In this blog post, we will explore neighborhoods in New York City using the Foursquare API. We will get the most common venue categories in each neighborhood, and then using the k-means clustering algorithm, group the neighborhoods into clusters.

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 
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 
import folium # map rendering library

print('Libraries imported.')
Libraries imported.

Explore the data

# download the data from a server
!wget -q -O 'newyork_data.json' https://cocl.us/new_york_dataset
print('Data downloaded.')
Data downloaded.
# load the data
with open('newyork_data.json') as json_data:
    newyork_data = json.load(json_data)
# explore the data
newyork_data
{'bbox': [-74.2492599487305,
  40.5033187866211,
  -73.7061614990234,
  40.9105606079102],
 'crs': {'properties': {'name': 'urn:ogc:def:crs:EPSG::4326'}, 'type': 'name'},
 'features': [{'geometry': {'coordinates': [-73.84720052054902,
     40.89470517661],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.1',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Wakefield',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.84720052054902,
     40.89470517661,
     -73.84720052054902,
     40.89470517661],
    'borough': 'Bronx',
    'name': 'Wakefield',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.82993910812398, 40.87429419303012],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.2',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Co-op',
    'annoline2': 'City',
    'annoline3': None,
    'bbox': [-73.82993910812398,
     40.87429419303012,
     -73.82993910812398,
     40.87429419303012],
    'borough': 'Bronx',
    'name': 'Co-op City',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.82780644716412, 40.887555677350775],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.3',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Eastchester',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.82780644716412,
     40.887555677350775,
     -73.82780644716412,
     40.887555677350775],
    'borough': 'Bronx',
    'name': 'Eastchester',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.90564259591682, 40.89543742690383],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.4',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Fieldston',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.90564259591682,
     40.89543742690383,
     -73.90564259591682,
     40.89543742690383],
    'borough': 'Bronx',
    'name': 'Fieldston',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.9125854610857, 40.890834493891305],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.5',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Riverdale',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.9125854610857,
     40.890834493891305,
     -73.9125854610857,
     40.890834493891305],
    'borough': 'Bronx',
    'name': 'Riverdale',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.90281798724604, 40.88168737120521],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.6',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Kingsbridge',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.90281798724604,
     40.88168737120521,
     -73.90281798724604,
     40.88168737120521],
    'borough': 'Bronx',
    'name': 'Kingsbridge',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.91065965862981, 40.87655077879964],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.7',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Marble',
    'annoline2': 'Hill',
    'annoline3': None,
    'bbox': [-73.91065965862981,
     40.87655077879964,
     -73.91065965862981,
     40.87655077879964],
    'borough': 'Manhattan',
    'name': 'Marble Hill',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.86731496814176, 40.89827261213805],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.8',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Woodlawn',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.86731496814176,
     40.89827261213805,
     -73.86731496814176,
     40.89827261213805],
    'borough': 'Bronx',
    'name': 'Woodlawn',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.8793907395681, 40.87722415599446],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.9',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Norwood',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.8793907395681,
     40.87722415599446,
     -73.8793907395681,
     40.87722415599446],
    'borough': 'Bronx',
    'name': 'Norwood',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.85744642974207, 40.88103887819211],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.10',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Williamsbridge',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.85744642974207,
     40.88103887819211,
     -73.85744642974207,
     40.88103887819211],
    'borough': 'Bronx',
    'name': 'Williamsbridge',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.83579759808117, 40.866858107252696],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.11',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Baychester',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.83579759808117,
     40.866858107252696,
     -73.83579759808117,
     40.866858107252696],
    'borough': 'Bronx',
    'name': 'Baychester',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.85475564017999, 40.85741349808865],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.12',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Pelham Parkway',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.85475564017999,
     40.85741349808865,
     -73.85475564017999,
     40.85741349808865],
    'borough': 'Bronx',
    'name': 'Pelham Parkway',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.78648845267413, 40.84724670491813],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.13',
   'properties': {'annoangle': 0.0,
    'annoline1': 'City',
    'annoline2': 'Island',
    'annoline3': None,
    'bbox': [-73.78648845267413,
     40.84724670491813,
     -73.78648845267413,
     40.84724670491813],
    'borough': 'Bronx',
    'name': 'City Island',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.8855121841913, 40.870185164975325],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.14',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Bedford',
    'annoline2': 'Park',
    'annoline3': None,
    'bbox': [-73.8855121841913,
     40.870185164975325,
     -73.8855121841913,
     40.870185164975325],
    'borough': 'Bronx',
    'name': 'Bedford Park',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.9104159619131, 40.85572707719664],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.15',
   'properties': {'annoangle': 0.0,
    'annoline1': 'University',
    'annoline2': 'Heights',
    'annoline3': None,
    'bbox': [-73.9104159619131,
     40.85572707719664,
     -73.9104159619131,
     40.85572707719664],
    'borough': 'Bronx',
    'name': 'University Heights',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.91967159119565, 40.84789792606271],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.16',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Morris',
    'annoline2': 'Heights',
    'annoline3': None,
    'bbox': [-73.91967159119565,
     40.84789792606271,
     -73.91967159119565,
     40.84789792606271],
    'borough': 'Bronx',
    'name': 'Morris Heights',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.89642655981623, 40.86099679638654],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.17',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Fordham',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.89642655981623,
     40.86099679638654,
     -73.89642655981623,
     40.86099679638654],
    'borough': 'Bronx',
    'name': 'Fordham',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.88735617532338, 40.84269615786053],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.18',
   'properties': {'annoangle': 0.0,
    'annoline1': 'East',
    'annoline2': 'Tremont',
    'annoline3': None,
    'bbox': [-73.88735617532338,
     40.84269615786053,
     -73.88735617532338,
     40.84269615786053],
    'borough': 'Bronx',
    'name': 'East Tremont',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.87774474910545, 40.83947505672653],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.19',
   'properties': {'annoangle': 0.0,
    'annoline1': 'West',
    'annoline2': 'Farms',
    'annoline3': None,
    'bbox': [-73.87774474910545,
     40.83947505672653,
     -73.87774474910545,
     40.83947505672653],
    'borough': 'Bronx',
    'name': 'West Farms',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.9261020935813, 40.836623010706056],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.20',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Highbridge',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.9261020935813,
     40.836623010706056,
     -73.9261020935813,
     40.836623010706056],
    'borough': 'Bronx',
    'name': 'High  Bridge',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.90942160757436, 40.819754370594936],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.21',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Melrose',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.90942160757436,
     40.819754370594936,
     -73.90942160757436,
     40.819754370594936],
    'borough': 'Bronx',
    'name': 'Melrose',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.91609987487575, 40.80623874935177],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.22',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Mott Haven',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.91609987487575,
     40.80623874935177,
     -73.91609987487575,
     40.80623874935177],
    'borough': 'Bronx',
    'name': 'Mott Haven',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.91322139386135, 40.801663627756206],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.23',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Port',
    'annoline2': 'Morris',
    'annoline3': None,
    'bbox': [-73.91322139386135,
     40.801663627756206,
     -73.91322139386135,
     40.801663627756206],
    'borough': 'Bronx',
    'name': 'Port Morris',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.8957882009446, 40.81509904545822],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.24',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Longwood',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.8957882009446,
     40.81509904545822,
     -73.8957882009446,
     40.81509904545822],
    'borough': 'Bronx',
    'name': 'Longwood',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.88331505955291, 40.80972987938709],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.25',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Hunts',
    'annoline2': 'Point',
    'annoline3': None,
    'bbox': [-73.88331505955291,
     40.80972987938709,
     -73.88331505955291,
     40.80972987938709],
    'borough': 'Bronx',
    'name': 'Hunts Point',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.90150648943059, 40.82359198585534],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.26',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Morrisania',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.90150648943059,
     40.82359198585534,
     -73.90150648943059,
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    'borough': 'Bronx',
    'name': 'Morrisania',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.86574609554924, 40.821012197914015],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.27',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Soundview',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.86574609554924,
     40.821012197914015,
     -73.86574609554924,
     40.821012197914015],
    'borough': 'Bronx',
    'name': 'Soundview',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.85414416189266, 40.80655112003589],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.28',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Clason',
    'annoline2': 'Point',
    'annoline3': None,
    'bbox': [-73.85414416189266,
     40.80655112003589,
     -73.85414416189266,
     40.80655112003589],
    'borough': 'Bronx',
    'name': 'Clason Point',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.81635002158441, 40.81510925804005],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.29',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Throgs Neck',
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    'annoline3': None,
    'bbox': [-73.81635002158441,
     40.81510925804005,
     -73.81635002158441,
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    'borough': 'Bronx',
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    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.8240992675385, 40.844245936947374],
    'type': 'Point'},
   'geometry_name': 'geom',
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   'properties': {'annoangle': 0.0,
    'annoline1': 'Country',
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    'bbox': [-73.8240992675385,
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    'borough': 'Bronx',
    'name': 'Country Club',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.85600310535783, 40.837937822267286],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.31',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Parkchester',
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    'annoline3': None,
    'bbox': [-73.85600310535783,
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    'borough': 'Bronx',
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    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.84219407604444, 40.8406194964327],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.32',
   'properties': {'annoangle': 0.0,
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    'bbox': [-73.84219407604444,
     40.8406194964327,
     -73.84219407604444,
     40.8406194964327],
    'borough': 'Bronx',
    'name': 'Westchester Square',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.8662991807561, 40.84360847124718],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.33',
   'properties': {'annoangle': 0.0,
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    'annoline2': 'Nest',
    'annoline3': None,
    'bbox': [-73.8662991807561,
     40.84360847124718,
     -73.8662991807561,
     40.84360847124718],
    'borough': 'Bronx',
    'name': 'Van Nest',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.85040178030421, 40.847549063536334],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.34',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Morris Park',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.85040178030421,
     40.847549063536334,
     -73.85040178030421,
     40.847549063536334],
    'borough': 'Bronx',
    'name': 'Morris Park',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.88845196134804, 40.85727710073895],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.35',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Belmont',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.88845196134804,
     40.85727710073895,
     -73.88845196134804,
     40.85727710073895],
    'borough': 'Bronx',
    'name': 'Belmont',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.91719048210393, 40.88139497727086],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.36',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Spuyten',
