{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing NYC's 311 Street Flooding Complaints from 2010 to 2020\n",
    "## Streets with the Most Street Flooding Complaints\n",
    "\n",
    "Author: Mark Bauer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "import seaborn as sns\n",
    "import geopandas as gpd\n",
    "\n",
    "plt.rcParams['savefig.facecolor'] = 'white'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printing verions of Python modules and packages with **watermark** - the IPython magic extension.  \n",
    "Documention for installing watermark: https://github.com/rasbt/watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.0\n",
      "IPython version      : 8.6.0\n",
      "\n",
      "numpy     : 1.23.4\n",
      "pandas    : 1.5.1\n",
      "geopandas : 0.12.1\n",
      "matplotlib: 3.6.2\n",
      "seaborn   : 0.12.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%reload_ext watermark\n",
    "%watermark -v -p numpy,pandas,geopandas,matplotlib,seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "README.md                               streets-clipped.gpkg\r\n",
      "street-flooding-complaints-cleaned.csv  streets.gpkg\r\n",
      "street-flooding-complaints.csv          water-main-breaks.csv\r\n"
     ]
    }
   ],
   "source": [
    "# list items in data folder\n",
    "%ls data/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Street Flooding Complaints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (24817, 27)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>cross_street_1</th>\n",
       "      <th>cross_street_2</th>\n",
       "      <th>address_type</th>\n",
       "      <th>...</th>\n",
       "      <th>incident_zip</th>\n",
       "      <th>city</th>\n",
       "      <th>x_coordinate_state_plane</th>\n",
       "      <th>y_coordinate_state_plane</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>location</th>\n",
       "      <th>incident_address</th>\n",
       "      <th>street_name</th>\n",
       "      <th>bbl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34783066</td>\n",
       "      <td>2016-11-15T09:27:00.000</td>\n",
       "      <td>2016-11-15T10:05:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INTERSECTION</td>\n",
       "      <td>...</td>\n",
       "      <td>10301.0</td>\n",
       "      <td>STATEN ISLAND</td>\n",
       "      <td>958594.0</td>\n",
       "      <td>170855.0</td>\n",
       "      <td>40.635597</td>\n",
       "      <td>-74.092438</td>\n",
       "      <td>{'latitude': '40.635596930697716', 'longitude'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36178846</td>\n",
       "      <td>2017-05-13T14:41:00.000</td>\n",
       "      <td>2017-11-08T11:05:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>32 AVE</td>\n",
       "      <td>78 ST</td>\n",
       "      <td>INTERSECTION</td>\n",
       "      <td>...</td>\n",
       "      <td>11370.0</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>1014871.0</td>\n",
       "      <td>215198.0</td>\n",
       "      <td>40.757292</td>\n",
       "      <td>-73.889472</td>\n",
       "      <td>{'latitude': '40.75729226742685', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31360389</td>\n",
       "      <td>2015-08-21T05:46:00.000</td>\n",
       "      <td>2015-08-26T10:27:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>110 AVE</td>\n",
       "      <td>110 RD</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>11433.0</td>\n",
       "      <td>JAMAICA</td>\n",
       "      <td>1043288.0</td>\n",
       "      <td>192114.0</td>\n",
       "      <td>40.693788</td>\n",
       "      <td>-73.787102</td>\n",
       "      <td>{'latitude': '40.69378840426638', 'longitude':...</td>\n",
       "      <td>110-07 164 PLACE</td>\n",
       "      <td>164 PLACE</td>\n",
       "      <td>4.101930e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32686230</td>\n",
       "      <td>2016-02-15T13:10:00.000</td>\n",
       "      <td>2016-02-16T14:30:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>ALDUS ST</td>\n",
       "      <td>HOE AVE</td>\n",
       "      <td>INTERSECTION</td>\n",
       "      <td>...</td>\n",
       "      <td>10459.0</td>\n",
       "      <td>BRONX</td>\n",
       "      <td>1014578.0</td>\n",
       "      <td>239190.0</td>\n",
       "      <td>40.823145</td>\n",
       "      <td>-73.890421</td>\n",
       "      <td>{'latitude': '40.82314481234778', 'longitude':...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41495135</td>\n",
       "      <td>2019-01-23T11:59:00.000</td>\n",
       "      <td>2019-01-28T13:05:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>102 ST</td>\n",
       "      <td>DEAD END</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>11414.0</td>\n",
       "      <td>HOWARD BEACH</td>\n",
       "      <td>1031172.0</td>\n",
       "      <td>179312.0</td>\n",
       "      <td>40.658722</td>\n",
       "      <td>-73.830883</td>\n",
       "      <td>{'latitude': '40.65872239939313', 'longitude':...</td>\n",
       "      <td>102-20 160 AVENUE</td>\n",
       "      <td>160 AVENUE</td>\n",
       "      <td>4.142340e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_key             created_date              closed_date agency  \\\n",
       "0    34783066  2016-11-15T09:27:00.000  2016-11-15T10:05:00.000    DEP   \n",
       "1    36178846  2017-05-13T14:41:00.000  2017-11-08T11:05:00.000    DEP   \n",
       "2    31360389  2015-08-21T05:46:00.000  2015-08-26T10:27:00.000    DEP   \n",
       "3    32686230  2016-02-15T13:10:00.000  2016-02-16T14:30:00.000    DEP   \n",
       "4    41495135  2019-01-23T11:59:00.000  2019-01-28T13:05:00.000    DEP   \n",
       "\n",
       "                              agency_name complaint_type  \\\n",
       "0  Department of Environmental Protection          Sewer   \n",
       "1  Department of Environmental Protection          Sewer   \n",
       "2  Department of Environmental Protection          Sewer   \n",
       "3  Department of Environmental Protection          Sewer   \n",
       "4  Department of Environmental Protection          Sewer   \n",
       "\n",
       "             descriptor cross_street_1 cross_street_2  address_type  ...  \\\n",
       "0  Street Flooding (SJ)            NaN            NaN  INTERSECTION  ...   \n",
       "1  Street Flooding (SJ)         32 AVE          78 ST  INTERSECTION  ...   \n",
       "2  Street Flooding (SJ)        110 AVE         110 RD       ADDRESS  ...   \n",
       "3  Street Flooding (SJ)       ALDUS ST        HOE AVE  INTERSECTION  ...   \n",
       "4  Street Flooding (SJ)         102 ST       DEAD END       ADDRESS  ...   \n",
       "\n",
       "  incident_zip           city x_coordinate_state_plane  \\\n",
       "0      10301.0  STATEN ISLAND                 958594.0   \n",
       "1      11370.0  East Elmhurst                1014871.0   \n",
       "2      11433.0        JAMAICA                1043288.0   \n",
       "3      10459.0          BRONX                1014578.0   \n",
       "4      11414.0   HOWARD BEACH                1031172.0   \n",
       "\n",
       "  y_coordinate_state_plane   latitude  longitude  \\\n",
       "0                 170855.0  40.635597 -74.092438   \n",
       "1                 215198.0  40.757292 -73.889472   \n",
       "2                 192114.0  40.693788 -73.787102   \n",
       "3                 239190.0  40.823145 -73.890421   \n",
       "4                 179312.0  40.658722 -73.830883   \n",
       "\n",
       "                                            location   incident_address  \\\n",
       "0  {'latitude': '40.635596930697716', 'longitude'...                NaN   \n",
       "1  {'latitude': '40.75729226742685', 'longitude':...                NaN   \n",
       "2  {'latitude': '40.69378840426638', 'longitude':...   110-07 164 PLACE   \n",
       "3  {'latitude': '40.82314481234778', 'longitude':...                NaN   \n",
       "4  {'latitude': '40.65872239939313', 'longitude':...  102-20 160 AVENUE   \n",
       "\n",
       "  street_name           bbl  \n",
       "0         NaN           NaN  \n",
       "1         NaN           NaN  \n",
       "2   164 PLACE  4.101930e+09  \n",
       "3         NaN           NaN  \n",
       "4  160 AVENUE  4.142340e+09  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read data as a dataframe\n",
    "path = 'data/street-flooding-complaints-cleaned.csv'\n",
    "df = pd.read_csv(path, low_memory=False)\n",
    "\n",
    "# preview data\n",
    "print(f'shape of data: {df.shape}')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 24817 entries, 0 to 24816\n",
      "Data columns (total 27 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   unique_key                      24817 non-null  int64  \n",
      " 1   created_date                    24817 non-null  object \n",
      " 2   closed_date                     24816 non-null  object \n",
      " 3   agency                          24817 non-null  object \n",
      " 4   agency_name                     24817 non-null  object \n",
      " 5   complaint_type                  24817 non-null  object \n",
      " 6   descriptor                      24817 non-null  object \n",
      " 7   cross_street_1                  21821 non-null  object \n",
      " 8   cross_street_2                  21816 non-null  object \n",
      " 9   address_type                    24817 non-null  object \n",
      " 10  status                          24817 non-null  object \n",
      " 11  resolution_description          24813 non-null  object \n",
      " 12  resolution_action_updated_date  24817 non-null  object \n",
      " 13  community_board                 24817 non-null  object \n",
      " 14  borough                         24817 non-null  object \n",
      " 15  open_data_channel_type          24817 non-null  object \n",
      " 16  park_borough                    24817 non-null  object \n",
      " 17  incident_zip                    24817 non-null  float64\n",
      " 18  city                            24817 non-null  object \n",
      " 19  x_coordinate_state_plane        24817 non-null  float64\n",
      " 20  y_coordinate_state_plane        24817 non-null  float64\n",
      " 21  latitude                        24817 non-null  float64\n",
      " 22  longitude                       24817 non-null  float64\n",
      " 23  location                        24817 non-null  object \n",
      " 24  incident_address                16002 non-null  object \n",
      " 25  street_name                     16002 non-null  object \n",
      " 26  bbl                             14603 non-null  float64\n",
      "dtypes: float64(6), int64(1), object(20)\n",
      "memory usage: 5.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# column info\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neighborhood Tabulation Areas (NTAs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-12T12:26:59.199514Z",
     "start_time": "2021-01-12T12:26:56.896376Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (262, 12)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shape_area</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>cdtaname</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>boroname</th>\n",
       "      <th>ntatype</th>\n",
       "      <th>nta2020</th>\n",
       "      <th>borocode</th>\n",
       "      <th>countyfips</th>\n",
       "      <th>ntaabbrev</th>\n",
       "      <th>cdta2020</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35321204.8204</td>\n",
       "      <td>Greenpoint</td>\n",
       "      <td>BK01 Williamsburg-Greenpoint (CD 1 Equivalent)</td>\n",
       "      <td>28912.5653122</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>0</td>\n",
       "      <td>BK0101</td>\n",
       "      <td>3</td>\n",
       "      <td>047</td>\n",
       "      <td>Grnpt</td>\n",
       "      <td>BK01</td>\n",
       "      <td>MULTIPOLYGON (((1003059.973 204572.243, 100299...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28854314.555</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>BK01 Williamsburg-Greenpoint (CD 1 Equivalent)</td>\n",
       "      <td>28098.0267744</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>0</td>\n",
       "      <td>BK0102</td>\n",
       "      <td>3</td>\n",
       "      <td>047</td>\n",
       "      <td>Wllmsbrg</td>\n",
       "      <td>BK01</td>\n",
       "      <td>MULTIPOLYGON (((995851.880 203199.535, 995969....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15208960.44</td>\n",
       "      <td>South Williamsburg</td>\n",
       "      <td>BK01 Williamsburg-Greenpoint (CD 1 Equivalent)</td>\n",
       "      <td>18250.2804159</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>0</td>\n",
       "      <td>BK0103</td>\n",
       "      <td>3</td>\n",
       "      <td>047</td>\n",
       "      <td>SWllmsbrg</td>\n",
       "      <td>BK01</td>\n",
       "      <td>MULTIPOLYGON (((998047.189 196303.521, 998157....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52266209.4439</td>\n",
       "      <td>East Williamsburg</td>\n",
       "      <td>BK01 Williamsburg-Greenpoint (CD 1 Equivalent)</td>\n",
       "      <td>43184.773814</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>0</td>\n",
       "      <td>BK0104</td>\n",
       "      <td>3</td>\n",
       "      <td>047</td>\n",
       "      <td>EWllmsbrg</td>\n",
       "      <td>BK01</td>\n",
       "      <td>MULTIPOLYGON (((1005302.485 199455.944, 100530...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9982321.73877</td>\n",
       "      <td>Brooklyn Heights</td>\n",
       "      <td>BK02 Downtown Brooklyn-Fort Greene (CD 2 Appro...</td>\n",
       "      <td>14312.506134</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>0</td>\n",
       "      <td>BK0201</td>\n",
       "      <td>3</td>\n",
       "      <td>047</td>\n",
       "      <td>BkHts</td>\n",
       "      <td>BK02</td>\n",
       "      <td>MULTIPOLYGON (((986737.292 194249.956, 986678....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      shape_area             ntaname  \\\n",
       "0  35321204.8204          Greenpoint   \n",
       "1   28854314.555        Williamsburg   \n",
       "2    15208960.44  South Williamsburg   \n",
       "3  52266209.4439   East Williamsburg   \n",
       "4  9982321.73877    Brooklyn Heights   \n",
       "\n",
       "                                            cdtaname     shape_leng  boroname  \\\n",
       "0     BK01 Williamsburg-Greenpoint (CD 1 Equivalent)  28912.5653122  Brooklyn   \n",
       "1     BK01 Williamsburg-Greenpoint (CD 1 Equivalent)  28098.0267744  Brooklyn   \n",
       "2     BK01 Williamsburg-Greenpoint (CD 1 Equivalent)  18250.2804159  Brooklyn   \n",
       "3     BK01 Williamsburg-Greenpoint (CD 1 Equivalent)   43184.773814  Brooklyn   \n",
       "4  BK02 Downtown Brooklyn-Fort Greene (CD 2 Appro...   14312.506134  Brooklyn   \n",
       "\n",
       "  ntatype nta2020 borocode countyfips  ntaabbrev cdta2020  \\\n",
       "0       0  BK0101        3        047      Grnpt     BK01   \n",
       "1       0  BK0102        3        047   Wllmsbrg     BK01   \n",
       "2       0  BK0103        3        047  SWllmsbrg     BK01   \n",
       "3       0  BK0104        3        047  EWllmsbrg     BK01   \n",
       "4       0  BK0201        3        047      BkHts     BK02   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((1003059.973 204572.243, 100299...  \n",
