Segmenting and Clustering Neighborhoods in New York City

Introduction

In this lab, you will learn how to convert addresses into their equivalent latitude and longitude values. Also, you will use the Foursquare API to explore neighborhoods in New York City. You will use the explore function to get the most common venue categories in each neighborhood, and then use this feature to group the neighborhoods into clusters. You will use the k-means clustering algorithm to complete this task. Finally, you will use the Folium library to visualize the neighborhoods in New York City and their emerging clusters.

Before we get the data and start exploring it, let's download all the dependencies that we will need.

In [1]:
import numpy as np # library to handle data in a vectorized manner

import pandas as pd # library for data analsysis
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

import json # library to handle JSON files

#!conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab
from geopy.geocoders import Nominatim # convert an address into latitude and longitude values

import requests # library to handle requests
from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe

# Matplotlib and associated plotting modules
import matplotlib.cm as cm
import matplotlib.colors as colors

# import k-means from clustering stage
from sklearn.cluster import KMeans

#!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab
import folium # map rendering library

print('Libraries imported.')
Libraries imported.

1. Download and Explore Dataset

Neighborhood has a total of 5 boroughs and 306 neighborhoods. In order to segement the neighborhoods and explore them, we will essentially need a dataset that contains the 5 boroughs and the neighborhoods that exist in each borough as well as the the latitude and logitude coordinates of each neighborhood.

Luckily, this dataset exists for free on the web. Feel free to try to find this dataset on your own, but here is the link to the dataset: https://geo.nyu.edu/catalog/nyu_2451_34572

For your convenience, I downloaded the files and placed it on the server, so you can simply run a wget command and access the data. So let's go ahead and do that.

In [2]:
!wget -q -O 'newyork_data.json' https://cocl.us/new_york_dataset
print('Data downloaded!')
Data downloaded!

Load and explore the data

Next, let's load the data.

In [3]:
with open('newyork_data.json') as json_data:
    newyork_data = json.load(json_data)

Let's take a quick look at the data.

In [4]:
newyork_data
Out[4]:
{'type': 'FeatureCollection',
 'totalFeatures': 306,
 'features': [{'type': 'Feature',
   'id': 'nyu_2451_34572.1',
   'geometry': {'type': 'Point',
    'coordinates': [-73.84720052054902, 40.89470517661]},
   'geometry_name': 'geom',
   'properties': {'name': 'Wakefield',
    'stacked': 1,
    'annoline1': 'Wakefield',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.84720052054902,
     40.89470517661,
     -73.84720052054902,
     40.89470517661]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.2',
   'geometry': {'type': 'Point',
    'coordinates': [-73.82993910812398, 40.87429419303012]},
   'geometry_name': 'geom',
   'properties': {'name': 'Co-op City',
    'stacked': 2,
    'annoline1': 'Co-op',
    'annoline2': 'City',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.82993910812398,
     40.87429419303012,
     -73.82993910812398,
     40.87429419303012]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.3',
   'geometry': {'type': 'Point',
    'coordinates': [-73.82780644716412, 40.887555677350775]},
   'geometry_name': 'geom',
   'properties': {'name': 'Eastchester',
    'stacked': 1,
    'annoline1': 'Eastchester',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.82780644716412,
     40.887555677350775,
     -73.82780644716412,
     40.887555677350775]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.4',
   'geometry': {'type': 'Point',
    'coordinates': [-73.90564259591682, 40.89543742690383]},
   'geometry_name': 'geom',
   'properties': {'name': 'Fieldston',
    'stacked': 1,
    'annoline1': 'Fieldston',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.90564259591682,
     40.89543742690383,
     -73.90564259591682,
     40.89543742690383]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.5',
   'geometry': {'type': 'Point',
    'coordinates': [-73.9125854610857, 40.890834493891305]},
   'geometry_name': 'geom',
   'properties': {'name': 'Riverdale',
    'stacked': 1,
    'annoline1': 'Riverdale',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.9125854610857,
     40.890834493891305,
     -73.9125854610857,
     40.890834493891305]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.6',
   'geometry': {'type': 'Point',
    'coordinates': [-73.90281798724604, 40.88168737120521]},
   'geometry_name': 'geom',
   'properties': {'name': 'Kingsbridge',
    'stacked': 1,
    'annoline1': 'Kingsbridge',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.90281798724604,
     40.88168737120521,
     -73.90281798724604,
     40.88168737120521]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.7',
   'geometry': {'type': 'Point',
    'coordinates': [-73.91065965862981, 40.87655077879964]},
   'geometry_name': 'geom',
   'properties': {'name': 'Marble Hill',
    'stacked': 2,
    'annoline1': 'Marble',
    'annoline2': 'Hill',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Manhattan',
    'bbox': [-73.91065965862981,
     40.87655077879964,
     -73.91065965862981,
     40.87655077879964]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.8',
   'geometry': {'type': 'Point',
    'coordinates': [-73.86731496814176, 40.89827261213805]},
   'geometry_name': 'geom',
   'properties': {'name': 'Woodlawn',
    'stacked': 1,
    'annoline1': 'Woodlawn',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.86731496814176,
     40.89827261213805,
     -73.86731496814176,
     40.89827261213805]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.9',
   'geometry': {'type': 'Point',
    'coordinates': [-73.8793907395681, 40.87722415599446]},
   'geometry_name': 'geom',
   'properties': {'name': 'Norwood',
    'stacked': 1,
    'annoline1': 'Norwood',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.8793907395681,
     40.87722415599446,
     -73.8793907395681,
     40.87722415599446]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.10',
   'geometry': {'type': 'Point',
    'coordinates': [-73.85744642974207, 40.88103887819211]},
   'geometry_name': 'geom',
   'properties': {'name': 'Williamsbridge',
    'stacked': 1,
    'annoline1': 'Williamsbridge',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.85744642974207,
     40.88103887819211,
     -73.85744642974207,
     40.88103887819211]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.11',
   'geometry': {'type': 'Point',
    'coordinates': [-73.83579759808117, 40.866858107252696]},
   'geometry_name': 'geom',
   'properties': {'name': 'Baychester',
    'stacked': 1,
    'annoline1': 'Baychester',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.83579759808117,
     40.866858107252696,
     -73.83579759808117,
     40.866858107252696]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.12',
   'geometry': {'type': 'Point',
    'coordinates': [-73.85475564017999, 40.85741349808865]},
   'geometry_name': 'geom',
   'properties': {'name': 'Pelham Parkway',
    'stacked': 1,
    'annoline1': 'Pelham Parkway',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.85475564017999,
     40.85741349808865,
     -73.85475564017999,
     40.85741349808865]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.13',
   'geometry': {'type': 'Point',
    'coordinates': [-73.78648845267413, 40.84724670491813]},
   'geometry_name': 'geom',
   'properties': {'name': 'City Island',
    'stacked': 2,
    'annoline1': 'City',
    'annoline2': 'Island',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.78648845267413,
     40.84724670491813,
     -73.78648845267413,
     40.84724670491813]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.14',
   'geometry': {'type': 'Point',
    'coordinates': [-73.8855121841913, 40.870185164975325]},
   'geometry_name': 'geom',
   'properties': {'name': 'Bedford Park',
    'stacked': 2,
    'annoline1': 'Bedford',
    'annoline2': 'Park',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.8855121841913,
     40.870185164975325,
     -73.8855121841913,
     40.870185164975325]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.15',
   'geometry': {'type': 'Point',
    'coordinates': [-73.9104159619131, 40.85572707719664]},
   'geometry_name': 'geom',
   'properties': {'name': 'University Heights',
    'stacked': 2,
    'annoline1': 'University',
    'annoline2': 'Heights',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.9104159619131,
     40.85572707719664,
     -73.9104159619131,
     40.85572707719664]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.16',
   'geometry': {'type': 'Point',
    'coordinates': [-73.91967159119565, 40.84789792606271]},
   'geometry_name': 'geom',
   'properties': {'name': 'Morris Heights',
    'stacked': 2,
    'annoline1': 'Morris',
    'annoline2': 'Heights',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.91967159119565,
     40.84789792606271,
     -73.91967159119565,
     40.84789792606271]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.17',
   'geometry': {'type': 'Point',
    'coordinates': [-73.89642655981623, 40.86099679638654]},
   'geometry_name': 'geom',
   'properties': {'name': 'Fordham',
    'stacked': 1,
    'annoline1': 'Fordham',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.89642655981623,
     40.86099679638654,
     -73.89642655981623,
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  {'type': 'Feature',
   'id': 'nyu_2451_34572.18',
   'geometry': {'type': 'Point',
    'coordinates': [-73.88735617532338, 40.84269615786053]},
   'geometry_name': 'geom',
   'properties': {'name': 'East Tremont',
    'stacked': 2,
    'annoline1': 'East',
    'annoline2': 'Tremont',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.88735617532338,
     40.84269615786053,
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     40.84269615786053]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.19',
   'geometry': {'type': 'Point',
    'coordinates': [-73.87774474910545, 40.83947505672653]},
   'geometry_name': 'geom',
   'properties': {'name': 'West Farms',
    'stacked': 2,
    'annoline1': 'West',
    'annoline2': 'Farms',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.87774474910545,
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  {'type': 'Feature',
   'id': 'nyu_2451_34572.20',
   'geometry': {'type': 'Point',
    'coordinates': [-73.9261020935813, 40.836623010706056]},
   'geometry_name': 'geom',
   'properties': {'name': 'High  Bridge',
    'stacked': 1,
    'annoline1': 'Highbridge',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.9261020935813,
     40.836623010706056,
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  {'type': 'Feature',
   'id': 'nyu_2451_34572.21',
   'geometry': {'type': 'Point',
    'coordinates': [-73.90942160757436, 40.819754370594936]},
   'geometry_name': 'geom',
   'properties': {'name': 'Melrose',
    'stacked': 1,
    'annoline1': 'Melrose',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.90942160757436,
     40.819754370594936,
     -73.90942160757436,
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  {'type': 'Feature',
   'id': 'nyu_2451_34572.22',
   'geometry': {'type': 'Point',
    'coordinates': [-73.91609987487575, 40.80623874935177]},
   'geometry_name': 'geom',
   'properties': {'name': 'Mott Haven',
    'stacked': 1,
    'annoline1': 'Mott Haven',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.91609987487575,
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  {'type': 'Feature',
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   'geometry': {'type': 'Point',
    'coordinates': [-73.91322139386135, 40.801663627756206]},
   'geometry_name': 'geom',
   'properties': {'name': 'Port Morris',
    'stacked': 2,
    'annoline1': 'Port',
    'annoline2': 'Morris',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
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  {'type': 'Feature',
   'id': 'nyu_2451_34572.24',
   'geometry': {'type': 'Point',
    'coordinates': [-73.8957882009446, 40.81509904545822]},
   'geometry_name': 'geom',
   'properties': {'name': 'Longwood',
    'stacked': 1,
    'annoline1': 'Longwood',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.8957882009446,
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     -73.8957882009446,
     40.81509904545822]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.25',
   'geometry': {'type': 'Point',
    'coordinates': [-73.88331505955291, 40.80972987938709]},
   'geometry_name': 'geom',
   'properties': {'name': 'Hunts Point',
    'stacked': 2,
    'annoline1': 'Hunts',
    'annoline2': 'Point',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.88331505955291,
     40.80972987938709,
     -73.88331505955291,
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  {'type': 'Feature',
   'id': 'nyu_2451_34572.26',
   'geometry': {'type': 'Point',
    'coordinates': [-73.90150648943059, 40.82359198585534]},
   'geometry_name': 'geom',
   'properties': {'name': 'Morrisania',
    'stacked': 1,
    'annoline1': 'Morrisania',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.90150648943059,
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  {'type': 'Feature',
   'id': 'nyu_2451_34572.27',
   'geometry': {'type': 'Point',
    'coordinates': [-73.86574609554924, 40.821012197914015]},
   'geometry_name': 'geom',
   'properties': {'name': 'Soundview',
    'stacked': 1,
    'annoline1': 'Soundview',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.86574609554924,
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  {'type': 'Feature',
   'id': 'nyu_2451_34572.28',
   'geometry': {'type': 'Point',
    'coordinates': [-73.85414416189266, 40.80655112003589]},
   'geometry_name': 'geom',
   'properties': {'name': 'Clason Point',
    'stacked': 2,
    'annoline1': 'Clason',
    'annoline2': 'Point',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.85414416189266,
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  {'type': 'Feature',
   'id': 'nyu_2451_34572.29',
   'geometry': {'type': 'Point',
    'coordinates': [-73.81635002158441, 40.81510925804005]},
   'geometry_name': 'geom',
   'properties': {'name': 'Throgs Neck',
    'stacked': 1,
    'annoline1': 'Throgs Neck',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.81635002158441,
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  {'type': 'Feature',
   'id': 'nyu_2451_34572.30',
   'geometry': {'type': 'Point',
    'coordinates': [-73.8240992675385, 40.844245936947374]},
   'geometry_name': 'geom',
   'properties': {'name': 'Country Club',
    'stacked': 2,
    'annoline1': 'Country',
    'annoline2': 'Club',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.8240992675385,
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  {'type': 'Feature',
   'id': 'nyu_2451_34572.31',
   'geometry': {'type': 'Point',
    'coordinates': [-73.85600310535783, 40.837937822267286]},
   'geometry_name': 'geom',
   'properties': {'name': 'Parkchester',
    'stacked': 1,
    'annoline1': 'Parkchester',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.85600310535783,
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     -73.85600310535783,
     40.837937822267286]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.32',
   'geometry': {'type': 'Point',
    'coordinates': [-73.84219407604444, 40.8406194964327]},
   'geometry_name': 'geom',
   'properties': {'name': 'Westchester Square',
    'stacked': 2,
    'annoline1': 'Westchester',
    'annoline2': 'Square',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.84219407604444,
     40.8406194964327,
     -73.84219407604444,
     40.8406194964327]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.33',
   'geometry': {'type': 'Point',
    'coordinates': [-73.8662991807561, 40.84360847124718]},
   'geometry_name': 'geom',
   'properties': {'name': 'Van Nest',
    'stacked': 2,
    'annoline1': 'Van',
    'annoline2': 'Nest',
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.8662991807561,
     40.84360847124718,
     -73.8662991807561,
     40.84360847124718]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.34',
   'geometry': {'type': 'Point',
    'coordinates': [-73.85040178030421, 40.847549063536334]},
   'geometry_name': 'geom',
   'properties': {'name': 'Morris Park',
    'stacked': 1,
    'annoline1': 'Morris Park',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.85040178030421,
     40.847549063536334,
     -73.85040178030421,
     40.847549063536334]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.35',
   'geometry': {'type': 'Point',
    'coordinates': [-73.88845196134804, 40.85727710073895]},
   'geometry_name': 'geom',
   'properties': {'name': 'Belmont',
    'stacked': 1,
    'annoline1': 'Belmont',
    'annoline2': None,
    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Bronx',
    'bbox': [-73.88845196134804,
     40.85727710073895,
     -73.88845196134804,
     40.85727710073895]}},
  {'type': 'Feature',
   'id': 'nyu_2451_34572.36',
   'geometry': {'type': 'Point',
    'coordinates': [-73.91719048210393, 40.88139497727086]},
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    'annoline3': None,
    'annoangle': 0.0,
    'borough': 'Staten Island',
    'bbox': [-74.08173992211962,
     40.61731079252983,
     -74.08173992211962,
     40.61731079252983]}}],
 'crs': {'type': 'name', 'properties': {'name': 'urn:ogc:def:crs:EPSG::4326'}},
 'bbox': [-74.2492599487305,
  40.5033187866211,
  -73.7061614990234,
  40.9105606079102]}

Notice how all the relevant data is in the features key, which is basically a list of the neighborhoods. So, let's define a new variable that includes this data.