    'annoline2': 'Duyvil',
    'annoline3': None,
    'bbox': [-73.91719048210393,
     40.88139497727086,
     -73.91719048210393,
     40.88139497727086],
    'borough': 'Bronx',
    'name': 'Spuyten Duyvil',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.90453054908927, 40.90854282950666],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.37',
   'properties': {'annoangle': 0.0,
    'annoline1': 'North',
    'annoline2': 'Riverdale',
    'annoline3': None,
    'bbox': [-73.90453054908927,
     40.90854282950666,
     -73.90453054908927,
     40.90854282950666],
    'borough': 'Bronx',
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  {'geometry': {'coordinates': [-73.96355614094303, 40.76028033131374],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.272',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Sutton',
    'annoline2': 'Place',
    'annoline3': None,
    'bbox': [-73.96355614094303,
     40.76028033131374,
     -73.96355614094303,
     40.76028033131374],
    'borough': 'Manhattan',
    'name': 'Sutton Place',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.95386782130745, 40.743414090073536],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.273',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Hunters',
    'annoline2': 'Point',
    'annoline3': None,
    'bbox': [-73.95386782130745,
     40.743414090073536,
     -73.95386782130745,
     40.743414090073536],
    'borough': 'Queens',
    'name': 'Hunters Point',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.96770824581834, 40.75204236950722],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.274',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Turtle',
    'annoline2': 'Bay',
    'annoline3': None,
    'bbox': [-73.96770824581834,
     40.75204236950722,
     -73.96770824581834,
     40.75204236950722],
    'borough': 'Manhattan',
    'name': 'Turtle Bay',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.97121928722265, 40.746917410740195],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.275',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Tudor',
    'annoline2': 'City',
    'annoline3': None,
    'bbox': [-73.97121928722265,
     40.746917410740195,
     -73.97121928722265,
     40.746917410740195],
    'borough': 'Manhattan',
    'name': 'Tudor City',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.97405170469203, 40.73099955477061],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.276',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Stuyvesant',
    'annoline2': 'Town',
    'annoline3': None,
    'bbox': [-73.97405170469203,
     40.73099955477061,
     -73.97405170469203,
     40.73099955477061],
    'borough': 'Manhattan',
    'name': 'Stuyvesant Town',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.9909471052826, 40.739673047638426],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.277',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Flatiron',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.9909471052826,
     40.739673047638426,
     -73.9909471052826,
     40.739673047638426],
    'borough': 'Manhattan',
    'name': 'Flatiron',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.91819286431682, 40.74565180608076],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.278',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Sunnyside',
    'annoline2': 'Gardens',
    'annoline3': None,
    'bbox': [-73.91819286431682,
     40.74565180608076,
     -73.91819286431682,
     40.74565180608076],
    'borough': 'Queens',
    'name': 'Sunnyside Gardens',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.93244235260178, 40.73725071694497],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.279',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Blissville',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.93244235260178,
     40.73725071694497,
     -73.93244235260178,
     40.73725071694497],
    'borough': 'Queens',
    'name': 'Blissville',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.99550751888415, 40.70328109093014],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.280',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Fulton',
    'annoline2': 'Ferry',
    'annoline3': None,
    'bbox': [-73.99550751888415,
     40.70328109093014,
     -73.99550751888415,
     40.70328109093014],
    'borough': 'Brooklyn',
    'name': 'Fulton Ferry',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.98111603592393, 40.70332149882874],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.281',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Vinegar',
    'annoline2': 'Hill',
    'annoline3': None,
    'bbox': [-73.98111603592393,
     40.70332149882874,
     -73.98111603592393,
     40.70332149882874],
    'borough': 'Brooklyn',
    'name': 'Vinegar Hill',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.93053108817338, 40.67503986503237],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.282',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Weeksville',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.93053108817338,
     40.67503986503237,
     -73.93053108817338,
     40.67503986503237],
    'borough': 'Brooklyn',
    'name': 'Weeksville',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.90331684852599, 40.67786104769531],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.283',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Broadway',
    'annoline2': 'Junction',
    'annoline3': None,
    'bbox': [-73.90331684852599,
     40.67786104769531,
     -73.90331684852599,
     40.67786104769531],
    'borough': 'Brooklyn',
    'name': 'Broadway Junction',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.9887528074504, 40.70317632822692],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.284',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Dumbo',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.9887528074504,
     40.70317632822692,
     -73.9887528074504,
     40.70317632822692],
    'borough': 'Brooklyn',
    'name': 'Dumbo',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-74.12059399718001, 40.60180957631444],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.285',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Manor',
    'annoline2': 'Heights',
    'annoline3': None,
    'bbox': [-74.12059399718001,
     40.60180957631444,
     -74.12059399718001,
     40.60180957631444],
    'borough': 'Staten Island',
    'name': 'Manor Heights',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-74.13208447484298, 40.60370692627371],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.286',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Willowbrook',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-74.13208447484298,
     40.60370692627371,
     -74.13208447484298,
     40.60370692627371],
    'borough': 'Staten Island',
    'name': 'Willowbrook',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-74.21776636068567, 40.541139922091766],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.287',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Sandy',
    'annoline2': 'Ground',
    'annoline3': None,
    'bbox': [-74.21776636068567,
     40.541139922091766,
     -74.21776636068567,
     40.541139922091766],
    'borough': 'Staten Island',
    'name': 'Sandy Ground',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-74.12727240604946, 40.579118742961214],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.288',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Egbertville',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-74.12727240604946,
     40.579118742961214,
     -74.12727240604946,
     40.579118742961214],
    'borough': 'Staten Island',
    'name': 'Egbertville',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.89213760232822, 40.56737588957032],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.289',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Roxbury',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.89213760232822,
     40.56737588957032,
     -73.89213760232822,
     40.56737588957032],
    'borough': 'Queens',
    'name': 'Roxbury',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.95918459428702, 40.598525095137255],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.290',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Homecrest',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.95918459428702,
     40.598525095137255,
     -73.95918459428702,
     40.598525095137255],
    'borough': 'Brooklyn',
    'name': 'Homecrest',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.88114319200604, 40.716414511158185],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.291',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Middle',
    'annoline2': 'Village',
    'annoline3': None,
    'bbox': [-73.88114319200604,
     40.716414511158185,
     -73.88114319200604,
     40.716414511158185],
    'borough': 'Queens',
    'name': 'Middle Village',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-74.20152556457658, 40.52626406734812],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.292',
   'properties': {'annoangle': 0.0,
    'annoline1': "Prince's",
    'annoline2': 'Bay',
    'annoline3': None,
    'bbox': [-74.20152556457658,
     40.52626406734812,
     -74.20152556457658,
     40.52626406734812],
    'borough': 'Staten Island',
    'name': "Prince's Bay",
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-74.13792663771568, 40.57650629379489],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.293',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Lighthouse',
    'annoline2': 'Hill',
    'annoline3': None,
    'bbox': [-74.13792663771568,
     40.57650629379489,
     -74.13792663771568,
     40.57650629379489],
    'borough': 'Staten Island',
    'name': 'Lighthouse Hill',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-74.22957080626941, 40.51954145748909],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.294',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Richmond',
    'annoline2': 'Valley',
    'annoline3': None,
    'bbox': [-74.22957080626941,
     40.51954145748909,
     -74.22957080626941,
     40.51954145748909],
    'borough': 'Staten Island',
    'name': 'Richmond Valley',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.82667757138641, 40.79060155670148],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.295',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Malba',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.82667757138641,
     40.79060155670148,
     -73.82667757138641,
     40.79060155670148],
    'borough': 'Queens',
    'name': 'Malba',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.890345709872, 40.6819989345173],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.296',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Highland',
    'annoline2': 'Park',
    'annoline3': None,
    'bbox': [-73.890345709872,
     40.6819989345173,
     -73.890345709872,
     40.6819989345173],
    'borough': 'Brooklyn',
    'name': 'Highland Park',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.94841515328893, 40.60937770113766],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.297',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Madison',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.94841515328893,
     40.60937770113766,
     -73.94841515328893,
     40.60937770113766],
    'borough': 'Brooklyn',
    'name': 'Madison',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.86172577555115, 40.85272297633017],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.298',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Bronxdale',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.86172577555115,
     40.85272297633017,
     -73.86172577555115,
     40.85272297633017],
    'borough': 'Bronx',
    'name': 'Bronxdale',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.85931863221647, 40.86578787802982],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.299',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Allerton',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.85931863221647,
     40.86578787802982,
     -73.85931863221647,
     40.86578787802982],
    'borough': 'Bronx',
    'name': 'Allerton',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.90152264513144, 40.8703923914147],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.300',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Kingsbridge',
    'annoline2': 'Heights',
    'annoline3': None,
    'bbox': [-73.90152264513144,
     40.8703923914147,
     -73.90152264513144,
     40.8703923914147],
    'borough': 'Bronx',
    'name': 'Kingsbridge Heights',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.94817709920184, 40.64692606658579],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.301',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Erasmus',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.94817709920184,
     40.64692606658579,
     -73.94817709920184,
     40.64692606658579],
    'borough': 'Brooklyn',
    'name': 'Erasmus',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-74.00011136202637, 40.75665808227519],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.302',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Hudson',
    'annoline2': 'Yards',
    'annoline3': None,
    'bbox': [-74.00011136202637,
     40.75665808227519,
     -74.00011136202637,
     40.75665808227519],
    'borough': 'Manhattan',
    'name': 'Hudson Yards',
    'stacked': 2},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.80553002968718, 40.58733774018741],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.303',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Hammels',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.80553002968718,
     40.58733774018741,
     -73.80553002968718,
     40.58733774018741],
    'borough': 'Queens',
    'name': 'Hammels',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.76596781445627, 40.611321691283834],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.304',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Bayswater',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.76596781445627,
     40.611321691283834,
     -73.76596781445627,
     40.611321691283834],
    'borough': 'Queens',
    'name': 'Bayswater',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-73.94563070334091, 40.756091297094706],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.305',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Queensbridge',
    'annoline2': None,
    'annoline3': None,
    'bbox': [-73.94563070334091,
     40.756091297094706,
     -73.94563070334091,
     40.756091297094706],
    'borough': 'Queens',
    'name': 'Queensbridge',
    'stacked': 1},
   'type': 'Feature'},
  {'geometry': {'coordinates': [-74.08173992211962, 40.61731079252983],
    'type': 'Point'},
   'geometry_name': 'geom',
   'id': 'nyu_2451_34572.306',
   'properties': {'annoangle': 0.0,
    'annoline1': 'Fox',
    'annoline2': 'Hills',
    'annoline3': None,
    'bbox': [-74.08173992211962,
     40.61731079252983,
     -74.08173992211962,
     40.61731079252983],
    'borough': 'Staten Island',
    'name': 'Fox Hills',
    'stacked': 2},
   'type': 'Feature'}],
 'totalFeatures': 306,
 'type': 'FeatureCollection'}

All the relevant data is in the features key, which is basically a list of the neighborhoods.
Let’s define a new variable that includes this data.

neighborhoods_data = newyork_data['features']

Let’s check the first item in the list.