       "1  MULTIPOLYGON (((995851.880 203199.535, 995969....  \n",
       "2  MULTIPOLYGON (((998047.189 196303.521, 998157....  \n",
       "3  MULTIPOLYGON (((1005302.485 199455.944, 100530...  \n",
       "4  MULTIPOLYGON (((986737.292 194249.956, 986678....  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing nta boundaries\n",
    "url = 'https://data.cityofnewyork.us/resource/9nt8-h7nd.geojson'\n",
    "nta_gdf = gpd.read_file(url).to_crs(epsg=2263)\n",
    "\n",
    "# previewing first five rows in data\n",
    "print(f'shape of data: {nta_gdf.shape}')\n",
    "nta_gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check plot\n",
    "nta_gdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Streets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (99324, 12)\n",
      "street id is unique: True\n",
      "epsg:2263\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>physicalid</th>\n",
       "      <th>st_label</th>\n",
       "      <th>st_name</th>\n",
       "      <th>full_stree</th>\n",
       "      <th>rw_type</th>\n",
       "      <th>rw_type_name</th>\n",
       "      <th>st_width</th>\n",
       "      <th>frm_lvl_co</th>\n",
       "      <th>to_lvl_co</th>\n",
       "      <th>borocode</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>262.77781228</td>\n",
       "      <td>MULTILINESTRING ((979278.595 196555.690, 97929...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>259.415988519</td>\n",
       "      <td>MULTILINESTRING ((979377.413 196797.951, 97950...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>280.444780871</td>\n",
       "      <td>MULTILINESTRING ((979503.289 197024.782, 97964...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0701391509</td>\n",
       "      <td>MULTILINESTRING ((979553.746 196059.826, 97952...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>206.27185039</td>\n",
       "      <td>MULTILINESTRING ((980288.092 195963.182, 98026...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  physicalid    st_label  st_name  full_stree rw_type rw_type_name st_width  \\\n",
       "0          3  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "1          5  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "2          6  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "3          8  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "4         14  BATTERY PL  BATTERY  BATTERY PL       1       Street     24.0   \n",
       "\n",
       "  frm_lvl_co to_lvl_co borocode     shape_leng  \\\n",
       "0         13        13        1   262.77781228   \n",
       "1         13        13        1  259.415988519   \n",
       "2         13        13        1  280.444780871   \n",
       "3         13        13        1  32.0701391509   \n",
       "4         13        13        1   206.27185039   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTILINESTRING ((979278.595 196555.690, 97929...  \n",
       "1  MULTILINESTRING ((979377.413 196797.951, 97950...  \n",
       "2  MULTILINESTRING ((979503.289 197024.782, 97964...  \n",
       "3  MULTILINESTRING ((979553.746 196059.826, 97952...  \n",
       "4  MULTILINESTRING ((980288.092 195963.182, 98026...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# streets path\n",
    "path = 'data/streets.gpkg'\n",
    "streets = gpd.read_file(path)\n",
    "\n",
    "# sanity checks\n",
    "print(f'shape of data: {streets.shape}')\n",
    "print(f\"street id is unique: {streets['physicalid'].is_unique}\")\n",
    "print(streets.crs)\n",
    "\n",
    "# preview\n",
    "streets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 99324 entries, 0 to 99323\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count  Dtype   \n",
      "---  ------        --------------  -----   \n",
      " 0   physicalid    99324 non-null  object  \n",
      " 1   st_label      99324 non-null  object  \n",
      " 2   st_name       99324 non-null  object  \n",
      " 3   full_stree    99324 non-null  object  \n",
      " 4   rw_type       99324 non-null  object  \n",
      " 5   rw_type_name  99324 non-null  object  \n",
      " 6   st_width      99324 non-null  object  \n",
      " 7   frm_lvl_co    99324 non-null  object  \n",
      " 8   to_lvl_co     99324 non-null  object  \n",
      " 9   borocode      99324 non-null  object  \n",
      " 10  shape_leng    99324 non-null  object  \n",
      " 11  geometry      99324 non-null  geometry\n",
      "dtypes: geometry(1), object(11)\n",
      "memory usage: 9.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# column info\n",
    "streets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiLineString    99324\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine counts of geom types\n",
    "streets.geom_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check plot\n",
    "streets.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assigning NTA (Neighborhood) Information to Street Complaints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34783066</td>\n",
       "      <td>POINT (958594.000 170855.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36178846</td>\n",
       "      <td>POINT (1014871.000 215198.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31360389</td>\n",
       "      <td>POINT (1043288.000 192114.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32686230</td>\n",
       "      <td>POINT (1014578.000 239190.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41495135</td>\n",
       "      <td>POINT (1031172.000 179312.000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_key                        geometry\n",
       "0    34783066   POINT (958594.000 170855.000)\n",
       "1    36178846  POINT (1014871.000 215198.000)\n",
       "2    31360389  POINT (1043288.000 192114.000)\n",
       "3    32686230  POINT (1014578.000 239190.000)\n",
       "4    41495135  POINT (1031172.000 179312.000)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert to geodataframe from x,y points\n",
    "crs = 2263\n",
    "geometry = gpd.points_from_xy(\n",
    "    df['x_coordinate_state_plane'],\n",
    "    df['y_coordinate_state_plane']\n",
    ")\n",
    "\n",
    "# make geodataframe\n",
    "gdf = gpd.GeoDataFrame(\n",
    "    df, \n",
    "    geometry=geometry,\n",
    "    crs=crs\n",
    ")\n",
    "\n",
    "# preview geodataframe\n",
    "gdf.iloc[:, [0, -1]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check plot\n",
    "gdf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (24814, 40)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>cross_street_1</th>\n",
       "      <th>cross_street_2</th>\n",
       "      <th>address_type</th>\n",
       "      <th>...</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>cdtaname</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>boroname</th>\n",
       "      <th>ntatype</th>\n",
       "      <th>nta2020</th>\n",
       "      <th>borocode</th>\n",
       "      <th>countyfips</th>\n",
       "      <th>ntaabbrev</th>\n",
       "      <th>cdta2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34783066</td>\n",
       "      <td>2016-11-15T09:27:00.000</td>\n",
       "      <td>2016-11-15T10:05:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INTERSECTION</td>\n",
       "      <td>...</td>\n",
       "      <td>St. George-New Brighton</td>\n",
       "      <td>SI01 North Shore (CD 1 Equivalent)</td>\n",
       "      <td>31943.5246384</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>0</td>\n",
       "      <td>SI0101</td>\n",
       "      <td>5</td>\n",
       "      <td>085</td>\n",
       "      <td>StGrg</td>\n",
       "      <td>SI01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>16585559</td>\n",
       "      <td>2010-05-04T09:20:00.000</td>\n",
       "      <td>2010-05-08T09:00:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>ST PETER'S PL</td>\n",
       "      <td>BEND</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>St. George-New Brighton</td>\n",
       "      <td>SI01 North Shore (CD 1 Equivalent)</td>\n",
       "      <td>31943.5246384</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>0</td>\n",
       "      <td>SI0101</td>\n",
       "      <td>5</td>\n",
       "      <td>085</td>\n",
       "      <td>StGrg</td>\n",
       "      <td>SI01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>18255249</td>\n",
       "      <td>2010-07-13T13:20:00.000</td>\n",
       "      <td>2010-07-13T15:10:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>ST PETER'S PL</td>\n",
       "      <td>BEND</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>St. George-New Brighton</td>\n",
       "      <td>SI01 North Shore (CD 1 Equivalent)</td>\n",
       "      <td>31943.5246384</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>0</td>\n",
       "      <td>SI0101</td>\n",
       "      <td>5</td>\n",
       "      <td>085</td>\n",
       "      <td>StGrg</td>\n",
       "      <td>SI01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>18380954</td>\n",
       "      <td>2010-07-30T11:08:00.000</td>\n",
       "      <td>2010-07-30T11:20:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>DANIEL LOW TER</td>\n",
       "      <td>BEND</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>St. George-New Brighton</td>\n",
       "      <td>SI01 North Shore (CD 1 Equivalent)</td>\n",
       "      <td>31943.5246384</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>0</td>\n",
       "      <td>SI0101</td>\n",
       "      <td>5</td>\n",
       "      <td>085</td>\n",
       "      <td>StGrg</td>\n",
       "      <td>SI01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1511</th>\n",
       "      <td>18449016</td>\n",
       "      <td>2010-08-09T13:49:00.000</td>\n",
       "      <td>2010-08-09T14:30:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>ST PETER'S PL</td>\n",
       "      <td>BEND</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>St. George-New Brighton</td>\n",
       "      <td>SI01 North Shore (CD 1 Equivalent)</td>\n",
       "      <td>31943.5246384</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>0</td>\n",
       "      <td>SI0101</td>\n",
       "      <td>5</td>\n",
       "      <td>085</td>\n",
       "      <td>StGrg</td>\n",
       "      <td>SI01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      unique_key             created_date              closed_date agency  \\\n",
       "0       34783066  2016-11-15T09:27:00.000  2016-11-15T10:05:00.000    DEP   \n",
       "926     16585559  2010-05-04T09:20:00.000  2010-05-08T09:00:00.000    DEP   \n",
       "1257    18255249  2010-07-13T13:20:00.000  2010-07-13T15:10:00.000    DEP   \n",
       "1341    18380954  2010-07-30T11:08:00.000  2010-07-30T11:20:00.000    DEP   \n",
       "1511    18449016  2010-08-09T13:49:00.000  2010-08-09T14:30:00.000    DEP   \n",
       "\n",
       "                                 agency_name complaint_type  \\\n",
       "0     Department of Environmental Protection          Sewer   \n",
       "926   Department of Environmental Protection          Sewer   \n",
       "1257  Department of Environmental Protection          Sewer   \n",
       "1341  Department of Environmental Protection          Sewer   \n",
       "1511  Department of Environmental Protection          Sewer   \n",
       "\n",
       "                descriptor  cross_street_1 cross_street_2  address_type  ...  \\\n",
       "0     Street Flooding (SJ)             NaN            NaN  INTERSECTION  ...   \n",
       "926   Street Flooding (SJ)   ST PETER'S PL           BEND       ADDRESS  ...   \n",
       "1257  Street Flooding (SJ)   ST PETER'S PL           BEND       ADDRESS  ...   \n",
       "1341  Street Flooding (SJ)  DANIEL LOW TER           BEND       ADDRESS  ...   \n",
       "1511  Street Flooding (SJ)   ST PETER'S PL           BEND       ADDRESS  ...   \n",
       "\n",
       "                      ntaname                            cdtaname  \\\n",
       "0     St. George-New Brighton  SI01 North Shore (CD 1 Equivalent)   \n",
       "926   St. George-New Brighton  SI01 North Shore (CD 1 Equivalent)   \n",
       "1257  St. George-New Brighton  SI01 North Shore (CD 1 Equivalent)   \n",
       "1341  St. George-New Brighton  SI01 North Shore (CD 1 Equivalent)   \n",
       "1511  St. George-New Brighton  SI01 North Shore (CD 1 Equivalent)   \n",
       "\n",
       "         shape_leng       boroname ntatype nta2020 borocode  countyfips  \\\n",
       "0     31943.5246384  Staten Island       0  SI0101        5         085   \n",
       "926   31943.5246384  Staten Island       0  SI0101        5         085   \n",
       "1257  31943.5246384  Staten Island       0  SI0101        5         085   \n",
       "1341  31943.5246384  Staten Island       0  SI0101        5         085   \n",
       "1511  31943.5246384  Staten Island       0  SI0101        5         085   \n",
       "\n",
       "     ntaabbrev  cdta2020  \n",
       "0        StGrg      SI01  \n",
       "926      StGrg      SI01  \n",
       "1257     StGrg      SI01  \n",
       "1341     StGrg      SI01  \n",
       "1511     StGrg      SI01  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# spatial join nta to points\n",
    "gdf = gpd.sjoin(\n",
    "    gdf,\n",
    "    nta_gdf,\n",
    "    how=\"inner\",\n",
    "    predicate='within'\n",
    ")\n",
    "\n",
    "print(f'shape of data: {gdf.shape}')\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "Int64Index: 24814 entries, 0 to 16453\n",
      "Data columns (total 40 columns):\n",
      " #   Column                          Non-Null Count  Dtype   \n",
      "---  ------                          --------------  -----   \n",
      " 0   unique_key                      24814 non-null  int64   \n",
      " 1   created_date                    24814 non-null  object  \n",
      " 2   closed_date                     24813 non-null  object  \n",
      " 3   agency                          24814 non-null  object  \n",
      " 4   agency_name                     24814 non-null  object  \n",
      " 5   complaint_type                  24814 non-null  object  \n",
      " 6   descriptor                      24814 non-null  object  \n",
      " 7   cross_street_1                  21821 non-null  object  \n",
      " 8   cross_street_2                  21816 non-null  object  \n",
      " 9   address_type                    24814 non-null  object  \n",
      " 10  status                          24814 non-null  object  \n",
      " 11  resolution_description          24810 non-null  object  \n",
      " 12  resolution_action_updated_date  24814 non-null  object  \n",
      " 13  community_board                 24814 non-null  object  \n",
      " 14  borough                         24814 non-null  object  \n",
      " 15  open_data_channel_type          24814 non-null  object  \n",
      " 16  park_borough                    24814 non-null  object  \n",
      " 17  incident_zip                    24814 non-null  float64 \n",
      " 18  city                            24814 non-null  object  \n",
      " 19  x_coordinate_state_plane        24814 non-null  float64 \n",
      " 20  y_coordinate_state_plane        24814 non-null  float64 \n",
      " 21  latitude                        24814 non-null  float64 \n",
      " 22  longitude                       24814 non-null  float64 \n",
      " 23  location                        24814 non-null  object  \n",