In [5]:
neighborhoods_data = newyork_data['features']

Let's take a look at the first item in this list.

In [6]:
neighborhoods_data[0]
Out[6]:
{'type': 'Feature',
 'id': 'nyu_2451_34572.1',
 'geometry': {'type': 'Point',
  'coordinates': [-73.84720052054902, 40.89470517661]},
 'geometry_name': 'geom',
 'properties': {'name': 'Wakefield',
  'stacked': 1,
  'annoline1': 'Wakefield',
  'annoline2': None,
  'annoline3': None,
  'annoangle': 0.0,
  'borough': 'Bronx',
  'bbox': [-73.84720052054902,
   40.89470517661,
   -73.84720052054902,
   40.89470517661]}}

Tranform the data into a pandas dataframe

The next task is essentially transforming this data of nested Python dictionaries into a pandas dataframe. So let's start by creating an empty dataframe.

In [7]:
# define the dataframe columns
column_names = ['Borough', 'Neighborhood', 'Latitude', 'Longitude'] 

# instantiate the dataframe
neighborhoods = pd.DataFrame(columns=column_names)

Take a look at the empty dataframe to confirm that the columns are as intended.

In [8]:
neighborhoods
Out[8]:
Borough Neighborhood Latitude Longitude

Then let's loop through the data and fill the dataframe one row at a time.

In [9]:
for data in neighborhoods_data:
    borough = neighborhood_name = data['properties']['borough'] 
    neighborhood_name = data['properties']['name']
        
    neighborhood_latlon = data['geometry']['coordinates']
    neighborhood_lat = neighborhood_latlon[1]
    neighborhood_lon = neighborhood_latlon[0]
    
    neighborhoods = neighborhoods.append({'Borough': borough,
                                          'Neighborhood': neighborhood_name,
                                          'Latitude': neighborhood_lat,
                                          'Longitude': neighborhood_lon}, ignore_index=True)

Quickly examine the resulting dataframe.

In [10]:
neighborhoods.head()
Out[10]:
Borough Neighborhood Latitude Longitude
0 Bronx Wakefield 40.894705 -73.847201
1 Bronx Co-op City 40.874294 -73.829939
2 Bronx Eastchester 40.887556 -73.827806
3 Bronx Fieldston 40.895437 -73.905643
4 Bronx Riverdale 40.890834 -73.912585

And make sure that the dataset has all 5 boroughs and 306 neighborhoods.

In [11]:
print('The dataframe has {} boroughs and {} neighborhoods.'.format(
        len(neighborhoods['Borough'].unique()),
        neighborhoods.shape[0]
    )
)
The dataframe has 5 boroughs and 306 neighborhoods.

Use geopy library to get the latitude and longitude values of New York City.

In order to define an instance of the geocoder, we need to define a user_agent. We will name our agent ny_explorer, as shown below.

In [12]:
address = 'New York City, NY'

geolocator = Nominatim(user_agent="ny_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geograpical coordinate of New York City are {}, {}.'.format(latitude, longitude))
The geograpical coordinate of New York City are 40.7127281, -74.0060152.

Create a map of New York with neighborhoods superimposed on top.

In [13]:
# create map of New York using latitude and longitude values
map_newyork = folium.Map(location=[latitude, longitude], zoom_start=10)

# add markers to map
for lat, lng, borough, neighborhood in zip(neighborhoods['Latitude'], neighborhoods['Longitude'], neighborhoods['Borough'], neighborhoods['Neighborhood']):
    label = '{}, {}'.format(neighborhood, borough)
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_newyork)  
    
map_newyork
Out[13]:

Folium is a great visualization library. Feel free to zoom into the above map, and click on each circle mark to reveal the name of the neighborhood and its respective borough.

However, for illustration purposes, let's simplify the above map and segment and cluster only the neighborhoods in Manhattan. So let's slice the original dataframe and create a new dataframe of the Manhattan data.

In [14]:
manhattan_data = neighborhoods[neighborhoods['Borough'] == 'Manhattan'].reset_index(drop=True)
manhattan_data.head()
Out[14]:
Borough Neighborhood Latitude Longitude
0 Manhattan Marble Hill 40.876551 -73.910660
1 Manhattan Chinatown 40.715618 -73.994279
2 Manhattan Washington Heights 40.851903 -73.936900
3 Manhattan Inwood 40.867684 -73.921210
4 Manhattan Hamilton Heights 40.823604 -73.949688

Let's get the geographical coordinates of Manhattan.

In [15]:
address = 'Manhattan, NY'

geolocator = Nominatim(user_agent="ny_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geograpical coordinate of Manhattan are {}, {}.'.format(latitude, longitude))
The geograpical coordinate of Manhattan are 40.7900869, -73.9598295.

As we did with all of New York City, let's visualizat Manhattan the neighborhoods in it.

In [16]:
# create map of Manhattan using latitude and longitude values
map_manhattan = folium.Map(location=[latitude, longitude], zoom_start=11)

# add markers to map
for lat, lng, label in zip(manhattan_data['Latitude'], manhattan_data['Longitude'], manhattan_data['Neighborhood']):
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_manhattan)  
    
map_manhattan
Out[16]:

Next, we are going to start utilizing the Foursquare API to explore the neighborhoods and segment them.

Define Foursquare Credentials and Version

In [17]:
CLIENT_ID = 'your Foursquare ID' # your Foursquare ID
CLIENT_SECRET = 'your Foursquare Secret' # your Foursquare Secret
VERSION = '20180605' # Foursquare API version

print('Your credentails:')
print('CLIENT_ID: ' + CLIENT_ID)
print('CLIENT_SECRET:' + CLIENT_SECRET)
Your credentails:
CLIENT_ID: BPAX1A44J1YTPYCUOCZXBY41JTWTHB3H2M2ISKZTYCO5LQMA
CLIENT_SECRET:LSLUNFQNMUBH4KXC1S1O41RTPWP2J41IYQFMO44NU5GTJSER

Let's explore the first neighborhood in our dataframe.

Get the neighborhood's name.

In [18]:
manhattan_data.loc[0, 'Neighborhood']
Out[18]:
'Marble Hill'

Get the neighborhood's latitude and longitude values.

In [19]:
neighborhood_latitude = manhattan_data.loc[0, 'Latitude'] # neighborhood latitude value
neighborhood_longitude = manhattan_data.loc[0, 'Longitude'] # neighborhood longitude value

neighborhood_name = manhattan_data.loc[0, 'Neighborhood'] # neighborhood name

print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, 
                                                               neighborhood_latitude, 
                                                               neighborhood_longitude))
Latitude and longitude values of Marble Hill are 40.87655077879964, -73.91065965862981.

Now, let's get the top 100 venues that are in Marble Hill within a radius of 500 meters.

First, let's create the GET request URL. Name your URL url.

In [22]:
# type your answer here
radius = 500 # define radius
LIMIT = 100 # limit of number of venues returned by Foursquare API
url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
    CLIENT_ID, 
    CLIENT_SECRET, 
    VERSION, 
    neighborhood_latitude, 
    neighborhood_longitude, 
    radius, 
    LIMIT)
url
Out[22]:
'https://api.foursquare.com/v2/venues/explore?&client_id=BPAX1A44J1YTPYCUOCZXBY41JTWTHB3H2M2ISKZTYCO5LQMA&client_secret=LSLUNFQNMUBH4KXC1S1O41RTPWP2J41IYQFMO44NU5GTJSER&v=20180605&ll=40.87655077879964,-73.91065965862981&radius=500&limit=100'

Double-click here for the solution.