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

Transform the data into a pandas dataframe

We will essentially be transforming this data of nested Python dictionaries into a pandas dataframe.
Let’s start by creating an empty dataframe.

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

# instantiate the dataframe
neighborhoods = pd.DataFrame(columns=column_names)
# empty dataframe
neighborhoods
Borough Neighborhood Latitude Longitude

Let’s loop through the data and fill the dataframe one row at a time.

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)
# examine the dataframe
neighborhoods.head()
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
# check whether the dataframe has all 5 boroughs and 306 neighborhoods
print('The dataframe has {} boroughs and {} neighborhoods.'.format(
    len(neighborhoods['Borough'].unique()), neighborhoods.shape[0]))
The dataframe has 5 boroughs and 306 neighborhoods.

Visualize the data

We will use the 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.

address = 'New York City, NY'

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

Let’s create a map of New York with the neighborhoods superimposed on top.

# create map of New York using latitude and longitude values
map_newyork = folium.Map(location=[latitude, longitude], zoom_start=11)

# 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

If you were working in a notebook and using Folium, you could zoom into the above map, and click on each circle mark to reveal the name of the neighborhood and its respective borough.

Let’s simplify the above map, and segment and cluster only the neighborhoods in Manhattan. We will slice the original dataframe and create a new dataframe of the Manhattan data.

manhattan_data = neighborhoods[neighborhoods['Borough'] == 'Manhattan'].reset_index(drop=True)
manhattan_data.head()
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 geographiical coordinates of Manhattan.

address = 'Manhattan, NY'

geolocator = Nominatim(user_agent='ny_explorer')
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geographical coordinates of Manhattan are {}, {}.'.format(latitude, longitude))
The geographical coordinates of Manhattan are 40.7896239, -73.9598939.

Let’s visualize the neighborhoods in Manhattan.

# create a map of Manhattan using latitude and longitude values
map_manhattan = folium.Map(location=[latitude, longitude], zoom_start=12)

# 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

Explore the neighborhoods in Manhattan

We will utilize the Foursquare API to explore the neighborhoods and segment them.

# define Foursquare Credentials
CLIENT_ID = '**********' 
CLIENT_SECRET = '**********'
VERSION = '20191219'

Explore the first neighborhood in our dataframe.

# get the neighborhood's name
manhattan_data.loc[0, 'Neighborhood']
'Marble Hill'
# get the neighborhood's latitude and longitude values
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('The geographical coordinates of {} are {}, {}.'.format(neighborhood_name, neighborhood_latitude, neighborhood_longitude))
The geographical coordinates of Marble Hill are 40.87655077879964, -73.91065965862981.

Let’s get the top 100 venues that are in Marble Hill within a radius of 500 meters.
First, create the GET request URL.

# limit the number of venues returned by the foursquare API
LIMIT = 100

# define radius
radius = 500

# Create URL
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)

# display URL
url
'https://api.foursquare.com/v2/venues/explore?&client_id=B50RYBOWHJ3ZMRVEFTLHJCXGOYNCXGI13VDT5FMYTUQSUTQC&client_secret=ST2IN4ZGOQ3BXEWJ2HW1LKUML2BBGK1JX1QD2WKEPET31W4W&v=20191219&ll=40.87655077879964,-73.91065965862981&radius=500&limit=100'

Send the GET request and examine the results.