      " 24  incident_address                16002 non-null  object  \n",
      " 25  street_name                     16002 non-null  object  \n",
      " 26  bbl                             14603 non-null  float64 \n",
      " 27  geometry                        24814 non-null  geometry\n",
      " 28  index_right                     24814 non-null  int64   \n",
      " 29  shape_area                      24814 non-null  object  \n",
      " 30  ntaname                         24814 non-null  object  \n",
      " 31  cdtaname                        24814 non-null  object  \n",
      " 32  shape_leng                      24814 non-null  object  \n",
      " 33  boroname                        24814 non-null  object  \n",
      " 34  ntatype                         24814 non-null  object  \n",
      " 35  nta2020                         24814 non-null  object  \n",
      " 36  borocode                        24814 non-null  object  \n",
      " 37  countyfips                      24814 non-null  object  \n",
      " 38  ntaabbrev                       24814 non-null  object  \n",
      " 39  cdta2020                        24814 non-null  object  \n",
      "dtypes: float64(6), geometry(1), int64(2), object(31)\n",
      "memory usage: 7.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# column info\n",
    "gdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "Int64Index: 24814 entries, 0 to 16453\n",
      "Data columns (total 37 columns):\n",
      " #   Column                          Non-Null Count  Dtype   \n",
      "---  ------                          --------------  -----   \n",
      " 0   unique_key                      24814 non-null  int64   \n",
      " 1   created_date                    24814 non-null  object  \n",
      " 2   closed_date                     24813 non-null  object  \n",
      " 3   agency                          24814 non-null  object  \n",
      " 4   agency_name                     24814 non-null  object  \n",
      " 5   complaint_type                  24814 non-null  object  \n",
      " 6   descriptor                      24814 non-null  object  \n",
      " 7   cross_street_1                  21821 non-null  object  \n",
      " 8   cross_street_2                  21816 non-null  object  \n",
      " 9   address_type                    24814 non-null  object  \n",
      " 10  status                          24814 non-null  object  \n",
      " 11  resolution_description          24810 non-null  object  \n",
      " 12  resolution_action_updated_date  24814 non-null  object  \n",
      " 13  community_board                 24814 non-null  object  \n",
      " 14  borough                         24814 non-null  object  \n",
      " 15  open_data_channel_type          24814 non-null  object  \n",
      " 16  park_borough                    24814 non-null  object  \n",
      " 17  incident_zip                    24814 non-null  float64 \n",
      " 18  city                            24814 non-null  object  \n",
      " 19  x_coordinate_state_plane        24814 non-null  float64 \n",
      " 20  y_coordinate_state_plane        24814 non-null  float64 \n",
      " 21  latitude                        24814 non-null  float64 \n",
      " 22  longitude                       24814 non-null  float64 \n",
      " 23  location                        24814 non-null  object  \n",
      " 24  incident_address                16002 non-null  object  \n",
      " 25  street_name                     16002 non-null  object  \n",
      " 26  bbl                             14603 non-null  float64 \n",
      " 27  geometry                        24814 non-null  geometry\n",
      " 28  ntaname                         24814 non-null  object  \n",
      " 29  cdtaname                        24814 non-null  object  \n",
      " 30  boroname                        24814 non-null  object  \n",
      " 31  ntatype                         24814 non-null  object  \n",
      " 32  nta2020                         24814 non-null  object  \n",
      " 33  borocode                        24814 non-null  object  \n",
      " 34  countyfips                      24814 non-null  object  \n",
      " 35  ntaabbrev                       24814 non-null  object  \n",
      " 36  cdta2020                        24814 non-null  object  \n",
      "dtypes: float64(6), geometry(1), int64(1), object(29)\n",
      "memory usage: 7.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# exclude specified columns\n",
    "cols = ['shape_leng', 'shape_area', 'index_right']\n",
    "exclude = gdf.columns.isin(cols)\n",
    "\n",
    "# locate columns besides ones above\n",
    "gdf = gdf.loc[:, gdf.columns[~exclude]]\n",
    "\n",
    "# sanity check\n",
    "gdf.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Snap Complaints to Streets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Methodology: https://medium.com/@brendan_ward/how-to-leverage-geopandas-for-faster-snapping-of-points-to-lines-6113c94e59aa\n",
    "\n",
    "The code below is from Brendan's awesome post."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (24814,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0                     [91978, 43274, 43275]\n",
       "926     [90144, 36999, 36779, 87014, 87015]\n",
       "1257    [90144, 36999, 36779, 87014, 87015]\n",
       "1341                                [36939]\n",
       "1511    [90144, 36999, 36779, 87014, 87015]\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# offset of match (ft.)\n",
    "offset = 80\n",
    "bbox = gdf.bounds + [-offset, -offset, offset, offset]\n",
    "\n",
    "# match points to streets based on distance\n",
    "hits = bbox.apply(lambda row: list(streets.sindex.intersection(row)), axis=1)\n",
    "\n",
    "print(f'shape of data: {hits.shape}')\n",
    "hits.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (82780, 15)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pt_idx</th>\n",
       "      <th>line_i</th>\n",
       "      <th>physicalid</th>\n",
       "      <th>st_label</th>\n",
       "      <th>st_name</th>\n",
       "      <th>full_stree</th>\n",
       "      <th>rw_type</th>\n",
       "      <th>rw_type_name</th>\n",
       "      <th>st_width</th>\n",
       "      <th>frm_lvl_co</th>\n",
       "      <th>to_lvl_co</th>\n",
       "      <th>borocode</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>geometry</th>\n",
       "      <th>point</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>91978.0</td>\n",
       "      <td>170110</td>\n",
       "      <td>LAFAYETTE AVE</td>\n",
       "      <td>LAFAYETTE</td>\n",
       "      <td>LAFAYETTE AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>275.583357823</td>\n",
       "      <td>MULTILINESTRING ((958432.654 171078.516, 95859...</td>\n",
       "      <td>POINT (958594.000 170855.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>43274.0</td>\n",
       "      <td>52391</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>BRIGHTON</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>40.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>115.32307671</td>\n",
       "      <td>MULTILINESTRING ((958593.707 170854.890, 95851...</td>\n",
       "      <td>POINT (958594.000 170855.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>43275.0</td>\n",
       "      <td>52392</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>BRIGHTON</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>172.36564876</td>\n",
       "      <td>MULTILINESTRING ((958765.405 170839.741, 95859...</td>\n",
       "      <td>POINT (958594.000 170855.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>926</td>\n",
       "      <td>90144.0</td>\n",
       "      <td>163531</td>\n",
       "      <td>RICHMOND TER</td>\n",
       "      <td>RICHMOND</td>\n",
       "      <td>RICHMOND TER</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1109.12608333</td>\n",
       "      <td>MULTILINESTRING ((961854.877 175109.904, 96145...</td>\n",
       "      <td>POINT (960718.000 175485.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>926</td>\n",
       "      <td>36999.0</td>\n",
       "      <td>45130</td>\n",
       "      <td>ST PETERS PL</td>\n",
       "      <td>ST PETERS</td>\n",
       "      <td>ST PETERS PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>263.241339318</td>\n",
       "      <td>MULTILINESTRING ((960796.937 175434.744, 96073...</td>\n",
       "      <td>POINT (960718.000 175485.000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pt_idx   line_i physicalid       st_label    st_name     full_stree  \\\n",
       "0       0  91978.0     170110  LAFAYETTE AVE  LAFAYETTE  LAFAYETTE AVE   \n",
       "1       0  43274.0      52391   BRIGHTON AVE   BRIGHTON   BRIGHTON AVE   \n",
       "2       0  43275.0      52392   BRIGHTON AVE   BRIGHTON   BRIGHTON AVE   \n",
       "3     926  90144.0     163531   RICHMOND TER   RICHMOND   RICHMOND TER   \n",
       "4     926  36999.0      45130   ST PETERS PL  ST PETERS   ST PETERS PL   \n",
       "\n",
       "  rw_type rw_type_name st_width frm_lvl_co to_lvl_co borocode     shape_leng  \\\n",
       "0       1       Street     30.0         13        13        5  275.583357823   \n",
       "1       1       Street     40.0         13        13        5   115.32307671   \n",
       "2       1       Street     30.0         13        13        5   172.36564876   \n",
       "3       1       Street     34.0         13        13        5  1109.12608333   \n",
       "4       1       Street     20.0         13        13        5  263.241339318   \n",
       "\n",
       "                                            geometry  \\\n",
       "0  MULTILINESTRING ((958432.654 171078.516, 95859...   \n",
       "1  MULTILINESTRING ((958593.707 170854.890, 95851...   \n",
       "2  MULTILINESTRING ((958765.405 170839.741, 95859...   \n",
       "3  MULTILINESTRING ((961854.877 175109.904, 96145...   \n",
       "4  MULTILINESTRING ((960796.937 175434.744, 96073...   \n",
       "\n",
       "                           point  \n",
       "0  POINT (958594.000 170855.000)  \n",
       "1  POINT (958594.000 170855.000)  \n",
       "2  POINT (958594.000 170855.000)  \n",
       "3  POINT (960718.000 175485.000)  \n",
       "4  POINT (960718.000 175485.000)  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# position1: index of points table\n",
    "# position2: ordinal position of line - access via iloc later\n",
    "points_to_lines_dict = {\n",
    "    'pt_idx': np.repeat(hits.index, hits.apply(len)),\n",
    "    'line_i': np.concatenate(hits.values)\n",
    "}\n",
    "    \n",
    "tmp = pd.DataFrame(points_to_lines_dict)\n",
    "# join back to the lines on line_i\n",
    "# join back to the original points to get their geometry, rename the point geometry as \"point\"\n",
    "tmp = (\n",
    "    tmp\n",
    "    .join(streets, on=\"line_i\")\n",
    "    .join(gdf['geometry'].rename(\"point\"), on=\"pt_idx\")\n",
    ")\n",
    "\n",
    "print(f'shape of data: {tmp.shape}')\n",
    "tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (82780, 15)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pt_idx</th>\n",
       "      <th>line_i</th>\n",
       "      <th>physicalid</th>\n",
       "      <th>st_label</th>\n",
       "      <th>st_name</th>\n",
       "      <th>full_stree</th>\n",
       "      <th>rw_type</th>\n",
       "      <th>rw_type_name</th>\n",
       "      <th>st_width</th>\n",
       "      <th>frm_lvl_co</th>\n",
       "      <th>to_lvl_co</th>\n",
       "      <th>borocode</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>geometry</th>\n",
       "      <th>point</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>91978.0</td>\n",
       "      <td>170110</td>\n",
       "      <td>LAFAYETTE AVE</td>\n",
       "      <td>LAFAYETTE</td>\n",
       "      <td>LAFAYETTE AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>275.583357823</td>\n",
       "      <td>MULTILINESTRING ((958432.654 171078.516, 95859...</td>\n",
       "      <td>POINT (958594.000 170855.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>43274.0</td>\n",
       "      <td>52391</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>BRIGHTON</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>40.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>115.32307671</td>\n",
       "      <td>MULTILINESTRING ((958593.707 170854.890, 95851...</td>\n",
       "      <td>POINT (958594.000 170855.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>43275.0</td>\n",
       "      <td>52392</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>BRIGHTON</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>172.36564876</td>\n",
       "      <td>MULTILINESTRING ((958765.405 170839.741, 95859...</td>\n",
       "      <td>POINT (958594.000 170855.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>926</td>\n",
       "      <td>90144.0</td>\n",
       "      <td>163531</td>\n",
       "      <td>RICHMOND TER</td>\n",
       "      <td>RICHMOND</td>\n",
       "      <td>RICHMOND TER</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>1109.12608333</td>\n",
       "      <td>MULTILINESTRING ((961854.877 175109.904, 96145...</td>\n",
       "      <td>POINT (960718.000 175485.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>926</td>\n",
       "      <td>36999.0</td>\n",
       "      <td>45130</td>\n",
       "      <td>ST PETERS PL</td>\n",
       "      <td>ST PETERS</td>\n",
       "      <td>ST PETERS PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>263.241339318</td>\n",
       "      <td>MULTILINESTRING ((960796.937 175434.744, 96073...</td>\n",
       "      <td>POINT (960718.000 175485.000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pt_idx   line_i physicalid       st_label    st_name     full_stree  \\\n",
       "0       0  91978.0     170110  LAFAYETTE AVE  LAFAYETTE  LAFAYETTE AVE   \n",
       "1       0  43274.0      52391   BRIGHTON AVE   BRIGHTON   BRIGHTON AVE   \n",
       "2       0  43275.0      52392   BRIGHTON AVE   BRIGHTON   BRIGHTON AVE   \n",
       "3     926  90144.0     163531   RICHMOND TER   RICHMOND   RICHMOND TER   \n",
       "4     926  36999.0      45130   ST PETERS PL  ST PETERS   ST PETERS PL   \n",
       "\n",
       "  rw_type rw_type_name st_width frm_lvl_co to_lvl_co borocode     shape_leng  \\\n",
       "0       1       Street     30.0         13        13        5  275.583357823   \n",
       "1       1       Street     40.0         13        13        5   115.32307671   \n",
       "2       1       Street     30.0         13        13        5   172.36564876   \n",
       "3       1       Street     34.0         13        13        5  1109.12608333   \n",
       "4       1       Street     20.0         13        13        5  263.241339318   \n",
       "\n",
       "                                            geometry  \\\n",
       "0  MULTILINESTRING ((958432.654 171078.516, 95859...   \n",
       "1  MULTILINESTRING ((958593.707 170854.890, 95851...   \n",
       "2  MULTILINESTRING ((958765.405 170839.741, 95859...   \n",
       "3  MULTILINESTRING ((961854.877 175109.904, 96145...   \n",
       "4  MULTILINESTRING ((960796.937 175434.744, 96073...   \n",
       "\n",
       "                           point  \n",
       "0  POINT (958594.000 170855.000)  \n",
       "1  POINT (958594.000 170855.000)  \n",
       "2  POINT (958594.000 170855.000)  \n",
