\n",
    ")\n",
    "\n",
    "ny_state_tracts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "NYC_COUNTIES = [\n",
    "    \"005\", # Bronx\n",
    "    \"047\", # Kings (Brooklyn)\n",
    "    \"061\", # New York County (Manhattan)\n",
    "    \"081\", # Queens\n",
    "    \"085\", # Richmond (Staten Island)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_census_tracts = ny_state_tracts[\n",
    "    ny_state_tracts['COUNTYFP'].isin(NYC_COUNTIES)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2167"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nyc_census_tracts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find number of raids per tract and other Census data\n",
    "* Merge geodataframes and a Census tract to each MARCH incident\n",
    "* import data from a previous analysis of NYC Census tracts and merge with MARCH data (more is [here](https://github.com/BuzzFeedNews/2020-02-gentrification)). Data includes historical demographic information, median home value, median income and calculations on whether a tract had gentrified between 2000 and 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_gdf = gp.sjoin(\n",
    "    nyc_census_tracts,\n",
    "    march_data_lat_long_geodf,\n",
    "    how = \"inner\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>TRACTCE</th>\n",
       "      <th>GEOID</th>\n",
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       "      <th>...</th>\n",
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       "      <th>lot</th>\n",
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       "      <th>dob_violation_number</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
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       "      <td>0</td>\n",
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       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>QN</td>\n",
       "      <td>4210593</td>\n",
       "      <td>9844.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2014-08-15</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.789203</td>\n",
       "      <td>40.712368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>081</td>\n",
       "      <td>045800</td>\n",
       "      <td>36081045800</td>\n",
       "      <td>458</td>\n",
       "      <td>Census Tract 458</td>\n",
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       "      <td>245281</td>\n",
       "      <td>0</td>\n",
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       "      <td>4210593</td>\n",
       "      <td>9844.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2014-03-01</td>\n",
       "      <td>MARCH: NO PA PERMIT/ECB NOV ISSUED</td>\n",
       "      <td>35090900P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.789203</td>\n",
       "      <td>40.712368</td>\n",
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       "      <td>NaN</td>\n",
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       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP TRACTCE        GEOID NAME          NAMELSAD  MTFCC  \\\n",
       "1      36      081  045800  36081045800  458  Census Tract 458  G5020   \n",
       "1      36      081  045800  36081045800  458  Census Tract 458  G5020   \n",
       "1      36      081  045800  36081045800  458  Census Tract 458  G5020   \n",
       "1      36      081  045800  36081045800  458  Census Tract 458  G5020   \n",
       "1      36      081  045800  36081045800  458  Census Tract 458  G5020   \n",
       "\n",
       "  FUNCSTAT   ALAND  AWATER  ... borough_abbrev bin_number   block    lot  \\\n",
       "1        S  245281       0  ...             QN    4210594  9844.0  110.0   \n",
       "1        S  245281       0  ...             QN    4210594  9844.0  110.0   \n",
       "1        S  245281       0  ...             QN    4210593  9844.0  101.0   \n",
       "1        S  245281       0  ...             QN    4210593  9844.0  101.0   \n",
       "1        S  245281       0  ...             QN    4210593  9844.0  101.0   \n",
       "\n",
       "  inspection_date                            access_1 ecb_violation_number  \\\n",
       "1      2015-02-28  MARCH: NO ENFORCEMENT ACTION TAKEN                  NaN   \n",
       "1      2014-02-28  MARCH: NO ENFORCEMENT ACTION TAKEN                  NaN   \n",
       "1      2014-08-15  MARCH: NO ENFORCEMENT ACTION TAKEN                  NaN   \n",
       "1      2014-03-01  MARCH: NO PA PERMIT/ECB NOV ISSUED            35090900P   \n",
       "1      2013-08-09  MARCH: NO ENFORCEMENT ACTION TAKEN                  NaN   \n",
       "\n",
       "   dob_violation_number  longitude   latitude  \n",
       "1                   NaN -73.789768  40.712252  \n",
       "1                   NaN -73.789768  40.712252  \n",
       "1                   NaN -73.789203  40.712368  \n",
       "1                   NaN -73.789203  40.712368  \n",
       "1                   NaN -73.789203  40.712368  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "635\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>raids</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>36005007600</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>36005007900</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>36005008300</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GEOID  raids\n",
       "0   36005001900      2\n",
       "1   36005002500     12\n",
       "2   36005002702      1\n",
       "3   36005003900      2\n",
       "4   36005004300      2\n",
       "5   36005004800      2\n",
       "6   36005005002     14\n",
       "7   36005005100      8\n",
       "8   36005005200      1\n",
       "9   36005005400      1\n",
       "10  36005005600      1\n",
       "11  36005005902      1\n",
       "12  36005006100      7\n",
       "13  36005006200      4\n",
       "14  36005006300     13\n",
       "15  36005006700     13\n",
       "16  36005007000      1\n",
       "17  36005007600      3\n",
       "18  36005007900      3\n",
       "19  36005008300      8"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "march_raids_per_tract = (\n",
    "    merged_gdf\n",
    "    .groupby([\n",
    "        \"GEOID\",\n",
    "    ])\n",
    "    .size()\n",
    "    .to_frame(\"raids\")\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "print(len(march_raids_per_tract))\n",
    "march_raids_per_tract.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36005002702    1\n",
       "36047009600    1\n",
       "36047049700    1\n",
       "36061028300    1\n",
       "36081014201    1\n",
       "              ..\n",
       "36081039900    1\n",
       "36061009500    1\n",
       "36005034800    1\n",
       "36085006700    1\n",
       "36061005600    1\n",
       "Name: GEOID, Length: 635, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "march_raids_per_tract[\"GEOID\"].value_counts().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge demographic data with MARCH raid counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>name</th>\n",
       "      <th>total_population_17</th>\n",
       "      <th>total_population_25_over_17</th>\n",
       "      <th>median_income_17</th>\n",
       "      <th>median_home_value_17</th>\n",
       "      <th>educational_attainment_17</th>\n",
       "      <th>white_alone_17</th>\n",
       "      <th>black_alone_17</th>\n",
       "      <th>native_alone_17</th>\n",
       "      <th>...</th>\n",
       "      <th>pct_asian_alone_change</th>\n",
       "      <th>pct_native_alone_17</th>\n",
       "      <th>pct_native_alone_00</th>\n",
       "      <th>pct_native_alone_change</th>\n",
       "      <th>pct_native_hawaiian_pacific_islander_17</th>\n",
       "      <th>pct_native_hawaiian_pacific_islander_00</th>\n",
       "      <th>pct_native_hawaiian_pacific_islander_change</th>\n",
       "      <th>pct_hispanic_or_latino_17</th>\n",
       "      <th>pct_hispanic_or_latino_00</th>\n",
       "      <th>pct_hispanic_or_latino_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13089020100</td>\n",
       "      <td>Census Tract 201, DeKalb County, Georgia</td>\n",
       "      <td>2076</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>174526.0</td>\n",
       "      <td>617300.0</td>\n",
       "      <td>1231.0</td>\n",
       "      <td>1885.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.647</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.224</td>\n",
       "      <td>-0.224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.576</td>\n",
       "      <td>10.768</td>\n",
       "      <td>-6.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13089020200</td>\n",
       "      <td>Census Tract 202, DeKalb County, Georgia</td>\n",
       "      <td>2045</td>\n",
       "      <td>1657.0</td>\n",
       "      <td>87889.0</td>\n",
       "      <td>548200.0</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>1596.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.299</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.546</td>\n",
       "      <td>-0.546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.281</td>\n",
       "      <td>3.092</td>\n",
       "      <td>2.189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13089020300</td>\n",
       "      <td>Census Tract 203, DeKalb County, Georgia</td>\n",
       "      <td>4151</td>\n",
       "      <td>2934.0</td>\n",
       "      <td>113088.0</td>\n",
       "      <td>470300.0</td>\n",
       "      <td>2344.0</td>\n",
       "      <td>3612.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.522</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.589</td>\n",
       "      <td>2.210</td>\n",
       "      <td>1.379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13089020400</td>\n",
       "      <td>Census Tract 204, DeKalb County, Georgia</td>\n",
       "      <td>2574</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>78681.0</td>\n",
       "      <td>433000.0</td>\n",
       "      <td>1584.0</td>\n",
       "      <td>2251.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.014</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.517</td>\n",
       "      <td>-0.517</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.875</td>\n",
       "      <td>3.341</td>\n",
       "      <td>-0.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13089020500</td>\n",
       "      <td>Census Tract 205, DeKalb County, Georgia</td>\n",
       "      <td>3305</td>\n",
       "      <td>2685.0</td>\n",
       "      <td>55208.0</td>\n",
       "      <td>239600.0</td>\n",
       "      <td>1220.0</td>\n",
       "      <td>1596.0</td>\n",
       "      <td>1286.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.593</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.062</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.446</td>\n",
       "      <td>2.935</td>\n",
       "      <td>2.511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         GEOID                                      name  total_population_17  \\\n",
       "0  13089020100  Census Tract 201, DeKalb County, Georgia                 2076   \n",
       "1  13089020200  Census Tract 202, DeKalb County, Georgia                 2045   \n",
       "2  13089020300  Census Tract 203, DeKalb County, Georgia                 4151   \n",
       "3  13089020400  Census Tract 204, DeKalb County, Georgia                 2574   \n",
       "4  13089020500  Census Tract 205, DeKalb County, Georgia                 3305   \n",
       "\n",
       "   total_population_25_over_17  median_income_17  median_home_value_17  \\\n",
       "0                       1455.0          174526.0              617300.0   \n",
       "1                       1657.0           87889.0              548200.0   \n",
       "2                       2934.0          113088.0              470300.0   \n",
       "3                       1891.0           78681.0              433000.0   \n",
       "4                       2685.0           55208.0              239600.0   \n",
       "\n",
       "   educational_attainment_17  white_alone_17  black_alone_17  native_alone_17  \\\n",
       "0                     1231.0          1885.0            36.0              0.0   \n",
       "1                     1271.0          1596.0           187.0              0.0   \n",
       "2                     2344.0          3612.0           146.0             27.0   \n",
       "3                     1584.0          2251.0            74.0              0.0   \n",
       "4                     1220.0          1596.0          1286.0              0.0   \n",
       "\n",
       "   ...  pct_asian_alone_change  pct_native_alone_17  pct_native_alone_00  \\\n",
       "0  ...                  -0.647                 0.00                0.224   \n",