Send the GET request and examine the resutls

In [23]:
results = requests.get(url).json()
results
Out[23]:
{'meta': {'code': 200, 'requestId': '5cd0c1b49fb6b756cd95eed0'},
 'response': {'suggestedFilters': {'header': 'Tap to show:',
   'filters': [{'name': 'Open now', 'key': 'openNow'}]},
  'headerLocation': 'Marble Hill',
  'headerFullLocation': 'Marble Hill, New York',
  'headerLocationGranularity': 'neighborhood',
  'totalResults': 25,
  'suggestedBounds': {'ne': {'lat': 40.88105078329964,
    'lng': -73.90471933917806},
   'sw': {'lat': 40.87205077429964, 'lng': -73.91659997808156}},
  'groups': [{'type': 'Recommended Places',
    'name': 'recommended',
    'items': [{'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4b4429abf964a52037f225e3',
       'name': "Arturo's",
       'location': {'address': '5198 Broadway',
        'crossStreet': 'at 225th St.',
        'lat': 40.87441177110231,
        'lng': -73.91027100981574,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87441177110231,
          'lng': -73.91027100981574}],
        'distance': 240,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'New York',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['5198 Broadway (at 225th St.)',
         'New York, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d1ca941735',
         'name': 'Pizza Place',
         'pluralName': 'Pizza Places',
         'shortName': 'Pizza',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/pizza_',
          'suffix': '.png'},
         'primary': True}],
       'delivery': {'id': '72548',
        'url': 'https://www.seamless.com/menu/arturos-pizza-5189-broadway-ave-new-york/72548?affiliate=1131&utm_source=foursquare-affiliate-network&utm_medium=affiliate&utm_campaign=1131&utm_content=72548',
        'provider': {'name': 'seamless',
         'icon': {'prefix': 'https://fastly.4sqi.net/img/general/cap/',
          'sizes': [40, 50],
          'name': '/delivery_provider_seamless_20180129.png'}}},
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4b4429abf964a52037f225e3-0'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4baf59e8f964a520a6f93be3',
       'name': 'Bikram Yoga',
       'location': {'address': '5500 Broadway',
        'crossStreet': '230th Street',
        'lat': 40.876843690797934,
        'lng': -73.90620384419528,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.876843690797934,
          'lng': -73.90620384419528}],
        'distance': 376,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['5500 Broadway (230th Street)',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d102941735',
         'name': 'Yoga Studio',
         'pluralName': 'Yoga Studios',
         'shortName': 'Yoga Studio',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/gym_yogastudio_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4baf59e8f964a520a6f93be3-1'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4b79cc46f964a520c5122fe3',
       'name': 'Tibbett Diner',
       'location': {'address': '3033 Tibbett Ave',
        'crossStreet': 'btwn 230th & 231st',
        'lat': 40.8804044222466,
        'lng': -73.90893738006402,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.8804044222466,
          'lng': -73.90893738006402}],
        'distance': 452,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['3033 Tibbett Ave (btwn 230th & 231st)',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d147941735',
         'name': 'Diner',
         'pluralName': 'Diners',
         'shortName': 'Diner',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/diner_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4b79cc46f964a520c5122fe3-2'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4b5357adf964a520319827e3',
       'name': "Dunkin'",
       'location': {'address': '5501 Broadway',
        'crossStreet': 'W 230th St',
        'lat': 40.87713584201589,
        'lng': -73.90666550701411,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87713584201589,
          'lng': -73.90666550701411}],
        'distance': 342,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['5501 Broadway (W 230th St)',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d148941735',
         'name': 'Donut Shop',
         'pluralName': 'Donut Shops',
         'shortName': 'Donuts',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/donuts_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4b5357adf964a520319827e3-3'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '55f81cd2498ee903149fcc64',
       'name': 'Starbucks',
       'location': {'address': '171 W 230th St',
        'crossStreet': 'Kimberly Pl',
        'lat': 40.87753134921497,
        'lng': -73.90558216359267,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87753134921497,
          'lng': -73.90558216359267}],
        'distance': 441,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['171 W 230th St (Kimberly Pl)',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d1e0931735',
         'name': 'Coffee Shop',
         'pluralName': 'Coffee Shops',
         'shortName': 'Coffee Shop',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-55f81cd2498ee903149fcc64-4'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '55f751ca498eacc0307d1cfe',
       'name': 'Blink Fitness Riverdale',
       'location': {'address': '5520 Broadway',
        'crossStreet': 'at W 230th St',
        'lat': 40.87714687429521,
        'lng': -73.90583697267095,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87714687429521,
          'lng': -73.90583697267095}],
        'distance': 411,
        'postalCode': '10463',
        'cc': 'US',
        'neighborhood': 'Kingsbridge',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['5520 Broadway (at W 230th St)',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d176941735',
         'name': 'Gym',
         'pluralName': 'Gyms',
         'shortName': 'Gym',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/building/gym_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-55f751ca498eacc0307d1cfe-5'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4a725fa1f964a520f6da1fe3',
       'name': 'TCR The Club of Riverdale',
       'location': {'address': '2600 Netherland Ave',
        'lat': 40.8786283,
        'lng': -73.9145678,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.8786283,
          'lng': -73.9145678}],
        'distance': 402,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['2600 Netherland Ave',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4e39a891bd410d7aed40cbc2',
         'name': 'Tennis Stadium',
         'pluralName': 'Tennis Stadiums',
         'shortName': 'Tennis',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/arts_entertainment/stadium_tennis_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []},
       'venuePage': {'id': '40358759'}},
      'referralId': 'e-0-4a725fa1f964a520f6da1fe3-6'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4b9c9c6af964a520b27236e3',
       'name': 'Land & Sea Restaurant',
       'location': {'address': '5535 Broadway',
        'crossStreet': '231st St',
        'lat': 40.87788463309788,
        'lng': -73.90587282193539,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87788463309788,
          'lng': -73.90587282193539}],
        'distance': 429,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['5535 Broadway (231st St)',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d1ce941735',
         'name': 'Seafood Restaurant',
         'pluralName': 'Seafood Restaurants',
         'shortName': 'Seafood',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/seafood_',
          'suffix': '.png'},
         'primary': True}],
       'delivery': {'id': '277380',
        'url': 'https://www.seamless.com/menu/land--sea-restaurant-5535-broadway-ave-bronx/277380?affiliate=1131&utm_source=foursquare-affiliate-network&utm_medium=affiliate&utm_campaign=1131&utm_content=277380',
        'provider': {'name': 'seamless',
         'icon': {'prefix': 'https://fastly.4sqi.net/img/general/cap/',
          'sizes': [40, 50],
          'name': '/delivery_provider_seamless_20180129.png'}}},
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4b9c9c6af964a520b27236e3-7'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '57655be738faa66160da7527',
       'name': 'Starbucks',
       'location': {'address': '50 W 225th St',
        'lat': 40.873754554218515,
        'lng': -73.90861305343668,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.873754554218515,
          'lng': -73.90861305343668}],
        'distance': 355,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'New York',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['50 W 225th St',
         'New York, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d1e0931735',
         'name': 'Coffee Shop',
         'pluralName': 'Coffee Shops',
         'shortName': 'Coffee Shop',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-57655be738faa66160da7527-8'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '546d31ca498e561c698a0320',
       'name': 'T.J. Maxx',
       'location': {'lat': 40.87723198343352,
        'lng': -73.90504239962168,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87723198343352,
          'lng': -73.90504239962168}],
        'distance': 478,
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['Bronx, NY', 'United States']},
       'categories': [{'id': '4bf58dd8d48988d1f6941735',
         'name': 'Department Store',
         'pluralName': 'Department Stores',
         'shortName': 'Department Store',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/departmentstore_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-546d31ca498e561c698a0320-9'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4b9f030af964a520eb0f37e3',
       'name': 'GameStop',
       'location': {'address': '90 W 225th St Ste A-B',
        'crossStreet': 'btw Broadway & Exterior St.',
        'lat': 40.874266802124836,
        'lng': -73.90934218062803,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.874266802124836,
          'lng': -73.90934218062803}],
        'distance': 277,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['90 W 225th St Ste A-B (btw Broadway & Exterior St.)',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d10b951735',
         'name': 'Video Game Store',
         'pluralName': 'Video Game Stores',
         'shortName': 'Video Games',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/videogames_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4b9f030af964a520eb0f37e3-10'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4b9c9c43f964a520ac7236e3',
       'name': 'Lot Less Closeouts',
       'location': {'address': '5545 Broadway',
        'crossStreet': '231st St',
        'lat': 40.878270422202085,
        'lng': -73.9052646742604,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.878270422202085,
          'lng': -73.9052646742604}],
        'distance': 492,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['5545 Broadway (231st St)',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '52dea92d3cf9994f4e043dbb',
         'name': 'Discount Store',
         'pluralName': 'Discount Stores',
         'shortName': 'Discount Store',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/discountstore_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4b9c9c43f964a520ac7236e3-11'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4b88e053f964a5208a1132e3',
       'name': 'Rite Aid',
       'location': {'address': '5237 Broadway',
        'crossStreet': '228th Street',
        'lat': 40.875466574434704,
        'lng': -73.90890629016033,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.875466574434704,
          'lng': -73.90890629016033}],
        'distance': 190,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['5237 Broadway (228th Street)',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d10f951735',
         'name': 'Pharmacy',
         'pluralName': 'Pharmacies',
         'shortName': 'Pharmacy',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/pharmacy_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4b88e053f964a5208a1132e3-12'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '5631194e498e2de074de661c',
       'name': 'Vitamin Shoppe',
       'location': {'address': '5510 Broadway',
        'lat': 40.87716,
        'lng': -73.905632,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87716,
          'lng': -73.905632}],
        'distance': 428,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['5510 Broadway',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '5744ccdfe4b0c0459246b4cd',
         'name': 'Supplement Shop',
         'pluralName': 'Supplement Shops',
         'shortName': 'Supplement Shop',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/education/lab_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-5631194e498e2de074de661c-13'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4dfe40df8877333e195b68fc',
       'name': 'Parrilla Latina',
       'location': {'address': '230th St & Broadway',
        'lat': 40.87747294351472,
        'lng': -73.90607346968568,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87747294351472,
          'lng': -73.90607346968568}],
        'distance': 399,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['230th St & Broadway',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d1cc941735',
         'name': 'Steakhouse',
         'pluralName': 'Steakhouses',
         'shortName': 'Steakhouse',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/steakhouse_',
          'suffix': '.png'},
         'primary': True}],
       'delivery': {'id': '330981',
        'url': 'https://www.seamless.com/menu/parrilla-latina-5523-broadway-bronx/330981?affiliate=1131&utm_source=foursquare-affiliate-network&utm_medium=affiliate&utm_campaign=1131&utm_content=330981',
        'provider': {'name': 'seamless',
         'icon': {'prefix': 'https://fastly.4sqi.net/img/general/cap/',
          'sizes': [40, 50],
          'name': '/delivery_provider_seamless_20180129.png'}}},
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4dfe40df8877333e195b68fc-14'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4c7e760d0b2a9eb0fb73651f',
       'name': 'Payless ShoeSource',
       'location': {'address': '60 W 225th St',
        'lat': 40.87372764344239,
        'lng': -73.90848340039689,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87372764344239,
          'lng': -73.90848340039689}],
        'distance': 363,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['60 W 225th St',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d107951735',
         'name': 'Shoe Store',
         'pluralName': 'Shoe Stores',
         'shortName': 'Shoes',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/apparel_shoestore_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4c7e760d0b2a9eb0fb73651f-15'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '56229ff8498e2abb44b6f12b',
       'name': 'Five Below',
       'location': {'address': '171 W 230th St Fl 2',
        'lat': 40.87763977050781,
        'lng': -73.90499114990234,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87763977050781,
          'lng': -73.90499114990234}],
        'distance': 492,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['171 W 230th St Fl 2',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '52dea92d3cf9994f4e043dbb',
         'name': 'Discount Store',
         'pluralName': 'Discount Stores',
         'shortName': 'Discount Store',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/discountstore_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-56229ff8498e2abb44b6f12b-16'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4ec68016cc21b428e1d2060a',
       'name': 'TD Bank',
       'location': {'address': '281 W 230th St',
        'lat': 40.8794958,
        'lng': -73.9092856,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.8794958,
          'lng': -73.9092856}],
        'distance': 347,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['281 W 230th St',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d10a951735',
         'name': 'Bank',
         'pluralName': 'Banks',
         'shortName': 'Bank',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/financial_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4ec68016cc21b428e1d2060a-17'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4e4e4517bd4101d0d7a67568',
       'name': 'Baskin-Robbins',
       'location': {'address': '5501 Broadway',
        'lat': 40.8769755336728,
        'lng': -73.90675193198494,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.8769755336728,
          'lng': -73.90675193198494}],
        'distance': 332,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['5501 Broadway',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d1c9941735',
         'name': 'Ice Cream Shop',
         'pluralName': 'Ice Cream Shops',
         'shortName': 'Ice Cream',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/icecream_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4e4e4517bd4101d0d7a67568-18'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '585c205665e7c70a2f1055ea',
       'name': 'Boston Market',
       'location': {'address': '5520 Broadway',
        'lat': 40.87743,
        'lng': -73.9054121,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87743,
          'lng': -73.9054121}],
        'distance': 452,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['5520 Broadway',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d14e941735',
         'name': 'American Restaurant',
         'pluralName': 'American Restaurants',
         'shortName': 'American',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/default_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-585c205665e7c70a2f1055ea-19'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '5802a20c38fa8638a305e241',
       'name': "Auntie Anne's",
       'location': {'address': '5532 Broadway W230th Str',
        'lat': 40.8773995,
        'lng': -73.9049467,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.8773995,
          'lng': -73.9049467}],
        'distance': 490,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['5532 Broadway W230th Str',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d16a941735',
         'name': 'Bakery',
         'pluralName': 'Bakeries',
         'shortName': 'Bakery',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/bakery_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-5802a20c38fa8638a305e241-20'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4e4ce4debd413c4cc66d05d0',
       'name': 'SUBWAY',
       'location': {'address': '5549 Broadway',
        'lat': 40.87849271667849,
        'lng': -73.90538547211088,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87849271667849,
          'lng': -73.90538547211088}],
        'distance': 493,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['5549 Broadway',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d1c5941735',
         'name': 'Sandwich Place',
         'pluralName': 'Sandwich Places',
         'shortName': 'Sandwiches',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/deli_',
          'suffix': '.png'},
         'primary': True}],
       'delivery': {'id': '774886',
        'url': 'https://www.seamless.com/menu/subway-5549-broadway-bronx/774886?affiliate=1131&utm_source=foursquare-affiliate-network&utm_medium=affiliate&utm_campaign=1131&utm_content=774886',
        'provider': {'name': 'seamless',
         'icon': {'prefix': 'https://fastly.4sqi.net/img/general/cap/',
          'sizes': [40, 50],
          'name': '/delivery_provider_seamless_20180129.png'}}},
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4e4ce4debd413c4cc66d05d0-21'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4c852173dc018cfa2bc3e56c',
       'name': "The Children's Place",
       'location': {'address': '44 W 225th St',
        'lat': 40.873671591133125,
        'lng': -73.90815619608166,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.873671591133125,
          'lng': -73.90815619608166}],
        'distance': 383,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['44 W 225th St',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d105951735',
         'name': 'Kids Store',
         'pluralName': 'Kids Stores',
         'shortName': 'Kids Store',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/apparel_kids_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4c852173dc018cfa2bc3e56c-22'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4a0eb511f964a520ea751fe3',
       'name': 'Target',
       'location': {'address': '40 W 225th St',
        'crossStreet': 'at Exterior St',
        'lat': 40.873437410462145,
        'lng': -73.90772557370363,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.873437410462145,
          'lng': -73.90772557370363}],
        'distance': 425,
        'postalCode': '10463',
        'cc': 'US',
        'city': 'Bronx',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['40 W 225th St (at Exterior St)',
         'Bronx, NY 10463',
         'United States']},
       'categories': [{'id': '52f2ab2ebcbc57f1066b8b42',
         'name': 'Big Box Store',
         'pluralName': 'Big Box Stores',
         'shortName': 'Big Box Store',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/default_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4a0eb511f964a520ea751fe3-23'},
     {'reasons': {'count': 0,
       'items': [{'summary': 'This spot is popular',
         'type': 'general',
         'reasonName': 'globalInteractionReason'}]},
      'venue': {'id': '4ed7956b8b81b2bf28adc714',
       'name': 'Terrace View Delicatessen',
       'location': {'address': '135 Terrace View Ave.',
        'lat': 40.87647647652852,
        'lng': -73.91274586964578,
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87647647652852,
          'lng': -73.91274586964578}],
        'distance': 175,
        'postalCode': '10034',
        'cc': 'US',
        'city': 'New York',
        'state': 'NY',
        'country': 'United States',
        'formattedAddress': ['135 Terrace View Ave.',
         'New York, NY 10034',
         'United States']},
       'categories': [{'id': '4bf58dd8d48988d146941735',
         'name': 'Deli / Bodega',
         'pluralName': 'Delis / Bodegas',
         'shortName': 'Deli / Bodega',
         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/deli_',
          'suffix': '.png'},
         'primary': True}],
       'photos': {'count': 0, 'groups': []}},
      'referralId': 'e-0-4ed7956b8b81b2bf28adc714-24'}]}]}}

From the Foursquare lab in the previous module, we know that all the information is in the items key. Before we proceed, let's borrow the get_category_type function from the Foursquare lab.

In [24]:
# function that extracts the category of the venue
def get_category_type(row):
    try:
        categories_list = row['categories']
    except:
        categories_list = row['venue.categories']
        
    if len(categories_list) == 0:
        return None
    else:
        return categories_list[0]['name']

Now we are ready to clean the json and structure it into a pandas dataframe.

In [25]:
venues = results['response']['groups'][0]['items']
    
nearby_venues = json_normalize(venues) # flatten JSON

# filter columns
filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']
nearby_venues =nearby_venues.loc[:, filtered_columns]

# filter the category for each row
nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)

# clean columns
nearby_venues.columns = [col.split(".")[-1] for col in nearby_venues.columns]

nearby_venues.head()
Out[25]:
name categories lat lng
0 Arturo's Pizza Place 40.874412 -73.910271
1 Bikram Yoga Yoga Studio 40.876844 -73.906204
2 Tibbett Diner Diner 40.880404 -73.908937
3 Dunkin' Donut Shop 40.877136 -73.906666
4 Starbucks Coffee Shop 40.877531 -73.905582

And how many venues were returned by Foursquare?

In [26]:
print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))
25 venues were returned by Foursquare.

2. Explore Neighborhoods in Manhattan

Let's create a function to repeat the same process to all the neighborhoods in Manhattan

In [27]:
def getNearbyVenues(names, latitudes, longitudes, radius=500):
    
    venues_list=[]
    for name, lat, lng in zip(names, latitudes, longitudes):
        print(name)
            
        # create the API request URL
        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
            CLIENT_ID, 
            CLIENT_SECRET, 
            VERSION, 
            lat, 
            lng, 
            radius, 
            LIMIT)
            
        # make the GET request
        results = requests.get(url).json()["response"]['groups'][0]['items']
        
        # return only relevant information for each nearby venue
        venues_list.append([(
            name, 
            lat, 
            lng, 
            v['venue']['name'], 
            v['venue']['location']['lat'], 
            v['venue']['location']['lng'],  
            v['venue']['categories'][0]['name']) for v in results])

    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
    nearby_venues.columns = ['Neighborhood', 
                  'Neighborhood Latitude', 
                  'Neighborhood Longitude', 
                  'Venue', 
                  'Venue Latitude', 
                  'Venue Longitude', 
                  'Venue Category']
    
    return(nearby_venues)

Now write the code to run the above function on each neighborhood and create a new dataframe called manhattan_venues.

In [28]:
# type your answer here

manhattan_venues = getNearbyVenues(names=manhattan_data['Neighborhood'],
                                   latitudes=manhattan_data['Latitude'],
                                   longitudes=manhattan_data['Longitude']
                                  )
Marble Hill
Chinatown
Washington Heights
Inwood
Hamilton Heights
Manhattanville
Central Harlem
East Harlem
Upper East Side
Yorkville
Lenox Hill
Roosevelt Island
Upper West Side
Lincoln Square
Clinton
Midtown
Murray Hill
Chelsea
Greenwich Village
East Village
Lower East Side
Tribeca
Little Italy
Soho
West Village
Manhattan Valley
Morningside Heights
Gramercy
Battery Park City
Financial District
Carnegie Hill
Noho
Civic Center
Midtown South
Sutton Place
Turtle Bay
Tudor City
Stuyvesant Town
Flatiron
Hudson Yards

Double-click here for the solution.

Let's check the size of the resulting dataframe

In [29]:
print(manhattan_venues.shape)
manhattan_venues.head()
(3317, 7)
Out[29]:
Neighborhood Neighborhood Latitude Neighborhood Longitude Venue Venue Latitude Venue Longitude Venue Category
0 Marble Hill 40.876551 -73.91066 Arturo's 40.874412 -73.910271 Pizza Place
1 Marble Hill 40.876551 -73.91066 Bikram Yoga 40.876844 -73.906204 Yoga Studio
2 Marble Hill 40.876551 -73.91066 Tibbett Diner 40.880404 -73.908937 Diner
3 Marble Hill 40.876551 -73.91066 Dunkin' 40.877136 -73.906666 Donut Shop
4 Marble Hill 40.876551 -73.91066 Starbucks 40.877531 -73.905582 Coffee Shop

Let's check how many venues were returned for each neighborhood

In [30]:
manhattan_venues.groupby('Neighborhood').count()
Out[30]:
Neighborhood Latitude Neighborhood Longitude Venue Venue Latitude Venue Longitude Venue Category
Neighborhood
Battery Park City 100 100 100 100 100 100
Carnegie Hill 100 100 100 100 100 100
Central Harlem 43 43 43 43 43 43
Chelsea 100 100 100 100 100 100
Chinatown 100 100 100 100 100 100
Civic Center 100 100 100 100 100 100
Clinton 100 100 100 100 100 100
East Harlem 41 41 41 41 41 41
East Village 100 100 100 100 100 100
Financial District 100 100 100 100 100 100
Flatiron 100 100 100 100 100 100
Gramercy 100 100 100 100 100 100
Greenwich Village 100 100 100 100 100 100
Hamilton Heights 60 60 60 60 60 60
Hudson Yards 73 73 73 73 73 73
Inwood 57 57 57 57 57 57
Lenox Hill 100 100 100 100 100 100
Lincoln Square 100 100 100 100 100 100
Little Italy 100 100 100 100 100 100
Lower East Side 63 63 63 63 63 63
Manhattan Valley 60 60 60 60 60 60
Manhattanville 41 41 41 41 41 41
Marble Hill 25 25 25 25 25 25
Midtown 100 100 100 100 100 100
Midtown South 100 100 100 100 100 100
Morningside Heights 42 42 42 42 42 42
Murray Hill 100 100 100 100 100 100
Noho 100 100 100 100 100 100
Roosevelt Island 26 26 26 26 26 26
Soho 100 100 100 100 100 100
Stuyvesant Town 19 19 19 19 19 19
Sutton Place 100 100 100 100 100 100
Tribeca 100 100 100 100 100 100
Tudor City 82 82 82 82 82 82
Turtle Bay 100 100 100 100 100 100
Upper East Side 100 100 100 100 100 100
Upper West Side 100 100 100 100 100 100
Washington Heights 85 85 85 85 85 85
West Village 100 100 100 100 100 100
Yorkville 100 100 100 100 100 100

Let's find out how many unique categories can be curated from all the returned venues

In [31]:
print('There are {} uniques categories.'.format(len(manhattan_venues['Venue Category'].unique())))
There are 332 uniques categories.