       "3  POINT (960718.000 175485.000)  \n",
       "4  POINT (960718.000 175485.000)  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert back to a GeoDataFrame, so we can do spatial ops\n",
    "tmp = gpd.GeoDataFrame(\n",
    "    tmp,\n",
    "    geometry='geometry',\n",
    "    crs=gdf.crs\n",
    ")\n",
    "\n",
    "print(f'shape of data: {tmp.shape}')\n",
    "tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>snap_dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>67983.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.063428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.771920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.378533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.738963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>28.693191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79.996887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          snap_dist\n",
       "count  67983.000000\n",
       "mean      16.063428\n",
       "std       23.771920\n",
       "min        0.000003\n",
       "25%        0.378533\n",
       "50%        2.738963\n",
       "75%       28.693191\n",
       "max       79.996887"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# discard any lines that are greater than tolerance from points\n",
    "# sort on ascending snap distance, so that closest goes to top\n",
    "tmp[\"snap_dist\"] = tmp['geometry'].distance(gpd.GeoSeries(tmp.point))\n",
    "\n",
    "tmp = (\n",
    "    tmp\n",
    "    .loc[tmp.snap_dist <= offset]\n",
    "    .sort_values(by=[\"snap_dist\"])\n",
    ")\n",
    "\n",
    "# sanity check distance ceiling\n",
    "tmp.loc[:, ['snap_dist']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>line_i</th>\n",
       "      <th>physicalid</th>\n",
       "      <th>st_label</th>\n",
       "      <th>st_name</th>\n",
       "      <th>full_stree</th>\n",
       "      <th>rw_type</th>\n",
       "      <th>rw_type_name</th>\n",
       "      <th>st_width</th>\n",
       "      <th>frm_lvl_co</th>\n",
       "      <th>to_lvl_co</th>\n",
       "      <th>borocode</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>geometry</th>\n",
       "      <th>point</th>\n",
       "      <th>snap_dist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pt_idx</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43275.0</td>\n",
       "      <td>52392</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>BRIGHTON</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>172.36564876</td>\n",
       "      <td>MULTILINESTRING ((958765.405 170839.741, 95859...</td>\n",
       "      <td>POINT (958594.000 170855.000)</td>\n",
       "      <td>0.135523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27507.0</td>\n",
       "      <td>33544</td>\n",
       "      <td>78 ST</td>\n",
       "      <td>78</td>\n",
       "      <td>78 ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>781.721951111</td>\n",
       "      <td>MULTILINESTRING ((1014870.958 215198.450, 1014...</td>\n",
       "      <td>POINT (1014871.000 215198.000)</td>\n",
       "      <td>0.020164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67718.0</td>\n",
       "      <td>83402</td>\n",
       "      <td>164 PL</td>\n",
       "      <td>164</td>\n",
       "      <td>164 PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>431.178187788</td>\n",
       "      <td>MULTILINESTRING ((1043229.478 192213.204, 1043...</td>\n",
       "      <td>POINT (1043288.000 192114.000)</td>\n",
       "      <td>2.996554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53494.0</td>\n",
       "      <td>64250</td>\n",
       "      <td>ALDUS ST</td>\n",
       "      <td>ALDUS</td>\n",
       "      <td>ALDUS ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>380.137331178</td>\n",
       "      <td>MULTILINESTRING ((1014198.807 239165.631, 1014...</td>\n",
       "      <td>POINT (1014578.000 239190.000)</td>\n",
       "      <td>0.021552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27836.0</td>\n",
       "      <td>33887</td>\n",
       "      <td>160 AVE</td>\n",
       "      <td>160</td>\n",
       "      <td>160 AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>205.969197341</td>\n",
       "      <td>MULTILINESTRING ((1031112.252 179304.566, 1031...</td>\n",
       "      <td>POINT (1031172.000 179312.000)</td>\n",
       "      <td>4.058630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         line_i physicalid      st_label   st_name    full_stree rw_type  \\\n",
       "pt_idx                                                                     \n",
       "0       43275.0      52392  BRIGHTON AVE  BRIGHTON  BRIGHTON AVE       1   \n",
       "1       27507.0      33544         78 ST        78         78 ST       1   \n",
       "2       67718.0      83402        164 PL       164        164 PL       1   \n",
       "3       53494.0      64250      ALDUS ST     ALDUS      ALDUS ST       1   \n",
       "4       27836.0      33887       160 AVE       160       160 AVE       1   \n",
       "\n",
       "       rw_type_name st_width frm_lvl_co to_lvl_co borocode     shape_leng  \\\n",
       "pt_idx                                                                      \n",
       "0            Street     30.0         13        13        5   172.36564876   \n",
       "1            Street     30.0         13        13        4  781.721951111   \n",
       "2            Street     30.0         13        13        4  431.178187788   \n",
       "3            Street     30.0         13        13        2  380.137331178   \n",
       "4            Street     30.0         13        13        4  205.969197341   \n",
       "\n",
       "                                                 geometry  \\\n",
       "pt_idx                                                      \n",
       "0       MULTILINESTRING ((958765.405 170839.741, 95859...   \n",
       "1       MULTILINESTRING ((1014870.958 215198.450, 1014...   \n",
       "2       MULTILINESTRING ((1043229.478 192213.204, 1043...   \n",
       "3       MULTILINESTRING ((1014198.807 239165.631, 1014...   \n",
       "4       MULTILINESTRING ((1031112.252 179304.566, 1031...   \n",
       "\n",
       "                                 point  snap_dist  \n",
       "pt_idx                                             \n",
       "0        POINT (958594.000 170855.000)   0.135523  \n",
       "1       POINT (1014871.000 215198.000)   0.020164  \n",
       "2       POINT (1043288.000 192114.000)   2.996554  \n",
       "3       POINT (1014578.000 239190.000)   0.021552  \n",
       "4       POINT (1031172.000 179312.000)   4.058630  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by the index of the points and take the first, which is the closest line \n",
    "closest = (\n",
    "    tmp\n",
    "    .groupby(\"pt_idx\")\n",
    "    .first()\n",
    ")\n",
    "\n",
    "# construct a GeoDataFrame of the closest lines\n",
    "closest = gpd.GeoDataFrame(closest, geometry=\"geometry\")\n",
    "\n",
    "closest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 64 rows or 0.26% of street flooding complaint points, which were more than 80 feet from the closest street center line.\n"
     ]
    }
   ],
   "source": [
    "counts = gdf.shape[0] - closest.shape[0]\n",
    "counts_perc = round((1 - (closest.shape[0] / gdf.shape[0])) * 100, 2)\n",
    "\n",
    "msg = f'Dropped {counts} rows or {counts_perc}% of street flooding complaint points, \\\n",
    "which were more than 80 feet from the closest street center line.'\n",
    "\n",
    "print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>line_i</th>\n",
       "      <th>physicalid</th>\n",
       "      <th>st_label</th>\n",
       "      <th>st_name</th>\n",
       "      <th>full_stree</th>\n",
       "      <th>rw_type</th>\n",
       "      <th>rw_type_name</th>\n",
       "      <th>st_width</th>\n",
       "      <th>frm_lvl_co</th>\n",
       "      <th>to_lvl_co</th>\n",
       "      <th>borocode</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>geometry</th>\n",
       "      <th>point</th>\n",
       "      <th>snap_dist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pt_idx</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>43275.0</td>\n",
       "      <td>52392</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>BRIGHTON</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>172.36564876</td>\n",
       "      <td>POINT (958593.988 170854.865)</td>\n",
       "      <td>POINT (958594.000 170855.000)</td>\n",
       "      <td>0.135523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27507.0</td>\n",
       "      <td>33544</td>\n",
       "      <td>78 ST</td>\n",
       "      <td>78</td>\n",
       "      <td>78 ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>781.721951111</td>\n",
       "      <td>POINT (1014871.020 215198.003)</td>\n",
       "      <td>POINT (1014871.000 215198.000)</td>\n",
       "      <td>0.020164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67718.0</td>\n",
       "      <td>83402</td>\n",
       "      <td>164 PL</td>\n",
       "      <td>164</td>\n",
       "      <td>164 PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>431.178187788</td>\n",
       "      <td>POINT (1043285.380 192112.545)</td>\n",
       "      <td>POINT (1043288.000 192114.000)</td>\n",
       "      <td>2.996554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53494.0</td>\n",
       "      <td>64250</td>\n",
       "      <td>ALDUS ST</td>\n",
       "      <td>ALDUS</td>\n",
       "      <td>ALDUS ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>380.137331178</td>\n",
       "      <td>POINT (1014578.001 239189.978)</td>\n",
       "      <td>POINT (1014578.000 239190.000)</td>\n",
       "      <td>0.021552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27836.0</td>\n",
       "      <td>33887</td>\n",
       "      <td>160 AVE</td>\n",
       "      <td>160</td>\n",
       "      <td>160 AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>205.969197341</td>\n",
       "      <td>POINT (1031171.229 179315.985)</td>\n",
       "      <td>POINT (1031172.000 179312.000)</td>\n",
       "      <td>4.058630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         line_i physicalid      st_label   st_name    full_stree rw_type  \\\n",
       "pt_idx                                                                     \n",
       "0       43275.0      52392  BRIGHTON AVE  BRIGHTON  BRIGHTON AVE       1   \n",
       "1       27507.0      33544         78 ST        78         78 ST       1   \n",
       "2       67718.0      83402        164 PL       164        164 PL       1   \n",
       "3       53494.0      64250      ALDUS ST     ALDUS      ALDUS ST       1   \n",
       "4       27836.0      33887       160 AVE       160       160 AVE       1   \n",
       "\n",
       "       rw_type_name st_width frm_lvl_co to_lvl_co borocode     shape_leng  \\\n",
       "pt_idx                                                                      \n",
       "0            Street     30.0         13        13        5   172.36564876   \n",
       "1            Street     30.0         13        13        4  781.721951111   \n",
       "2            Street     30.0         13        13        4  431.178187788   \n",
       "3            Street     30.0         13        13        2  380.137331178   \n",
       "4            Street     30.0         13        13        4  205.969197341   \n",
       "\n",
       "                              geometry                           point  \\\n",
       "pt_idx                                                                   \n",
       "0        POINT (958593.988 170854.865)   POINT (958594.000 170855.000)   \n",
       "1       POINT (1014871.020 215198.003)  POINT (1014871.000 215198.000)   \n",
       "2       POINT (1043285.380 192112.545)  POINT (1043288.000 192114.000)   \n",
       "3       POINT (1014578.001 239189.978)  POINT (1014578.000 239190.000)   \n",
       "4       POINT (1031171.229 179315.985)  POINT (1031172.000 179312.000)   \n",
       "\n",
       "        snap_dist  \n",
       "pt_idx             \n",
       "0        0.135523  \n",
       "1        0.020164  \n",
       "2        2.996554  \n",
       "3        0.021552  \n",
       "4        4.058630  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# position of nearest point from start of the line and get new point location geometry\n",
    "pos = closest['geometry'].project(gpd.GeoSeries(closest.point))\n",
    "new_pts = closest['geometry'].interpolate(pos)\n",
    "\n",
    "# create a new GeoDataFrame from the columns from the closest line and \n",
    "# new point geometries (which will be called \"geometries\")\n",
    "snapped = gpd.GeoDataFrame(closest, geometry=new_pts)\n",
    "\n",
    "snapped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "Int64Index: 24814 entries, 0 to 16453\n",
      "Data columns (total 37 columns):\n",
      " #   Column                          Non-Null Count  Dtype   \n",
      "---  ------                          --------------  -----   \n",
      " 0   unique_key                      24814 non-null  int64   \n",
      " 1   created_date                    24814 non-null  object  \n",
      " 2   closed_date                     24813 non-null  object  \n",
      " 3   agency                          24814 non-null  object  \n",
      " 4   agency_name                     24814 non-null  object  \n",
      " 5   complaint_type                  24814 non-null  object  \n",
      " 6   descriptor                      24814 non-null  object  \n",
      " 7   cross_street_1                  21821 non-null  object  \n",
      " 8   cross_street_2                  21816 non-null  object  \n",
      " 9   address_type                    24814 non-null  object  \n",
      " 10  status                          24814 non-null  object  \n",
      " 11  resolution_description          24810 non-null  object  \n",
      " 12  resolution_action_updated_date  24814 non-null  object  \n",
      " 13  community_board                 24814 non-null  object  \n",
      " 14  borough                         24814 non-null  object  \n",
      " 15  open_data_channel_type          24814 non-null  object  \n",
      " 16  park_borough                    24814 non-null  object  \n",
      " 17  incident_zip                    24814 non-null  float64 \n",
      " 18  city                            24814 non-null  object  \n",
      " 19  x_coordinate_state_plane        24814 non-null  float64 \n",
      " 20  y_coordinate_state_plane        24814 non-null  float64 \n",
      " 21  latitude                        24814 non-null  float64 \n",
      " 22  longitude                       24814 non-null  float64 \n",
      " 23  location                        24814 non-null  object  \n",
      " 24  incident_address                16002 non-null  object  \n",
      " 25  street_name                     16002 non-null  object  \n",
      " 26  bbl                             14603 non-null  float64 \n",
      " 27  geometry                        24814 non-null  geometry\n",
      " 28  ntaname                         24814 non-null  object  \n",
      " 29  cdtaname                        24814 non-null  object  \n",
      " 30  boroname                        24814 non-null  object  \n",
      " 31  ntatype                         24814 non-null  object  \n",