results = requests.get(url).json()
results
{'meta': {'code': 200, 'requestId': '5e68ce37618f43001c2e7a8e'},
 'response': {'groups': [{'items': [{'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4b4429abf964a52037f225e3-0',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/pizza_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d1ca941735',
         'name': 'Pizza Place',
         'pluralName': 'Pizza Places',
         'primary': True,
         'shortName': 'Pizza'}],
       'delivery': {'id': '72548',
        'provider': {'icon': {'name': '/delivery_provider_seamless_20180129.png',
          'prefix': 'https://fastly.4sqi.net/img/general/cap/',
          'sizes': [40, 50]},
         'name': 'seamless'},
        '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'},
       'id': '4b4429abf964a52037f225e3',
       'location': {'address': '5198 Broadway',
        'cc': 'US',
        'city': 'New York',
        'country': 'United States',
        'crossStreet': 'at 225th St.',
        'distance': 240,
        'formattedAddress': ['5198 Broadway (at 225th St.)',
         'New York, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87441177110231,
          'lng': -73.91027100981574}],
        'lat': 40.87441177110231,
        'lng': -73.91027100981574,
        'postalCode': '10463',
        'state': 'NY'},
       'name': "Arturo's",
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4baf59e8f964a520a6f93be3-1',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/gym_yogastudio_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d102941735',
         'name': 'Yoga Studio',
         'pluralName': 'Yoga Studios',
         'primary': True,
         'shortName': 'Yoga Studio'}],
       'id': '4baf59e8f964a520a6f93be3',
       'location': {'address': '5500 Broadway',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'crossStreet': '230th Street',
        'distance': 376,
        'formattedAddress': ['5500 Broadway (230th Street)',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.876843690797934,
          'lng': -73.90620384419528}],
        'lat': 40.876843690797934,
        'lng': -73.90620384419528,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Bikram Yoga',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4b79cc46f964a520c5122fe3-2',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/diner_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d147941735',
         'name': 'Diner',
         'pluralName': 'Diners',
         'primary': True,
         'shortName': 'Diner'}],
       'id': '4b79cc46f964a520c5122fe3',
       'location': {'address': '3033 Tibbett Ave',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'crossStreet': 'btwn 230th & 231st',
        'distance': 452,
        'formattedAddress': ['3033 Tibbett Ave (btwn 230th & 231st)',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.8804044222466,
          'lng': -73.90893738006402}],
        'lat': 40.8804044222466,
        'lng': -73.90893738006402,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Tibbett Diner',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4b5357adf964a520319827e3-3',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/donuts_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d148941735',
         'name': 'Donut Shop',
         'pluralName': 'Donut Shops',
         'primary': True,
         'shortName': 'Donuts'}],
       'id': '4b5357adf964a520319827e3',
       'location': {'address': '5501 Broadway',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'crossStreet': 'W 230th St',
        'distance': 342,
        'formattedAddress': ['5501 Broadway (W 230th St)',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87713584201589,
          'lng': -73.90666550701411}],
        'lat': 40.87713584201589,
        'lng': -73.90666550701411,
        'postalCode': '10463',
        'state': 'NY'},
       'name': "Dunkin'",
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-55f81cd2498ee903149fcc64-4',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d1e0931735',
         'name': 'Coffee Shop',
         'pluralName': 'Coffee Shops',
         'primary': True,
         'shortName': 'Coffee Shop'}],
       'id': '55f81cd2498ee903149fcc64',
       'location': {'address': '171 W 230th St',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'crossStreet': 'Kimberly Pl',
        'distance': 441,
        'formattedAddress': ['171 W 230th St (Kimberly Pl)',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87753134921497,
          'lng': -73.90558216359267}],
        'lat': 40.87753134921497,
        'lng': -73.90558216359267,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Starbucks',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4cf6ae55d3a8a1cd71a9d243-5',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/building/gym_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d176941735',
         'name': 'Gym',
         'pluralName': 'Gyms',
         'primary': True,
         'shortName': 'Gym'}],
       'id': '4cf6ae55d3a8a1cd71a9d243',
       'location': {'address': '5500 Broadway',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'crossStreet': '230th St',
        'distance': 361,
        'formattedAddress': ['5500 Broadway (230th St)',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87670526507199,
          'lng': -73.90637207670373}],
        'lat': 40.87670526507199,
        'lng': -73.90637207670373,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Astral Fitness & Wellness Center',
       'photos': {'count': 0, 'groups': []},
       'venuePage': {'id': '75803748'}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-55f751ca498eacc0307d1cfe-6',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/building/gym_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d176941735',
         'name': 'Gym',
         'pluralName': 'Gyms',
         'primary': True,
         'shortName': 'Gym'}],
       'id': '55f751ca498eacc0307d1cfe',
       'location': {'address': '5520 Broadway',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'crossStreet': 'at W 230th St',
        'distance': 433,
        'formattedAddress': ['5520 Broadway (at W 230th St)',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.877271495944626,
          'lng': -73.90559491338075}],
        'lat': 40.877271495944626,
        'lng': -73.90559491338075,
        'neighborhood': 'Kingsbridge',
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Blink Fitness',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-546d31ca498e561c698a0320-7',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/departmentstore_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d1f6941735',
         'name': 'Department Store',
         'pluralName': 'Department Stores',
         'primary': True,
         'shortName': 'Department Store'}],
       'id': '546d31ca498e561c698a0320',
       'location': {'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'distance': 478,
        'formattedAddress': ['Bronx, NY', 'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87723198343352,
          'lng': -73.90504239962168}],
        'lat': 40.87723198343352,
        'lng': -73.90504239962168,
        'state': 'NY'},
       'name': 'T.J. Maxx',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4b9c9c6af964a520b27236e3-8',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/seafood_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d1ce941735',
         'name': 'Seafood Restaurant',
         'pluralName': 'Seafood Restaurants',
         'primary': True,
         'shortName': 'Seafood'}],
       'delivery': {'id': '277380',
        'provider': {'icon': {'name': '/delivery_provider_seamless_20180129.png',
          'prefix': 'https://fastly.4sqi.net/img/general/cap/',
          'sizes': [40, 50]},
         'name': 'seamless'},
        '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'},
       'id': '4b9c9c6af964a520b27236e3',
       'location': {'address': '5535 Broadway',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'crossStreet': '231st St',
        'distance': 429,
        'formattedAddress': ['5535 Broadway (231st St)',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87788463309788,
          'lng': -73.90587282193539}],
        'lat': 40.87788463309788,
        'lng': -73.90587282193539,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Land & Sea Restaurant',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4a725fa1f964a520f6da1fe3-9',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/arts_entertainment/stadium_tennis_',
          'suffix': '.png'},
         'id': '4e39a891bd410d7aed40cbc2',
         'name': 'Tennis Stadium',
         'pluralName': 'Tennis Stadiums',
         'primary': True,
         'shortName': 'Tennis'}],
       'id': '4a725fa1f964a520f6da1fe3',
       'location': {'address': '2600 Netherland Ave',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'distance': 402,
        'formattedAddress': ['2600 Netherland Ave',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.8786283,
          'lng': -73.9145678}],
        'lat': 40.8786283,
        'lng': -73.9145678,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'TCR The Club of Riverdale',
       'photos': {'count': 0, 'groups': []},
       'venuePage': {'id': '40358759'}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4b88e053f964a5208a1132e3-10',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/pharmacy_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d10f951735',
         'name': 'Pharmacy',
         'pluralName': 'Pharmacies',
         'primary': True,
         'shortName': 'Pharmacy'}],
       'id': '4b88e053f964a5208a1132e3',
       'location': {'address': '5237 Broadway',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'crossStreet': '228th Street',
        'distance': 190,
        'formattedAddress': ['5237 Broadway (228th Street)',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.875466574434704,
          'lng': -73.90890629016033}],
        'lat': 40.875466574434704,
        'lng': -73.90890629016033,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Rite Aid',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-57655be738faa66160da7527-11',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d1e0931735',
         'name': 'Coffee Shop',
         'pluralName': 'Coffee Shops',
         'primary': True,
         'shortName': 'Coffee Shop'}],
       'id': '57655be738faa66160da7527',
       'location': {'address': '50 W 225th St',
        'cc': 'US',
        'city': 'New York',
        'country': 'United States',
        'distance': 355,
        'formattedAddress': ['50 W 225th St',
         'New York, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.873754554218515,
          'lng': -73.90861305343668}],
        'lat': 40.873754554218515,
        'lng': -73.90861305343668,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Starbucks',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4b9c9c43f964a520ac7236e3-12',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/discountstore_',
          'suffix': '.png'},
         'id': '52dea92d3cf9994f4e043dbb',
         'name': 'Discount Store',
         'pluralName': 'Discount Stores',
         'primary': True,
         'shortName': 'Discount Store'}],
       'id': '4b9c9c43f964a520ac7236e3',
       'location': {'address': '5545 Broadway',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'crossStreet': '231st St',
        'distance': 492,
        'formattedAddress': ['5545 Broadway (231st St)',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.878270422202085,
          'lng': -73.9052646742604}],
        'lat': 40.878270422202085,
        'lng': -73.9052646742604,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Lot Less Closeouts',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-5631194e498e2de074de661c-13',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/education/lab_',
          'suffix': '.png'},
         'id': '5744ccdfe4b0c0459246b4cd',
         'name': 'Supplement Shop',
         'pluralName': 'Supplement Shops',
         'primary': True,
         'shortName': 'Supplement Shop'}],
       'id': '5631194e498e2de074de661c',
       'location': {'address': '5510 Broadway',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'distance': 428,
        'formattedAddress': ['5510 Broadway',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87716,
          'lng': -73.905632}],
        'lat': 40.87716,
        'lng': -73.905632,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Vitamin Shoppe',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4e4e4517bd4101d0d7a67568-14',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/icecream_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d1c9941735',
         'name': 'Ice Cream Shop',
         'pluralName': 'Ice Cream Shops',
         'primary': True,
         'shortName': 'Ice Cream'}],
       'id': '4e4e4517bd4101d0d7a67568',
       'location': {'address': '5501 Broadway',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'distance': 343,
        'formattedAddress': ['5501 Broadway',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87714929478882,
          'lng': -73.90665810372622}],
        'lat': 40.87714929478882,
        'lng': -73.90665810372622,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Baskin-Robbins',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-56229ff8498e2abb44b6f12b-15',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/default_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d1ff941735',
         'name': 'Miscellaneous Shop',
         'pluralName': 'Miscellaneous Shops',
         'primary': True,
         'shortName': 'Shop'}],
       'id': '56229ff8498e2abb44b6f12b',
       'location': {'address': '171 W 230th St',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'distance': 487,
        'formattedAddress': ['171 W 230th St',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.8772564,
          'lng': -73.9049384}],
        'lat': 40.8772564,
        'lng': -73.9049384,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Five Below',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-585c205665e7c70a2f1055ea-16',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/default_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d14e941735',
         'name': 'American Restaurant',
         'pluralName': 'American Restaurants',
         'primary': True,
         'shortName': 'American'}],
       'delivery': {'id': '1436334',
        'provider': {'icon': {'name': '/delivery_provider_seamless_20180129.png',
          'prefix': 'https://fastly.4sqi.net/img/general/cap/',
          'sizes': [40, 50]},
         'name': 'seamless'},
        'url': 'https://www.seamless.com/menu/boston-market-5520-broadway-bronx/1436334?affiliate=1131&utm_source=foursquare-affiliate-network&utm_medium=affiliate&utm_campaign=1131&utm_content=1436334'},
       'id': '585c205665e7c70a2f1055ea',
       'location': {'address': '5520 Broadway',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'distance': 452,
        'formattedAddress': ['5520 Broadway',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87743,
          'lng': -73.9054121}],
        'lat': 40.87743,
        'lng': -73.9054121,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Boston Market',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4e4ce4debd413c4cc66d05d0-17',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/deli_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d1c5941735',
         'name': 'Sandwich Place',
         'pluralName': 'Sandwich Places',
         'primary': True,
         'shortName': 'Sandwiches'}],
       'delivery': {'id': '774886',
        'provider': {'icon': {'name': '/delivery_provider_seamless_20180129.png',
          'prefix': 'https://fastly.4sqi.net/img/general/cap/',
          'sizes': [40, 50]},
         'name': 'seamless'},
        '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'},
       'id': '4e4ce4debd413c4cc66d05d0',
       'location': {'address': '5549 Broadway',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'distance': 482,
        'formattedAddress': ['5549 Broadway',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.878464979323944,
          'lng': -73.9055176422437}],
        'lat': 40.878464979323944,
        'lng': -73.9055176422437,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'SUBWAY',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4ec68016cc21b428e1d2060a-18',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/financial_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d10a951735',
         'name': 'Bank',
         'pluralName': 'Banks',
         'primary': True,
         'shortName': 'Bank'}],
       'id': '4ec68016cc21b428e1d2060a',
       'location': {'address': '281 W 230th St',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'distance': 347,
        'formattedAddress': ['281 W 230th St',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.8794958,
          'lng': -73.9092856}],
        'lat': 40.8794958,
        'lng': -73.9092856,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'TD Bank',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4dfe40df8877333e195b68fc-19',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/steakhouse_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d1cc941735',
         'name': 'Steakhouse',
         'pluralName': 'Steakhouses',
         'primary': True,
         'shortName': 'Steakhouse'}],
       'delivery': {'id': '330981',
        'provider': {'icon': {'name': '/delivery_provider_seamless_20180129.png',
          'prefix': 'https://fastly.4sqi.net/img/general/cap/',
          'sizes': [40, 50]},
         'name': 'seamless'},
        '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'},
       'id': '4dfe40df8877333e195b68fc',
       'location': {'address': '230th St & Broadway',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'distance': 399,
        'formattedAddress': ['230th St & Broadway',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87747294351472,
          'lng': -73.90607346968568}],
        'lat': 40.87747294351472,
        'lng': -73.90607346968568,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Parrilla Latina',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4c852173dc018cfa2bc3e56c-20',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/apparel_kids_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d105951735',
         'name': 'Kids Store',
         'pluralName': 'Kids Stores',
         'primary': True,
         'shortName': 'Kids Store'}],
       'id': '4c852173dc018cfa2bc3e56c',
       'location': {'address': '44 W 225th St',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'distance': 383,
        'formattedAddress': ['44 W 225th St',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.873671591133125,
          'lng': -73.90815619608166}],
        'lat': 40.873671591133125,
        'lng': -73.90815619608166,
        'postalCode': '10463',
        'state': 'NY'},
       'name': "The Children's Place",
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-53319bb511d2ef06787f02b4-21',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/mall_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d1fd941735',
         'name': 'Shopping Mall',
         'pluralName': 'Shopping Malls',
         'primary': True,
         'shortName': 'Mall'}],
       'id': '53319bb511d2ef06787f02b4',
       'location': {'address': '171 W 231st St',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'distance': 456,
        'formattedAddress': ['171 W 231st St',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.87753906779665,
          'lng': -73.90539578168178}],
        'lat': 40.87753906779665,
        'lng': -73.90539578168178,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Broadway Plaza',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4d0a529133d6b60cf4cf9985-22',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/deli_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d1c5941735',
         'name': 'Sandwich Place',
         'pluralName': 'Sandwich Places',
         'primary': True,
         'shortName': 'Sandwiches'}],
       'id': '4d0a529133d6b60cf4cf9985',
       'location': {'address': '5209 Broadway',
        'cc': 'US',
        'city': 'Bronx',
        'country': 'United States',
        'distance': 463,
        'formattedAddress': ['5209 Broadway',
         'Bronx, NY 10463',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.877720351115315,
          'lng': -73.90537973066263}],
        'lat': 40.877720351115315,
        'lng': -73.90537973066263,
        'postalCode': '10463',
        'state': 'NY'},
       'name': 'Subway',
       'photos': {'count': 0, 'groups': []}}},
     {'reasons': {'count': 0,
       'items': [{'reasonName': 'globalInteractionReason',
         'summary': 'This spot is popular',
         'type': 'general'}]},
      'referralId': 'e-0-4ed7956b8b81b2bf28adc714-23',
      'venue': {'categories': [{'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/deli_',
          'suffix': '.png'},
         'id': '4bf58dd8d48988d146941735',
         'name': 'Deli / Bodega',
         'pluralName': 'Delis / Bodegas',
         'primary': True,
         'shortName': 'Deli / Bodega'}],
       'id': '4ed7956b8b81b2bf28adc714',
       'location': {'address': '135 Terrace View Ave.',
        'cc': 'US',
        'city': 'New York',
        'country': 'United States',
        'distance': 218,
        'formattedAddress': ['135 Terrace View Ave.',
         'New York, NY 10034',
         'United States'],
        'labeledLatLngs': [{'label': 'display',
          'lat': 40.875995,
          'lng': -73.913151}],
        'lat': 40.875995,
        'lng': -73.913151,
        'postalCode': '10034',
        'state': 'NY'},
       'name': 'Terrace View Delicatessen',
       'photos': {'count': 0, 'groups': []}}}],
    'name': 'recommended',
    'type': 'Recommended Places'}],
  'headerFullLocation': 'Marble Hill, New York',
  'headerLocation': 'Marble Hill',
  'headerLocationGranularity': 'neighborhood',
  'suggestedBounds': {'ne': {'lat': 40.88105078329964,
    'lng': -73.90471933917806},
   'sw': {'lat': 40.87205077429964, 'lng': -73.91659997808156}},
  'suggestedFilters': {'filters': [{'key': 'openNow', 'name': 'Open now'}],
   'header': 'Tap to show:'},
  'totalResults': 24}}