       "1  ...                   2.299                 0.00                0.546   \n",
       "2  ...                   0.539                 0.65                0.522   \n",
       "3  ...                   3.014                 0.00                0.517   \n",
       "4  ...                   2.593                 0.00                0.062   \n",
       "\n",
       "   pct_native_alone_change  pct_native_hawaiian_pacific_islander_17  \\\n",
       "0                   -0.224                                      0.0   \n",
       "1                   -0.546                                      0.0   \n",
       "2                    0.128                                      0.0   \n",
       "3                   -0.517                                      0.0   \n",
       "4                   -0.062                                      0.0   \n",
       "\n",
       "  pct_native_hawaiian_pacific_islander_00  \\\n",
       "0                                     0.0   \n",
       "1                                     0.0   \n",
       "2                                     0.0   \n",
       "3                                     0.0   \n",
       "4                                     0.0   \n",
       "\n",
       "  pct_native_hawaiian_pacific_islander_change pct_hispanic_or_latino_17  \\\n",
       "0                                         0.0                     4.576   \n",
       "1                                         0.0                     5.281   \n",
       "2                                         0.0                     3.589   \n",
       "3                                         0.0                     2.875   \n",
       "4                                         0.0                     5.446   \n",
       "\n",
       "  pct_hispanic_or_latino_00 pct_hispanic_or_latino_change  \n",
       "0                    10.768                        -6.192  \n",
       "1                     3.092                         2.189  \n",
       "2                     2.210                         1.379  \n",
       "3                     3.341                        -0.466  \n",
       "4                     2.935                         2.511  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gentrification = pd.read_csv(\n",
    "    \"../data/gentrification.csv\",\n",
    "    dtype={\"GEOID\":str}\n",
    ")\n",
    "\n",
    "gentrification.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GEOID', 'name', 'total_population_17', 'total_population_25_over_17',\n",
       "       'median_income_17', 'median_home_value_17', 'educational_attainment_17',\n",
       "       'white_alone_17', 'black_alone_17', 'native_alone_17', 'asian_alone_17',\n",
       "       'native_hawaiian_pacific_islander_17', 'some_other_race_alone',\n",
       "       'two_or_more', 'hispanic_or_latino_17', 'city', 'metro_area', 'state',\n",
       "       'county', 'tract', 'total_population_00', 'white_alone_00',\n",
       "       'black_alone_00', 'native_alone_00', 'asian_alone_00',\n",
       "       'native_hawaiian_pacific_islander_00', 'hispanic_or_latino_00',\n",
       "       'total_population_25_over_00', 'median_income_00',\n",
       "       'median_home_value_00', 'educational_attainment_00',\n",
       "       'educational_attainment_pct_17', 'educational_attainment_pct_00',\n",
       "       'educational_attainment_change', 'home_pct_change',\n",
       "       'metro_median_income_00_q40', 'metro_median_home_value_00_q40',\n",
       "       'educational_attainment_change_q66', 'home_pct_change_q66',\n",
       "       'low_population', 'median_home_value_00_qualifies',\n",
       "       'median_40th_income_00_qualifies', 'educational_increase_is_top_3rd',\n",
       "       'median_home_increased', 'median_home_increase_is_top_3rd',\n",
       "       'eligible_for_gentrification', 'gentrified',\n",
       "       'eligible_not_gentrified_highpop', 'not_eligible_highpop',\n",
       "       'pct_white_alone_17', 'pct_white_alone_00', 'pct_white_alone_change',\n",
       "       'pct_black_alone_17', 'pct_black_alone_00', 'pct_black_alone_change',\n",
       "       'pct_asian_alone_17', 'pct_asian_alone_00', 'pct_asian_alone_change',\n",
       "       'pct_native_alone_17', 'pct_native_alone_00', 'pct_native_alone_change',\n",
       "       'pct_native_hawaiian_pacific_islander_17',\n",
       "       'pct_native_hawaiian_pacific_islander_00',\n",
       "       'pct_native_hawaiian_pacific_islander_change',\n",
       "       'pct_hispanic_or_latino_17', 'pct_hispanic_or_latino_00',\n",
       "       'pct_hispanic_or_latino_change'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gentrification.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2799 635\n"
     ]
    }
   ],
   "source": [
    "gentrification_targets = pd.merge(\n",
    "    gentrification,\n",
    "    march_raids_per_tract,\n",
    "    on=\"GEOID\"\n",
    ")\n",
    "\n",
    "print(len(gentrification), len(gentrification_targets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>name</th>\n",
       "      <th>total_population_17</th>\n",
       "      <th>total_population_25_over_17</th>\n",
       "      <th>median_income_17</th>\n",
       "      <th>median_home_value_17</th>\n",
       "      <th>educational_attainment_17</th>\n",
       "      <th>white_alone_17</th>\n",
       "      <th>black_alone_17</th>\n",
       "      <th>native_alone_17</th>\n",
       "      <th>...</th>\n",
       "      <th>pct_native_alone_17</th>\n",
       "      <th>pct_native_alone_00</th>\n",
       "      <th>pct_native_alone_change</th>\n",
       "      <th>pct_native_hawaiian_pacific_islander_17</th>\n",
       "      <th>pct_native_hawaiian_pacific_islander_00</th>\n",
       "      <th>pct_native_hawaiian_pacific_islander_change</th>\n",
       "      <th>pct_hispanic_or_latino_17</th>\n",
       "      <th>pct_hispanic_or_latino_00</th>\n",
       "      <th>pct_hispanic_or_latino_change</th>\n",
       "      <th>raids</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36005001900</td>\n",
       "      <td>Census Tract 19, Bronx County, New York</td>\n",
       "      <td>2817</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>40117.0</td>\n",
       "      <td>375000.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.568</td>\n",
       "      <td>-0.568</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.390</td>\n",
       "      <td>62.311</td>\n",
       "      <td>-8.921</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36005002500</td>\n",
       "      <td>Census Tract 25, Bronx County, New York</td>\n",
       "      <td>5760</td>\n",
       "      <td>3466.0</td>\n",
       "      <td>22771.0</td>\n",
       "      <td>493300.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>1161.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.684</td>\n",
       "      <td>79.037</td>\n",
       "      <td>-2.353</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36005002702</td>\n",
       "      <td>Census Tract 27.02, Bronx County, New York</td>\n",
       "      <td>4538</td>\n",
       "      <td>2415.0</td>\n",
       "      <td>13216.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.410</td>\n",
       "      <td>0.211</td>\n",
       "      <td>1.199</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.580</td>\n",
       "      <td>74.894</td>\n",
       "      <td>-9.314</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36005003900</td>\n",
       "      <td>Census Tract 39, Bronx County, New York</td>\n",
       "      <td>6473</td>\n",
       "      <td>3815.0</td>\n",
       "      <td>30357.0</td>\n",
       "      <td>222100.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.182</td>\n",
       "      <td>78.794</td>\n",
       "      <td>-1.612</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36005004300</td>\n",
       "      <td>Census Tract 43, Bronx County, New York</td>\n",
       "      <td>6190</td>\n",
       "      <td>3706.0</td>\n",
       "      <td>22940.0</td>\n",
       "      <td>424500.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1597.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.018</td>\n",
       "      <td>77.532</td>\n",
       "      <td>-6.514</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         GEOID                                        name  \\\n",
       "0  36005001900     Census Tract 19, Bronx County, New York   \n",
       "1  36005002500     Census Tract 25, Bronx County, New York   \n",
       "2  36005002702  Census Tract 27.02, Bronx County, New York   \n",
       "3  36005003900     Census Tract 39, Bronx County, New York   \n",
       "4  36005004300     Census Tract 43, Bronx County, New York   \n",
       "\n",
       "   total_population_17  total_population_25_over_17  median_income_17  \\\n",
       "0                 2817                       1837.0           40117.0   \n",
       "1                 5760                       3466.0           22771.0   \n",
       "2                 4538                       2415.0           13216.0   \n",
       "3                 6473                       3815.0           30357.0   \n",
       "4                 6190                       3706.0           22940.0   \n",
       "\n",
       "   median_home_value_17  educational_attainment_17  white_alone_17  \\\n",
       "0              375000.0                      420.0           218.0   \n",
       "1              493300.0                      310.0           141.0   \n",
       "2                   NaN                      178.0            80.0   \n",
       "3              222100.0                      343.0           443.0   \n",
       "4              424500.0                      316.0             5.0   \n",
       "\n",
       "   black_alone_17  native_alone_17  ...  pct_native_alone_17  \\\n",
       "0           932.0              0.0  ...                0.000   \n",
       "1          1161.0             27.0  ...                0.469   \n",
       "2          1460.0             64.0  ...                1.410   \n",
       "3          1034.0             39.0  ...                0.603   \n",
       "4          1597.0             35.0  ...                0.565   \n",
       "\n",
       "   pct_native_alone_00  pct_native_alone_change  \\\n",
       "0                0.568                   -0.568   \n",
       "1                0.196                    0.273   \n",
       "2                0.211                    1.199   \n",
       "3                0.066                    0.537   \n",
       "4                0.271                    0.294   \n",
       "\n",
       "   pct_native_hawaiian_pacific_islander_17  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "\n",
       "   pct_native_hawaiian_pacific_islander_00  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "\n",
       "  pct_native_hawaiian_pacific_islander_change pct_hispanic_or_latino_17  \\\n",
       "0                                         0.0                    53.390   \n",
       "1                                         0.0                    76.684   \n",
       "2                                         0.0                    65.580   \n",
       "3                                         0.0                    77.182   \n",
       "4                                         0.0                    71.018   \n",
       "\n",
       "  pct_hispanic_or_latino_00 pct_hispanic_or_latino_change raids  \n",
       "0                    62.311                        -8.921     2  \n",
       "1                    79.037                        -2.353    12  \n",
       "2                    74.894                        -9.314     1  \n",
       "3                    78.794                        -1.612     2  \n",
       "4                    77.532                        -6.514     2  \n",
       "\n",
       "[5 rows x 68 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gentrification_targets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GEOID</th>\n",
       "      <th>name</th>\n",
       "      <th>total_population_17</th>\n",
       "      <th>total_population_25_over_17</th>\n",
       "      <th>median_income_17</th>\n",
       "      <th>median_home_value_17</th>\n",
       "      <th>educational_attainment_17</th>\n",
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       "      <th>native_alone_17</th>\n",
       "      <th>...</th>\n",
       "      <th>pct_native_alone_17</th>\n",
       "      <th>pct_native_alone_00</th>\n",
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       "      <th>pct_native_hawaiian_pacific_islander_17</th>\n",
       "      <th>pct_native_hawaiian_pacific_islander_00</th>\n",