      " 32  nta2020                         24814 non-null  object  \n",
      " 33  borocode                        24814 non-null  object  \n",
      " 34  countyfips                      24814 non-null  object  \n",
      " 35  ntaabbrev                       24814 non-null  object  \n",
      " 36  cdta2020                        24814 non-null  object  \n",
      "dtypes: float64(6), geometry(1), int64(1), object(29)\n",
      "memory usage: 7.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# column info\n",
    "gdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
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       "</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>cross_street_1</th>\n",
       "      <th>cross_street_2</th>\n",
       "      <th>address_type</th>\n",
       "      <th>...</th>\n",
       "      <th>full_stree</th>\n",
       "      <th>rw_type</th>\n",
       "      <th>rw_type_name</th>\n",
       "      <th>st_width</th>\n",
       "      <th>frm_lvl_co</th>\n",
       "      <th>to_lvl_co</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>geometry</th>\n",
       "      <th>point</th>\n",
       "      <th>snap_dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34783066</td>\n",
       "      <td>2016-11-15T09:27:00.000</td>\n",
       "      <td>2016-11-15T10:05:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INTERSECTION</td>\n",
       "      <td>...</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>172.36564876</td>\n",
       "      <td>POINT (958593.988 170854.865)</td>\n",
       "      <td>POINT (958594.000 170855.000)</td>\n",
       "      <td>0.135523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16585559</td>\n",
       "      <td>2010-05-04T09:20:00.000</td>\n",
       "      <td>2010-05-08T09:00:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>ST PETER'S PL</td>\n",
       "      <td>BEND</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>RICHMOND TER</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>602.593212361</td>\n",
       "      <td>POINT (960713.101 175471.573)</td>\n",
       "      <td>POINT (960718.000 175485.000)</td>\n",
       "      <td>14.292678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18255249</td>\n",
       "      <td>2010-07-13T13:20:00.000</td>\n",
       "      <td>2010-07-13T15:10:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>ST PETER'S PL</td>\n",
       "      <td>BEND</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>RICHMOND TER</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>602.593212361</td>\n",
       "      <td>POINT (960713.101 175471.573)</td>\n",
       "      <td>POINT (960718.000 175485.000)</td>\n",
       "      <td>14.292678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18380954</td>\n",
       "      <td>2010-07-30T11:08:00.000</td>\n",
       "      <td>2010-07-30T11:20:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>DANIEL LOW TER</td>\n",
       "      <td>BEND</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>BELMONT PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>432.344943452</td>\n",
       "      <td>POINT (961777.231 173950.696)</td>\n",
       "      <td>POINT (961777.000 173953.000)</td>\n",
       "      <td>2.315312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18449016</td>\n",
       "      <td>2010-08-09T13:49:00.000</td>\n",
       "      <td>2010-08-09T14:30:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>ST PETER'S PL</td>\n",
       "      <td>BEND</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>RICHMOND TER</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>602.593212361</td>\n",
       "      <td>POINT (960713.101 175471.573)</td>\n",
       "      <td>POINT (960718.000 175485.000)</td>\n",
       "      <td>14.292678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_key             created_date              closed_date agency  \\\n",
       "0    34783066  2016-11-15T09:27:00.000  2016-11-15T10:05:00.000    DEP   \n",
       "1    16585559  2010-05-04T09:20:00.000  2010-05-08T09:00:00.000    DEP   \n",
       "2    18255249  2010-07-13T13:20:00.000  2010-07-13T15:10:00.000    DEP   \n",
       "3    18380954  2010-07-30T11:08:00.000  2010-07-30T11:20:00.000    DEP   \n",
       "4    18449016  2010-08-09T13:49:00.000  2010-08-09T14:30:00.000    DEP   \n",
       "\n",
       "                              agency_name complaint_type  \\\n",
       "0  Department of Environmental Protection          Sewer   \n",
       "1  Department of Environmental Protection          Sewer   \n",
       "2  Department of Environmental Protection          Sewer   \n",
       "3  Department of Environmental Protection          Sewer   \n",
       "4  Department of Environmental Protection          Sewer   \n",
       "\n",
       "             descriptor  cross_street_1 cross_street_2  address_type  ...  \\\n",
       "0  Street Flooding (SJ)             NaN            NaN  INTERSECTION  ...   \n",
       "1  Street Flooding (SJ)   ST PETER'S PL           BEND       ADDRESS  ...   \n",
       "2  Street Flooding (SJ)   ST PETER'S PL           BEND       ADDRESS  ...   \n",
       "3  Street Flooding (SJ)  DANIEL LOW TER           BEND       ADDRESS  ...   \n",
       "4  Street Flooding (SJ)   ST PETER'S PL           BEND       ADDRESS  ...   \n",
       "\n",
       "     full_stree rw_type rw_type_name st_width frm_lvl_co to_lvl_co  \\\n",
       "0  BRIGHTON AVE       1       Street     30.0         13        13   \n",
       "1  RICHMOND TER       1       Street     34.0         13        13   \n",
       "2  RICHMOND TER       1       Street     34.0         13        13   \n",
       "3    BELMONT PL       1       Street     30.0         13        13   \n",
       "4  RICHMOND TER       1       Street     34.0         13        13   \n",
       "\n",
       "      shape_leng                       geometry  \\\n",
       "0   172.36564876  POINT (958593.988 170854.865)   \n",
       "1  602.593212361  POINT (960713.101 175471.573)   \n",
       "2  602.593212361  POINT (960713.101 175471.573)   \n",
       "3  432.344943452  POINT (961777.231 173950.696)   \n",
       "4  602.593212361  POINT (960713.101 175471.573)   \n",
       "\n",
       "                           point  snap_dist  \n",
       "0  POINT (958594.000 170855.000)   0.135523  \n",
       "1  POINT (960718.000 175485.000)  14.292678  \n",
       "2  POINT (960718.000 175485.000)  14.292678  \n",
       "3  POINT (961777.000 173953.000)   2.315312  \n",
       "4  POINT (960718.000 175485.000)  14.292678  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join back to the original points and drop any that did not join\n",
    "updated_points = (\n",
    "    gdf\n",
    "    .drop(columns=[\"geometry\"])\n",
    "    .join(snapped.drop(columns=[\"borocode\"]))\n",
    "    .dropna(subset=[\"geometry\"])\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "updated_points.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of snap_dist (ft.)')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of snap distances\n",
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "sns.histplot(updated_points['snap_dist'])\n",
    "plt.title('Histogram of snap_dist (ft.)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>physicalid</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100019</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100041</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  physicalid  count\n",
       "0     100019      1\n",
       "1     100020      1\n",
       "2      10003      1\n",
       "3      10004      2\n",
       "4     100041      1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine counts per street id\n",
    "gdf_count = (\n",
    "    updated_points\n",
    "    .groupby(by='physicalid')['created_date']\n",
    "    .count()\n",
    "    .reset_index()\n",
    "    .rename(columns={\"created_date\": \"count\"})\n",
    ")\n",
    "\n",
    "gdf_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (99324, 13)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>physicalid</th>\n",
       "      <th>st_label</th>\n",
       "      <th>st_name</th>\n",
       "      <th>full_stree</th>\n",
       "      <th>rw_type</th>\n",
       "      <th>rw_type_name</th>\n",
       "      <th>st_width</th>\n",
       "      <th>frm_lvl_co</th>\n",
       "      <th>to_lvl_co</th>\n",
       "      <th>borocode</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>geometry</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>262.77781228</td>\n",
       "      <td>MULTILINESTRING ((979278.595 196555.690, 97929...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>259.415988519</td>\n",
       "      <td>MULTILINESTRING ((979377.413 196797.951, 97950...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>280.444780871</td>\n",
       "      <td>MULTILINESTRING ((979503.289 197024.782, 97964...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0701391509</td>\n",
       "      <td>MULTILINESTRING ((979553.746 196059.826, 97952...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>206.27185039</td>\n",
       "      <td>MULTILINESTRING ((980288.092 195963.182, 98026...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  physicalid    st_label  st_name  full_stree rw_type rw_type_name st_width  \\\n",
       "0          3  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "1          5  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "2          6  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "3          8  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "4         14  BATTERY PL  BATTERY  BATTERY PL       1       Street     24.0   \n",
       "\n",
       "  frm_lvl_co to_lvl_co borocode     shape_leng  \\\n",
       "0         13        13        1   262.77781228   \n",
       "1         13        13        1  259.415988519   \n",
       "2         13        13        1  280.444780871   \n",
       "3         13        13        1  32.0701391509   \n",
       "4         13        13        1   206.27185039   \n",
       "\n",
       "                                            geometry  count  \n",
       "0  MULTILINESTRING ((979278.595 196555.690, 97929...      0  \n",
       "1  MULTILINESTRING ((979377.413 196797.951, 97950...      0  \n",
       "2  MULTILINESTRING ((979503.289 197024.782, 97964...      0  \n",
       "3  MULTILINESTRING ((979553.746 196059.826, 97952...      0  \n",
       "4  MULTILINESTRING ((980288.092 195963.182, 98026...      0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join our street data to our flood complaints data\n",
    "streets_with_count = streets.merge(\n",
    "    gdf_count, \n",
    "    on='physicalid',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "streets_with_count['count'] = streets_with_count['count'].fillna(0).astype(int)\n",
    "\n",
    "print(f'shape of data: {streets_with_count.shape}')\n",
    "streets_with_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "Int64Index: 99324 entries, 0 to 99323\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype   \n",
      "---  ------        --------------  -----   \n",
      " 0   physicalid    99324 non-null  object  \n",
      " 1   st_label      99324 non-null  object  \n",
      " 2   st_name       99324 non-null  object  \n",
      " 3   full_stree    99324 non-null  object  \n",
      " 4   rw_type       99324 non-null  object  \n",
      " 5   rw_type_name  99324 non-null  object  \n",
      " 6   st_width      99324 non-null  object  \n",
      " 7   frm_lvl_co    99324 non-null  object  \n",
      " 8   to_lvl_co     99324 non-null  object  \n",
      " 9   borocode      99324 non-null  object  \n",
      " 10  shape_leng    99324 non-null  object  \n",
      " 11  geometry      99324 non-null  geometry\n",
      " 12  count         99324 non-null  int64   \n",
      "dtypes: geometry(1), int64(1), object(11)\n",
      "memory usage: 10.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# column info\n",
    "streets_with_count.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>physicalid</th>\n",
       "      <th>st_label</th>\n",
       "      <th>st_name</th>\n",
       "      <th>full_stree</th>\n",
       "      <th>rw_type</th>\n",
       "      <th>rw_type_name</th>\n",
       "      <th>st_width</th>\n",
       "      <th>frm_lvl_co</th>\n",
       "      <th>to_lvl_co</th>\n",
       "      <th>borocode</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>geometry</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76015</th>\n",
       "      <td>93488</td>\n",
       "      <td>157 ST</td>\n",
       "      <td>157</td>\n",
       "      <td>157 ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>499.592808383</td>\n",
       "      <td>MULTILINESTRING ((1045395.176 182129.994, 1045...</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36568</th>\n",
       "      <td>44654</td>\n",
       "      <td>MILL RD</td>\n",
       "      <td>MILL</td>\n",
       "      <td>MILL RD</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>60.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>404.81289109</td>\n",
       "      <td>MULTILINESTRING ((952047.247 142027.744, 95184...</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18877</th>\n",
       "      <td>23726</td>\n",
       "      <td>141 ST</td>\n",
       "      <td>141</td>\n",
       "      <td>141 ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>678.503924658</td>\n",
       "      <td>MULTILINESTRING ((1039008.416 188480.641, 1039...</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85763</th>\n",
       "      <td>109590</td>\n",
       "      <td>SAPPHIRE ST</td>\n",
       "      <td>SAPPHIRE</td>\n",
       "      <td>SAPPHIRE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>22.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>554.170960128</td>\n",
       "      <td>MULTILINESTRING ((1023856.014 183443.026, 1023...</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67778</th>\n",
       "      <td>83475</td>\n",
       "      <td>BEDELL ST</td>\n",
       "      <td>BEDELL</td>\n",
       "      <td>BEDELL ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>651.973468023</td>\n",
       "      <td>MULTILINESTRING ((1043449.919 189921.878, 1043...</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      physicalid     st_label   st_name   full_stree rw_type rw_type_name  \\\n",
       "76015      93488       157 ST       157       157 ST       1       Street   \n",
       "36568      44654      MILL RD      MILL      MILL RD       1       Street   \n",
       "18877      23726       141 ST       141       141 ST       1       Street   \n",
       "85763     109590  SAPPHIRE ST  SAPPHIRE  SAPPHIRE ST       1       Street   \n",
       "67778      83475    BEDELL ST    BEDELL    BEDELL ST       1       Street   \n",
       "\n",
       "      st_width frm_lvl_co to_lvl_co borocode     shape_leng  \\\n",
       "76015     35.0         13        13        4  499.592808383   \n",
       "36568     60.0         13        13        5   404.81289109   \n",
       "18877     30.0         13        13        4  678.503924658   \n",
       "85763     22.0         13        13        3  554.170960128   \n",
       "67778     30.0         13        13        4  651.973468023   \n",
       "\n",
       "                                                geometry  count  \n",