All the information is in the items key.
Let’s borrow the get_category_type function from the Foursquare lab.

# 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']

Clean the json and structure it into a pandas dataframe.

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()
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
print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))
24 venues were returned by Foursquare.

Explore all neighborhoods in Manhattan

Let’s create a function to repeat the same process for all the neighborhoods in Manhattan.

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)

Run the above function on each neighborhood and create a new dataframe.

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

Let’s check the size of the resulting dataframe.

print(manhattan_venues.shape)
manhattan_venues.head()
(3313, 7)
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.

manhattan_venues.groupby('Neighborhood').count()
Neighborhood Latitude Neighborhood Longitude Venue Venue Latitude Venue Longitude Venue Category
Neighborhood
Battery Park City 95 95 95 95 95 95
Carnegie Hill 100 100 100 100 100 100
Central Harlem 46 46 46 46 46 46
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 61 61 61 61 61 61
Hudson Yards 84 84 84 84 84 84
Inwood 56 56 56 56 56 56
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 56 56 56 56 56 56
Manhattan Valley 51 51 51 51 51 51
Manhattanville 42 42 42 42 42 42
Marble Hill 24 24 24 24 24 24
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 30 30 30 30 30 30
Soho 100 100 100 100 100 100
Stuyvesant Town 16 16 16 16 16 16
Sutton Place 100 100 100 100 100 100
Tribeca 100 100 100 100 100 100
Tudor City 79 79 79 79 79 79
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 90 90 90 90 90 90
West Village 100 100 100 100 100 100
Yorkville 100 100 100 100 100 100

How many unique categories can be curated from all the returned venues?

print('There are {} unique categories'.format(len(manhattan_venues['Venue Category'].unique())))
There are 343 unique categories

Analyze each neighborhood

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

# add neighborhood column back to the 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()
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 BBQ Joint Baby Store Bagel Shop Bakery Bank Bar Baseball Field Basketball Court Bed & Breakfast 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 Bridge Bubble Tea Shop Building Burger Joint Burrito Place Bus Line Bus Station Bus Stop Butcher Cafeteria Café Cajun / Creole Restaurant Cambodian Restaurant Camera Store Candy Store Caribbean Restaurant Caucasian Restaurant Cheese Shop Chinese Restaurant Chocolate Shop Circus Climbing Gym Clothing Store Club House Cocktail Bar Coffee Shop College Academic Building College Arts Building College Bookstore College Cafeteria College Gym College Theater Comedy Club Comfort Food Restaurant Community Center Concert Hall Convenience Store Cooking School 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 Doctor's Office 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 Stand 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 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 Kitchen Supply Store Korean Restaurant Kosher Restaurant Latin American Restaurant Laundry Service Leather Goods Store 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 Great Outdoors Outdoor Sculpture Outdoor Supply Store Outdoors & Recreation Paella Restaurant Pakistani Restaurant Paper / Office Supplies Store Park Pastry Shop Pedestrian Plaza Performing Arts Venue Persian Restaurant Peruvian Restaurant Pet Café Pet Service Pet Store Pharmacy Photography Studio Physical Therapist Piano Bar Pie Shop Pilates Studio Pizza Place Playground Plaza Poke Place Pool Pop-Up Shop Pub Public Art Ramen Restaurant Record Shop Recording Studio Recreation Center 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 Scandinavian Restaurant Scenic Lookout School Sculpture Garden Seafood Restaurant Shanghai Restaurant Shipping Store Shoe Store Shopping Mall Skate Park Skating Rink Smoke Shop Smoothie Shop Snack Place Soba Restaurant Soccer Field 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 Stables 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 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 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 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
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 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 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
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 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
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 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 0 0 0 0 0 0 0 0 0 0 0 0
# examine the new dataframe size
manhattan_onehot.shape
(3313, 344)

Let’s group rows by neighborhood and by taking the mean of the frequency of occurrence of each category.