       "      <th>pct_native_hawaiian_pacific_islander_change</th>\n",
       "      <th>pct_hispanic_or_latino_17</th>\n",
       "      <th>pct_hispanic_or_latino_00</th>\n",
       "      <th>pct_hispanic_or_latino_change</th>\n",
       "      <th>raids</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36005001900</td>\n",
       "      <td>Census Tract 19, Bronx County, New York</td>\n",
       "      <td>2817</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>40117.0</td>\n",
       "      <td>375000.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.568</td>\n",
       "      <td>-0.568</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.390</td>\n",
       "      <td>62.311</td>\n",
       "      <td>-8.921</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36005002500</td>\n",
       "      <td>Census Tract 25, Bronx County, New York</td>\n",
       "      <td>5760</td>\n",
       "      <td>3466.0</td>\n",
       "      <td>22771.0</td>\n",
       "      <td>493300.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>1161.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.684</td>\n",
       "      <td>79.037</td>\n",
       "      <td>-2.353</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36005002702</td>\n",
       "      <td>Census Tract 27.02, Bronx County, New York</td>\n",
       "      <td>4538</td>\n",
       "      <td>2415.0</td>\n",
       "      <td>13216.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.410</td>\n",
       "      <td>0.211</td>\n",
       "      <td>1.199</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.580</td>\n",
       "      <td>74.894</td>\n",
       "      <td>-9.314</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36005003900</td>\n",
       "      <td>Census Tract 39, Bronx County, New York</td>\n",
       "      <td>6473</td>\n",
       "      <td>3815.0</td>\n",
       "      <td>30357.0</td>\n",
       "      <td>222100.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.182</td>\n",
       "      <td>78.794</td>\n",
       "      <td>-1.612</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36005004300</td>\n",
       "      <td>Census Tract 43, Bronx County, New York</td>\n",
       "      <td>6190</td>\n",
       "      <td>3706.0</td>\n",
       "      <td>22940.0</td>\n",
       "      <td>424500.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1597.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.018</td>\n",
       "      <td>77.532</td>\n",
       "      <td>-6.514</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 68 columns</p>\n",
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      ],
      "text/plain": [
       "         GEOID                                        name  \\\n",
       "0  36005001900     Census Tract 19, Bronx County, New York   \n",
       "1  36005002500     Census Tract 25, Bronx County, New York   \n",
       "2  36005002702  Census Tract 27.02, Bronx County, New York   \n",
       "3  36005003900     Census Tract 39, Bronx County, New York   \n",
       "4  36005004300     Census Tract 43, Bronx County, New York   \n",
       "\n",
       "   total_population_17  total_population_25_over_17  median_income_17  \\\n",
       "0                 2817                       1837.0           40117.0   \n",
       "1                 5760                       3466.0           22771.0   \n",
       "2                 4538                       2415.0           13216.0   \n",
       "3                 6473                       3815.0           30357.0   \n",
       "4                 6190                       3706.0           22940.0   \n",
       "\n",
       "   median_home_value_17  educational_attainment_17  white_alone_17  \\\n",
       "0              375000.0                      420.0           218.0   \n",
       "1              493300.0                      310.0           141.0   \n",
       "2                   NaN                      178.0            80.0   \n",
       "3              222100.0                      343.0           443.0   \n",
       "4              424500.0                      316.0             5.0   \n",
       "\n",
       "   black_alone_17  native_alone_17  ...  pct_native_alone_17  \\\n",
       "0           932.0              0.0  ...                0.000   \n",
       "1          1161.0             27.0  ...                0.469   \n",
       "2          1460.0             64.0  ...                1.410   \n",
       "3          1034.0             39.0  ...                0.603   \n",
       "4          1597.0             35.0  ...                0.565   \n",
       "\n",
       "   pct_native_alone_00  pct_native_alone_change  \\\n",
       "0                0.568                   -0.568   \n",
       "1                0.196                    0.273   \n",
       "2                0.211                    1.199   \n",
       "3                0.066                    0.537   \n",
       "4                0.271                    0.294   \n",
       "\n",
       "   pct_native_hawaiian_pacific_islander_17  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "\n",
       "   pct_native_hawaiian_pacific_islander_00  \\\n",
       "0                                      0.0   \n",
       "1                                      0.0   \n",
       "2                                      0.0   \n",
       "3                                      0.0   \n",
       "4                                      0.0   \n",
       "\n",
       "  pct_native_hawaiian_pacific_islander_change pct_hispanic_or_latino_17  \\\n",
       "0                                         0.0                    53.390   \n",
       "1                                         0.0                    76.684   \n",
       "2                                         0.0                    65.580   \n",
       "3                                         0.0                    77.182   \n",
       "4                                         0.0                    71.018   \n",
       "\n",
       "  pct_hispanic_or_latino_00 pct_hispanic_or_latino_change raids  \n",
       "0                    62.311                        -8.921     2  \n",
       "1                    79.037                        -2.353    12  \n",
       "2                    74.894                        -9.314     1  \n",
       "3                    78.794                        -1.612     2  \n",
       "4                    77.532                        -6.514     2  \n",
       "\n",
       "[5 rows x 68 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gentrification_targets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GEOID', 'name', 'total_population_17', 'total_population_25_over_17',\n",
       "       'median_income_17', 'median_home_value_17', 'educational_attainment_17',\n",
       "       'white_alone_17', 'black_alone_17', 'native_alone_17', 'asian_alone_17',\n",
       "       'native_hawaiian_pacific_islander_17', 'some_other_race_alone',\n",
       "       'two_or_more', 'hispanic_or_latino_17', 'city', 'metro_area', 'state',\n",
       "       'county', 'tract', 'total_population_00', 'white_alone_00',\n",
       "       'black_alone_00', 'native_alone_00', 'asian_alone_00',\n",
       "       'native_hawaiian_pacific_islander_00', 'hispanic_or_latino_00',\n",
       "       'total_population_25_over_00', 'median_income_00',\n",
       "       'median_home_value_00', 'educational_attainment_00',\n",
       "       'educational_attainment_pct_17', 'educational_attainment_pct_00',\n",
       "       'educational_attainment_change', 'home_pct_change',\n",
       "       'metro_median_income_00_q40', 'metro_median_home_value_00_q40',\n",
       "       'educational_attainment_change_q66', 'home_pct_change_q66',\n",
       "       'low_population', 'median_home_value_00_qualifies',\n",
       "       'median_40th_income_00_qualifies', 'educational_increase_is_top_3rd',\n",
       "       'median_home_increased', 'median_home_increase_is_top_3rd',\n",
       "       'eligible_for_gentrification', 'gentrified',\n",
       "       'eligible_not_gentrified_highpop', 'not_eligible_highpop',\n",
       "       'pct_white_alone_17', 'pct_white_alone_00', 'pct_white_alone_change',\n",
       "       'pct_black_alone_17', 'pct_black_alone_00', 'pct_black_alone_change',\n",
       "       'pct_asian_alone_17', 'pct_asian_alone_00', 'pct_asian_alone_change',\n",
       "       'pct_native_alone_17', 'pct_native_alone_00', 'pct_native_alone_change',\n",
       "       'pct_native_hawaiian_pacific_islander_17',\n",
       "       'pct_native_hawaiian_pacific_islander_00',\n",
       "       'pct_native_hawaiian_pacific_islander_change',\n",
       "       'pct_hispanic_or_latino_17', 'pct_hispanic_or_latino_00',\n",
       "       'pct_hispanic_or_latino_change', 'raids'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gentrification_targets.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>TRACTCE</th>\n",
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       "      <td>36</td>\n",
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       "      <td>045800</td>\n",
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       "      <td>2015-02-28</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
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       "      <td>40.712252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>081</td>\n",
       "      <td>045800</td>\n",
       "      <td>36081045800</td>\n",
       "      <td>458</td>\n",
       "      <td>Census Tract 458</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>245281</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>QN</td>\n",
       "      <td>4210594</td>\n",
       "      <td>9844.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.789768</td>\n",
       "      <td>40.712252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>081</td>\n",
       "      <td>045800</td>\n",
       "      <td>36081045800</td>\n",
       "      <td>458</td>\n",
       "      <td>Census Tract 458</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>245281</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>QN</td>\n",
       "      <td>4210593</td>\n",
       "      <td>9844.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2014-08-15</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.789203</td>\n",
       "      <td>40.712368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>081</td>\n",
       "      <td>045800</td>\n",
       "      <td>36081045800</td>\n",
       "      <td>458</td>\n",
       "      <td>Census Tract 458</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>245281</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>QN</td>\n",
       "      <td>4210593</td>\n",
       "      <td>9844.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2014-03-01</td>\n",
       "      <td>MARCH: NO PA PERMIT/ECB NOV ISSUED</td>\n",
       "      <td>35090900P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.789203</td>\n",
       "      <td>40.712368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>081</td>\n",
       "      <td>045800</td>\n",
       "      <td>36081045800</td>\n",
       "      <td>458</td>\n",
       "      <td>Census Tract 458</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>245281</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>QN</td>\n",
       "      <td>4210593</td>\n",
       "      <td>9844.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2013-08-09</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.789203</td>\n",
       "      <td>40.712368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP TRACTCE        GEOID NAME          NAMELSAD  MTFCC  \\\n",
       "1      36      081  045800  36081045800  458  Census Tract 458  G5020   \n",
       "1      36      081  045800  36081045800  458  Census Tract 458  G5020   \n",
       "1      36      081  045800  36081045800  458  Census Tract 458  G5020   \n",
       "1      36      081  045800  36081045800  458  Census Tract 458  G5020   \n",
       "1      36      081  045800  36081045800  458  Census Tract 458  G5020   \n",
       "\n",
       "  FUNCSTAT   ALAND  AWATER  ... borough_abbrev bin_number   block    lot  \\\n",
       "1        S  245281       0  ...             QN    4210594  9844.0  110.0   \n",
       "1        S  245281       0  ...             QN    4210594  9844.0  110.0   \n",
       "1        S  245281       0  ...             QN    4210593  9844.0  101.0   \n",
       "1        S  245281       0  ...             QN    4210593  9844.0  101.0   \n",
       "1        S  245281       0  ...             QN    4210593  9844.0  101.0   \n",
       "\n",
       "  inspection_date                            access_1 ecb_violation_number  \\\n",