       "76015  MULTILINESTRING ((1045395.176 182129.994, 1045...     91  \n",
       "36568  MULTILINESTRING ((952047.247 142027.744, 95184...     87  \n",
       "18877  MULTILINESTRING ((1039008.416 188480.641, 1039...     71  \n",
       "85763  MULTILINESTRING ((1023856.014 183443.026, 1023...     71  \n",
       "67778  MULTILINESTRING ((1043449.919 189921.878, 1043...     63  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# examine highest counts\n",
    "streets_with_count.sort_values(by='count', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>physicalid</th>\n",
       "      <th>st_label</th>\n",
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       "      <th>full_stree</th>\n",
       "      <th>rw_type</th>\n",
       "      <th>rw_type_name</th>\n",
       "      <th>st_width</th>\n",
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       "      <th>borocode</th>\n",
       "      <th>shape_leng</th>\n",
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       "      <th>count_per_100ft</th>\n",
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       "      <th>0</th>\n",
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       "      <td>BATTERY</td>\n",
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       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
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       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
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       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>32.070147</td>\n",
       "      <td>MULTILINESTRING ((979553.746 196059.826, 97952...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>206.256713</td>\n",
       "      <td>MULTILINESTRING ((980288.092 195963.182, 98026...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  physicalid    st_label  st_name  full_stree rw_type rw_type_name st_width  \\\n",
       "0          3  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "1          5  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "2          6  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "3          8  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "4         14  BATTERY PL  BATTERY  BATTERY PL       1       Street     24.0   \n",
       "\n",
       "  frm_lvl_co to_lvl_co borocode  shape_leng  \\\n",
       "0         13        13        1  262.778330   \n",
       "1         13        13        1  259.416503   \n",
       "2         13        13        1  280.445341   \n",
       "3         13        13        1   32.070147   \n",
       "4         13        13        1  206.256713   \n",
       "\n",
       "                                            geometry  count  count_per_100ft  \n",
       "0  MULTILINESTRING ((979278.595 196555.690, 97929...      0              0.0  \n",
       "1  MULTILINESTRING ((979377.413 196797.951, 97950...      0              0.0  \n",
       "2  MULTILINESTRING ((979503.289 197024.782, 97964...      0              0.0  \n",
       "3  MULTILINESTRING ((979553.746 196059.826, 97952...      0              0.0  \n",
       "4  MULTILINESTRING ((980288.092 195963.182, 98026...      0              0.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize counts\n",
    "streets_with_count['shape_leng'] = streets_with_count['geometry'].length\n",
    "count_norm = (streets_with_count['count'] / streets_with_count['shape_leng'].replace(0, np.nan) * 100)\n",
    "\n",
    "# counts per 100 ft\n",
    "streets_with_count['count_per_100ft'] = round(count_norm, 2)\n",
    "\n",
    "streets_with_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>count_per_100ft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>99324.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.095102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.979545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>228.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count_per_100ft\n",
       "count     99324.000000\n",
       "mean          0.095102\n",
       "std           0.979545\n",
       "min           0.000000\n",
       "25%           0.000000\n",
       "50%           0.000000\n",
       "75%           0.000000\n",
       "max         228.670000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
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    }
   ],
   "source": [
    "# examine values\n",
    "streets_with_count.loc[:, ['count_per_100ft']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>155472</td>\n",
       "      <td>W 228 ST</td>\n",
       "      <td>228</td>\n",
       "      <td>W  228 ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>44.0</td>\n",
       "      <td>13</td>\n",
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       "      <td>1</td>\n",
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       "      <td>228.67</td>\n",
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       "      <th>2264</th>\n",
       "      <td>3350</td>\n",
       "      <td>E 47 ST</td>\n",
       "      <td>47</td>\n",
       "      <td>E  47 ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>45.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>36.576249</td>\n",
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       "      <td>19</td>\n",
       "      <td>51.95</td>\n",
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       "      <th>87173</th>\n",
       "      <td>130213</td>\n",
       "      <td>E 68 ST</td>\n",
       "      <td>68</td>\n",
       "      <td>E  68 ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>38.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>15.462558</td>\n",
       "      <td>MULTILINESTRING ((1007289.058 166879.286, 1007...</td>\n",
       "      <td>6</td>\n",
       "      <td>38.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18223</th>\n",
       "      <td>22994</td>\n",
       "      <td>SHORE BLVD</td>\n",
       "      <td>SHORE</td>\n",
       "      <td>SHORE BLVD</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>36.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>23.642131</td>\n",
       "      <td>MULTILINESTRING ((1004233.755 222267.727, 1004...</td>\n",
       "      <td>9</td>\n",
       "      <td>38.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98528</th>\n",
       "      <td>199921</td>\n",
       "      <td>HERKIMER ST</td>\n",
       "      <td>HERKIMER</td>\n",
       "      <td>HERKIMER ST</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>17.255823</td>\n",
       "      <td>MULTILINESTRING ((1011761.619 185801.742, 1011...</td>\n",
       "      <td>6</td>\n",
       "      <td>34.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      physicalid     st_label   st_name   full_stree rw_type rw_type_name  \\\n",
       "89319     155472     W 228 ST       228    W  228 ST       1       Street   \n",
       "2264        3350      E 47 ST        47     E  47 ST       1       Street   \n",
       "87173     130213      E 68 ST        68     E  68 ST       1       Street   \n",
       "18223      22994   SHORE BLVD     SHORE   SHORE BLVD       1       Street   \n",
       "98528     199921  HERKIMER ST  HERKIMER  HERKIMER ST       1       Street   \n",
       "\n",
       "      st_width frm_lvl_co to_lvl_co borocode  shape_leng  \\\n",
       "89319     44.0         13        13        1   11.807495   \n",
       "2264      45.0         13        13        1   36.576249   \n",
       "87173     38.0         13        13        3   15.462558   \n",
       "18223     36.0         13        13        4   23.642131   \n",
       "98528     30.0         13        13        3   17.255823   \n",
       "\n",
       "                                                geometry  count  \\\n",
       "89319  MULTILINESTRING ((1009588.318 258326.241, 1009...     27   \n",
       "2264   MULTILINESTRING ((993217.323 213226.959, 99323...     19   \n",
       "87173  MULTILINESTRING ((1007289.058 166879.286, 1007...      6   \n",
       "18223  MULTILINESTRING ((1004233.755 222267.727, 1004...      9   \n",
       "98528  MULTILINESTRING ((1011761.619 185801.742, 1011...      6   \n",
       "\n",
       "       count_per_100ft  \n",
       "89319           228.67  \n",
       "2264             51.95  \n",
       "87173            38.80  \n",
       "18223            38.07  \n",
       "98528            34.77  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort descending\n",
    "streets_with_count.sort_values(by='count_per_100ft', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joining Streets and Counts to Neighborhoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (24750, 50)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>cross_street_1</th>\n",
       "      <th>cross_street_2</th>\n",
       "      <th>address_type</th>\n",
       "      <th>...</th>\n",
       "      <th>full_stree</th>\n",
       "      <th>rw_type</th>\n",
       "      <th>rw_type_name</th>\n",
       "      <th>st_width</th>\n",
       "      <th>frm_lvl_co</th>\n",
       "      <th>to_lvl_co</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>geometry</th>\n",
       "      <th>point</th>\n",
       "      <th>snap_dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34783066</td>\n",
       "      <td>2016-11-15T09:27:00.000</td>\n",
       "      <td>2016-11-15T10:05:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INTERSECTION</td>\n",
       "      <td>...</td>\n",
       "      <td>BRIGHTON AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>172.36564876</td>\n",
       "      <td>POINT (958593.988 170854.865)</td>\n",
       "      <td>POINT (958594.000 170855.000)</td>\n",
       "      <td>0.135523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16585559</td>\n",
       "      <td>2010-05-04T09:20:00.000</td>\n",
       "      <td>2010-05-08T09:00:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>ST PETER'S PL</td>\n",
       "      <td>BEND</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>RICHMOND TER</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>602.593212361</td>\n",
       "      <td>POINT (960713.101 175471.573)</td>\n",
       "      <td>POINT (960718.000 175485.000)</td>\n",
       "      <td>14.292678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18255249</td>\n",
       "      <td>2010-07-13T13:20:00.000</td>\n",
       "      <td>2010-07-13T15:10:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>ST PETER'S PL</td>\n",
       "      <td>BEND</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>RICHMOND TER</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>602.593212361</td>\n",
       "      <td>POINT (960713.101 175471.573)</td>\n",
       "      <td>POINT (960718.000 175485.000)</td>\n",
       "      <td>14.292678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18380954</td>\n",
       "      <td>2010-07-30T11:08:00.000</td>\n",
       "      <td>2010-07-30T11:20:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>DANIEL LOW TER</td>\n",
       "      <td>BEND</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>BELMONT PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>432.344943452</td>\n",
       "      <td>POINT (961777.231 173950.696)</td>\n",
       "      <td>POINT (961777.000 173953.000)</td>\n",
       "      <td>2.315312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18449016</td>\n",
       "      <td>2010-08-09T13:49:00.000</td>\n",
       "      <td>2010-08-09T14:30:00.000</td>\n",
       "      <td>DEP</td>\n",
       "      <td>Department of Environmental Protection</td>\n",
       "      <td>Sewer</td>\n",
       "      <td>Street Flooding (SJ)</td>\n",
       "      <td>ST PETER'S PL</td>\n",
       "      <td>BEND</td>\n",
       "      <td>ADDRESS</td>\n",
       "      <td>...</td>\n",
       "      <td>RICHMOND TER</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>34.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>602.593212361</td>\n",
       "      <td>POINT (960713.101 175471.573)</td>\n",
       "      <td>POINT (960718.000 175485.000)</td>\n",
       "      <td>14.292678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_key             created_date              closed_date agency  \\\n",
       "0    34783066  2016-11-15T09:27:00.000  2016-11-15T10:05:00.000    DEP   \n",
       "1    16585559  2010-05-04T09:20:00.000  2010-05-08T09:00:00.000    DEP   \n",
       "2    18255249  2010-07-13T13:20:00.000  2010-07-13T15:10:00.000    DEP   \n",
       "3    18380954  2010-07-30T11:08:00.000  2010-07-30T11:20:00.000    DEP   \n",
       "4    18449016  2010-08-09T13:49:00.000  2010-08-09T14:30:00.000    DEP   \n",
       "\n",
       "                              agency_name complaint_type  \\\n",
       "0  Department of Environmental Protection          Sewer   \n",
       "1  Department of Environmental Protection          Sewer   \n",
       "2  Department of Environmental Protection          Sewer   \n",
       "3  Department of Environmental Protection          Sewer   \n",
       "4  Department of Environmental Protection          Sewer   \n",
       "\n",
       "             descriptor  cross_street_1 cross_street_2  address_type  ...  \\\n",
       "0  Street Flooding (SJ)             NaN            NaN  INTERSECTION  ...   \n",
       "1  Street Flooding (SJ)   ST PETER'S PL           BEND       ADDRESS  ...   \n",
       "2  Street Flooding (SJ)   ST PETER'S PL           BEND       ADDRESS  ...   \n",
       "3  Street Flooding (SJ)  DANIEL LOW TER           BEND       ADDRESS  ...   \n",
       "4  Street Flooding (SJ)   ST PETER'S PL           BEND       ADDRESS  ...   \n",
       "\n",
       "     full_stree rw_type rw_type_name st_width frm_lvl_co to_lvl_co  \\\n",
       "0  BRIGHTON AVE       1       Street     30.0         13        13   \n",
       "1  RICHMOND TER       1       Street     34.0         13        13   \n",
       "2  RICHMOND TER       1       Street     34.0         13        13   \n",
       "3    BELMONT PL       1       Street     30.0         13        13   \n",
       "4  RICHMOND TER       1       Street     34.0         13        13   \n",
       "\n",
       "      shape_leng                       geometry  \\\n",
       "0   172.36564876  POINT (958593.988 170854.865)   \n",
       "1  602.593212361  POINT (960713.101 175471.573)   \n",
       "2  602.593212361  POINT (960713.101 175471.573)   \n",
       "3  432.344943452  POINT (961777.231 173950.696)   \n",
       "4  602.593212361  POINT (960713.101 175471.573)   \n",
       "\n",
       "                           point  snap_dist  \n",
       "0  POINT (958594.000 170855.000)   0.135523  \n",
       "1  POINT (960718.000 175485.000)  14.292678  \n",
       "2  POINT (960718.000 175485.000)  14.292678  \n",
       "3  POINT (961777.000 173953.000)   2.315312  \n",
       "4  POINT (960718.000 175485.000)  14.292678  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'shape of data: {updated_points.shape}')\n",
    "updated_points.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 24750 entries, 0 to 24749\n",
      "Data columns (total 50 columns):\n",
      " #   Column                          Non-Null Count  Dtype   \n",
      "---  ------                          --------------  -----   \n",
      " 0   unique_key                      24750 non-null  int64   \n",
      " 1   created_date                    24750 non-null  object  \n",