3. Analyze Each Neighborhood

In [32]:
# one hot encoding
manhattan_onehot = pd.get_dummies(manhattan_venues[['Venue Category']], prefix="", prefix_sep="")

# add neighborhood column back to dataframe
manhattan_onehot['Neighborhood'] = manhattan_venues['Neighborhood'] 

# move neighborhood column to the first column
fixed_columns = [manhattan_onehot.columns[-1]] + list(manhattan_onehot.columns[:-1])
manhattan_onehot = manhattan_onehot[fixed_columns]

manhattan_onehot.head()
Out[32]:
Neighborhood Accessories Store Adult Boutique Afghan Restaurant African Restaurant American Restaurant Antique Shop Arcade Arepa Restaurant Argentinian Restaurant Art Gallery Art Museum Arts & Crafts Store Asian Restaurant Athletics & Sports Auditorium Australian Restaurant Austrian Restaurant Auto Workshop BBQ Joint Baby Store Bagel Shop Bakery Bank Bar Baseball Field Basketball Court Beer Bar Beer Garden Beer Store Big Box Store Bike Rental / Bike Share Bike Shop Bike Trail Bistro Board Shop Boat or Ferry Bookstore Boutique Boxing Gym Brazilian Restaurant Breakfast Spot Bridal Shop Bubble Tea Shop Building Burger Joint Burrito Place Bus Station Bus Stop Business Service Butcher Cafeteria Café Cajun / Creole Restaurant Cambodian Restaurant Camera Store Candy Store Cantonese Restaurant Caribbean Restaurant Caucasian Restaurant Cheese Shop Chinese Restaurant Chocolate Shop Church Circus Climbing Gym Clothing Store Club House Cocktail Bar Coffee Shop College Academic Building College Bookstore College Cafeteria College Gym College Theater Comedy Club Community Center Concert Hall Convenience Store Cosmetics Shop Coworking Space Creperie Cuban Restaurant Cultural Center Cupcake Shop Cycle Studio Czech Restaurant Dance Studio Daycare Deli / Bodega Department Store Design Studio Dessert Shop Dim Sum Restaurant Diner Discount Store Dive Bar Dog Run Donut Shop Drugstore Dry Cleaner Dumpling Restaurant Duty-free Shop Eastern European Restaurant Electronics Store Empanada Restaurant English Restaurant Ethiopian Restaurant Event Space Exhibit Falafel Restaurant Farmers Market Fast Food Restaurant Filipino Restaurant Fish Market Flea Market Flower Shop Food & Drink Shop Food Court Food Truck Fountain French Restaurant Fried Chicken Joint Frozen Yogurt Shop Furniture / Home Store Gaming Cafe Garden Garden Center Gas Station Gastropub Gay Bar General College & University General Entertainment German Restaurant Gift Shop Golf Course Gourmet Shop Greek Restaurant Grocery Store Gym Gym / Fitness Center Gym Pool Gymnastics Gym Harbor / Marina Hardware Store Hawaiian Restaurant Health & Beauty Service Health Food Store Heliport Herbs & Spices Store High School Himalayan Restaurant Historic Site History Museum Hobby Shop Hookah Bar Hostel Hot Dog Joint Hotel Hotel Bar Hotpot Restaurant Ice Cream Shop Indian Restaurant Indie Movie Theater Indie Theater Intersection Irish Pub Israeli Restaurant Italian Restaurant Japanese Curry Restaurant Japanese Restaurant Jazz Club Jewelry Store Jewish Restaurant Juice Bar Karaoke Bar Kebab Restaurant Kids Store Korean Restaurant Kosher Restaurant Latin American Restaurant Laundry Service Lebanese Restaurant Library Lingerie Store Liquor Store Lounge Malay Restaurant Market Martial Arts Dojo Massage Studio Medical Center Mediterranean Restaurant Memorial Site Men's Store Metro Station Mexican Restaurant Middle Eastern Restaurant Mini Golf Miscellaneous Shop Mobile Phone Shop Modern European Restaurant Molecular Gastronomy Restaurant Monument / Landmark Moroccan Restaurant Movie Theater Museum Music School Music Venue Nail Salon New American Restaurant Newsstand Nightclub Non-Profit Noodle House North Indian Restaurant Office Opera House Optical Shop Organic Grocery Other Nightlife Outdoor Sculpture Outdoors & Recreation Paella Restaurant Pakistani Restaurant Paper / Office Supplies Store Park Pastry Shop Performing Arts Venue Persian Restaurant Peruvian Restaurant Pet Café Pet Service Pet Store Pharmacy Photography Studio Piano Bar Pie Shop Pilates Studio Pizza Place Playground Plaza Poke Place Pool Portuguese Restaurant Pub Public Art Ramen Restaurant Record Shop Rental Car Location Residential Building (Apartment / Condo) Resort Rest Area Restaurant Rock Climbing Spot Rock Club Roof Deck Russian Restaurant Sake Bar Salad Place Salon / Barbershop Sandwich Place Scenic Lookout School Sculpture Garden Seafood Restaurant Shanghai Restaurant Shipping Store Shoe Repair Shoe Store Shopping Mall Skate Park Ski Shop Smoke Shop Snack Place Soba Restaurant Social Club Soup Place South American Restaurant South Indian Restaurant Southern / Soul Food Restaurant Spa Spanish Restaurant Speakeasy Spiritual Center Sporting Goods Shop Sports Bar Sports Club Steakhouse Street Art Strip Club Supermarket Supplement Shop Sushi Restaurant Swiss Restaurant Szechuan Restaurant Taco Place Tailor Shop Taiwanese Restaurant Tapas Restaurant Tattoo Parlor Tea Room Tech Startup Tennis Court Tennis Stadium Thai Restaurant Theater Theme Park Ride / Attraction Thrift / Vintage Store Tiki Bar Tourist Information Center Toy / Game Store Trail Tree Turkish Restaurant Udon Restaurant Used Bookstore Vegetarian / Vegan Restaurant Venezuelan Restaurant Veterinarian Video Game Store Video Store Vietnamese Restaurant Volleyball Court Watch Shop Waterfront Weight Loss Center Whisky Bar Wine Bar Wine Shop Wings Joint Women's Store Yoga Studio
0 Marble Hill 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 Marble Hill 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
2 Marble Hill 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 Marble Hill 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 Marble Hill 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

And let's examine the new dataframe size.

In [33]:
manhattan_onehot.shape
Out[33]:
(3317, 333)

Next, let's group rows by neighborhood and by taking the mean of the frequency of occurrence of each category