       "1      2015-02-28  MARCH: NO ENFORCEMENT ACTION TAKEN                  NaN   \n",
       "1      2014-02-28  MARCH: NO ENFORCEMENT ACTION TAKEN                  NaN   \n",
       "1      2014-08-15  MARCH: NO ENFORCEMENT ACTION TAKEN                  NaN   \n",
       "1      2014-03-01  MARCH: NO PA PERMIT/ECB NOV ISSUED            35090900P   \n",
       "1      2013-08-09  MARCH: NO ENFORCEMENT ACTION TAKEN                  NaN   \n",
       "\n",
       "   dob_violation_number  longitude   latitude  \n",
       "1                   NaN -73.789768  40.712252  \n",
       "1                   NaN -73.789768  40.712252  \n",
       "1                   NaN -73.789203  40.712368  \n",
       "1                   NaN -73.789203  40.712368  \n",
       "1                   NaN -73.789203  40.712368  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find raids near Friends and Lovers\n",
    "\n",
    "In this part of the analysis, we identify raids in near Friends and Lovers. The geographical scope of this analysis includes raids in the Census tract where the bar Friends and Lovers is located as well as every adjacent Census tract. This list was then used to do on-the-ground reporting. \n",
    "\n",
    "Census tract IDs were found here: https://popfactfinder.planning.nyc.gov/#14/40.67871/-73.95851\n",
    "GEOIDs were then constructed based on Census Bureau conventions: https://www.census.gov/programs-surveys/geography/guidance/geo-identifiers.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36\n"
     ]
    },
    {
     "data": {
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       "    }\n",
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       "    .dataframe thead th {\n",
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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>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>TRACTCE</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>NAMELSAD</th>\n",
       "      <th>MTFCC</th>\n",
       "      <th>FUNCSTAT</th>\n",
       "      <th>ALAND</th>\n",
       "      <th>AWATER</th>\n",
       "      <th>...</th>\n",
       "      <th>borough_abbrev</th>\n",
       "      <th>bin_number</th>\n",
       "      <th>block</th>\n",
       "      <th>lot</th>\n",
       "      <th>inspection_date</th>\n",
       "      <th>access_1</th>\n",
       "      <th>ecb_violation_number</th>\n",
       "      <th>dob_violation_number</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>022700</td>\n",
       "      <td>36047022700</td>\n",
       "      <td>227</td>\n",
       "      <td>Census Tract 227</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>240700</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>BK</td>\n",
       "      <td>3057707</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2013-11-15</td>\n",
       "      <td>MARCH: FAILURE TO MAINTAIN BLDG/ECB NOV ISSUED</td>\n",
       "      <td>35064942Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.957403</td>\n",
       "      <td>40.681220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>022700</td>\n",
       "      <td>36047022700</td>\n",
       "      <td>227</td>\n",
       "      <td>Census Tract 227</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>240700</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>BK</td>\n",
       "      <td>3057707</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.957403</td>\n",
       "      <td>40.681220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>022700</td>\n",
       "      <td>36047022700</td>\n",
       "      <td>227</td>\n",
       "      <td>Census Tract 227</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>240700</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>BK</td>\n",
       "      <td>3420462</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>7503.0</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.954224</td>\n",
       "      <td>40.683398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1209</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>022100</td>\n",
       "      <td>36047022100</td>\n",
       "      <td>221</td>\n",
       "      <td>Census Tract 221</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>152083</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>BK</td>\n",
       "      <td>3030920</td>\n",
       "      <td>1225.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2016-12-10</td>\n",
       "      <td>MARCH: OCCUPANCY CONTRARY TO C OF O/NONE/ECB N...</td>\n",
       "      <td>35213315L</td>\n",
       "      <td>121016CERFC02</td>\n",
       "      <td>-73.953205</td>\n",
       "      <td>40.675154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>020100</td>\n",
       "      <td>36047020100</td>\n",
       "      <td>201</td>\n",
       "      <td>Census Tract 201</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>175379</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>BK</td>\n",
       "      <td>3057634</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2013-11-16</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.963014</td>\n",
       "      <td>40.682424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     STATEFP COUNTYFP TRACTCE        GEOID NAME          NAMELSAD  MTFCC  \\\n",
       "1197      36      047  022700  36047022700  227  Census Tract 227  G5020   \n",
       "1197      36      047  022700  36047022700  227  Census Tract 227  G5020   \n",
       "1197      36      047  022700  36047022700  227  Census Tract 227  G5020   \n",
       "1209      36      047  022100  36047022100  221  Census Tract 221  G5020   \n",
       "2249      36      047  020100  36047020100  201  Census Tract 201  G5020   \n",
       "\n",
       "     FUNCSTAT   ALAND  AWATER  ... borough_abbrev bin_number   block     lot  \\\n",
       "1197        S  240700       0  ...             BK    3057707  2016.0    22.0   \n",
       "1197        S  240700       0  ...             BK    3057707  2016.0    22.0   \n",
       "1197        S  240700       0  ...             BK    3420462  1996.0  7503.0   \n",
       "1209        S  152083       0  ...             BK    3030920  1225.0     8.0   \n",
       "2249        S  175379       0  ...             BK    3057634  2014.0    17.0   \n",
       "\n",
       "     inspection_date                                           access_1  \\\n",
       "1197      2013-11-15     MARCH: FAILURE TO MAINTAIN BLDG/ECB NOV ISSUED   \n",
       "1197      2015-06-28                 MARCH: NO ENFORCEMENT ACTION TAKEN   \n",
       "1197      2017-02-25                 MARCH: NO ENFORCEMENT ACTION TAKEN   \n",
       "1209      2016-12-10  MARCH: OCCUPANCY CONTRARY TO C OF O/NONE/ECB N...   \n",
       "2249      2013-11-16                 MARCH: NO ENFORCEMENT ACTION TAKEN   \n",
       "\n",
       "     ecb_violation_number  dob_violation_number  longitude   latitude  \n",
       "1197            35064942Y                   NaN -73.957403  40.681220  \n",
       "1197                  NaN                   NaN -73.957403  40.681220  \n",
       "1197                  NaN                   NaN -73.954224  40.683398  \n",
       "1209            35213315L         121016CERFC02 -73.953205  40.675154  \n",
       "2249                  NaN                   NaN -73.963014  40.682424  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_tracts = [\n",
    "    \"36047020100\",\n",
    "    \"36047022700\",\n",
    "    \"36047020300\", # ode to babel\n",
    "    \"36047020500\",\n",
    "    \"36047020700\",\n",
    "    \"36047021500\",\n",
    "    \"36047021700\",\n",
    "    \"36047021900\",\n",
    "    \"36047022100\",\n",
    "    \"36047024700\",\n",
    "    \"36047030500\" # friends and lovers \n",
    "]\n",
    "\n",
    "\n",
    "pared_down_raids = merged_gdf[merged_gdf[\"GEOID\"].isin(census_tracts)]\n",
    "\n",
    "print(len(pared_down_raids))\n",
    "pared_down_raids.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36047030500    12\n",
       "36047021700     8\n",
       "36047020300     7\n",
       "36047022700     3\n",
       "36047024700     3\n",
       "36047020100     2\n",
       "36047022100     1\n",
       "Name: GEOID, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pared_down_raids[\"GEOID\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "neighborhoodraids=(\n",
    "    pd.merge(\n",
    "        pared_down_raids,\n",
    "        gentrification_targets, \n",
    "        on=\"GEOID\"\n",
    "    )\n",
    "    [[\n",
    "        'GEOID',\n",
    "        'NAME',\n",
    "        'address',\n",
    "        'inspection_date', \n",
    "        'access_1',\n",
    "        'ecb_violation_number', \n",
    "        'dob_violation_number', \n",
    "        'longitude', \n",
    "        'latitude',\n",
    "        'total_population_17',\n",
    "        'median_income_17', \n",
    "        'median_home_value_17', \n",
    "        'educational_attainment_17',\n",
    "        'white_alone_17', \n",
    "        'black_alone_17', \n",
    "        'native_alone_17', \n",
    "        'asian_alone_17',\n",
    "        'native_hawaiian_pacific_islander_17', \n",
    "        'some_other_race_alone',\n",
    "        'two_or_more', \n",
    "        'hispanic_or_latino_17',\n",
    "        'educational_attainment_pct_17',\n",
    "        'educational_attainment_change', \n",
    "        'home_pct_change',\n",
    "        'pct_white_alone_17',  'pct_white_alone_change',\n",
    "        'pct_black_alone_17', 'pct_black_alone_change',\n",
    "        'pct_asian_alone_17',  'pct_asian_alone_change',\n",
    "        'pct_native_alone_17',  'pct_native_alone_change',\n",
    "        'pct_native_hawaiian_pacific_islander_17',\n",
    "        'pct_native_hawaiian_pacific_islander_change',\n",
    "        'pct_hispanic_or_latino_17', \n",
    "        'pct_hispanic_or_latino_change', 'raids' \n",
    "    ]]\n",
    ")\n",
    "\n",
    "len(neighborhoodraids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighborhoodraids.to_csv(\"../output/neighborhood_raids.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GEOID', 'NAME', 'address', 'inspection_date', 'access_1',\n",
       "       'ecb_violation_number', 'dob_violation_number', 'longitude', 'latitude',\n",
       "       'total_population_17', 'median_income_17', 'median_home_value_17',\n",
       "       'educational_attainment_17', 'white_alone_17', 'black_alone_17',\n",
       "       'native_alone_17', 'asian_alone_17',\n",
       "       'native_hawaiian_pacific_islander_17', 'some_other_race_alone',\n",
       "       'two_or_more', 'hispanic_or_latino_17', 'educational_attainment_pct_17',\n",
       "       'educational_attainment_change', 'home_pct_change',\n",
       "       'pct_white_alone_17', 'pct_white_alone_change', 'pct_black_alone_17',\n",
       "       'pct_black_alone_change', 'pct_asian_alone_17',\n",
       "       'pct_asian_alone_change', 'pct_native_alone_17',\n",
       "       'pct_native_alone_change', 'pct_native_hawaiian_pacific_islander_17',\n",
       "       'pct_native_hawaiian_pacific_islander_change',\n",
       "       'pct_hispanic_or_latino_17', 'pct_hispanic_or_latino_change', 'raids'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoodraids.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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>GEOID</th>\n",
       "      <th>NAME</th>\n",
       "      <th>address</th>\n",
       "      <th>inspection_date</th>\n",
       "      <th>access_1</th>\n",
       "      <th>ecb_violation_number</th>\n",
       "      <th>dob_violation_number</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>total_population_17</th>\n",
       "      <th>...</th>\n",
       "      <th>pct_black_alone_change</th>\n",
       "      <th>pct_asian_alone_17</th>\n",
       "      <th>pct_asian_alone_change</th>\n",
       "      <th>pct_native_alone_17</th>\n",
       "      <th>pct_native_alone_change</th>\n",
       "      <th>pct_native_hawaiian_pacific_islander_17</th>\n",
       "      <th>pct_native_hawaiian_pacific_islander_change</th>\n",
       "      <th>pct_hispanic_or_latino_17</th>\n",
       "      <th>pct_hispanic_or_latino_change</th>\n",
       "      <th>raids</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36047022700</td>\n",
       "      <td>227</td>\n",
       "      <td>1088 FULTON STREET</td>\n",
       "      <td>2013-11-15</td>\n",
       "      <td>MARCH: FAILURE TO MAINTAIN BLDG/ECB NOV ISSUED</td>\n",
       "      <td>35064942Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.957403</td>\n",
       "      <td>40.681220</td>\n",
       "      <td>3556</td>\n",