      " 2   closed_date                     24749 non-null  object  \n",
      " 3   agency                          24750 non-null  object  \n",
      " 4   agency_name                     24750 non-null  object  \n",
      " 5   complaint_type                  24750 non-null  object  \n",
      " 6   descriptor                      24750 non-null  object  \n",
      " 7   cross_street_1                  21759 non-null  object  \n",
      " 8   cross_street_2                  21754 non-null  object  \n",
      " 9   address_type                    24750 non-null  object  \n",
      " 10  status                          24750 non-null  object  \n",
      " 11  resolution_description          24747 non-null  object  \n",
      " 12  resolution_action_updated_date  24750 non-null  object  \n",
      " 13  community_board                 24750 non-null  object  \n",
      " 14  borough                         24750 non-null  object  \n",
      " 15  open_data_channel_type          24750 non-null  object  \n",
      " 16  park_borough                    24750 non-null  object  \n",
      " 17  incident_zip                    24750 non-null  float64 \n",
      " 18  city                            24750 non-null  object  \n",
      " 19  x_coordinate_state_plane        24750 non-null  float64 \n",
      " 20  y_coordinate_state_plane        24750 non-null  float64 \n",
      " 21  latitude                        24750 non-null  float64 \n",
      " 22  longitude                       24750 non-null  float64 \n",
      " 23  location                        24750 non-null  object  \n",
      " 24  incident_address                15947 non-null  object  \n",
      " 25  street_name                     15947 non-null  object  \n",
      " 26  bbl                             14553 non-null  float64 \n",
      " 27  ntaname                         24750 non-null  object  \n",
      " 28  cdtaname                        24750 non-null  object  \n",
      " 29  boroname                        24750 non-null  object  \n",
      " 30  ntatype                         24750 non-null  object  \n",
      " 31  nta2020                         24750 non-null  object  \n",
      " 32  borocode                        24750 non-null  object  \n",
      " 33  countyfips                      24750 non-null  object  \n",
      " 34  ntaabbrev                       24750 non-null  object  \n",
      " 35  cdta2020                        24750 non-null  object  \n",
      " 36  line_i                          24750 non-null  float64 \n",
      " 37  physicalid                      24750 non-null  object  \n",
      " 38  st_label                        24750 non-null  object  \n",
      " 39  st_name                         24750 non-null  object  \n",
      " 40  full_stree                      24750 non-null  object  \n",
      " 41  rw_type                         24750 non-null  object  \n",
      " 42  rw_type_name                    24750 non-null  object  \n",
      " 43  st_width                        24750 non-null  object  \n",
      " 44  frm_lvl_co                      24750 non-null  object  \n",
      " 45  to_lvl_co                       24750 non-null  object  \n",
      " 46  shape_leng                      24750 non-null  object  \n",
      " 47  geometry                        24750 non-null  geometry\n",
      " 48  point                           24750 non-null  geometry\n",
      " 49  snap_dist                       24750 non-null  float64 \n",
      "dtypes: float64(8), geometry(2), int64(1), object(39)\n",
      "memory usage: 9.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# column info\n",
    "updated_points.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_key</th>\n",
       "      <th>nta2020</th>\n",
       "      <th>countyfips</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>boroname</th>\n",
       "      <th>borocode</th>\n",
       "      <th>shape_leng</th>\n",
       "      <th>physicalid</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>34783066</td>\n",
       "      <td>SI0101</td>\n",
       "      <td>085</td>\n",
       "      <td>St. George-New Brighton</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>5</td>\n",
       "      <td>172.36564876</td>\n",
       "      <td>52392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16585559</td>\n",
       "      <td>SI0101</td>\n",
       "      <td>085</td>\n",
       "      <td>St. George-New Brighton</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>5</td>\n",
       "      <td>602.593212361</td>\n",
       "      <td>44892</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18255249</td>\n",
       "      <td>SI0101</td>\n",
       "      <td>085</td>\n",
       "      <td>St. George-New Brighton</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>5</td>\n",
       "      <td>602.593212361</td>\n",
       "      <td>44892</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18380954</td>\n",
       "      <td>SI0101</td>\n",
       "      <td>085</td>\n",
       "      <td>St. George-New Brighton</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>5</td>\n",
       "      <td>432.344943452</td>\n",
       "      <td>45070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18449016</td>\n",
       "      <td>SI0101</td>\n",
       "      <td>085</td>\n",
       "      <td>St. George-New Brighton</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>5</td>\n",
       "      <td>602.593212361</td>\n",
       "      <td>44892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_key nta2020 countyfips                  ntaname       boroname  \\\n",
       "0    34783066  SI0101        085  St. George-New Brighton  Staten Island   \n",
       "1    16585559  SI0101        085  St. George-New Brighton  Staten Island   \n",
       "2    18255249  SI0101        085  St. George-New Brighton  Staten Island   \n",
       "3    18380954  SI0101        085  St. George-New Brighton  Staten Island   \n",
       "4    18449016  SI0101        085  St. George-New Brighton  Staten Island   \n",
       "\n",
       "  borocode     shape_leng physicalid  \n",
       "0        5   172.36564876      52392  \n",
       "1        5  602.593212361      44892  \n",
       "2        5  602.593212361      44892  \n",
       "3        5  432.344943452      45070  \n",
       "4        5  602.593212361      44892  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve specific columns\n",
    "cols = [\n",
    "    'unique_key',\n",
    "    'nta2020',\n",
    "    'countyfips',\n",
    "    'ntaname',\n",
    "    'boroname',\n",
    "    'borocode',\n",
    "    'shape_leng',\n",
    "    'physicalid'\n",
    "]\n",
    "\n",
    "streets_with_nta = updated_points.loc[:, cols]\n",
    "\n",
    "streets_with_nta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>physicalid</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>boroname</th>\n",
       "      <th>count_complaints</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100019</td>\n",
       "      <td>Howard Beach-Lindenwood</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100020</td>\n",
       "      <td>Howard Beach-Lindenwood</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>Queens</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100041</td>\n",
       "      <td>Queens Village</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  physicalid                  ntaname boroname  count_complaints\n",
       "0     100019  Howard Beach-Lindenwood   Queens                 1\n",
       "1     100020  Howard Beach-Lindenwood   Queens                 1\n",
       "2      10003            East Elmhurst   Queens                 1\n",
       "3      10004            East Elmhurst   Queens                 2\n",
       "4     100041           Queens Village   Queens                 1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicates\n",
    "checking_for_duplicates = (\n",
    "    streets_with_nta\n",
    "    .groupby(by=['physicalid', 'ntaname', 'boroname'])['shape_leng']\n",
    "    .count()\n",
    "    .reset_index()\n",
    "    .rename(columns={\"shape_leng\": \"count_complaints\"})\n",
    ")\n",
    "\n",
    "checking_for_duplicates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "street id is unique: False\n"
     ]
    }
   ],
   "source": [
    "is_unique = checking_for_duplicates['physicalid'].is_unique\n",
    "\n",
    "print(f'street id is unique: {is_unique}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>physicalid</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>boroname</th>\n",
       "      <th>count_complaints</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>10042</td>\n",
       "      <td>Glendale</td>\n",
       "      <td>Queens</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>10042</td>\n",
       "      <td>Rego Park</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>10779</td>\n",
       "      <td>Middle Village</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>10779</td>\n",
       "      <td>Middle Village Cemetery</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>109590</td>\n",
       "      <td>Howard Beach-Lindenwood</td>\n",
       "      <td>Queens</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>109590</td>\n",
       "      <td>East New York-City Line</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>1128</td>\n",
       "      <td>Chelsea-Hudson Yards</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>1128</td>\n",
       "      <td>Midtown-Times Square</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>1134</td>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>1134</td>\n",
       "      <td>Midtown-Times Square</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    physicalid                  ntaname   boroname  count_complaints\n",
       "61       10042                 Glendale     Queens                 5\n",
       "62       10042                Rego Park     Queens                 1\n",
       "541      10779           Middle Village     Queens                 1\n",
       "542      10779  Middle Village Cemetery     Queens                 1\n",
       "557     109590  Howard Beach-Lindenwood     Queens                53\n",
       "556     109590  East New York-City Line   Brooklyn                18\n",
       "585       1128     Chelsea-Hudson Yards  Manhattan                 2\n",
       "586       1128     Midtown-Times Square  Manhattan                 2\n",
       "594       1134           Hell's Kitchen  Manhattan                 5\n",
       "595       1134     Midtown-Times Square  Manhattan                 3"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(checking_for_duplicates\n",
    " .loc[checking_for_duplicates.duplicated(subset=['physicalid'], keep=False) == True]\n",
    " .sort_values(by=['physicalid', 'count_complaints'], ascending=[True, False])\n",
    " .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count of duplicates: 130\n",
      "percent duplicates: 0.13%\n"
     ]
    }
   ],
   "source": [
    "count_duplicates = (\n",
    "    checking_for_duplicates\n",
    "    .loc[checking_for_duplicates.duplicated(subset=['physicalid'], keep=False) == True]\n",
    "    .shape[0]\n",
    ")\n",
    "\n",
    "counts = round(count_duplicates / streets_with_count.shape[0] * 100, 2)\n",
    "\n",
    "print(f'count of duplicates: {count_duplicates:,}')\n",
    "print(f'percent duplicates: {counts}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "physical id is unique: True\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>physicalid</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>boroname</th>\n",
       "      <th>count_complaints</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100019</td>\n",
       "      <td>Howard Beach-Lindenwood</td>\n",
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       "      <th>1</th>\n",
       "      <td>100020</td>\n",
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       "      <td>Queens</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10003</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10004</td>\n",
       "      <td>East Elmhurst</td>\n",
       "      <td>Queens</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100041</td>\n",
       "      <td>Queens Village</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  physicalid                  ntaname boroname  count_complaints\n",
       "0     100019  Howard Beach-Lindenwood   Queens                 1\n",
       "1     100020  Howard Beach-Lindenwood   Queens                 1\n",
       "2      10003            East Elmhurst   Queens                 1\n",
       "3      10004            East Elmhurst   Queens                 2\n",
       "4     100041           Queens Village   Queens                 1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorting descending by number of complaints on a street in a given NTA then removing duplicates\n",
    "unique_streets = (\n",
    "    checking_for_duplicates\n",
    "    .sort_values(by=['physicalid', 'count_complaints'], ascending=[True, False])\n",
    "    .drop_duplicates('physicalid')\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "print(f\"physical id is unique: {unique_streets['physicalid'].is_unique}\")\n",
    "unique_streets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of data: (99324, 17)\n"
     ]
    },
    {
     "data": {
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       "      <th>st_label</th>\n",
       "      <th>st_name</th>\n",
       "      <th>full_stree</th>\n",
       "      <th>rw_type</th>\n",
       "      <th>rw_type_name</th>\n",
       "      <th>st_width</th>\n",
       "      <th>frm_lvl_co</th>\n",
       "      <th>to_lvl_co</th>\n",
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       "      <th>geometry</th>\n",
       "      <th>count</th>\n",
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       "      <th>ntaname</th>\n",
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       "      <td>3</td>\n",
       "      <td>BATTERY PL</td>\n",
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       "      <td>6</td>\n",
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       "      <td>BATTERY PL</td>\n",
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       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>32.070147</td>\n",
       "      <td>MULTILINESTRING ((979553.746 196059.826, 97952...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>BATTERY PL</td>\n",
       "      <td>1</td>\n",
       "      <td>Street</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>206.256713</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  physicalid    st_label  st_name  full_stree rw_type rw_type_name st_width  \\\n",
       "0          3  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "1          5  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "2          6  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "3          8  BATTERY PL  BATTERY  BATTERY PL       1       Street     42.0   \n",
       "4         14  BATTERY PL  BATTERY  BATTERY PL       1       Street     24.0   \n",
       "\n",
       "  frm_lvl_co to_lvl_co borocode  shape_leng  \\\n",
       "0         13        13        1  262.778330   \n",
       "1         13        13        1  259.416503   \n",
       "2         13        13        1  280.445341   \n",
       "3         13        13        1   32.070147   \n",