In [34]:
manhattan_grouped = manhattan_onehot.groupby('Neighborhood').mean().reset_index()
manhattan_grouped
Out[34]:
Neighborhood Accessories Store Adult Boutique Afghan Restaurant African Restaurant American Restaurant Antique Shop Arcade Arepa Restaurant Argentinian Restaurant Art Gallery Art Museum Arts & Crafts Store Asian Restaurant Athletics & Sports Auditorium Australian Restaurant Austrian Restaurant Auto Workshop BBQ Joint Baby Store Bagel Shop Bakery Bank Bar Baseball Field Basketball Court Beer Bar Beer Garden Beer Store Big Box Store Bike Rental / Bike Share Bike Shop Bike Trail Bistro Board Shop Boat or Ferry Bookstore Boutique Boxing Gym Brazilian Restaurant Breakfast Spot Bridal Shop Bubble Tea Shop Building Burger Joint Burrito Place Bus Station Bus Stop Business Service Butcher Cafeteria Café Cajun / Creole Restaurant Cambodian Restaurant Camera Store Candy Store Cantonese Restaurant Caribbean Restaurant Caucasian Restaurant Cheese Shop Chinese Restaurant Chocolate Shop Church Circus Climbing Gym Clothing Store Club House Cocktail Bar Coffee Shop College Academic Building College Bookstore College Cafeteria College Gym College Theater Comedy Club Community Center Concert Hall Convenience Store Cosmetics Shop Coworking Space Creperie Cuban Restaurant Cultural Center Cupcake Shop Cycle Studio Czech Restaurant Dance Studio Daycare Deli / Bodega Department Store Design Studio Dessert Shop Dim Sum Restaurant Diner Discount Store Dive Bar Dog Run Donut Shop Drugstore Dry Cleaner Dumpling Restaurant Duty-free Shop Eastern European Restaurant Electronics Store Empanada Restaurant English Restaurant Ethiopian Restaurant Event Space Exhibit Falafel Restaurant Farmers Market Fast Food Restaurant Filipino Restaurant Fish Market Flea Market Flower Shop Food & Drink Shop Food Court Food Truck Fountain French Restaurant Fried Chicken Joint Frozen Yogurt Shop Furniture / Home Store Gaming Cafe Garden Garden Center Gas Station Gastropub Gay Bar General College & University General Entertainment German Restaurant Gift Shop Golf Course Gourmet Shop Greek Restaurant Grocery Store Gym Gym / Fitness Center Gym Pool Gymnastics Gym Harbor / Marina Hardware Store Hawaiian Restaurant Health & Beauty Service Health Food Store Heliport Herbs & Spices Store High School Himalayan Restaurant Historic Site History Museum Hobby Shop Hookah Bar Hostel Hot Dog Joint Hotel Hotel Bar Hotpot Restaurant Ice Cream Shop Indian Restaurant Indie Movie Theater Indie Theater Intersection Irish Pub Israeli Restaurant Italian Restaurant Japanese Curry Restaurant Japanese Restaurant Jazz Club Jewelry Store Jewish Restaurant Juice Bar Karaoke Bar Kebab Restaurant Kids Store Korean Restaurant Kosher Restaurant Latin American Restaurant Laundry Service Lebanese Restaurant Library Lingerie Store Liquor Store Lounge Malay Restaurant Market Martial Arts Dojo Massage Studio Medical Center Mediterranean Restaurant Memorial Site Men's Store Metro Station Mexican Restaurant Middle Eastern Restaurant Mini Golf Miscellaneous Shop Mobile Phone Shop Modern European Restaurant Molecular Gastronomy Restaurant Monument / Landmark Moroccan Restaurant Movie Theater Museum Music School Music Venue Nail Salon New American Restaurant Newsstand Nightclub Non-Profit Noodle House North Indian Restaurant Office Opera House Optical Shop Organic Grocery Other Nightlife Outdoor Sculpture Outdoors & Recreation Paella Restaurant Pakistani Restaurant Paper / Office Supplies Store Park Pastry Shop Performing Arts Venue Persian Restaurant Peruvian Restaurant Pet Café Pet Service Pet Store Pharmacy Photography Studio Piano Bar Pie Shop Pilates Studio Pizza Place Playground Plaza Poke Place Pool Portuguese Restaurant Pub Public Art Ramen Restaurant Record Shop Rental Car Location Residential Building (Apartment / Condo) Resort Rest Area Restaurant Rock Climbing Spot Rock Club Roof Deck Russian Restaurant Sake Bar Salad Place Salon / Barbershop Sandwich Place Scenic Lookout School Sculpture Garden Seafood Restaurant Shanghai Restaurant Shipping Store Shoe Repair Shoe Store Shopping Mall Skate Park Ski Shop Smoke Shop Snack Place Soba Restaurant Social Club Soup Place South American Restaurant South Indian Restaurant Southern / Soul Food Restaurant Spa Spanish Restaurant Speakeasy Spiritual Center Sporting Goods Shop Sports Bar Sports Club Steakhouse Street Art Strip Club Supermarket Supplement Shop Sushi Restaurant Swiss Restaurant Szechuan Restaurant Taco Place Tailor Shop Taiwanese Restaurant Tapas Restaurant Tattoo Parlor Tea Room Tech Startup Tennis Court Tennis Stadium Thai Restaurant Theater Theme Park Ride / Attraction Thrift / Vintage Store Tiki Bar Tourist Information Center Toy / Game Store Trail Tree Turkish Restaurant Udon Restaurant Used Bookstore Vegetarian / Vegan Restaurant Venezuelan Restaurant Veterinarian Video Game Store Video Store Vietnamese Restaurant Volleyball Court Watch Shop Waterfront Weight Loss Center Whisky Bar Wine Bar Wine Shop Wings Joint Women's Store Yoga Studio
0 Battery Park City 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.010000 0.01 0.00 0.00 0.000000 0.020000 0.00 0.000000 0.010000 0.000000 0.000000 0.000000 0.000000 0.000000 0.01000 0.00 0.00 0.00 0.000000 0.00000 0.010000 0.00 0.010000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.010000 0.020000 0.01 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.01 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.00000 0.030000 0.00 0.000000 0.070000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.00000 0.00 0.02 0.000000 0.00 0.000000 0.00 0.000000 0.020000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.01 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.020000 0.010000 0.020000 0.000000 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.00 0.01 0.000000 0.010000 0.040000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.050000 0.000000 0.00 0.020000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.030000 0.00000 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.010000 0.02 0.01 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.00 0.00 0.01 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.080000 0.00 0.010000 0.00 0.000000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.00 0.00 0.020000 0.020000 0.020000 0.00 0.000000 0.00 0.010000 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.020000 0.010000 0.000000 0.01 0.010000 0.000000 0.000000 0.00 0.000000 0.02 0.00 0.00 0.010000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.010000 0.00000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.01 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.030000 0.000000 0.020000 0.000000
1 Carnegie Hill 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.010000 0.000000 0.01 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.020000 0.000000 0.030000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.030000 0.000000 0.00 0.00 0.010000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.040000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.00000 0.010000 0.00 0.010000 0.050000 0.00 0.00 0.00000 0.01 0.00 0.000000 0.010000 0.010000 0.000000 0.040000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.01 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.010000 0.000000 0.030000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.01 0.00 0.00 0.000000 0.010000 0.00 0.01 0.000000 0.030000 0.030000 0.020000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.010000 0.020000 0.00 0.00 0.000000 0.00 0.00 0.020000 0.00000 0.030000 0.000000 0.00 0.000000 0.000000 0.01 0.00 0.000000 0.000000 0.01 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.010000 0.00 0.00 0.000000 0.020000 0.00 0.00 0.00 0.010000 0.00 0.00 0.00 0.00 0.00 0.01 0.000000 0.000000 0.01 0.010000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.00 0.00 0.060000 0.010000 0.000000 0.00 0.000000 0.00 0.010000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.010000 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.030000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.020000 0.000000 0.010000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.020000 0.00 0.00 0.000000 0.00 0.00 0.010000 0.030000 0.000000 0.010000 0.030000
2 Central Harlem 0.000000 0.00 0.00 0.069767 0.046512 0.00 0.00 0.000000 0.000000 0.023256 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.023256 0.00 0.023256 0.000000 0.000000 0.023256 0.000000 0.000000 0.023256 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.023256 0.023256 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.023256 0.023256 0.00 0.00 0.000000 0.00 0.00 0.023256 0.000000 0.00 0.046512 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.046512 0.00 0.00 0.00000 0.00 0.00 0.023256 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.023256 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.023256 0.023256 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.046512 0.023256 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.023256 0.046512 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.023256 0.00 0.000000 0.023256 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.023256 0.000000 0.023256 0.000000 0.000000 0.023256 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.023256 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.023256 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.023256 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.046512 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.046512 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.023256 0.023256 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000
3 Chelsea 0.000000 0.00 0.00 0.000000 0.040000 0.01 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.010000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.040000 0.000000 0.010000 0.000000 0.000000 0.010000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.020000 0.000000 0.00 0.00 0.010000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.00000 0.000000 0.00 0.010000 0.060000 0.00 0.00 0.00000 0.00 0.01 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.01 0.00000 0.00 0.02 0.020000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.01 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.01 0.01 0.00 0.00000 0.000000 0.000000 0.000000 0.020000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.020000 0.00 0.00 0.000000 0.010000 0.010000 0.010000 0.00 0.00 0.000000 0.00 0.000000 0.02 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.030000 0.000000 0.00 0.050000 0.010000 0.00 0.01 0.000000 0.00 0.01 0.050000 0.00000 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.020000 0.000000 0.00 0.00 0.000000 0.00 0.01 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.040000 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.01 0.000000 0.000000 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.010000 0.000000 0.01 0.00 0.00 0.00 0.010000 0.000000 0.000000 0.01 0.010000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.010000 0.010000 0.000000 0.00 0.030000 0.000000 0.000000 0.01 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.010000 0.00 0.010000 0.00 0.00 0.000000 0.00000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.010000 0.000000 0.00 0.020000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.030000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.020000 0.000000 0.010000 0.000000
4 Chinatown 0.000000 0.00 0.00 0.000000 0.040000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.020000 0.000000 0.00 0.00 0.01 0.000000 0.000000 0.00 0.000000 0.020000 0.000000 0.030000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.010000 0.00000 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.030000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.01 0.000000 0.000000 0.00 0.100000 0.00 0.00 0.00 0.00000 0.000000 0.00 0.040000 0.010000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.02 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.03000 0.00 0.00 0.00 0.000000 0.01 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.020000 0.00 0.000000 0.01 0.000000 0.000000 0.00 0.00 0.01 0.000000 0.010000 0.00 0.00 0.010000 0.010000 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.02 0.030000 0.000000 0.01 0.00 0.000000 0.00 0.00 0.010000 0.00000 0.000000 0.000000 0.02 0.000000 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.020000 0.000000 0.000000 0.02 0.00 0.000000 0.00 0.01 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.01 0.000000 0.000000 0.01 0.010000 0.00 0.000000 0.00 0.03 0.00 0.00 0.00 0.02 0.01 0.00000 0.00000 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.00 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.01 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.000000 0.01 0.00 0.00 0.000000 0.020000 0.020000 0.000000 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.000000 0.01 0.00 0.00 0.00 0.00 0.00 0.000000 0.020000 0.010000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.01 0.000000 0.00 0.01 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.040000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000
5 Civic Center 0.000000 0.00 0.00 0.000000 0.020000 0.01 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.010000 0.000000 0.00 0.01 0.00 0.000000 0.000000 0.01 0.010000 0.040000 0.000000 0.010000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.010000 0.000000 0.01 0.00 0.000000 0.00 0.010000 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.000000 0.000000 0.020000 0.01 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00000 0.010000 0.00 0.030000 0.040000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.01000 0.00 0.00 0.000000 0.00 0.020000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.010000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.020000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.040000 0.000000 0.000000 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.01 0.000000 0.000000 0.00 0.01 0.000000 0.000000 0.020000 0.050000 0.00 0.00 0.000000 0.00 0.000000 0.01 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.040000 0.020000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.060000 0.00000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.01 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.010000 0.01 0.01 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.00 0.01 0.01 0.00 0.00 0.01 0.000000 0.000000 0.01 0.010000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.030000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.000000 0.010000 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.040000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.030000 0.000000 0.000000 0.00 0.030000 0.00 0.00 0.000000 0.00000 0.01 0.000000 0.000000 0.020000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.00 0.00 0.01 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.010000 0.010000 0.010000 0.000000 0.030000
6 Clinton 0.000000 0.00 0.00 0.000000 0.040000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.000000 0.020000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.010000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.010000 0.00 0.00 0.00 0.00000 0.000000 0.00 0.030000 0.020000 0.00 0.00 0.00000 0.00 0.00 0.010000 0.000000 0.010000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.01 0.010000 0.00 0.01 0.020000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.020000 0.000000 0.000000 0.010000 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.010000 0.020000 0.050000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.010000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.040000 0.010000 0.00 0.010000 0.000000 0.00 0.02 0.000000 0.00 0.00 0.040000 0.00000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.020000 0.000000 0.000000 0.000000 0.00 0.00 0.010000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.010000 0.000000 0.00 0.020000 0.00 0.000000 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.00 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.01 0.00 0.020000 0.000000 0.000000 0.01 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.000000 0.010000 0.01 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.020000 0.000000 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.030000 0.000000 0.000000 0.00 0.010000 0.01 0.00 0.010000 0.00000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.010000 0.130000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.020000 0.030000 0.000000 0.000000 0.000000
7 East Harlem 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.097561 0.000000 0.000000 0.000000 0.000000 0.024390 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.024390 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00000 0.024390 0.00 0.024390 0.024390 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.024390 0.000000 0.00 0.00 0.02439 0.00 0.00 0.000000 0.00 0.024390 0.00 0.073171 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.024390 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.024390 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.024390 0.024390 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.024390 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.073171 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.121951 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.024390 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.024390 0.024390 0.00 0.00 0.00 0.00 0.024390 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.024390 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.024390 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.024390 0.024390 0.000000 0.00 0.000000 0.00 0.00 0.024390 0.02439 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.024390 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.048780 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000
8 East Village 0.000000 0.00 0.00 0.000000 0.020000 0.01 0.00 0.020000 0.010000 0.010000 0.00 0.010000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.020000 0.010000 0.000000 0.060000 0.000000 0.000000 0.000000 0.00000 0.01 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.010000 0.000000 0.01 0.040000 0.00 0.00 0.00 0.00000 0.000000 0.00 0.030000 0.030000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.01 0.000000 0.00 0.000000 0.00 0.010000 0.000000 0.00 0.020000 0.00 0.000000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.010000 0.00 0.000000 0.010000 0.000000 0.010000 0.00 0.00 0.01 0.00000 0.000000 0.000000 0.000000 0.010000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.00 0.01 0.010000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.01 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.040000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.020000 0.00000 0.010000 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.020000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.040000 0.01 0.00 0.00 0.000000 0.00 0.00 0.00 0.01 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.00 0.000000 0.010000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.040000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.030000 0.02 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.020000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.01 0.00 0.00 0.000000 0.010000 0.000000 0.020000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.000000 0.000000 0.000000 0.01 0.000000 0.000000 0.010000 0.02 0.010000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.01 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.01 0.03 0.00 0.000000 0.000000 0.00 0.020000 0.00 0.00 0.000000 0.00 0.00 0.050000 0.020000 0.000000 0.000000 0.000000
9 Financial District 0.010000 0.00 0.00 0.000000 0.030000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.010000 0.000000 0.030000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.030000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.00000 0.000000 0.00 0.010000 0.080000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.01 0.00 0.01000 0.00 0.00 0.010000 0.00 0.000000 0.00 0.010000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.020000 0.00 0.020000 0.010000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.010000 0.020000 0.000000 0.010000 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.010000 0.000000 0.040000 0.030000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.040000 0.000000 0.00 0.010000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.030000 0.01000 0.020000 0.000000 0.02 0.000000 0.020000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.020000 0.00 0.00 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.00 0.00 0.02 0.00 0.00 0.01 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.030000 0.00 0.010000 0.00 0.000000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.00 0.00 0.030000 0.000000 0.010000 0.00 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.000000 0.01 0.00 0.00 0.010000 0.000000 0.010000 0.000000 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.010000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.020000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.040000 0.00000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.010000 0.040000 0.000000 0.010000 0.000000
10 Flatiron 0.000000 0.00 0.00 0.000000 0.040000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.030000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.00 0.010000 0.00 0.00 0.000000 0.000000 0.01 0.010000 0.00 0.00 0.00 0.00000 0.030000 0.00 0.000000 0.010000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.030000 0.00 0.00 0.01000 0.00 0.00 0.030000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.030000 0.00 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.010000 0.000000 0.000000 0.000000 0.000000 0.020000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.01 0.000000 0.010000 0.00 0.01 0.000000 0.010000 0.040000 0.040000 0.00 0.00 0.000000 0.00 0.000000 0.01 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.010000 0.00 0.00 0.000000 0.00 0.01 0.010000 0.00000 0.040000 0.000000 0.00 0.000000 0.010000 0.00 0.01 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.010000 0.000000 0.010000 0.00 0.00 0.020000 0.00 0.00 0.000000 0.020000 0.00 0.00 0.01 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.030000 0.00 0.000000 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.01 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.01 0.01 0.00 0.010000 0.030000 0.010000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.020000 0.000000 0.010000 0.00 0.030000 0.00 0.01 0.000000 0.00000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.01 0.000000 0.00 0.010000 0.000000 0.00 0.00 0.00 0.00 0.01 0.000000 0.00 0.00 0.00 0.00 0.02 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.01 0.00 0.000000 0.020000 0.000000 0.020000 0.040000
11 Gramercy 0.000000 0.00 0.00 0.000000 0.040000 0.00 0.01 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.040000 0.010000 0.000000 0.030000 0.000000 0.000000 0.010000 0.00000 0.00 0.00 0.01 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.01 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.00 0.040000 0.030000 0.00 0.00 0.00000 0.00 0.00 0.020000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.00000 0.00 0.01 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.00 0.00000 0.000000 0.010000 0.000000 0.000000 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.01 0.000000 0.030000 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.030000 0.010000 0.00 0.010000 0.000000 0.00 0.00 0.000000 0.01 0.01 0.050000 0.00000 0.010000 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.000000 0.000000 0.010000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.030000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.010000 0.01 0.000000 0.00 0.010000 0.00 0.00 0.01 0.00 0.00 0.01 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.01 0.040000 0.020000 0.000000 0.00 0.010000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.020000 0.00 0.000000 0.01 0.00 0.00 0.000000 0.010000 0.010000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.01 0.00 0.00 0.01 0.000000 0.020000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.020000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.030000 0.000000 0.00 0.04 0.01 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.020000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.020000 0.000000 0.000000 0.010000
12 Greenwich Village 0.000000 0.00 0.00 0.000000 0.020000 0.00 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.020000 0.000000 0.010000 0.000000 0.000000 0.010000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.00 0.000000 0.00 0.010000 0.000000 0.020000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.030000 0.00 0.00 0.000000 0.00 0.00 0.020000 0.000000 0.00 0.020000 0.00 0.00 0.00 0.00000 0.040000 0.00 0.020000 0.020000 0.00 0.00 0.00000 0.00 0.00 0.010000 0.000000 0.000000 0.000000 0.020000 0.00 0.01 0.01000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.020000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.01 0.02 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.040000 0.000000 0.000000 0.000000 0.01 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.02 0.000000 0.000000 0.020000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.010000 0.000000 0.00 0.020000 0.030000 0.01 0.00 0.000000 0.00 0.00 0.100000 0.00000 0.000000 0.010000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.01 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.010000 0.00 0.00 0.000000 0.010000 0.01 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.01 0.010000 0.010000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.01 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.000000 0.000000 0.020000 0.000000 0.000000 0.00 0.030000 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.000000 0.01 0.00 0.00 0.00 0.00 0.00 0.000000 0.020000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.000000 0.000000 0.040000 0.00 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.01 0.00 0.01 0.00 0.000000 0.000000 0.00 0.020000 0.00 0.01 0.000000 0.00 0.00 0.010000 0.000000 0.000000 0.000000 0.010000
13 Hamilton Heights 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.033333 0.016667 0.016667 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.016667 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.066667 0.00 0.00 0.000000 0.00 0.00 0.033333 0.000000 0.00 0.033333 0.00 0.00 0.00 0.00000 0.000000 0.00 0.033333 0.066667 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.016667 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.033333 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.016667 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.016667 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.016667 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.016667 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.016667 0.000000 0.00 0.016667 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.033333 0.00 0.00 0.000000 0.00 0.00 0.016667 0.00000 0.016667 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.016667 0.00 0.00 0.000000 0.000000 0.033333 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.016667 0.00 0.00 0.000000 0.083333 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.016667 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.066667 0.000000 0.000000 0.00 0.000000 0.00 0.016667 0.000000 0.000000 0.00 0.016667 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.033333 0.000000 0.033333 0.00 0.016667 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.016667 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.016667 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.000000 0.000000 0.033333 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.016667 0.000000 0.000000 0.000000 0.033333
14 Hudson Yards 0.000000 0.00 0.00 0.000000 0.068493 0.00 0.00 0.000000 0.000000 0.013699 0.00 0.000000 0.013699 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.013699 0.000000 0.013699 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.013699 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.013699 0.027397 0.00 0.013699 0.000000 0.00 0.000000 0.000000 0.041096 0.00 0.00 0.013699 0.00 0.00 0.000000 0.013699 0.00 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.00 0.013699 0.054795 0.00 0.00 0.00000 0.00 0.00 0.013699 0.000000 0.013699 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.013699 0.013699 0.00 0.000000 0.00 0.000000 0.00 0.00 0.027397 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.013699 0.013699 0.000000 0.000000 0.013699 0.000000 0.013699 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.013699 0.000000 0.027397 0.041096 0.00 0.00 0.000000 0.00 0.000000 0.00 0.013699 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.041096 0.013699 0.00 0.013699 0.000000 0.00 0.00 0.000000 0.00 0.00 0.054795 0.00000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.013699 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.013699 0.000000 0.00 0.000000 0.00 0.013699 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.013699 0.000000 0.027397 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.013699 0.000000 0.00 0.000000 0.013699 0.00 0.000000 0.027397 0.00 0.000000 0.00 0.00 0.00 0.013699 0.000000 0.013699 0.013699 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.013699 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.013699 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.013699 0.00000 0.00 0.013699 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.027397 0.041096 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.013699 0.000000 0.000000 0.000000 0.000000
15 Inwood 0.000000 0.00 0.00 0.000000 0.035088 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.035088 0.000000 0.017544 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.017544 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.017544 0.000000 0.00 0.000000 0.000000 0.070175 0.00 0.00 0.000000 0.00 0.00 0.017544 0.000000 0.00 0.035088 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.017544 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.035088 0.000000 0.00 0.000000 0.00 0.017544 0.00 0.00 0.017544 0.017544 0.00 0.000000 0.00000 0.00 0.00 0.00 0.017544 0.00 0.000000 0.000000 0.00 0.000000 0.017544 0.017544 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.035088 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.017544 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.017544 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.017544 0.00 0.00 0.000000 0.00000 0.000000 0.000000 0.00 0.000000 0.017544 0.00 0.00 0.000000 0.000000 0.00 0.017544 0.00 0.00 0.000000 0.000000 0.000000 0.070175 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.070175 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.035088 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.017544 0.017544 0.00 0.00 0.00 0.00 0.052632 0.017544 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.017544 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.017544 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.017544 0.000000 0.00 0.000000 0.00 0.00 0.017544 0.00000 0.00 0.017544 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.017544 0.00 0.00 0.00 0.00 0.00 0.00 0.017544 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.035088 0.017544 0.000000 0.000000 0.017544
16 Lenox Hill 0.000000 0.00 0.01 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.00 0.010000 0.020000 0.010000 0.010000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.01 0.000000 0.000000 0.030000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.00000 0.000000 0.01 0.020000 0.060000 0.01 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.030000 0.00 0.00 0.01000 0.00 0.00 0.020000 0.01 0.000000 0.00 0.020000 0.000000 0.00 0.010000 0.00 0.010000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.010000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.00 0.01 0.010000 0.010000 0.030000 0.030000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.010000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.060000 0.00000 0.010000 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.010000 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.020000 0.01 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.00 0.01 0.040000 0.010000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.020000 0.010000 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.010000 0.000000 0.00 0.030000 0.00 0.00 0.010000 0.00000 0.00 0.000000 0.010000 0.050000 0.00 0.000000 0.010000 0.000000 0.00 0.000000 0.00 0.01 0.00 0.000000 0.00 0.020000 0.000000 0.00 0.00 0.00 0.00 0.01 0.000000 0.00 0.02 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.010000 0.020000 0.000000 0.010000 0.000000