       "      <td>...</td>\n",
       "      <td>-32.250</td>\n",
       "      <td>3.375</td>\n",
       "      <td>1.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.294</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.309</td>\n",
       "      <td>7.593</td>\n",
       "      <td>0.752</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36047022700</td>\n",
       "      <td>227</td>\n",
       "      <td>1088 FULTON STREET</td>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.957403</td>\n",
       "      <td>40.681220</td>\n",
       "      <td>3556</td>\n",
       "      <td>...</td>\n",
       "      <td>-32.250</td>\n",
       "      <td>3.375</td>\n",
       "      <td>1.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.294</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.309</td>\n",
       "      <td>7.593</td>\n",
       "      <td>0.752</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36047022700</td>\n",
       "      <td>227</td>\n",
       "      <td>1188 BEDFORD AVENUE</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.954224</td>\n",
       "      <td>40.683398</td>\n",
       "      <td>3556</td>\n",
       "      <td>...</td>\n",
       "      <td>-32.250</td>\n",
       "      <td>3.375</td>\n",
       "      <td>1.790</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.294</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.309</td>\n",
       "      <td>7.593</td>\n",
       "      <td>0.752</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36047022100</td>\n",
       "      <td>221</td>\n",
       "      <td>1401 BEDFORD AVENUE</td>\n",
       "      <td>2016-12-10</td>\n",
       "      <td>MARCH: OCCUPANCY CONTRARY TO C OF O/NONE/ECB N...</td>\n",
       "      <td>35213315L</td>\n",
       "      <td>121016CERFC02</td>\n",
       "      <td>-73.953205</td>\n",
       "      <td>40.675154</td>\n",
       "      <td>4174</td>\n",
       "      <td>...</td>\n",
       "      <td>-41.273</td>\n",
       "      <td>4.432</td>\n",
       "      <td>3.846</td>\n",
       "      <td>1.485</td>\n",
       "      <td>1.154</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>12.266</td>\n",
       "      <td>2.134</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36047020100</td>\n",
       "      <td>201</td>\n",
       "      <td>946 FULTON STREET</td>\n",
       "      <td>2013-11-16</td>\n",
       "      <td>MARCH: NO ENFORCEMENT ACTION TAKEN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.963014</td>\n",
       "      <td>40.682424</td>\n",
       "      <td>3735</td>\n",
       "      <td>...</td>\n",
       "      <td>-36.572</td>\n",
       "      <td>4.471</td>\n",
       "      <td>1.112</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.415</td>\n",
       "      <td>2.761</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         GEOID NAME              address inspection_date  \\\n",
       "0  36047022700  227   1088 FULTON STREET      2013-11-15   \n",
       "1  36047022700  227   1088 FULTON STREET      2015-06-28   \n",
       "2  36047022700  227  1188 BEDFORD AVENUE      2017-02-25   \n",
       "3  36047022100  221  1401 BEDFORD AVENUE      2016-12-10   \n",
       "4  36047020100  201    946 FULTON STREET      2013-11-16   \n",
       "\n",
       "                                            access_1 ecb_violation_number  \\\n",
       "0     MARCH: FAILURE TO MAINTAIN BLDG/ECB NOV ISSUED            35064942Y   \n",
       "1                 MARCH: NO ENFORCEMENT ACTION TAKEN                  NaN   \n",
       "2                 MARCH: NO ENFORCEMENT ACTION TAKEN                  NaN   \n",
       "3  MARCH: OCCUPANCY CONTRARY TO C OF O/NONE/ECB N...            35213315L   \n",
       "4                 MARCH: NO ENFORCEMENT ACTION TAKEN                  NaN   \n",
       "\n",
       "  dob_violation_number  longitude   latitude  total_population_17  ...  \\\n",
       "0                  NaN -73.957403  40.681220                 3556  ...   \n",
       "1                  NaN -73.957403  40.681220                 3556  ...   \n",
       "2                  NaN -73.954224  40.683398                 3556  ...   \n",
       "3        121016CERFC02 -73.953205  40.675154                 4174  ...   \n",
       "4                  NaN -73.963014  40.682424                 3735  ...   \n",
       "\n",
       "   pct_black_alone_change  pct_asian_alone_17  pct_asian_alone_change  \\\n",
       "0                 -32.250               3.375                   1.790   \n",
       "1                 -32.250               3.375                   1.790   \n",
       "2                 -32.250               3.375                   1.790   \n",
       "3                 -41.273               4.432                   3.846   \n",
       "4                 -36.572               4.471                   1.112   \n",
       "\n",
       "   pct_native_alone_17  pct_native_alone_change  \\\n",
       "0                0.000                   -0.294   \n",
       "1                0.000                   -0.294   \n",
       "2                0.000                   -0.294   \n",
       "3                1.485                    1.154   \n",
       "4                0.000                   -0.110   \n",
       "\n",
       "   pct_native_hawaiian_pacific_islander_17  \\\n",
       "0                                    0.309   \n",
       "1                                    0.309   \n",
       "2                                    0.309   \n",
       "3                                    0.000   \n",
       "4                                    0.000   \n",
       "\n",
       "   pct_native_hawaiian_pacific_islander_change  pct_hispanic_or_latino_17  \\\n",
       "0                                        0.309                      7.593   \n",
       "1                                        0.309                      7.593   \n",
       "2                                        0.309                      7.593   \n",
       "3                                        0.000                     12.266   \n",
       "4                                        0.000                     10.415   \n",
       "\n",
       "   pct_hispanic_or_latino_change  raids  \n",
       "0                          0.752      3  \n",
       "1                          0.752      3  \n",
       "2                          0.752      3  \n",
       "3                          2.134      1  \n",
       "4                          2.761      2  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoodraids.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find a list of the most raided bars in the Census tracts surrounding \"Friends and Lovers\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "778 FRANKLIN AVENUE    7\n",
       "990 ATLANTIC AVENUE    6\n",
       "960 ATLANTIC AVENUE    6\n",
       "566 NOSTRAND AVENUE    3\n",
       "899 BERGEN STREET      2\n",
       "1088 FULTON STREET     2\n",
       "1401 BEDFORD AVENUE    1\n",
       "559 GRAND AVENUE       1\n",
       "946 FULTON STREET      1\n",
       "676 FRANKLIN AVENUE    1\n",
       "617 GRAND AVENUE       1\n",
       "856 ATLANTIC AVENUE    1\n",
       "766 FRANKLIN AVENUE    1\n",
       "1065 PACIFIC STREET    1\n",
       "1188 BEDFORD AVENUE    1\n",
       "15 PUTNAM AVENUE       1\n",
       "Name: address, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhoodraids[\"address\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighborhoodraids[\"address\"].value_counts().to_csv(\"../output/neighborhood_target_list.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some statistics for the neighborhood\n",
    "- demographic changes for Census tract in which Ode to Babel and Friends and Lovers are \n",
    "- to adjust numbers for inflation. The rate of 1.44 came from the [Bureau of Labor Statistics Inflation Calculator](https://www.bls.gov/data/inflation_calculator.htm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>147</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GEOID</th>\n",
       "      <td>36047020300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>Census Tract 203, Kings County, New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_population_17</th>\n",
       "      <td>1764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_population_25_over_17</th>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income_17</th>\n",
       "      <td>103571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_home_value_17</th>\n",
       "      <td>918600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_attainment_17</th>\n",
       "      <td>833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white_alone_17</th>\n",
       "      <td>675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black_alone_17</th>\n",
       "      <td>496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native_alone_17</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asian_alone_17</th>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native_hawaiian_pacific_islander_17</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>some_other_race_alone</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_or_more</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic_or_latino_17</th>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <td>New York City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metro_area</th>\n",
       "      <td>New York-Newark-Jersey City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county</th>\n",
       "      <td>Kings County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tract</th>\n",
       "      <td>Census Tract 203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_population_00</th>\n",
       "      <td>1192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white_alone_00</th>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black_alone_00</th>\n",
       "      <td>684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native_alone_00</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asian_alone_00</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native_hawaiian_pacific_islander_00</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic_or_latino_00</th>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_population_25_over_00</th>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income_00</th>\n",
       "      <td>38942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_home_value_00</th>\n",
       "      <td>192900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_attainment_00</th>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_attainment_pct_17</th>\n",
       "      <td>64.0769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_attainment_pct_00</th>\n",
       "      <td>21.3889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_attainment_change</th>\n",
       "      <td>42.688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_pct_change</th>\n",
       "      <td>0.697609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metro_median_income_00_q40</th>\n",
       "      <td>43583.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metro_median_home_value_00_q40</th>\n",
       "      <td>181600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_attainment_change_q66</th>\n",
       "      <td>11.1645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_pct_change_q66</th>\n",
       "      <td>0.375903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low_population</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_home_value_00_qualifies</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_40th_income_00_qualifies</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_increase_is_top_3rd</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_home_increased</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_home_increase_is_top_3rd</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eligible_for_gentrification</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gentrified</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eligible_not_gentrified_highpop</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not_eligible_highpop</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_white_alone_17</th>\n",
       "      <td>38.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_white_alone_00</th>\n",
       "      <td>11.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_white_alone_change</th>\n",
       "      <td>26.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_black_alone_17</th>\n",
       "      <td>28.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_black_alone_00</th>\n",
       "      <td>57.383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_black_alone_change</th>\n",