       "4         13        13        1  206.256713   \n",
       "\n",
       "                                            geometry  count  count_per_100ft  \\\n",
       "0  MULTILINESTRING ((979278.595 196555.690, 97929...      0              0.0   \n",
       "1  MULTILINESTRING ((979377.413 196797.951, 97950...      0              0.0   \n",
       "2  MULTILINESTRING ((979503.289 197024.782, 97964...      0              0.0   \n",
       "3  MULTILINESTRING ((979553.746 196059.826, 97952...      0              0.0   \n",
       "4  MULTILINESTRING ((980288.092 195963.182, 98026...      0              0.0   \n",
       "\n",
       "  ntaname boroname  count_complaints  \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  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# joining streets with count complaints to unique streets\n",
    "streets_with_count_nta = streets_with_count.merge(\n",
    "    unique_streets, \n",
    "    left_on='physicalid', \n",
    "    right_on='physicalid', \n",
    "    how='left'\n",
    ")\n",
    "\n",
    "print(f'shape of data: {streets_with_count_nta.shape}')\n",
    "streets_with_count_nta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "Int64Index: 99324 entries, 0 to 99323\n",
      "Data columns (total 17 columns):\n",
      " #   Column            Non-Null Count  Dtype   \n",
      "---  ------            --------------  -----   \n",
      " 0   physicalid        99324 non-null  object  \n",
      " 1   st_label          99324 non-null  object  \n",
      " 2   st_name           99324 non-null  object  \n",
      " 3   full_stree        99324 non-null  object  \n",
      " 4   rw_type           99324 non-null  object  \n",
      " 5   rw_type_name      99324 non-null  object  \n",
      " 6   st_width          99324 non-null  object  \n",
      " 7   frm_lvl_co        99324 non-null  object  \n",
      " 8   to_lvl_co         99324 non-null  object  \n",
      " 9   borocode          99324 non-null  object  \n",
      " 10  shape_leng        99324 non-null  float64 \n",
      " 11  geometry          99324 non-null  geometry\n",
      " 12  count             99324 non-null  int64   \n",
      " 13  count_per_100ft   99324 non-null  float64 \n",
      " 14  ntaname           11991 non-null  object  \n",
      " 15  boroname          11991 non-null  object  \n",
      " 16  count_complaints  11991 non-null  float64 \n",
      "dtypes: float64(3), geometry(1), int64(1), object(12)\n",
      "memory usage: 13.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# examine columns\n",
    "streets_with_count_nta.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>physicalid</th>\n",
       "      <th>full_stree</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>boroname</th>\n",
       "      <th>count</th>\n",
       "      <th>count_per_100ft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76015</th>\n",
       "      <td>93488</td>\n",
       "      <td>157 ST</td>\n",
       "      <td>Springfield Gardens (South)-Brookville</td>\n",
       "      <td>Queens</td>\n",
       "      <td>91</td>\n",
       "      <td>18.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36568</th>\n",
       "      <td>44654</td>\n",
       "      <td>MILL RD</td>\n",
       "      <td>Oakwood-Richmondtown</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>87</td>\n",
       "      <td>21.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18877</th>\n",
       "      <td>23726</td>\n",
       "      <td>141 ST</td>\n",
       "      <td>Baisley Park</td>\n",
       "      <td>Queens</td>\n",
       "      <td>71</td>\n",
       "      <td>10.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85763</th>\n",
       "      <td>109590</td>\n",
       "      <td>SAPPHIRE ST</td>\n",
       "      <td>Howard Beach-Lindenwood</td>\n",
       "      <td>Queens</td>\n",
       "      <td>71</td>\n",
       "      <td>12.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67778</th>\n",
       "      <td>83475</td>\n",
       "      <td>BEDELL ST</td>\n",
       "      <td>Baisley Park</td>\n",
       "      <td>Queens</td>\n",
       "      <td>63</td>\n",
       "      <td>9.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      physicalid   full_stree                                 ntaname  \\\n",
       "76015      93488       157 ST  Springfield Gardens (South)-Brookville   \n",
       "36568      44654      MILL RD                    Oakwood-Richmondtown   \n",
       "18877      23726       141 ST                            Baisley Park   \n",
       "85763     109590  SAPPHIRE ST                 Howard Beach-Lindenwood   \n",
       "67778      83475    BEDELL ST                            Baisley Park   \n",
       "\n",
       "            boroname  count  count_per_100ft  \n",
       "76015         Queens     91            18.21  \n",
       "36568  Staten Island     87            21.49  \n",
       "18877         Queens     71            10.46  \n",
       "85763         Queens     71            12.81  \n",
       "67778         Queens     63             9.66  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# retrieve desired columns\n",
    "cols = [\n",
    "    'physicalid','full_stree', 'ntaname', 'boroname',\n",
    "    'count', 'count_per_100ft'\n",
    "]\n",
    "\n",
    "count_by_nta = (\n",
    "    streets_with_count_nta\n",
    "    .loc[:, cols]\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "# sort on count desc\n",
    "count_by_nta.sort_values(by='count', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>physicalid</th>\n",
       "      <th>full_stree</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>boroname</th>\n",
       "      <th>count</th>\n",
       "      <th>count_per_100ft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89319</th>\n",
       "      <td>155472</td>\n",
       "      <td>W  228 ST</td>\n",
       "      <td>Kingsbridge-Marble Hill</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>27</td>\n",
       "      <td>228.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2264</th>\n",
       "      <td>3350</td>\n",
       "      <td>E  47 ST</td>\n",
       "      <td>United Nations</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>19</td>\n",
       "      <td>51.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87173</th>\n",
       "      <td>130213</td>\n",
       "      <td>E  68 ST</td>\n",
       "      <td>Marine Park-Mill Basin-Bergen Beach</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>6</td>\n",
       "      <td>38.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18223</th>\n",
       "      <td>22994</td>\n",
       "      <td>SHORE BLVD</td>\n",
       "      <td>Old Astoria-Hallets Point</td>\n",
       "      <td>Queens</td>\n",
       "      <td>9</td>\n",
       "      <td>38.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98528</th>\n",
       "      <td>199921</td>\n",
       "      <td>HERKIMER ST</td>\n",
       "      <td>Ocean Hill</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>6</td>\n",
       "      <td>34.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      physicalid   full_stree                              ntaname   boroname  \\\n",
       "89319     155472    W  228 ST              Kingsbridge-Marble Hill      Bronx   \n",
       "2264        3350     E  47 ST                       United Nations  Manhattan   \n",
       "87173     130213     E  68 ST  Marine Park-Mill Basin-Bergen Beach   Brooklyn   \n",
       "18223      22994   SHORE BLVD            Old Astoria-Hallets Point     Queens   \n",
       "98528     199921  HERKIMER ST                           Ocean Hill   Brooklyn   \n",
       "\n",
       "       count  count_per_100ft  \n",
       "89319     27           228.67  \n",
       "2264      19            51.95  \n",
       "87173      6            38.80  \n",
       "18223      9            38.07  \n",
       "98528      6            34.77  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort on count per 100ft desc\n",
    "count_by_nta.sort_values(by='count_per_100ft', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>count</th>\n",
       "      <th>count_per_100ft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>99324.000000</td>\n",
       "      <td>99324.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.249184</td>\n",
       "      <td>0.095102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.310524</td>\n",
       "      <td>0.979545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>91.000000</td>\n",
       "      <td>228.670000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count  count_per_100ft\n",
       "count  99324.000000     99324.000000\n",
       "mean       0.249184         0.095102\n",
       "std        1.310524         0.979545\n",
       "min        0.000000         0.000000\n",
       "25%        0.000000         0.000000\n",
       "50%        0.000000         0.000000\n",
       "75%        0.000000         0.000000\n",
       "max       91.000000       228.670000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary statistics\n",
    "(streets_with_count\n",
    " .groupby(by=['physicalid', 'full_stree'])[['count', 'count_per_100ft']]\n",
    " .sum()\n",
    " .reset_index()\n",
    " .describe()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>physicalid</th>\n",
       "      <th>full_stree</th>\n",
       "      <th>ntaname</th>\n",
       "      <th>boroname</th>\n",
       "      <th>count</th>\n",
       "      <th>count_per_100ft</th>\n",
       "      <th>ntaname_full</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76015</th>\n",
       "      <td>93488</td>\n",
       "      <td>157 ST</td>\n",
       "      <td>Springfield Gardens (South)-Brookville</td>\n",
       "      <td>Queens</td>\n",
       "      <td>91</td>\n",
       "      <td>18.21</td>\n",
       "      <td>157 ST (id: 93488), Springfield Gardens (South...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36568</th>\n",
       "      <td>44654</td>\n",
       "      <td>MILL RD</td>\n",
       "      <td>Oakwood-Richmondtown</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>87</td>\n",
       "      <td>21.49</td>\n",
       "      <td>MILL RD (id: 44654), Oakwood-Richmondtown, Sta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18877</th>\n",
       "      <td>23726</td>\n",
       "      <td>141 ST</td>\n",
       "      <td>Baisley Park</td>\n",
       "      <td>Queens</td>\n",
       "      <td>71</td>\n",
       "      <td>10.46</td>\n",
       "      <td>141 ST (id: 23726), Baisley Park, Queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85763</th>\n",
       "      <td>109590</td>\n",
       "      <td>SAPPHIRE ST</td>\n",
       "      <td>Howard Beach-Lindenwood</td>\n",
       "      <td>Queens</td>\n",
       "      <td>71</td>\n",
       "      <td>12.81</td>\n",
       "      <td>SAPPHIRE ST (id: 109590), Howard Beach-Lindenw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67778</th>\n",
       "      <td>83475</td>\n",
       "      <td>BEDELL ST</td>\n",
       "      <td>Baisley Park</td>\n",
       "      <td>Queens</td>\n",
       "      <td>63</td>\n",
       "      <td>9.66</td>\n",
       "      <td>BEDELL ST (id: 83475), Baisley Park, Queens</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      physicalid   full_stree                                 ntaname  \\\n",
       "76015      93488       157 ST  Springfield Gardens (South)-Brookville   \n",
       "36568      44654      MILL RD                    Oakwood-Richmondtown   \n",
       "18877      23726       141 ST                            Baisley Park   \n",
       "85763     109590  SAPPHIRE ST                 Howard Beach-Lindenwood   \n",
       "67778      83475    BEDELL ST                            Baisley Park   \n",
       "\n",
       "            boroname  count  count_per_100ft  \\\n",
       "76015         Queens     91            18.21   \n",
       "36568  Staten Island     87            21.49   \n",
       "18877         Queens     71            10.46   \n",
       "85763         Queens     71            12.81   \n",
       "67778         Queens     63             9.66   \n",
       "\n",
       "                                            ntaname_full  \n",
       "76015  157 ST (id: 93488), Springfield Gardens (South...  \n",
       "36568  MILL RD (id: 44654), Oakwood-Richmondtown, Sta...  \n",
       "18877           141 ST (id: 23726), Baisley Park, Queens  \n",
       "85763  SAPPHIRE ST (id: 109590), Howard Beach-Lindenw...  \n",
       "67778        BEDELL ST (id: 83475), Baisley Park, Queens  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding nta information\n",
    "count_by_nta['ntaname_full'] = (\n",
    "    count_by_nta['full_stree']\n",
    "    + \" (id: \"\n",
    "    + count_by_nta['physicalid']\n",
    "    + \"), \" + count_by_nta['ntaname']\n",
    "    + \", \"\n",
    "    + count_by_nta['boroname']\n",
    ")\n",
    "\n",
    "count_by_nta.sort_values(by='count', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "data = count_by_nta.sort_values(by='count', ascending=False).head(10)\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='ntaname_full',\n",
    "    x='count',\n",
    "    color='#1f77b4'\n",
    ")\n",
    "\n",
    "label = 'Count of NYC 311 Street Flooding Complaints by Street Segment from 2010 to 2020'\n",
    "fig.suptitle(label, fontsize=12)\n",
    "plt.xlabel('Count', fontsize=12)\n",
    "plt.ylabel('Street Segment\\n', fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "data = count_by_nta.sort_values(by='count_per_100ft', ascending=False).head(10)\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='ntaname_full',\n",
    "    x='count_per_100ft',\n",
    "    color='#1f77b4'\n",
    ")\n",
    "\n",
    "label = 'Count of NYC 311 Street Flooding Complaints per 100 ft. by Street Segment from 2010 to 2020'\n",
    "fig.suptitle(label, fontsize=12)\n",
    "plt.xlabel('Count per 100 ft.', fontsize=12)\n",
    "plt.ylabel('Street Segment', fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(10, 8))\n",
    "\n",
    "# first plot\n",
    "data = count_by_nta.sort_values(by='count', ascending=False).head(10)\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='ntaname_full',\n",
    "    x='count',\n",
    "    color='#1f77b4',\n",
    "    ax=axs[0]\n",
    ")\n",
    "\n",
    "label = 'Count of NYC 311 Street Flooding Complaints by Street Segment from 2010 to 2020'\n",
    "axs[0].set_title(label, fontsize=12, pad=10, x=-.4)\n",
    "axs[0].set_xlabel('Count', fontsize=12)\n",
    "axs[0].set_ylabel('Street Segment\\n', fontsize=12, labelpad=10)\n",
    "\n",
    "# second plot\n",
    "data = count_by_nta.sort_values(by='count_per_100ft', ascending=False).head(10)\n",
    "sns.barplot(\n",
    "    data=data,\n",
    "    y='ntaname_full',\n",
    "    x='count_per_100ft',\n",
    "    color='#1f77b4',\n",
    "    ax=axs[1]\n",
    ")\n",
    "\n",
    "label = 'Count of NYC 311 Street Flooding Complaints per 100 ft. by Street Segment from 2010 to 2020'\n",
    "axs[1].set_title(label, fontsize=12, pad=10, x=-.4)\n",
    "axs[1].set_xlabel('Count per 100 ft.', fontsize=12, labelpad=10)\n",
    "axs[1].set_ylabel('Street Segment', fontsize=12, labelpad=10)\n",
    "\n",
    "fig.tight_layout(pad=.9)\n",
    "plt.savefig('figures/count-street-segment.png', dpi=250, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}