17 Lincoln Square 0.000000 0.00 0.00 0.000000 0.020000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.020000 0.000000 0.010000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.050000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.01 0.01000 0.000000 0.00 0.000000 0.010000 0.00 0.01 0.00000 0.00 0.00 0.000000 0.000000 0.050000 0.000000 0.020000 0.00 0.00 0.00000 0.00 0.00 0.020000 0.00 0.000000 0.00 0.000000 0.010000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.01 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.010000 0.010000 0.040000 0.000000 0.000000 0.020000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.01 0.000000 0.010000 0.00 0.00 0.000000 0.010000 0.020000 0.060000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.020000 0.000000 0.00 0.000000 0.000000 0.03 0.01 0.000000 0.00 0.00 0.050000 0.00000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.010000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.020000 0.00 0.00 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.02 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.030000 0.00 0.040000 0.00 0.000000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.00 0.00 0.000000 0.010000 0.050000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.01 0.00 0.00 0.010000 0.000000 0.000000 0.000000 0.010000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.000000 0.00000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.060000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.010000 0.020000 0.000000 0.000000 0.010000
18 Little Italy 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.000000 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.050000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.00 0.010000 0.00 0.020000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.040000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.020000 0.01 0.00 0.00 0.00000 0.030000 0.00 0.020000 0.010000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.020000 0.00 0.00 0.00000 0.00 0.00 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.01 0.010000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.01000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.000000 0.000000 0.00 0.01 0.00 0.00000 0.000000 0.000000 0.000000 0.010000 0.000000 0.000000 0.020000 0.00 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.00 0.01 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.020000 0.000000 0.00 0.030000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.030000 0.00000 0.020000 0.000000 0.01 0.000000 0.000000 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.020000 0.000000 0.010000 0.02 0.00 0.030000 0.00 0.00 0.000000 0.010000 0.01 0.00 0.01 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.01 0.000000 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.01 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.010000 0.030000 0.030000 0.000000 0.000000 0.00 0.030000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.01 0.00 0.00 0.01 0.00 0.00 0.000000 0.010000 0.010000 0.010000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.010000 0.000000 0.000000 0.01 0.000000 0.000000 0.000000 0.00 0.000000 0.01 0.01 0.00 0.000000 0.00 0.020000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.000000 0.00 0.00 0.020000 0.010000 0.000000 0.020000 0.020000
19 Lower East Side 0.000000 0.00 0.00 0.000000 0.015873 0.00 0.00 0.000000 0.015873 0.031746 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.015873 0.000000 0.00 0.015873 0.031746 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.015873 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.015873 0.000000 0.047619 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.047619 0.00 0.00 0.00 0.00000 0.015873 0.00 0.031746 0.047619 0.00 0.00 0.00000 0.00 0.00 0.000000 0.015873 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.015873 0.00 0.015873 0.000000 0.00 0.015873 0.00 0.015873 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.015873 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.015873 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.015873 0.015873 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.015873 0.000000 0.00 0.00 0.000000 0.00 0.00 0.015873 0.00000 0.031746 0.000000 0.00 0.000000 0.015873 0.00 0.00 0.000000 0.000000 0.00 0.015873 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.015873 0.00 0.00 0.000000 0.015873 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.015873 0.00 0.000000 0.00 0.015873 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.015873 0.00 0.015873 0.00 0.000000 0.015873 0.000000 0.000000 0.015873 0.00 0.00 0.00 0.00 0.031746 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.047619 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.015873 0.00 0.00 0.00 0.000000 0.000000 0.031746 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.031746 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.015873 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.015873 0.00 0.000000 0.00 0.00 0.00 0.015873 0.00 0.015873 0.015873 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.015873 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.015873 0.015873
20 Manhattan Valley 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.016667 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.016667 0.000000 0.033333 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.016667 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.016667 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.033333 0.00 0.00 0.000000 0.00 0.00 0.016667 0.000000 0.00 0.016667 0.00 0.00 0.00 0.00000 0.016667 0.00 0.000000 0.050000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.016667 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.033333 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.016667 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.016667 0.000000 0.00 0.016667 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.016667 0.016667 0.000000 0.016667 0.00 0.000000 0.00 0.000000 0.016667 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.016667 0.000000 0.016667 0.00 0.00 0.000000 0.00 0.016667 0.00 0.016667 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.016667 0.00 0.000000 0.000000 0.00 0.016667 0.050000 0.00 0.00 0.000000 0.00 0.00 0.016667 0.00000 0.016667 0.000000 0.00 0.000000 0.016667 0.00 0.00 0.000000 0.016667 0.00 0.016667 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.016667 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.033333 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.016667 0.00 0.000000 0.00 0.016667 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.050000 0.033333 0.016667 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.033333 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.000000 0.000000 0.016667 0.00 0.016667 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.033333 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.016667 0.00 0.00 0.000000 0.00 0.00 0.000000 0.016667 0.016667 0.000000 0.033333
21 Manhattanville 0.000000 0.00 0.00 0.000000 0.024390 0.00 0.00 0.000000 0.000000 0.024390 0.00 0.000000 0.024390 0.000000 0.00 0.00 0.00 0.000000 0.024390 0.00 0.000000 0.000000 0.000000 0.024390 0.000000 0.000000 0.000000 0.02439 0.00 0.00 0.00 0.000000 0.02439 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.024390 0.00 0.024390 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.048780 0.00 0.00 0.00 0.02439 0.000000 0.00 0.000000 0.073171 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.02439 0.00 0.00 0.000000 0.00 0.000000 0.00 0.024390 0.000000 0.00 0.000000 0.00 0.024390 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.02439 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.02439 0.000000 0.000000 0.000000 0.000000 0.024390 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.024390 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.024390 0.00 0.00 0.000000 0.00 0.00 0.048780 0.02439 0.000000 0.000000 0.00 0.000000 0.024390 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.024390 0.024390 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.048780 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.024390 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02439 0.00000 0.000000 0.00 0.000000 0.000000 0.048780 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.024390 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.048780 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.024390 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.024390 0.000000 0.024390 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000
22 Marble Hill 0.000000 0.00 0.00 0.000000 0.040000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.040000 0.040000 0.000000 0.000000 0.000000 0.000000 0.00000 0.00 0.04 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.080000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.040000 0.040000 0.00 0.000000 0.00 0.040000 0.08 0.00 0.000000 0.040000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.040000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.040000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.040000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.040000 0.00 0.00 0.00 0.00 0.040000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.040000 0.000000 0.000000 0.00 0.040000 0.000000 0.000000 0.00 0.040000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.040000 0.00000 0.00 0.000000 0.040000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.04 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.040000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.040000
23 Midtown 0.000000 0.00 0.00 0.000000 0.030000 0.00 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.030000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.030000 0.000000 0.01 0.01 0.000000 0.00 0.000000 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.01 0.00 0.00 0.00000 0.050000 0.00 0.040000 0.030000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.010000 0.000000 0.020000 0.00 0.00 0.01000 0.00 0.00 0.020000 0.00 0.000000 0.00 0.010000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.040000 0.000000 0.020000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.01 0.00 0.000000 0.000000 0.00 0.01 0.020000 0.010000 0.020000 0.000000 0.00 0.00 0.000000 0.00 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.080000 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.020000 0.000000 0.01 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.02 0.00 0.010000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.01 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.01 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.010000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.030000 0.000000 0.000000 0.00 0.030000 0.00 0.00 0.040000 0.00000 0.00 0.000000 0.000000 0.020000 0.00 0.010000 0.000000 0.010000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.040000 0.01 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.010000 0.010000
24 Midtown South 0.000000 0.00 0.00 0.000000 0.020000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.030000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.00 0.01 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.010000 0.020000 0.00 0.00 0.000000 0.00 0.000000 0.010000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00000 0.020000 0.00 0.030000 0.050000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.030000 0.00 0.00 0.00000 0.00 0.01 0.000000 0.00 0.000000 0.00 0.000000 0.010000 0.00 0.020000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.010000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.01 0.00000 0.020000 0.010000 0.000000 0.000000 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.020000 0.00 0.00 0.00 0.000000 0.010000 0.01 0.00 0.000000 0.010000 0.010000 0.030000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.060000 0.050000 0.00 0.000000 0.000000 0.00 0.01 0.000000 0.00 0.00 0.030000 0.00000 0.040000 0.000000 0.01 0.000000 0.010000 0.01 0.00 0.000000 0.150000 0.00 0.000000 0.00 0.00 0.000000 0.020000 0.000000 0.010000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.020000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.000000 0.010000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.01 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.010000 0.00000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.01 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.010000 0.000000 0.000000 0.000000 0.020000
25 Morningside Heights 0.000000 0.00 0.00 0.000000 0.071429 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.023810 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.071429 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.047619 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.047619 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.095238 0.00 0.00 0.02381 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.047619 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.023810 0.000000 0.00 0.000000 0.023810 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.047619 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.023810 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.023810 0.023810 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.023810 0.023810 0.00 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.047619 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.02381 0.000000 0.00 0.000000 0.000000 0.071429 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.023810 0.00 0.00 0.00 0.00 0.023810 0.000000 0.000000 0.00 0.000000 0.00 0.023810 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.023810 0.000000 0.023810 0.000000 0.000000 0.00 0.023810 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.023810 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.047619 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000
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27 Noho 0.000000 0.01 0.00 0.000000 0.020000 0.00 0.00 0.000000 0.010000 0.030000 0.00 0.000000 0.010000 0.000000 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.020000 0.000000 0.010000 0.000000 0.000000 0.010000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.020000 0.030000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.00 0.050000 0.030000 0.00 0.00 0.00000 0.00 0.00 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.01 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.020000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.010000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.050000 0.000000 0.000000 0.010000 0.00 0.020000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.030000 0.00 0.01 0.010000 0.030000 0.010000 0.010000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.01 0.00 0.01 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.030000 0.010000 0.00 0.020000 0.010000 0.01 0.00 0.000000 0.00 0.00 0.060000 0.00000 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.030000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.010000 0.00 0.010000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.02 0.020000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.030000 0.00 0.00 0.01 0.000000 0.000000 0.020000 0.000000 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.010000 0.010000 0.000000 0.010000 0.00 0.000000 0.00 0.00 0.010000 0.01000 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.020000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.010000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.01 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.01 0.010000 0.020000 0.010000 0.000000 0.010000
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29 Soho 0.010000 0.00 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.030000 0.01 0.010000 0.000000 0.000000 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.020000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.060000 0.01 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00000 0.100000 0.00 0.000000 0.020000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.01 0.010000 0.00 0.010000 0.00 0.000000 0.000000 0.02 0.020000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.01 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.020000 0.000000 0.000000 0.030000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.020000 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.030000 0.00000 0.010000 0.000000 0.00 0.000000 0.000000 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.030000 0.00 0.04 0.000000 0.010000 0.00 0.00 0.01 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.01 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.020000 0.000000 0.000000 0.000000 0.00 0.020000 0.000000 0.000000 0.00 0.040000 0.00 0.00 0.01 0.000000 0.00 0.00 0.00 0.01 0.00 0.00 0.000000 0.010000 0.010000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.010000 0.000000 0.000000 0.01 0.000000 0.000000 0.000000 0.00 0.010000 0.01 0.02 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.02 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.01 0.000000 0.00 0.00 0.010000 0.000000 0.000000 0.060000 0.020000
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31 Sutton Place 0.000000 0.01 0.00 0.000000 0.030000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.010000 0.000000 0.020000 0.000000 0.000000 0.010000 0.02000 0.01 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.01 0.000000 0.01 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.02 0.010000 0.00 0.000000 0.00 0.010000 0.020000 0.01 0.030000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.020000 0.000000 0.000000 0.040000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.01 0.010000 0.020000 0.030000 0.060000 0.00 0.00 0.000000 0.00 0.000000 0.01 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.00 0.020000 0.040000 0.00 0.00 0.000000 0.01 0.00 0.050000 0.00000 0.000000 0.000000 0.00 0.000000 0.030000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.000000 0.000000 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.020000 0.00 0.00 0.000000 0.020000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.010000 0.01 0.000000 0.01 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.02 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.020000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.01 0.010000 0.00 0.00 0.010000 0.00000 0.01 0.000000 0.000000 0.020000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.01 0.00 0.010000 0.00 0.010000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.010000 0.010000 0.000000 0.000000 0.020000
32 Tribeca 0.000000 0.00 0.00 0.000000 0.040000 0.00 0.00 0.000000 0.010000 0.010000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.020000 0.000000 0.000000 0.000000 0.010000 0.000000 0.00000 0.00 0.00 0.01 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.040000 0.00 0.00 0.000000 0.01 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.050000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.020000 0.00 0.00 0.00 0.00000 0.010000 0.00 0.020000 0.030000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.00 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.010000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.020000 0.000000 0.000000 0.010000 0.00 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.030000 0.000000 0.030000 0.000000 0.01 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.020000 0.000000 0.00 0.000000 0.010000 0.01 0.01 0.000000 0.00 0.00 0.050000 0.00000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.02 0.000000 0.000000 0.00 0.01 0.00 0.000000 0.01 0.00 0.00 0.00 0.00 0.00 0.000000 0.010000 0.00 0.010000 0.00 0.000000 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.050000 0.00 0.010000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.010000 0.020000 0.000000 0.02 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.01 0.00 0.00 0.020000 0.000000 0.000000 0.020000 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.01 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.040000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.020000 0.00000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.01 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.010000 0.01 0.00 0.000000 0.00 0.01 0.030000 0.030000 0.000000 0.000000 0.010000
33 Tudor City 0.000000 0.00 0.00 0.000000 0.012195 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.036585 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.012195 0.000000 0.012195 0.000000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.024390 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.048780 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.012195 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.012195 0.000000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.036585 0.000000 0.00 0.000000 0.00 0.024390 0.00 0.00 0.036585 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.012195 0.000000 0.012195 0.000000 0.00 0.012195 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.012195 0.00 0.00 0.048780 0.000000 0.012195 0.012195 0.00 0.00 0.000000 0.00 0.012195 0.00 0.000000 0.012195 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.036585 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.012195 0.00000 0.012195 0.000000 0.00 0.012195 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.012195 0.00 0.00 0.000000 0.012195 0.012195 0.012195 0.000000 0.000000 0.012195 0.00 0.00 0.000000 0.00 0.00 0.000000 0.060976 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.060976 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.012195 0.000000 0.00 0.00 0.00 0.00 0.036585 0.000000 0.012195 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.024390 0.00 0.000000 0.00 0.00 0.00 0.000000 0.012195 0.024390 0.000000 0.000000 0.00 0.012195 0.012195 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.024390 0.024390 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.000000 0.000000 0.036585 0.00 0.000000 0.012195 0.000000 0.00 0.000000 0.00 0.00 0.00 0.012195 0.00 0.024390 0.000000 0.00 0.00 0.00 0.00 0.00 0.012195 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.024390 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.012195
34 Turtle Bay 0.000000 0.00 0.00 0.000000 0.020000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.000000 0.010000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.01 0.000000 0.000000 0.030000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.01 0.000000 0.00 0.01 0.00 0.00000 0.000000 0.00 0.010000 0.040000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.010000 0.000000 0.00 0.000000 0.00 0.010000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.00000 0.01 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.010000 0.000000 0.030000 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.010000 0.020000 0.00 0.00 0.020000 0.010000 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.010000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.050000 0.000000 0.00 0.000000 0.030000 0.00 0.00 0.000000 0.00 0.00 0.060000 0.01000 0.030000 0.000000 0.00 0.000000 0.000000 0.02 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.010000 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.01 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.01 0.000000 0.000000 0.030000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.010000 0.010000 0.00 0.00 0.00 0.00 0.010000 0.000000 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.030000 0.00 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.01 0.00 0.01 0.000000 0.000000 0.010000 0.000000 0.000000 0.00 0.020000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.01 0.00 0.00 0.00 0.00 0.000000 0.020000 0.010000 0.000000 0.00 0.000000 0.00 0.00 0.050000 0.00000 0.00 0.000000 0.000000 0.050000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.00 0.010000 0.000000 0.00 0.00 0.00 0.01 0.00 0.000000 0.00 0.01 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.040000 0.000000 0.000000 0.000000 0.000000
35 Upper East Side 0.000000 0.00 0.00 0.000000 0.020000 0.00 0.00 0.000000 0.000000 0.040000 0.01 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.040000 0.000000 0.010000 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.010000 0.020000 0.00 0.00 0.000000 0.01 0.000000 0.000000 0.010000 0.01 0.000000 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.01 0.00 0.00 0.00000 0.010000 0.00 0.030000 0.050000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.00000 0.00 0.00 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.010000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.01 0.000000 0.00 0.000000 0.000000 0.07 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.030000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.01 0.000000 0.020000 0.000000 0.040000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.030000 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.080000 0.00000 0.010000 0.010000 0.00 0.000000 0.040000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.010000 0.00 0.00 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.010000 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.020000 0.010000 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.010000 0.000000 0.000000 0.01 0.010000 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.000000 0.01 0.00 0.00 0.00 0.00 0.00 0.000000 0.030000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.01 0.00 0.00 0.02 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.020000 0.000000 0.010000 0.020000
36 Upper West Side 0.010000 0.00 0.00 0.000000 0.020000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.010000 0.010000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.030000 0.000000 0.040000 0.000000 0.000000 0.000000 0.00000 0.01 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.020000 0.000000 0.00 0.00 0.020000 0.00 0.000000 0.000000 0.030000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.00000 0.010000 0.00 0.000000 0.030000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.020000 0.00 0.00 0.00000 0.00 0.01 0.000000 0.00 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.010000 0.000000 0.01 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.01 0.01000 0.000000 0.000000 0.000000 0.010000 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.00 0.00 0.010000 0.000000 0.010000 0.010000 0.00 0.00 0.000000 0.00 0.000000 0.01 0.000000 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.020000 0.030000 0.00 0.00 0.000000 0.00 0.00 0.060000 0.00000 0.010000 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.010000 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.030000 0.00 0.00 0.000000 0.010000 0.02 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.01 0.01 0.000000 0.000000 0.01 0.010000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.010000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.00 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.020000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.010000 0.00 0.020000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.010000 0.000000 0.000000 0.010000 0.00 0.000000 0.01 0.00 0.000000 0.01000 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.020000 0.010000 0.00 0.00 0.00 0.00 0.01 0.010000 0.00 0.01 0.00 0.01 0.03 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.040000 0.010000 0.000000 0.000000 0.010000
37 Washington Heights 0.011765 0.00 0.00 0.000000 0.011765 0.00 0.00 0.011765 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.047059 0.000000 0.011765 0.000000 0.000000 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.011765 0.00 0.000000 0.000000 0.011765 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.058824 0.00 0.00 0.000000 0.00 0.00 0.011765 0.000000 0.00 0.023529 0.00 0.00 0.00 0.00000 0.011765 0.00 0.011765 0.011765 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.023529 0.011765 0.00 0.000000 0.00 0.011765 0.00 0.00 0.000000 0.011765 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.000000 0.011765 0.011765 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.035294 0.023529 0.011765 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.011765 0.00 0.00 0.000000 0.00 0.00 0.011765 0.00000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.011765 0.000000 0.00 0.023529 0.00 0.00 0.000000 0.000000 0.011765 0.011765 0.000000 0.011765 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.023529 0.00 0.00 0.00 0.035294 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.023529 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.011765 0.023529 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.011765 0.011765 0.00 0.00 0.00 0.00 0.023529 0.000000 0.011765 0.00 0.011765 0.00 0.000000 0.000000 0.011765 0.00 0.011765 0.000000 0.00 0.011765 0.011765 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.023529 0.011765 0.000000 0.00 0.011765 0.000000 0.011765 0.00 0.023529 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.023529 0.000000 0.00 0.011765 0.00 0.00 0.000000 0.00000 0.00 0.023529 0.023529 0.011765 0.00 0.000000 0.000000 0.000000 0.00 0.023529 0.00 0.00 0.00 0.000000 0.00 0.011765 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.011765 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.011765 0.023529 0.000000 0.011765 0.000000
38 West Village 0.010000 0.00 0.00 0.000000 0.040000 0.00 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.01 0.000000 0.000000 0.00 0.000000 0.030000 0.000000 0.000000 0.000000 0.000000 0.010000 0.00000 0.00 0.00 0.00 0.000000 0.00000 0.000000 0.01 0.000000 0.010000 0.010000 0.00 0.01 0.010000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.01 0.00 0.000000 0.000000 0.00 0.020000 0.00 0.00 0.00 0.00000 0.010000 0.00 0.020000 0.020000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.050000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.010000 0.000000 0.00 0.010000 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.01000 0.000000 0.000000 0.000000 0.030000 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.040000 0.02 0.00 0.00 0.000000 0.000000 0.00 0.02 0.000000 0.000000 0.010000 0.010000 0.00 0.00 0.000000 0.01 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.020000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.100000 0.00000 0.020000 0.040000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.010000 0.00 0.00 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.01 0.00 0.00 0.000000 0.000000 0.00 0.050000 0.01 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.030000 0.00 0.010000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.01 0.00 0.00 0.020000 0.010000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.000000 0.010000 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.020000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.010000 0.000000 0.020000 0.00 0.000000 0.010000 0.000000 0.00 0.000000 0.00 0.01 0.00 0.000000 0.00 0.000000 0.010000 0.00 0.01 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00 0.00 0.040000 0.000000 0.000000 0.000000 0.000000
39 Yorkville 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.010000 0.010000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.020000 0.010000 0.010000 0.060000 0.000000 0.000000 0.000000 0.00000 0.01 0.00 0.00 0.000000 0.00000 0.010000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.050000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.000000 0.01 0.030000 0.000000 0.00 0.020000 0.00 0.030000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.00 0.00 0.00000 0.000000 0.000000 0.000000 0.010000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.00 0.01 0.000000 0.000000 0.060000 0.010000 0.00 0.01 0.000000 0.00 0.000000 0.01 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.01 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.030000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.060000 0.00000 0.030000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.000000 0.000000 0.010000 0.010000 0.000000 0.000000 0.000000 0.00 0.00 0.010000 0.00 0.00 0.000000 0.030000 0.00 0.00 0.00 0.000000 0.00 0.00 0.01 0.00 0.00 0.00 0.000000 0.000000 0.01 0.010000 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00000 0.00000 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.00 0.010000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.00 0.00 0.040000 0.000000 0.000000 0.00 0.010000 0.00 0.020000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.020000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.020000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.00000 0.00 0.000000 0.000000 0.040000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.020000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.01 0.020000 0.00 0.00 0.000000 0.00 0.00 0.010000 0.030000 0.000000 0.000000 0.000000