       "      <td>-29.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_asian_alone_17</th>\n",
       "      <td>15.363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_asian_alone_00</th>\n",
       "      <td>3.943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_asian_alone_change</th>\n",
       "      <td>11.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_native_alone_17</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_native_alone_00</th>\n",
       "      <td>0.336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_native_alone_change</th>\n",
       "      <td>-0.336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_native_hawaiian_pacific_islander_17</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_native_hawaiian_pacific_islander_00</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_native_hawaiian_pacific_islander_change</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_hispanic_or_latino_17</th>\n",
       "      <td>15.136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_hispanic_or_latino_00</th>\n",
       "      <td>25.419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_hispanic_or_latino_change</th>\n",
       "      <td>-10.283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raids</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                  147\n",
       "GEOID                                                                     36047020300\n",
       "name                                         Census Tract 203, Kings County, New York\n",
       "total_population_17                                                              1764\n",
       "total_population_25_over_17                                                      1300\n",
       "median_income_17                                                               103571\n",
       "median_home_value_17                                                           918600\n",
       "educational_attainment_17                                                         833\n",
       "white_alone_17                                                                    675\n",
       "black_alone_17                                                                    496\n",
       "native_alone_17                                                                     0\n",
       "asian_alone_17                                                                    271\n",
       "native_hawaiian_pacific_islander_17                                                 0\n",
       "some_other_race_alone                                                              21\n",
       "two_or_more                                                                        34\n",
       "hispanic_or_latino_17                                                             267\n",
       "city                                                                    New York City\n",
       "metro_area                                                New York-Newark-Jersey City\n",
       "state                                                                              NY\n",
       "county                                                                   Kings County\n",
       "tract                                                                Census Tract 203\n",
       "total_population_00                                                              1192\n",
       "white_alone_00                                                                    140\n",
       "black_alone_00                                                                    684\n",
       "native_alone_00                                                                     4\n",
       "asian_alone_00                                                                     47\n",
       "native_hawaiian_pacific_islander_00                                                 0\n",
       "hispanic_or_latino_00                                                             303\n",
       "total_population_25_over_00                                                       720\n",
       "median_income_00                                                                38942\n",
       "median_home_value_00                                                           192900\n",
       "educational_attainment_00                                                         154\n",
       "educational_attainment_pct_17                                                 64.0769\n",
       "educational_attainment_pct_00                                                 21.3889\n",
       "educational_attainment_change                                                  42.688\n",
       "home_pct_change                                                              0.697609\n",
       "metro_median_income_00_q40                                                    43583.6\n",
       "metro_median_home_value_00_q40                                                 181600\n",
       "educational_attainment_change_q66                                             11.1645\n",
       "home_pct_change_q66                                                          0.375903\n",
       "low_population                                                                  False\n",
       "median_home_value_00_qualifies                                                  False\n",
       "median_40th_income_00_qualifies                                                  True\n",
       "educational_increase_is_top_3rd                                                  True\n",
       "median_home_increased                                                            True\n",
       "median_home_increase_is_top_3rd                                                  True\n",
       "eligible_for_gentrification                                                     False\n",
       "gentrified                                                                      False\n",
       "eligible_not_gentrified_highpop                                                 False\n",
       "not_eligible_highpop                                                             True\n",
       "pct_white_alone_17                                                             38.265\n",
       "pct_white_alone_00                                                             11.745\n",
       "pct_white_alone_change                                                          26.52\n",
       "pct_black_alone_17                                                             28.118\n",
       "pct_black_alone_00                                                             57.383\n",
       "pct_black_alone_change                                                        -29.265\n",
       "pct_asian_alone_17                                                             15.363\n",
       "pct_asian_alone_00                                                              3.943\n",
       "pct_asian_alone_change                                                          11.42\n",
       "pct_native_alone_17                                                                 0\n",
       "pct_native_alone_00                                                             0.336\n",
       "pct_native_alone_change                                                        -0.336\n",
       "pct_native_hawaiian_pacific_islander_17                                             0\n",
       "pct_native_hawaiian_pacific_islander_00                                             0\n",
       "pct_native_hawaiian_pacific_islander_change                                         0\n",
       "pct_hispanic_or_latino_17                                                      15.136\n",
       "pct_hispanic_or_latino_00                                                      25.419\n",
       "pct_hispanic_or_latino_change                                                 -10.283\n",
       "raids                                                                               7"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "gentrification_targets[gentrification_targets[\"GEOID\"] == \"36047020300\"].T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* demographic changes for Census tract where Friends and Lovers is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>167</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GEOID</th>\n",
       "      <td>36047030500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>Census Tract 305, Kings County, New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_population_17</th>\n",
       "      <td>6267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_population_25_over_17</th>\n",
       "      <td>5310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income_17</th>\n",
       "      <td>70125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_home_value_17</th>\n",
       "      <td>544800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_attainment_17</th>\n",
       "      <td>3034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white_alone_17</th>\n",
       "      <td>2483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black_alone_17</th>\n",
       "      <td>2471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native_alone_17</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asian_alone_17</th>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native_hawaiian_pacific_islander_17</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>some_other_race_alone</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two_or_more</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic_or_latino_17</th>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <td>New York City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metro_area</th>\n",
       "      <td>New York-Newark-Jersey City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>county</th>\n",
       "      <td>Kings County</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tract</th>\n",
       "      <td>Census Tract 305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_population_00</th>\n",
       "      <td>4777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white_alone_00</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black_alone_00</th>\n",
       "      <td>3684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native_alone_00</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asian_alone_00</th>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>native_hawaiian_pacific_islander_00</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic_or_latino_00</th>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_population_25_over_00</th>\n",
       "      <td>2909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income_00</th>\n",
       "      <td>28331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_home_value_00</th>\n",
       "      <td>141397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_attainment_00</th>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_attainment_pct_17</th>\n",
       "      <td>57.1375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_attainment_pct_00</th>\n",
       "      <td>12.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_attainment_change</th>\n",
       "      <td>45.0715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_pct_change</th>\n",
       "      <td>0.626264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metro_median_income_00_q40</th>\n",
       "      <td>43583.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metro_median_home_value_00_q40</th>\n",
       "      <td>181600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_attainment_change_q66</th>\n",
       "      <td>11.1645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_pct_change_q66</th>\n",
       "      <td>0.375903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low_population</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_home_value_00_qualifies</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_40th_income_00_qualifies</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educational_increase_is_top_3rd</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_home_increased</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_home_increase_is_top_3rd</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eligible_for_gentrification</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gentrified</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eligible_not_gentrified_highpop</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not_eligible_highpop</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_white_alone_17</th>\n",
       "      <td>39.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_white_alone_00</th>\n",