manhattan_grouped = manhattan_onehot.groupby('Neighborhood').mean().reset_index()
manhattan_grouped
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 BBQ Joint Baby Store Bagel Shop Bakery Bank Bar Baseball Field Basketball Court Bed & Breakfast 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 Bridge Bubble Tea Shop Building Burger Joint Burrito Place Bus Line Bus Station Bus Stop Butcher Cafeteria Café Cajun / Creole Restaurant Cambodian Restaurant Camera Store Candy Store Caribbean Restaurant Caucasian Restaurant Cheese Shop Chinese Restaurant Chocolate Shop Circus Climbing Gym Clothing Store Club House Cocktail Bar Coffee Shop College Academic Building College Arts Building College Bookstore College Cafeteria College Gym College Theater Comedy Club Comfort Food Restaurant Community Center Concert Hall Convenience Store Cooking School 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 Doctor's Office 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 Stand 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 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 Kitchen Supply Store Korean Restaurant Kosher Restaurant Latin American Restaurant Laundry Service Leather Goods Store 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 Great Outdoors Outdoor Sculpture Outdoor Supply Store Outdoors & Recreation Paella Restaurant Pakistani Restaurant Paper / Office Supplies Store Park Pastry Shop Pedestrian Plaza Performing Arts Venue Persian Restaurant Peruvian Restaurant Pet Café Pet Service Pet Store Pharmacy Photography Studio Physical Therapist Piano Bar Pie Shop Pilates Studio Pizza Place Playground Plaza Poke Place Pool Pop-Up Shop Pub Public Art Ramen Restaurant Record Shop Recording Studio Recreation Center 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 Scandinavian Restaurant Scenic Lookout School Sculpture Garden Seafood Restaurant Shanghai Restaurant Shipping Store Shoe Store Shopping Mall Skate Park Skating Rink Smoke Shop Smoothie Shop Snack Place Soba Restaurant Soccer Field 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 Stables 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 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 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 0.010526 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.010526 0.010526 0.00 0.00 0.021053 0.00 0.000000 0.010526 0.000000 0.000000 0.0000 0.00 0.00 0.000000 0.010526 0.00 0.00 0.00 0.00 0.00000 0.010526 0.00 0.021053 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.010526 0.010526 0.010526 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.010526 0.000000 0.000000 0.00 0.010526 0.00 0.00 0.00000 0.021053 0.00 0.000000 0.063158 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.010526 0.010526 0.00 0.00 0.00000 0.00 0.010526 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.010526 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.010526 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.021053 0.00 0.010526 0.0000 0.000000 0.000000 0.000000 0.000000 0.00 0.010526 0.00 0.00000 0.010526 0.00 0.00 0.00 0.00 0.00 0.00 0.021053 0.000000 0.021053 0.031579 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.010526 0.000000 0.00 0.000000 0.000000 0.00 0.052632 0.010526 0.00 0.021053 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.021053 0.00000 0.000000 0.000000 0.010526 0.000000 0.010526 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010526 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010526 0.031579 0.021053 0.000000 0.010526 0.00 0.00 0.000000 0.000000 0.00 0.00 0.010526 0.00 0.010526 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.073684 0.00 0.000000 0.010526 0.00 0.000000 0.000000 0.0000 0.010526 0.000000 0.00 0.00 0.00 0.00 0.00 0.021053 0.010526 0.021053 0.00 0.000000 0.00 0.010526 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010526 0.000000 0.021053 0.00 0.010526 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.031579 0.00 0.00 0.010526 0.00 0.00 0.00 0.000000 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 0.010526 0.00000 0.00 0.000000 0.000000 0.010526 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.010526 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 0.000000 0.031579 0.000000 0.031579 0.000000
1 Carnegie Hill 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.000000 0.01 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.010000 0.030000 0.000000 0.020000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.030000 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.000000 0.000000 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.050000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00000 0.010000 0.00 0.020000 0.060000 0.00 0.00 0.00 0.00000 0.01 0.00 0.000000 0.00 0.01 0.010000 0.000000 0.000000 0.030000 0.00 0.00 0.00000 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.01 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.01 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.010000 0.0000 0.030000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.010000 0.000000 0.030000 0.030000 0.030000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.01 0.010000 0.000000 0.00 0.000000 0.020000 0.00 0.000000 0.000000 0.00 0.00 0.020000 0.00000 0.030000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.00 0.00 0.000000 0.000000 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 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.01 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.010000 0.000000 0.00 0.00 0.00 0.00 0.00 0.060000 0.010000 0.000000 0.00 0.000000 0.00 0.020000 0.000000 0.010000 0.00 0.00 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 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.020000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00000 0.00 0.010000 0.000000 0.010000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.020000 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 0.065217 0.043478 0.00 0.00 0.000000 0.000000 0.021739 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.021739 0.00 0.021739 0.000000 0.000000 0.043478 0.0000 0.00 0.00 0.021739 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.021739 0.021739 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.021739 0.021739 0.00 0.00 0.000000 0.000000 0.021739 0.000000 0.00 0.043478 0.00 0.00 0.00000 0.000000 0.00 0.021739 0.000000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.043478 0.00 0.00 0.00000 0.00 0.000000 0.021739 0.00 0.000000 0.00 0.021739 0.000000 0.00 0.021739 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.021739 0.021739 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.043478 0.043478 0.000000 0.000000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.021739 0.021739 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 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 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.021739 0.000000 0.000000 0.021739 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.021739 0.000000 0.000000 0.000000 0.00 0.021739 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.021739 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.021739 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.021739 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.021739 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.021739 0.000000 0.00 0.00 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 0.00 0.000000 0.000000 0.00 0.043478 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.021739 0.021739 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.021739 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 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 0.000000 0.000000 0.000000 0.000000 0.000000
3 Chelsea 0.000000 0.00 0.00 0.000000 0.030000 0.00 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.010000 0.050000 0.000000 0.010000 0.0000 0.00 0.00 0.010000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.020000 0.000000 0.000000 0.00 0.020000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.01 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.01 0.010000 0.00 0.00 0.00000 0.010000 0.00 0.020000 0.060000 0.00 0.00 0.00 0.00000 0.00 0.01 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.01 0.00000 0.00 0.020000 0.020000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.01 0.01 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.020000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.010000 0.000000 0.010000 0.010000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.030000 0.000000 0.00 0.030000 0.010000 0.00 0.000000 0.000000 0.00 0.01 0.050000 0.00000 0.020000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.020000 0.00 0.00 0.00 0.000000 0.000000 0.010000 0.000000 0.010000 0.01 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.020000 0.00 0.01 0.00 0.010000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.01 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.010000 0.000000 0.01 0.01 0.00 0.00 0.00 0.010000 0.000000 0.000000 0.01 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 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.010000 0.00 0.010000 0.000000 0.00 0.020000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.01 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.010000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00000 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.010000 0.00 0.00 0.020000 0.00 0.00 0.000000 0.000000 0.010000 0.030000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 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 0.000000 0.030000 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 0.000000 0.00 0.01 0.000000 0.00 0.000000 0.030000 0.000000 0.020000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.01 0.00000 0.000000 0.00 0.000000 0.000000 0.020000 0.000000 0.00 0.000000 0.00 0.000000 0.010000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.090000 0.00 0.00 0.00000 0.000000 0.00 0.050000 0.010000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.00000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.020000 0.020000 0.010000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.02000 0.00 0.00 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.000000 0.000000 0.000000 0.020000 0.00 0.000000 0.01 0.00000 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.000000 0.020000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.03 0.020000 0.000000 0.01 0.000000 0.000000 0.00 0.00 0.010000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.01 0.00 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.02 0.000000 0.00 0.01 0.00 0.000000 0.000000 0.000000 0.000000 0.020000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.01 0.000000 0.010000 0.00 0.010000 0.00 0.000000 0.00 0.02 0.00 0.000000 0.00 0.03 0.01 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.000000 0.000000 0.00 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 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.01 0.00 0.01 0.000000 0.030000 0.020000 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.01 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.030000 0.010000 0.000000 0.00 0.000000 0.00 0.01 0.000000 0.000000 0.00000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.01 0.000000 0.000000 0.010000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.040000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.010000
5 Civic Center 0.000000 0.00 0.00 0.000000 0.030000 0.01 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.010000 0.000000 0.000000 0.01 0.00 0.000000 0.01 0.010000 0.030000 0.000000 0.010000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.010000 0.000000 0.010000 0.00 0.000000 0.00 0.000000 0.010000 0.010000 0.010000 0.010000 0.000000 0.000000 0.000000 0.00 0.000000 0.020000 0.01 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00000 0.010000 0.00 0.030000 0.060000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.01000 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.020000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.040000 0.000000 0.000000 0.010000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.01 0.00 0.00 0.00 0.010000 0.000000 0.000000 0.020000 0.050000 0.00 0.00 0.000000 0.000000 0.000000 0.01 0.000000 0.000000 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 0.000000 0.000000 0.00 0.00 0.020000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.01 0.00 0.00 0.000000 0.000000 0.000000 0.010000 0.00 0.000000 0.02 0.00 0.01 0.000000 0.000000 0.010000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.01 0.010000 0.00 0.000000 0.01 0.000000 0.000000 0.01 0.010000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.030000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.010000 0.000000 0.00 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 0.00 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.030000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.030000 0.000000 0.000000 0.00 0.020000 0.00 0.00 0.000000 0.000000 0.00000 0.01 0.000000 0.000000 0.020000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.010000 0.00 0.00000 0.00 0.00 0.01 0.000000 0.000000 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 0.020000 0.020000 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 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.010000 0.000000 0.010000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.000000 0.010000 0.00 0.010000 0.00 0.00 0.00000 0.000000 0.00 0.020000 0.040000 0.00 0.00 0.00 0.00000 0.00 0.00 0.010000 0.00 0.00 0.010000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.010000 0.000000 0.00 0.000000 0.010000 0.010000 0.000000 0.01 0.00 0.010000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.020000 0.00 0.000000 0.0000 0.010000 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.000000 0.020000 0.050000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.010000 0.000000 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 0.010000 0.000000 0.00 0.00 0.050000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.020000 0.00 0.000000 0.00 0.00 0.00 0.020000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.010000 0.00 0.010000 0.000000 0.00 0.020000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.01 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.010000 0.00 0.010000 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.01 0.00 0.010000 0.000000 0.000000 0.01 0.000000 0.00 0.010000 0.000000 0.010000 0.00 0.00 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 0.030000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.030000 0.000000 0.000000 0.00 0.010000 0.01 0.00 0.000000 0.010000 0.00000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.01 0.010000 0.000000 0.000000 0.090000 0.00 0.00000 0.01 0.00 0.00 0.000000 0.000000 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 0.020000 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 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.097561 0.000000 0.000000 0.0000 0.00 0.00 0.024390 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.024390 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00000 0.000000 0.00 0.024390 0.000000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.024390 0.000000 0.000000 0.00 0.00 0.02439 0.00 0.000000 0.000000 0.00 0.024390 0.00 0.048780 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.024390 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.024390 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.02439 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.024390 0.024390 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 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 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.073171 0.00 0.00 0.00 0.000000 0.000000 0.024390 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.121951 0.00 0.00 0.000000 0.000000 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 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.024390 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.000000 0.024390 0.00 0.00 0.00 0.00 0.00 0.024390 0.024390 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.024390 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.024390 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 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 0.024390 0.02439 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.024390 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.073171 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 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 0.000000 0.000000 0.000000 0.000000 0.000000