Let's confirm the new size

In [35]:
manhattan_grouped.shape
Out[35]:
(40, 333)

Let's print each neighborhood along with the top 5 most common venues

In [36]:
num_top_venues = 5

for hood in manhattan_grouped['Neighborhood']:
    print("----"+hood+"----")
    temp = manhattan_grouped[manhattan_grouped['Neighborhood'] == hood].T.reset_index()
    temp.columns = ['venue','freq']
    temp = temp.iloc[1:]
    temp['freq'] = temp['freq'].astype(float)
    temp = temp.round({'freq': 2})
    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))
    print('\n')
----Battery Park City----
                venue  freq
0                Park  0.08
1         Coffee Shop  0.07
2               Hotel  0.05
3                 Gym  0.04
4  Italian Restaurant  0.03


----Carnegie Hill----
            venue  freq
0     Pizza Place  0.06
1     Coffee Shop  0.05
2            Café  0.04
3  Cosmetics Shop  0.04
4     Yoga Studio  0.03


----Central Harlem----
                 venue  freq
0   African Restaurant  0.07
1    French Restaurant  0.05
2   Chinese Restaurant  0.05
3  American Restaurant  0.05
4   Seafood Restaurant  0.05


----Chelsea----
                venue  freq
0         Coffee Shop  0.06
1  Italian Restaurant  0.05
2      Ice Cream Shop  0.05
3           Nightclub  0.04
4              Bakery  0.04


----Chinatown----
                   venue  freq
0     Chinese Restaurant  0.10
1           Cocktail Bar  0.04
2    American Restaurant  0.04
3  Vietnamese Restaurant  0.04
4         Ice Cream Shop  0.03


----Civic Center----
                  venue  freq
0    Italian Restaurant  0.06
1  Gym / Fitness Center  0.05
2           Coffee Shop  0.04
3     French Restaurant  0.04
4        Sandwich Place  0.04


----Clinton----
                  venue  freq
0               Theater  0.13
1  Gym / Fitness Center  0.05
2                 Hotel  0.04
3    Italian Restaurant  0.04
4   American Restaurant  0.04


----East Harlem----
                       venue  freq
0         Mexican Restaurant  0.12
1                     Bakery  0.10
2              Deli / Bodega  0.07
3  Latin American Restaurant  0.07
4            Thai Restaurant  0.05


----East Village----
                venue  freq
0                 Bar  0.06
1            Wine Bar  0.05
2      Ice Cream Shop  0.04
3  Mexican Restaurant  0.04
4         Pizza Place  0.04


----Financial District----
         venue  freq
0  Coffee Shop  0.08
1   Steakhouse  0.04
2    Wine Shop  0.04
3        Hotel  0.04
4          Gym  0.04


----Flatiron----
                  venue  freq
0           Yoga Studio  0.04
1   American Restaurant  0.04
2   Japanese Restaurant  0.04
3  Gym / Fitness Center  0.04
4                   Gym  0.04


----Gramercy----
                    venue  freq
0      Italian Restaurant  0.05
1            Cocktail Bar  0.04
2     American Restaurant  0.04
3  Thrift / Vintage Store  0.04
4             Pizza Place  0.04


----Greenwich Village----
                venue  freq
0  Italian Restaurant  0.10
1      Clothing Store  0.04
2   French Restaurant  0.04
3    Sushi Restaurant  0.04
4  Seafood Restaurant  0.03


----Hamilton Heights----
                venue  freq
0  Mexican Restaurant  0.08
1         Pizza Place  0.07
2         Coffee Shop  0.07
3                Café  0.07
4         Yoga Studio  0.03


----Hudson Yards----
                 venue  freq
0  American Restaurant  0.07
1          Coffee Shop  0.05
2   Italian Restaurant  0.05
3                Hotel  0.04
4              Theater  0.04


----Inwood----
                venue  freq
0  Mexican Restaurant  0.07
1                Café  0.07
2              Lounge  0.07
3         Pizza Place  0.05
4       Deli / Bodega  0.04


----Lenox Hill----
                venue  freq
0  Italian Restaurant  0.06
1         Coffee Shop  0.06
2    Sushi Restaurant  0.05
3         Pizza Place  0.04
4        Burger Joint  0.03


----Lincoln Square----
                  venue  freq
0  Gym / Fitness Center  0.06
1               Theater  0.06
2    Italian Restaurant  0.05
3                 Plaza  0.05
4                  Café  0.05


----Little Italy----
                venue  freq
0              Bakery  0.05
1                Café  0.04
2      Ice Cream Shop  0.03
3  Salon / Barbershop  0.03
4      Sandwich Place  0.03


----Lower East Side----
                 venue  freq
0     Ramen Restaurant  0.05
1          Coffee Shop  0.05
2   Chinese Restaurant  0.05
3                 Café  0.05
4  Japanese Restaurant  0.03


----Manhattan Valley----
               venue  freq
0        Coffee Shop  0.05
1  Indian Restaurant  0.05
2        Pizza Place  0.05
3        Yoga Studio  0.03
4         Playground  0.03


----Manhattanville----
                venue  freq
0         Coffee Shop  0.07
1  Chinese Restaurant  0.05
2                Park  0.05
3  Seafood Restaurant  0.05
4  Italian Restaurant  0.05


----Marble Hill----
            venue  freq
0  Discount Store  0.08
1     Coffee Shop  0.08
2          Bakery  0.04
3   Big Box Store  0.04
4  Tennis Stadium  0.04


----Midtown----
            venue  freq
0           Hotel  0.08
1  Clothing Store  0.05
2      Steakhouse  0.04
3         Theater  0.04
4      Food Truck  0.04


----Midtown South----
                 venue  freq
0    Korean Restaurant  0.15
1                Hotel  0.06
2            Hotel Bar  0.05
3          Coffee Shop  0.05
4  Japanese Restaurant  0.04


----Morningside Heights----
                 venue  freq
0          Coffee Shop  0.10
1                 Park  0.07
2  American Restaurant  0.07
3            Bookstore  0.07
4                 Café  0.05


----Murray Hill----
                 venue  freq
0          Coffee Shop  0.04
1                Hotel  0.04
2       Sandwich Place  0.04
3  Japanese Restaurant  0.04
4   Italian Restaurant  0.03


----Noho----
                venue  freq
0  Italian Restaurant  0.06
1   French Restaurant  0.05
2        Cocktail Bar  0.05
3           Gift Shop  0.03
4            Boutique  0.03


----Roosevelt Island----
            venue  freq
0   Deli / Bodega  0.08
1     Coffee Shop  0.08
2  Sandwich Place  0.08
3   Metro Station  0.04
4    Liquor Store  0.04