       "      <td>2.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_white_alone_change</th>\n",
       "      <td>37.087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_black_alone_17</th>\n",
       "      <td>39.429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_black_alone_00</th>\n",
       "      <td>77.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_black_alone_change</th>\n",
       "      <td>-37.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_asian_alone_17</th>\n",
       "      <td>5.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_asian_alone_00</th>\n",
       "      <td>2.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_asian_alone_change</th>\n",
       "      <td>2.902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_native_alone_17</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_native_alone_00</th>\n",
       "      <td>0.356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_native_alone_change</th>\n",
       "      <td>-0.356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_native_hawaiian_pacific_islander_17</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_native_hawaiian_pacific_islander_00</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_native_hawaiian_pacific_islander_change</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_hispanic_or_latino_17</th>\n",
       "      <td>14.409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_hispanic_or_latino_00</th>\n",
       "      <td>16.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_hispanic_or_latino_change</th>\n",
       "      <td>-2.401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raids</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                  167\n",
       "GEOID                                                                     36047030500\n",
       "name                                         Census Tract 305, Kings County, New York\n",
       "total_population_17                                                              6267\n",
       "total_population_25_over_17                                                      5310\n",
       "median_income_17                                                                70125\n",
       "median_home_value_17                                                           544800\n",
       "educational_attainment_17                                                        3034\n",
       "white_alone_17                                                                   2483\n",
       "black_alone_17                                                                   2471\n",
       "native_alone_17                                                                     0\n",
       "asian_alone_17                                                                    317\n",
       "native_hawaiian_pacific_islander_17                                                 0\n",
       "some_other_race_alone                                                               0\n",
       "two_or_more                                                                        93\n",
       "hispanic_or_latino_17                                                             903\n",
       "city                                                                    New York City\n",
       "metro_area                                                New York-Newark-Jersey City\n",
       "state                                                                              NY\n",
       "county                                                                   Kings County\n",
       "tract                                                                Census Tract 305\n",
       "total_population_00                                                              4777\n",
       "white_alone_00                                                                    121\n",
       "black_alone_00                                                                   3684\n",
       "native_alone_00                                                                    17\n",
       "asian_alone_00                                                                    103\n",
       "native_hawaiian_pacific_islander_00                                                 0\n",
       "hispanic_or_latino_00                                                             803\n",
       "total_population_25_over_00                                                      2909\n",
       "median_income_00                                                                28331\n",
       "median_home_value_00                                                           141397\n",
       "educational_attainment_00                                                         351\n",
       "educational_attainment_pct_17                                                 57.1375\n",
       "educational_attainment_pct_00                                                  12.066\n",
       "educational_attainment_change                                                 45.0715\n",
       "home_pct_change                                                              0.626264\n",
       "metro_median_income_00_q40                                                    43583.6\n",
       "metro_median_home_value_00_q40                                                 181600\n",
       "educational_attainment_change_q66                                             11.1645\n",
       "home_pct_change_q66                                                          0.375903\n",
       "low_population                                                                  False\n",
       "median_home_value_00_qualifies                                                   True\n",
       "median_40th_income_00_qualifies                                                  True\n",
       "educational_increase_is_top_3rd                                                  True\n",
       "median_home_increased                                                            True\n",
       "median_home_increase_is_top_3rd                                                  True\n",
       "eligible_for_gentrification                                                      True\n",
       "gentrified                                                                       True\n",
       "eligible_not_gentrified_highpop                                                 False\n",
       "not_eligible_highpop                                                            False\n",
       "pct_white_alone_17                                                              39.62\n",
       "pct_white_alone_00                                                              2.533\n",
       "pct_white_alone_change                                                         37.087\n",
       "pct_black_alone_17                                                             39.429\n",
       "pct_black_alone_00                                                              77.12\n",
       "pct_black_alone_change                                                        -37.691\n",
       "pct_asian_alone_17                                                              5.058\n",
       "pct_asian_alone_00                                                              2.156\n",
       "pct_asian_alone_change                                                          2.902\n",
       "pct_native_alone_17                                                                 0\n",
       "pct_native_alone_00                                                             0.356\n",
       "pct_native_alone_change                                                        -0.356\n",
       "pct_native_hawaiian_pacific_islander_17                                             0\n",
       "pct_native_hawaiian_pacific_islander_00                                             0\n",
       "pct_native_hawaiian_pacific_islander_change                                         0\n",
       "pct_hispanic_or_latino_17                                                      14.409\n",
       "pct_hispanic_or_latino_00                                                       16.81\n",
       "pct_hispanic_or_latino_change                                                  -2.401\n",
       "raids                                                                              12"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gentrification_targets[gentrification_targets[\"GEOID\"] == \"36047030500\"].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "167    1.675686\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "friends_lovers_tract =gentrification_targets[gentrification_targets[\"GEOID\"] == \"36047030500\"]\n",
    "\n",
    "median_home_value_increase = ( \n",
    "    (friends_lovers_tract[\"median_home_value_17\"] - (friends_lovers_tract[\"median_home_value_00\"]* 1.44))/\n",
    "    (friends_lovers_tract[\"median_home_value_00\"]* 1.44)\n",
    ")\n",
    "\n",
    "print(median_home_value_increase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median_home_value_00:  167    203611.354848\n",
      "Name: median_home_value_00, dtype: float64 median_home_value_17:  167    544800.0\n",
      "Name: median_home_value_17, dtype: float64 median_income_00:  167    40796.666395\n",
      "Name: median_income_00, dtype: float64 median_income_17:  167    70125.0\n",
      "Name: median_income_17, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"median_home_value_00: \", friends_lovers_tract[\"median_home_value_00\"]* 1.44,\n",
    "    \"median_home_value_17: \", friends_lovers_tract[\"median_home_value_17\"], \n",
    "    \"median_income_00: \", friends_lovers_tract[\"median_income_00\"]* 1.44,\n",
    "    \"median_income_17: \", friends_lovers_tract[\"median_income_17\"], \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GEOID', 'name', 'total_population_17', 'total_population_25_over_17',\n",
       "       'median_income_17', 'median_home_value_17', 'educational_attainment_17',\n",
       "       'white_alone_17', 'black_alone_17', 'native_alone_17', 'asian_alone_17',\n",
       "       'native_hawaiian_pacific_islander_17', 'some_other_race_alone',\n",
       "       'two_or_more', 'hispanic_or_latino_17', 'city', 'metro_area', 'state',\n",
       "       'county', 'tract', 'total_population_00', 'white_alone_00',\n",
       "       'black_alone_00', 'native_alone_00', 'asian_alone_00',\n",
       "       'native_hawaiian_pacific_islander_00', 'hispanic_or_latino_00',\n",
       "       'total_population_25_over_00', 'median_income_00',\n",
       "       'median_home_value_00', 'educational_attainment_00',\n",
       "       'educational_attainment_pct_17', 'educational_attainment_pct_00',\n",
       "       'educational_attainment_change', 'home_pct_change',\n",
       "       'metro_median_income_00_q40', 'metro_median_home_value_00_q40',\n",
       "       'educational_attainment_change_q66', 'home_pct_change_q66',\n",
       "       'low_population', 'median_home_value_00_qualifies',\n",
       "       'median_40th_income_00_qualifies', 'educational_increase_is_top_3rd',\n",
       "       'median_home_increased', 'median_home_increase_is_top_3rd',\n",
       "       'eligible_for_gentrification', 'gentrified',\n",
       "       'eligible_not_gentrified_highpop', 'not_eligible_highpop',\n",
       "       'pct_white_alone_17', 'pct_white_alone_00', 'pct_white_alone_change',\n",
       "       'pct_black_alone_17', 'pct_black_alone_00', 'pct_black_alone_change',\n",
       "       'pct_asian_alone_17', 'pct_asian_alone_00', 'pct_asian_alone_change',\n",
       "       'pct_native_alone_17', 'pct_native_alone_00', 'pct_native_alone_change',\n",
       "       'pct_native_hawaiian_pacific_islander_17',\n",
       "       'pct_native_hawaiian_pacific_islander_00',\n",
       "       'pct_native_hawaiian_pacific_islander_change',\n",
       "       'pct_hispanic_or_latino_17', 'pct_hispanic_or_latino_00',\n",
       "       'pct_hispanic_or_latino_change', 'raids'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gentrification_targets.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Noise complaints analysis\n",
    "\n",
    "This part of the analysis looks at noise complaints affiliated with Ode To Babel's address. \n",
    "\n",
    "In the cells we will:\n",
    "- identify types of complaints that are relevant (noise)\n",
    "\n",
    "The data was downloaded from this portal with `772 Dean Street`, the bar's address, being a filter:\n",
    "https://nycopendata.socrata.com/Social-Services/311-Service-Requests-from-2010-to-Present/erm2-nwe9/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "220"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complaints = pd.read_csv(\n",