8 East Village 0.000000 0.00 0.00 0.000000 0.020000 0.00 0.00 0.010000 0.010000 0.010000 0.00 0.010000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.020000 0.020000 0.000000 0.070000 0.0000 0.00 0.00 0.010000 0.000000 0.01 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.010000 0.000000 0.01 0.040000 0.00 0.00 0.00000 0.000000 0.00 0.040000 0.020000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.010000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.020000 0.000000 0.000000 0.000000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.000000 0.010000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.010000 0.000000 0.000000 0.000000 0.00 0.000000 0.01 0.00000 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.010000 0.010000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.01 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.050000 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.030000 0.00000 0.030000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.030000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.040000 0.01 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.01 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.01 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.01 0.040000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.030000 0.01 0.00 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 0.01 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 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 0.000000 0.00000 0.00 0.000000 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.01 0.010000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.01 0.03 0.00 0.000000 0.000000 0.00 0.020000 0.00 0.000000 0.00 0.00 0.050000 0.010000 0.000000 0.000000 0.000000
9 Financial District 0.010000 0.00 0.00 0.000000 0.050000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.010000 0.000000 0.000000 0.050000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00000 0.000000 0.00 0.030000 0.090000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.01 0.00 0.01000 0.00 0.000000 0.010000 0.00 0.000000 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.01 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.030000 0.00 0.020000 0.010000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.010000 0.00 0.030000 0.0000 0.010000 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.010000 0.010000 0.010000 0.040000 0.030000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.030000 0.000000 0.00 0.010000 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.020000 0.01000 0.010000 0.000000 0.020000 0.000000 0.020000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.000000 0.000000 0.020000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.020000 0.00 0.000000 0.01 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.040000 0.000000 0.010000 0.00 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.01 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 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 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 0.030000 0.00000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 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 0.000000 0.030000 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 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.020000 0.000000 0.000000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.020000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.020000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.040000 0.00 0.00 0.010000 0.000000 0.000000 0.000000 0.01 0.010000 0.01 0.00 0.00000 0.030000 0.00 0.000000 0.020000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.030000 0.00 0.00 0.01000 0.00 0.000000 0.030000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.030000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.010000 0.000000 0.000000 0.020000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.010000 0.000000 0.010000 0.020000 0.040000 0.00 0.00 0.000000 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.01 0.020000 0.00000 0.040000 0.000000 0.000000 0.000000 0.010000 0.00 0.01 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.000000 0.01 0.00 0.00 0.030000 0.000000 0.000000 0.000000 0.020000 0.00 0.00 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.030000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.000000 0.010000 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.000000 0.01 0.01 0.00 0.010000 0.030000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.030000 0.000000 0.010000 0.00 0.010000 0.00 0.01 0.000000 0.000000 0.00000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.010000 0.00 0.00 0.000000 0.000000 0.010000 0.000000 0.00 0.00000 0.00 0.00 0.02 0.000000 0.000000 0.00 0.00 0.00 0.02 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.01 0.00 0.000000 0.020000 0.000000 0.010000 0.040000
11 Gramercy 0.000000 0.00 0.00 0.000000 0.030000 0.00 0.01 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.040000 0.010000 0.000000 0.050000 0.0000 0.00 0.01 0.010000 0.000000 0.00 0.00 0.01 0.00 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.020000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.01 0.000000 0.00 0.00 0.00000 0.000000 0.00 0.030000 0.030000 0.00 0.00 0.00 0.00000 0.00 0.00 0.020000 0.01 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.010000 0.000000 0.00 0.000000 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.030000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.010000 0.0000 0.000000 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.010000 0.000000 0.030000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.030000 0.000000 0.00 0.030000 0.000000 0.00 0.000000 0.000000 0.01 0.00 0.050000 0.00000 0.000000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.010000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.040000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.010000 0.00 0.000000 0.00 0.010000 0.00 0.00 0.00 0.000000 0.00 0.01 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.000000 0.000000 0.00 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 0.00 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 0.020000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.01 0.00 0.00 0.01 0.000000 0.030000 0.000000 0.010000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.020000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.030000 0.000000 0.00 0.03000 0.01 0.00 0.00 0.000000 0.000000 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 0.000000 0.010000 0.000000 0.000000 0.010000
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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 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.032787 0.016393 0.016393 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.016393 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.065574 0.00 0.00 0.000000 0.000000 0.032787 0.000000 0.00 0.032787 0.00 0.00 0.00000 0.000000 0.00 0.032787 0.065574 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.016393 0.00 0.00 0.00000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.065574 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.016393 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.016393 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.016393 0.0000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00000 0.016393 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.016393 0.000000 0.00 0.016393 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.032787 0.00 0.000000 0.000000 0.00 0.00 0.016393 0.00000 0.016393 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.016393 0.00 0.00 0.00 0.000000 0.000000 0.016393 0.000000 0.00 0.000000 0.00 0.00 0.00 0.016393 0.000000 0.000000 0.016393 0.049180 0.00 0.00 0.000000 0.000000 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 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.032787 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.098361 0.000000 0.000000 0.00 0.000000 0.00 0.016393 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.032787 0.00 0.000000 0.032787 0.00 0.016393 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.016393 0.00 0.00 0.00 0.000000 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 0.000000 0.00000 0.00 0.000000 0.000000 0.032787 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 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 0.016393 0.000000 0.000000 0.000000 0.032787
14 Hudson Yards 0.000000 0.00 0.00 0.000000 0.059524 0.00 0.00 0.000000 0.000000 0.023810 0.00 0.000000 0.011905 0.000000 0.000000 0.00 0.00 0.011905 0.00 0.000000 0.000000 0.000000 0.023810 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.011905 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.011905 0.011905 0.000000 0.000000 0.011905 0.000000 0.00 0.000000 0.035714 0.00 0.00 0.011905 0.000000 0.000000 0.011905 0.00 0.000000 0.00 0.00 0.00000 0.000000 0.00 0.011905 0.047619 0.00 0.00 0.00 0.00000 0.00 0.00 0.011905 0.00 0.00 0.011905 0.000000 0.000000 0.011905 0.00 0.00 0.00000 0.00 0.000000 0.011905 0.00 0.000000 0.00 0.011905 0.011905 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.023810 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.011905 0.00 0.000000 0.0000 0.000000 0.000000 0.000000 0.011905 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.011905 0.011905 0.000000 0.023810 0.047619 0.00 0.00 0.011905 0.000000 0.000000 0.00 0.011905 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.047619 0.011905 0.00 0.011905 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.047619 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.011905 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.011905 0.000000 0.00 0.000000 0.00 0.011905 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.011905 0.000000 0.023810 0.00 0.011905 0.011905 0.00 0.011905 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.011905 0.011905 0.000000 0.00 0.00 0.00 0.000000 0.011905 0.00 0.000000 0.023810 0.00 0.000000 0.00 0.00 0.00 0.011905 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.011905 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.011905 0.023810 0.000000 0.00 0.000000 0.00 0.00 0.011905 0.011905 0.00000 0.00 0.011905 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.011905 0.00 0.00 0.000000 0.000000 0.035714 0.011905 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 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 0.011905 0.011905 0.000000 0.000000 0.000000
15 Inwood 0.000000 0.00 0.00 0.000000 0.035714 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.035714 0.000000 0.017857 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.017857 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.017857 0.000000 0.00 0.000000 0.053571 0.00 0.00 0.000000 0.000000 0.017857 0.000000 0.00 0.035714 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.017857 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.017857 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.035714 0.000000 0.00 0.000000 0.000000 0.017857 0.000000 0.00 0.00 0.017857 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.017857 0.00 0.000000 0.000000 0.00 0.000000 0.017857 0.017857 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.000000 0.000000 0.035714 0.000000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.017857 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.017857 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 0.00000 0.000000 0.000000 0.000000 0.000000 0.017857 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.017857 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.071429 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.071429 0.00 0.00 0.000000 0.000000 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 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.035714 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.017857 0.017857 0.00 0.00 0.00 0.00 0.00 0.053571 0.017857 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.053571 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.017857 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.000000 0.035714 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00000 0.00 0.017857 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.017857 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.035714 0.017857 0.000000 0.000000 0.017857
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 0.000000 0.00 0.00 0.010000 0.00 0.010000 0.020000 0.000000 0.010000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.000000 0.01 0.000000 0.010000 0.000000 0.030000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.030000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00000 0.010000 0.01 0.040000 0.060000 0.01 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.01000 0.00 0.000000 0.020000 0.01 0.000000 0.00 0.010000 0.000000 0.00 0.010000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.01 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.010000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.010000 0.010000 0.010000 0.030000 0.030000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.010000 0.000000 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 0.000000 0.000000 0.00 0.00 0.070000 0.00000 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.010000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.020000 0.01 0.00 0.000000 0.000000 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 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.010000 0.000000 0.00 0.00 0.00 0.00 0.00 0.050000 0.020000 0.000000 0.00 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 0.00 0.000000 0.00 0.00 0.00 0.020000 0.020000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.010000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.000000 0.010000 0.000000 0.00 0.020000 0.00 0.00 0.000000 0.010000 0.00000 0.00 0.000000 0.000000 0.050000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.01 0.000000 0.000000 0.020000 0.000000 0.00 0.00000 0.00 0.00 0.01 0.000000 0.000000 0.01 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.010000 0.020000 0.000000 0.010000 0.000000
17 Lincoln Square 0.000000 0.00 0.00 0.000000 0.030000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.010000 0.020000 0.000000 0.010000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.050000 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.01 0.01000 0.010000 0.00 0.000000 0.020000 0.00 0.01 0.01 0.00000 0.00 0.00 0.000000 0.00 0.00 0.040000 0.000000 0.000000 0.020000 0.00 0.00 0.00000 0.00 0.000000 0.020000 0.00 0.000000 0.00 0.000000 0.010000 0.00 0.010000 0.000000 0.000000 0.000000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.020000 0.0100 0.020000 0.000000 0.000000 0.020000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.01 0.00 0.01 0.00 0.000000 0.010000 0.020000 0.020000 0.030000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 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 0.000000 0.000000 0.00 0.00 0.050000 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.020000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.020000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.02 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.030000 0.00 0.000000 0.040000 0.00 0.000000 0.000000 0.0000 0.010000 0.010000 0.00 0.00 0.00 0.00 0.00 0.000000 0.010000 0.060000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.01 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.01 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 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 0.000000 0.00000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.050000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 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 0.020000 0.020000 0.000000 0.000000 0.010000