----Soho----
            venue  freq
0  Clothing Store  0.10
1   Women's Store  0.06
2        Boutique  0.06
3     Men's Store  0.04
4      Shoe Store  0.04


----Stuyvesant Town----
              venue  freq
0               Bar  0.21
1              Park  0.11
2        Playground  0.11
3          Heliport  0.05
4  Basketball Court  0.05


----Sutton Place----
                    venue  freq
0    Gym / Fitness Center  0.06
1      Italian Restaurant  0.05
2  Furniture / Home Store  0.04
3       Indian Restaurant  0.04
4     American Restaurant  0.03


----Tribeca----
                 venue  freq
0   Italian Restaurant  0.05
1                 Café  0.05
2                 Park  0.05
3             Boutique  0.04
4  American Restaurant  0.04


----Tudor City----
                venue  freq
0  Mexican Restaurant  0.06
1                Park  0.06
2    Greek Restaurant  0.05
3                Café  0.05
4    Sushi Restaurant  0.04


----Turtle Bay----
                venue  freq
0  Italian Restaurant  0.06
1               Hotel  0.05
2          Steakhouse  0.05
3    Sushi Restaurant  0.05
4         Coffee Shop  0.04


----Upper East Side----
                  venue  freq
0    Italian Restaurant  0.08
1               Exhibit  0.07
2           Coffee Shop  0.05
3  Gym / Fitness Center  0.04
4             Juice Bar  0.04


----Upper West Side----
                      venue  freq
0        Italian Restaurant  0.06
1                  Wine Bar  0.04
2                       Bar  0.04
3               Coffee Shop  0.03
4  Mediterranean Restaurant  0.03


----Washington Heights----
                       venue  freq
0                       Café  0.06
1                     Bakery  0.05
2              Grocery Store  0.04
3          Mobile Phone Shop  0.04
4  Latin American Restaurant  0.02


----West Village----
                     venue  freq
0       Italian Restaurant  0.10
1  New American Restaurant  0.05
2           Cosmetics Shop  0.05
3                 Wine Bar  0.04
4                Jazz Club  0.04


----Yorkville----
                venue  freq
0                 Gym  0.06
1  Italian Restaurant  0.06
2                 Bar  0.06
3         Coffee Shop  0.05
4         Pizza Place  0.04


Let's put that into a pandas dataframe

First, let's write a function to sort the venues in descending order.

In [37]:
def return_most_common_venues(row, num_top_venues):
    row_categories = row.iloc[1:]
    row_categories_sorted = row_categories.sort_values(ascending=False)
    
    return row_categories_sorted.index.values[0:num_top_venues]

Now let's create the new dataframe and display the top 10 venues for each neighborhood.

In [38]:
num_top_venues = 10

indicators = ['st', 'nd', 'rd']

# create columns according to number of top venues
columns = ['Neighborhood']
for ind in np.arange(num_top_venues):
    try:
        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))
    except:
        columns.append('{}th Most Common Venue'.format(ind+1))

# create a new dataframe
neighborhoods_venues_sorted = pd.DataFrame(columns=columns)
neighborhoods_venues_sorted['Neighborhood'] = manhattan_grouped['Neighborhood']

for ind in np.arange(manhattan_grouped.shape[0]):
    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(manhattan_grouped.iloc[ind, :], num_top_venues)

neighborhoods_venues_sorted.head()
Out[38]:
Neighborhood 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue
0 Battery Park City Park Coffee Shop Hotel Gym Wine Shop Italian Restaurant Clothing Store Plaza Memorial Site Burger Joint
1 Carnegie Hill Pizza Place Coffee Shop Café Cosmetics Shop Grocery Store Bar Spa French Restaurant Japanese Restaurant Yoga Studio
2 Central Harlem African Restaurant Public Art Cosmetics Shop French Restaurant Chinese Restaurant Seafood Restaurant Gym / Fitness Center American Restaurant Park Southern / Soul Food Restaurant
3 Chelsea Coffee Shop Italian Restaurant Ice Cream Shop Nightclub Bakery American Restaurant Hotel Seafood Restaurant Theater Cupcake Shop
4 Chinatown Chinese Restaurant Cocktail Bar American Restaurant Vietnamese Restaurant Bubble Tea Shop Noodle House Bar Dumpling Restaurant Ice Cream Shop Hotpot Restaurant

4. Cluster Neighborhoods

Run k-means to cluster the neighborhood into 5 clusters.

In [39]:
# set number of clusters
kclusters = 5

manhattan_grouped_clustering = manhattan_grouped.drop('Neighborhood', 1)

# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(manhattan_grouped_clustering)

# check cluster labels generated for each row in the dataframe
kmeans.labels_[0:10] 
Out[39]:
array([3, 1, 1, 3, 1, 0, 0, 4, 1, 3], dtype=int32)

Let's create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood.

In [40]:
# add clustering labels
neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)

manhattan_merged = manhattan_data

# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood
manhattan_merged = manhattan_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')

manhattan_merged.head() # check the last columns!
Out[40]:
Borough Neighborhood Latitude Longitude Cluster Labels 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue
0 Manhattan Marble Hill 40.876551 -73.910660 4 Discount Store Coffee Shop Yoga Studio Seafood Restaurant Gym Tennis Stadium Big Box Store Supplement Shop Steakhouse Shoe Store
1 Manhattan Chinatown 40.715618 -73.994279 1 Chinese Restaurant Cocktail Bar American Restaurant Vietnamese Restaurant Bubble Tea Shop Noodle House Bar Dumpling Restaurant Ice Cream Shop Hotpot Restaurant
2 Manhattan Washington Heights 40.851903 -73.936900 4 Café Bakery Mobile Phone Shop Grocery Store Pizza Place Chinese Restaurant Latin American Restaurant Mexican Restaurant Tapas Restaurant Shoe Store
3 Manhattan Inwood 40.867684 -73.921210 4 Lounge Mexican Restaurant Café Pizza Place Wine Bar Deli / Bodega Park American Restaurant Frozen Yogurt Shop Chinese Restaurant
4 Manhattan Hamilton Heights 40.823604 -73.949688 4 Mexican Restaurant Café Coffee Shop Pizza Place Yoga Studio Sushi Restaurant Caribbean Restaurant Chinese Restaurant School Bakery

Finally, let's visualize the resulting clusters

In [41]:
# create map
map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)

# set color scheme for the clusters
x = np.arange(kclusters)
ys = [i + x + (i*x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]

# add markers to the map
markers_colors = []
for lat, lon, poi, cluster in zip(manhattan_merged['Latitude'], manhattan_merged['Longitude'], manhattan_merged['Neighborhood'], manhattan_merged['Cluster Labels']):
    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)
    folium.CircleMarker(
        [lat, lon],
        radius=5,
        popup=label,
        color=rainbow[cluster-1],
        fill=True,
        fill_color=rainbow[cluster-1],
        fill_opacity=0.7).add_to(map_clusters)
       
map_clusters
Out[41]:

5. Examine Clusters

Now, you can examine each cluster and determine the discriminating venue categories that distinguish each cluster. Based on the defining categories, you can then assign a name to each cluster. I will leave this exercise to you.

Cluster 1

In [42]:
manhattan_merged.loc[manhattan_merged['Cluster Labels'] == 0, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]
Out[42]:
Neighborhood 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue
8 Upper East Side Italian Restaurant Exhibit Coffee Shop Bakery Gym / Fitness Center Art Gallery Juice Bar Hotel Spa Cocktail Bar
13 Lincoln Square Gym / Fitness Center Theater Café Concert Hall Plaza Italian Restaurant Opera House Performing Arts Venue French Restaurant Indie Movie Theater
14 Clinton Theater Gym / Fitness Center Italian Restaurant American Restaurant Hotel Cocktail Bar Wine Shop Spa Indie Theater Lounge
18 Greenwich Village Italian Restaurant Sushi Restaurant French Restaurant Clothing Store Seafood Restaurant Indian Restaurant Café Gourmet Shop Cosmetics Shop Bakery
21 Tribeca Park Italian Restaurant Café Spa American Restaurant Boutique Wine Shop Wine Bar Gym Coffee Shop
24 West Village Italian Restaurant Cosmetics Shop New American Restaurant Gastropub Wine Bar Jazz Club American Restaurant Bakery French Restaurant Park
27 Gramercy Italian Restaurant American Restaurant Bagel Shop Thrift / Vintage Store Pizza Place Cocktail Bar Hotel Mexican Restaurant Thai Restaurant Grocery Store
31 Noho Italian Restaurant Cocktail Bar French Restaurant Boutique Grocery Store Art Gallery Gift Shop Hotel Rock Club Coffee Shop
32 Civic Center Italian Restaurant Gym / Fitness Center French Restaurant Hotel Sandwich Place Coffee Shop Bakery Yoga Studio Spa Cocktail Bar
35 Turtle Bay Italian Restaurant Sushi Restaurant Hotel Steakhouse Wine Bar Coffee Shop French Restaurant Park Café Japanese Restaurant
39 Hudson Yards American Restaurant Coffee Shop Italian Restaurant Gym / Fitness Center Theater Café Hotel Dog Run Gym Park

Cluster 2

In [43]:
manhattan_merged.loc[manhattan_merged['Cluster Labels'] == 1, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]
Out[43]:
Neighborhood 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue
1 Chinatown Chinese Restaurant Cocktail Bar American Restaurant Vietnamese Restaurant Bubble Tea Shop Noodle House Bar Dumpling Restaurant Ice Cream Shop Hotpot Restaurant
6 Central Harlem African Restaurant Public Art Cosmetics Shop French Restaurant Chinese Restaurant Seafood Restaurant Gym / Fitness Center American Restaurant Park Southern / Soul Food Restaurant
10 Lenox Hill Coffee Shop Italian Restaurant Sushi Restaurant Pizza Place Cosmetics Shop Burger Joint Gym Gym / Fitness Center Sporting Goods Shop Deli / Bodega
12 Upper West Side Italian Restaurant Bar Wine Bar Coffee Shop Mediterranean Restaurant Vegetarian / Vegan Restaurant Burger Joint Indian Restaurant Bakery Bookstore
15 Midtown Hotel Clothing Store Steakhouse Theater Cocktail Bar Food Truck Spa American Restaurant Coffee Shop Bookstore
16 Murray Hill Sandwich Place Japanese Restaurant Coffee Shop Hotel French Restaurant Gym Bar Italian Restaurant Jewish Restaurant Juice Bar
19 East Village Bar Wine Bar Chinese Restaurant Ice Cream Shop Mexican Restaurant Pizza Place Ramen Restaurant Coffee Shop Cocktail Bar Vegetarian / Vegan Restaurant
22 Little Italy Bakery Café Clothing Store Italian Restaurant Sandwich Place Mediterranean Restaurant Ice Cream Shop Seafood Restaurant Salon / Barbershop Massage Studio
23 Soho Clothing Store Boutique Women's Store Shoe Store Men's Store Art Gallery Mediterranean Restaurant Italian Restaurant Furniture / Home Store Yoga Studio
30 Carnegie Hill Pizza Place Coffee Shop Café Cosmetics Shop Grocery Store Bar Spa French Restaurant Japanese Restaurant Yoga Studio
33 Midtown South Korean Restaurant Hotel Hotel Bar Coffee Shop Japanese Restaurant Cosmetics Shop Italian Restaurant Gym / Fitness Center Bakery Cocktail Bar
34 Sutton Place Gym / Fitness Center Italian Restaurant Indian Restaurant Furniture / Home Store American Restaurant Gym Juice Bar Dessert Shop Yoga Studio Cupcake Shop
38 Flatiron Yoga Studio Japanese Restaurant Gym Gym / Fitness Center American Restaurant Cosmetics Shop Clothing Store New American Restaurant Salon / Barbershop Sporting Goods Shop

Cluster 3

In [44]:
manhattan_merged.loc[manhattan_merged['Cluster Labels'] == 2, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]
Out[44]:
Neighborhood 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue
37 Stuyvesant Town Bar Park Playground German Restaurant Basketball Court Baseball Field Fountain Harbor / Marina Cocktail Bar Coffee Shop

Cluster 4

In [45]:
manhattan_merged.loc[manhattan_merged['Cluster Labels'] == 3, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]
Out[45]:
Neighborhood 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue
5 Manhattanville Coffee Shop Mexican Restaurant Italian Restaurant Park Chinese Restaurant Seafood Restaurant Dumpling Restaurant Bar Bus Station Burger Joint
17 Chelsea Coffee Shop Italian Restaurant Ice Cream Shop Nightclub Bakery American Restaurant Hotel Seafood Restaurant Theater Cupcake Shop
26 Morningside Heights Coffee Shop American Restaurant Park Bookstore Café Food Truck Deli / Bodega Burger Joint New American Restaurant Tennis Court
28 Battery Park City Park Coffee Shop Hotel Gym Wine Shop Italian Restaurant Clothing Store Plaza Memorial Site Burger Joint
29 Financial District Coffee Shop Gym Wine Shop Hotel Steakhouse Pizza Place Café Bar American Restaurant Park

Cluster 5

In [46]:
manhattan_merged.loc[manhattan_merged['Cluster Labels'] == 4, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]
Out[46]:
Neighborhood 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue
0 Marble Hill Discount Store Coffee Shop Yoga Studio Seafood Restaurant Gym Tennis Stadium Big Box Store Supplement Shop Steakhouse Shoe Store
2 Washington Heights Café Bakery Mobile Phone Shop Grocery Store Pizza Place Chinese Restaurant Latin American Restaurant Mexican Restaurant Tapas Restaurant Shoe Store
3 Inwood Lounge Mexican Restaurant Café Pizza Place Wine Bar Deli / Bodega Park American Restaurant Frozen Yogurt Shop Chinese Restaurant
4 Hamilton Heights Mexican Restaurant Café Coffee Shop Pizza Place Yoga Studio Sushi Restaurant Caribbean Restaurant Chinese Restaurant School Bakery
7 East Harlem Mexican Restaurant Bakery Latin American Restaurant Deli / Bodega Thai Restaurant Convenience Store Café Taco Place Street Art Steakhouse
9 Yorkville Gym Italian Restaurant Bar Coffee Shop Pizza Place Sushi Restaurant Deli / Bodega Japanese Restaurant Ice Cream Shop Diner
11 Roosevelt Island Sandwich Place Deli / Bodega Coffee Shop Dog Run Café Farmers Market Supermarket Metro Station Outdoors & Recreation Dry Cleaner
20 Lower East Side Café Chinese Restaurant Coffee Shop Ramen Restaurant Japanese Restaurant Cocktail Bar Shoe Store Bakery Art Gallery Sandwich Place
25 Manhattan Valley Indian Restaurant Coffee Shop Pizza Place Yoga Studio Playground Spa Café Mexican Restaurant Deli / Bodega Bar
36 Tudor City Park Mexican Restaurant Greek Restaurant Café Sushi Restaurant Deli / Bodega Pizza Place Hotel Dog Run Asian Restaurant

Thank you for completing this lab!

This notebook was created by Alex Aklson and Polong Lin. I hope you found this lab interesting and educational. Feel free to contact us if you have any questions!

This notebook is part of a course on Coursera called Applied Data Science Capstone. If you accessed this notebook outside the course, you can take this course online by clicking here.


Copyright © 2018 Cognitive Class. This notebook and its source code are released under the terms of the MIT License.