    "    \"../data/311-complaints_772_deanstreet.csv\",\n",
    "    parse_dates = [\"Created Date\"]\n",
    ")\n",
    "\n",
    "len(complaints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unique Key                                 int64\n",
       "Created Date                      datetime64[ns]\n",
       "Closed Date                               object\n",
       "Agency                                    object\n",
       "Agency Name                               object\n",
       "Complaint Type                            object\n",
       "Descriptor                                object\n",
       "Location Type                             object\n",
       "Incident Zip                             float64\n",
       "Incident Address                          object\n",
       "Street Name                               object\n",
       "Cross Street 1                            object\n",
       "Cross Street 2                            object\n",
       "Intersection Street 1                     object\n",
       "Intersection Street 2                     object\n",
       "Address Type                              object\n",
       "City                                      object\n",
       "Landmark                                  object\n",
       "Facility Type                             object\n",
       "Status                                    object\n",
       "Due Date                                  object\n",
       "Resolution Description                    object\n",
       "Resolution Action Updated Date            object\n",
       "Community Board                           object\n",
       "BBL                                      float64\n",
       "Borough                                   object\n",
       "X Coordinate (State Plane)               float64\n",
       "Y Coordinate (State Plane)               float64\n",
       "Open Data Channel Type                    object\n",
       "Park Facility Name                        object\n",
       "Park Borough                              object\n",
       "Vehicle Type                             float64\n",
       "Taxi Company Borough                     float64\n",
       "Taxi Pick Up Location                    float64\n",
       "Bridge Highway Name                      float64\n",
       "Bridge Highway Direction                 float64\n",
       "Road Ramp                                float64\n",
       "Bridge Highway Segment                   float64\n",
       "Latitude                                 float64\n",
       "Longitude                                float64\n",
       "Location                                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complaints.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unique Key</th>\n",
       "      <th>Created Date</th>\n",
       "      <th>Closed Date</th>\n",
       "      <th>Agency</th>\n",
       "      <th>Agency Name</th>\n",
       "      <th>Complaint Type</th>\n",
       "      <th>Descriptor</th>\n",
       "      <th>Location Type</th>\n",
       "      <th>Incident Zip</th>\n",
       "      <th>Incident Address</th>\n",
       "      <th>...</th>\n",
       "      <th>Vehicle Type</th>\n",
       "      <th>Taxi Company Borough</th>\n",
       "      <th>Taxi Pick Up Location</th>\n",
       "      <th>Bridge Highway Name</th>\n",
       "      <th>Bridge Highway Direction</th>\n",
       "      <th>Road Ramp</th>\n",
       "      <th>Bridge Highway Segment</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47067063</td>\n",
       "      <td>2020-08-02 10:58:56</td>\n",
       "      <td>08/02/2020 11:08:28 AM</td>\n",
       "      <td>NYPD</td>\n",
       "      <td>New York City Police Department</td>\n",
       "      <td>Noise - Commercial</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>Club/Bar/Restaurant</td>\n",
       "      <td>11238.0</td>\n",
       "      <td>772 DEAN STREET</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>40.679474</td>\n",
       "      <td>-73.964574</td>\n",
       "      <td>(40.6794737, -73.9645739)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47058732</td>\n",
       "      <td>2020-08-01 13:35:57</td>\n",
       "      <td>08/01/2020 01:50:22 PM</td>\n",
       "      <td>NYPD</td>\n",
       "      <td>New York City Police Department</td>\n",
       "      <td>Noise - Commercial</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>Club/Bar/Restaurant</td>\n",
       "      <td>11238.0</td>\n",
       "      <td>772 DEAN STREET</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>40.679474</td>\n",
       "      <td>-73.964574</td>\n",
       "      <td>(40.6794737, -73.9645739)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47048119</td>\n",
       "      <td>2020-07-31 23:38:55</td>\n",
       "      <td>08/14/2020 02:51:57 PM</td>\n",
       "      <td>MAYOR?S OFFICE OF SPECIAL ENFORCEMENT</td>\n",
       "      <td>Mayor?s Office of Special Enforcement</td>\n",
       "      <td>NonCompliance with Phased Reopening</td>\n",
       "      <td>Business not in compliance</td>\n",
       "      <td>Store/Commercial</td>\n",
       "      <td>11238.0</td>\n",
       "      <td>772 DEAN STREET</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>40.679474</td>\n",
       "      <td>-73.964574</td>\n",
       "      <td>(40.6794737, -73.9645739)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46926376</td>\n",
       "      <td>2020-07-20 20:12:00</td>\n",
       "      <td>07/20/2020 08:12:00 PM</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Water System</td>\n",
       "      <td>Fire Hydrant Emergency (FHE)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11238.0</td>\n",
       "      <td>772 DEAN STREET</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>40.679474</td>\n",
       "      <td>-73.964574</td>\n",
       "      <td>(40.6794737, -73.9645739)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38375835</td>\n",
       "      <td>2018-02-04 00:16:31</td>\n",
       "      <td>02/04/2018 01:18:39 AM</td>\n",
       "      <td>NYPD</td>\n",
       "      <td>New York City Police Department</td>\n",
       "      <td>Noise - Commercial</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>Store/Commercial</td>\n",
       "      <td>11238.0</td>\n",
       "      <td>772 DEAN STREET</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>40.679474</td>\n",
       "      <td>-73.964574</td>\n",
       "      <td>(40.6794737, -73.9645739)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unique Key        Created Date             Closed Date  \\\n",
       "0    47067063 2020-08-02 10:58:56  08/02/2020 11:08:28 AM   \n",
       "1    47058732 2020-08-01 13:35:57  08/01/2020 01:50:22 PM   \n",
       "2    47048119 2020-07-31 23:38:55  08/14/2020 02:51:57 PM   \n",
       "3    46926376 2020-07-20 20:12:00  07/20/2020 08:12:00 PM   \n",
       "4    38375835 2018-02-04 00:16:31  02/04/2018 01:18:39 AM   \n",
       "\n",
       "                                  Agency  \\\n",
       "0                                   NYPD   \n",
       "1                                   NYPD   \n",
       "2  MAYOR?S OFFICE OF SPECIAL ENFORCEMENT   \n",
       "3                                    DEP   \n",
       "4                                   NYPD   \n",
       "\n",
       "                              Agency Name  \\\n",
       "0         New York City Police Department   \n",
       "1         New York City Police Department   \n",
       "2   Mayor?s Office of Special Enforcement   \n",
       "3  Department of Environmental Protection   \n",
       "4         New York City Police Department   \n",
       "\n",
       "                        Complaint Type                    Descriptor  \\\n",
       "0                   Noise - Commercial              Loud Music/Party   \n",
       "1                   Noise - Commercial              Loud Music/Party   \n",
       "2  NonCompliance with Phased Reopening    Business not in compliance   \n",
       "3                         Water System  Fire Hydrant Emergency (FHE)   \n",
       "4                   Noise - Commercial              Loud Music/Party   \n",
       "\n",
       "         Location Type  Incident Zip Incident Address  ... Vehicle Type  \\\n",
       "0  Club/Bar/Restaurant       11238.0  772 DEAN STREET  ...          NaN   \n",
       "1  Club/Bar/Restaurant       11238.0  772 DEAN STREET  ...          NaN   \n",
       "2     Store/Commercial       11238.0  772 DEAN STREET  ...          NaN   \n",
       "3                  NaN       11238.0  772 DEAN STREET  ...          NaN   \n",
       "4     Store/Commercial       11238.0  772 DEAN STREET  ...          NaN   \n",
       "\n",
       "  Taxi Company Borough Taxi Pick Up Location Bridge Highway Name  \\\n",
       "0                  NaN                   NaN                 NaN   \n",
       "1                  NaN                   NaN                 NaN   \n",
       "2                  NaN                   NaN                 NaN   \n",
       "3                  NaN                   NaN                 NaN   \n",
       "4                  NaN                   NaN                 NaN   \n",
       "\n",
       "  Bridge Highway Direction Road Ramp Bridge Highway Segment   Latitude  \\\n",
       "0                      NaN       NaN                    NaN  40.679474   \n",
       "1                      NaN       NaN                    NaN  40.679474   \n",
       "2                      NaN       NaN                    NaN  40.679474   \n",
       "3                      NaN       NaN                    NaN  40.679474   \n",
       "4                      NaN       NaN                    NaN  40.679474   \n",
       "\n",
       "   Longitude                   Location  \n",
       "0 -73.964574  (40.6794737, -73.9645739)  \n",
       "1 -73.964574  (40.6794737, -73.9645739)  \n",
       "2 -73.964574  (40.6794737, -73.9645739)  \n",
       "3 -73.964574  (40.6794737, -73.9645739)  \n",
       "4 -73.964574  (40.6794737, -73.9645739)  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complaints.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Noise - Commercial                     182\n",
       "Noise - Street/Sidewalk                 10\n",
       "Rodent                                   5\n",
       "Noise - Residential                      4\n",
       "General Construction/Plumbing            3\n",
       "Derelict Vehicles                        2\n",
       "Urinating in Public                      2\n",
       "Food Establishment                       2\n",
       "New Tree Request                         2\n",
       "Emergency Response Team (ERT)            1\n",
       "NonCompliance with Phased Reopening      1\n",
       "Missed Collection (All Materials)        1\n",
       "Drinking                                 1\n",
       "Water System                             1\n",
       "Overgrown Tree/Branches                  1\n",
       "Plumbing                                 1\n",
       "Building/Use                             1\n",
       "Name: Complaint Type, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complaints[\"Complaint Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "196"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_complaints = complaints[ complaints[\"Complaint Type\"].str.contains(\"Noise\")]\n",
    "len(noise_complaints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                              196\n",
       "mean     2018-11-15 08:32:42.025510400\n",
       "min                2015-09-25 23:21:10\n",
       "25%      2018-03-28 11:47:45.750000128\n",
       "50%                2019-03-13 14:48:02\n",
       "75%      2019-08-18 19:08:05.249999872\n",
       "max                2020-08-02 10:58:56\n",
       "Name: Created Date, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_complaints[\"Created Date\"].describe(datetime_is_numeric = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bfa53a0>"
      ]
     },
     "execution_count": 44,
     "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": [
    "(\n",
    "    noise_complaints\n",
    "    .groupby(pd.Grouper(\n",
    "        key = \"Created Date\",\n",
    "        freq = \"MS\"\n",
    "    ))\n",
    "    .size()\n",
    "    .plot.bar()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_complaints.set_index(\n",
    "        \"Created Date\"\n",
    ")[\"Incident Address\"].resample(\n",
    "    \"MS\"\n",
    ").count(\n",
    ").to_csv(\n",
    "    \"../output/noise_complaints_ode_to_babel.csv\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}