18 Little Italy 0.010000 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 0.000000 0.01 0.00 0.000000 0.00 0.000000 0.050000 0.000000 0.000000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.000000 0.010000 0.000000 0.00 0.000000 0.00 0.000000 0.040000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.060000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.020000 0.01 0.00 0.00000 0.020000 0.00 0.030000 0.020000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.020000 0.00 0.00 0.00000 0.00 0.000000 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.01 0.010000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.01000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.000000 0.000000 0.00 0.01 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.010000 0.000000 0.000000 0.010000 0.00 0.010000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.020000 0.010000 0.02 0.020000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.030000 0.00000 0.020000 0.000000 0.010000 0.000000 0.000000 0.01 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.01 0.01 0.00 0.030000 0.000000 0.000000 0.000000 0.010000 0.01 0.00 0.010000 0.000000 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 0.01 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.01 0.020000 0.000000 0.000000 0.00 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 0.00 0.010000 0.00 0.00 0.00 0.010000 0.030000 0.030000 0.00 0.000000 0.000000 0.00 0.020000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.01 0.00 0.000000 0.00 0.01 0.00 0.00 0.000000 0.020000 0.010000 0.010000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.01 0.02 0.000000 0.000000 0.020000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.010000 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 0.000000 0.00 0.00 0.000000 0.017857 0.053571 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.017857 0.035714 0.000000 0.000000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.017857 0.000000 0.000000 0.000000 0.000000 0.000000 0.017857 0.00 0.000000 0.053571 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.035714 0.00 0.00 0.00000 0.017857 0.00 0.035714 0.053571 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.000000 0.000000 0.00 0.017857 0.00 0.000000 0.017857 0.00 0.017857 0.017857 0.017857 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.017857 0.000000 0.017857 0.00 0.00 0.017857 0.00000 0.000000 0.00 0.000000 0.0000 0.017857 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.017857 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.017857 0.00000 0.035714 0.000000 0.000000 0.000000 0.017857 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.017857 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.017857 0.000000 0.000000 0.000000 0.017857 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.017857 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.017857 0.00 0.000000 0.017857 0.00 0.000000 0.017857 0.0000 0.000000 0.017857 0.00 0.00 0.00 0.00 0.00 0.053571 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.035714 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.017857 0.00 0.00 0.00 0.000000 0.000000 0.017857 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.017857 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.017857 0.000000 0.017857 0.017857 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.017857 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.017857 0.017857
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 0.019608 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.019608 0.000000 0.058824 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.019608 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.019608 0.00 0.00 0.000000 0.000000 0.019608 0.000000 0.00 0.019608 0.00 0.00 0.00000 0.019608 0.00 0.000000 0.039216 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.019608 0.00 0.00 0.00000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.039216 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.019608 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.019608 0.000000 0.00 0.019608 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.000000 0.000000 0.000000 0.019608 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.019608 0.000000 0.019608 0.00 0.00 0.000000 0.000000 0.019608 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.019608 0.00 0.000000 0.000000 0.00 0.019608 0.058824 0.00 0.000000 0.000000 0.00 0.00 0.019608 0.00000 0.019608 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.019608 0.000000 0.019608 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.039216 0.00 0.00 0.000000 0.000000 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 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.019608 0.00 0.000000 0.000000 0.00 0.019608 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.058824 0.039216 0.019608 0.00 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 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.019608 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00000 0.00 0.000000 0.000000 0.019608 0.00 0.019608 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.039216 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.019608 0.00 0.000000 0.00 0.00 0.000000 0.019608 0.019608 0.000000 0.039216
21 Manhattanville 0.000000 0.00 0.00 0.000000 0.023810 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.023810 0.00 0.000000 0.023810 0.023810 0.023810 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.02381 0.000000 0.00 0.000000 0.000000 0.023810 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.023810 0.000000 0.00 0.000000 0.023810 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.023810 0.00 0.00 0.02381 0.000000 0.00 0.000000 0.071429 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.023810 0.00 0.00 0.02381 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.047619 0.000000 0.00 0.000000 0.000000 0.023810 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.02381 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.023810 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.02381 0.000000 0.00 0.000000 0.0000 0.000000 0.023810 0.000000 0.000000 0.00 0.000000 0.00 0.00000 0.023810 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.023810 0.00 0.000000 0.000000 0.00 0.00 0.047619 0.02381 0.000000 0.000000 0.000000 0.000000 0.023810 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.023810 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.047619 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.023810 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.047619 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.023810 0.00 0.00 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 0.00 0.000000 0.000000 0.00 0.047619 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.000000 0.023810 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.00000 0.00 0.023810 0.000000 0.023810 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.02381 0.00 0.00 0.00 0.000000 0.000000 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 0.000000 0.000000 0.000000 0.000000 0.000000
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23 Midtown 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 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.030000 0.000000 0.010000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.030000 0.000000 0.010000 0.01 0.000000 0.00 0.000000 0.000000 0.000000 0.010000 0.010000 0.000000 0.000000 0.000000 0.00 0.000000 0.030000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00000 0.040000 0.00 0.030000 0.040000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.010000 0.000000 0.000000 0.020000 0.00 0.00 0.01000 0.00 0.000000 0.020000 0.00 0.000000 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.00000 0.010000 0.01 0.010000 0.0000 0.030000 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.00000 0.000000 0.00 0.01 0.00 0.00 0.00 0.00 0.010000 0.010000 0.010000 0.020000 0.010000 0.00 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.070000 0.000000 0.00 0.000000 0.020000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.020000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.01 0.00 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.010000 0.000000 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 0.00 0.000000 0.00 0.01 0.00 0.01 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.01 0.010000 0.000000 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.010000 0.000000 0.00 0.01 0.010000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.020000 0.000000 0.000000 0.00 0.030000 0.00 0.00 0.000000 0.030000 0.00000 0.00 0.000000 0.000000 0.020000 0.00 0.010000 0.000000 0.01 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.030000 0.01 0.00000 0.00 0.00 0.00 0.000000 0.000000 0.01 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.010000 0.010000
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28 Roosevelt Island 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 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.033333 0.000000 0.000000 0.000000 0.033333 0.000000 0.000000 0.00 0.000000 0.033333 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.066667 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.033333 0.00 0.00 0.00000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.033333 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.033333 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.033333 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.033333 0.000000 0.033333 0.033333 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 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 0.033333 0.000000 0.00 0.00 0.000000 0.00000 0.033333 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.033333 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.033333 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.033333 0.000000 0.00 0.00 0.000000 0.000000 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 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.033333 0.00 0.000000 0.000000 0.066667 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.033333 0.033333 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.033333 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.066667 0.00 0.033333 0.033333 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.033333 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 0.000000 0.00000 0.00 0.033333 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.033333 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.000000
29 Soho 0.020000 0.00 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.040000 0.01 0.010000 0.000000 0.000000 0.000000 0.01 0.00 0.000000 0.00 0.000000 0.030000 0.000000 0.000000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.000000 0.060000 0.010000 0.00 0.000000 0.00 0.000000 0.010000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00000 0.100000 0.00 0.010000 0.010000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.010000 0.010000 0.00 0.010000 0.00 0.000000 0.000000 0.01 0.020000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.010000 0.0000 0.020000 0.000000 0.000000 0.030000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.000000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.020000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.020000 0.00000 0.010000 0.000000 0.000000 0.000000 0.000000 0.01 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.000000 0.01 0.00 0.00 0.030000 0.000000 0.030000 0.000000 0.010000 0.00 0.00 0.010000 0.000000 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 0.00 0.000000 0.00 0.01 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.01 0.020000 0.000000 0.000000 0.00 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 0.00 0.000000 0.00 0.00 0.00 0.010000 0.020000 0.000000 0.00 0.000000 0.000000 0.00 0.020000 0.000000 0.000000 0.040000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.01 0.00 0.00 0.000000 0.020000 0.010000 0.000000 0.00 0.030000 0.00 0.00 0.000000 0.000000 0.00000 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.010000 0.01 0.01 0.000000 0.000000 0.000000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 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 0.010000 0.000000 0.000000 0.040000 0.020000
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34 Turtle Bay 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.020000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.010000 0.000000 0.010000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.010000 0.000000 0.010000 0.00 0.000000 0.00 0.010000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.030000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.01 0.000000 0.00 0.00 0.00000 0.000000 0.00 0.010000 0.050000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.010000 0.0000 0.030000 0.000000 0.000000 0.000000 0.00 0.020000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.01 0.01 0.00 0.000000 0.020000 0.010000 0.010000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.010000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.030000 0.000000 0.00 0.000000 0.030000 0.00 0.000000 0.000000 0.00 0.00 0.070000 0.01000 0.030000 0.000000 0.000000 0.000000 0.000000 0.02 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 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 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.01 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.01 0.01 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.01 0.000000 0.000000 0.030000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.010000 0.000000 0.00 0.00 0.00 0.00 0.00 0.010000 0.000000 0.020000 0.00 0.000000 0.00 0.010000 0.000000 0.030000 0.00 0.00 0.00 0.000000 0.010000 0.00 0.000000 0.010000 0.00 0.000000 0.00 0.00 0.01 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.020000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.01 0.000000 0.00 0.00 0.00 0.00 0.000000 0.010000 0.010000 0.000000 0.00 0.000000 0.00 0.00 0.000000 0.050000 0.00000 0.00 0.000000 0.000000 0.050000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.010000 0.000000 0.010000 0.000000 0.00 0.00000 0.00 0.01 0.00 0.010000 0.000000 0.02 0.00 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.040000 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 0.060000 0.02 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.050000 0.000000 0.010000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.010000 0.020000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.010000 0.010000 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.010000 0.01 0.00 0.00000 0.010000 0.00 0.020000 0.050000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.020000 0.00 0.00 0.00000 0.00 0.000000 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.06 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.030000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.01 0.00 0.010000 0.000000 0.020000 0.000000 0.040000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.030000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.060000 0.00000 0.010000 0.010000 0.000000 0.000000 0.040000 0.00 0.00 0.000000 0.01 0.000000 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.000000 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 0.00 0.000000 0.00 0.01 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.010000 0.010000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.0000 0.010000 0.000000 0.00 0.00 0.00 0.00 0.00 0.030000 0.010000 0.010000 0.00 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 0.00 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.010000 0.00 0.000000 0.000000 0.01 0.010000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 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 0.000000 0.00000 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.000000 0.00 0.00 0.010000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 0.01 0.00 0.00 0.01 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.000000 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 0.000000 0.00 0.00 0.000000 0.00 0.010000 0.030000 0.000000 0.040000 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.020000 0.000000 0.000000 0.00 0.020000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.040000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.040000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.010000 0.000000 0.00 0.010000 0.00 0.000000 0.000000 0.00 0.020000 0.000000 0.000000 0.000000 0.00 0.00 0.010000 0.000000 0.01 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.010000 0.01000 0.000000 0.00 0.000000 0.0000 0.010000 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.010000 0.010000 0.010000 0.020000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.00 0.020000 0.030000 0.00 0.000000 0.000000 0.00 0.01 0.060000 0.00000 0.010000 0.000000 0.000000 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.030000 0.000000 0.000000 0.000000 0.010000 0.02 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.010000 0.01 0.000000 0.000000 0.01 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.01 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.020000 0.000000 0.000000 0.00 0.000000 0.00 0.020000 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.020000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.01 0.010000 0.000000 0.000000 0.010000 0.00 0.000000 0.01 0.00 0.000000 0.000000 0.01000 0.00 0.000000 0.000000 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.020000 0.000000 0.00 0.00000 0.01 0.00 0.01 0.000000 0.000000 0.01 0.00 0.00 0.02 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.00 0.00 0.040000 0.010000 0.000000 0.000000 0.020000
37 Washington Heights 0.011111 0.00 0.00 0.000000 0.011111 0.00 0.00 0.011111 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.044444 0.011111 0.011111 0.0000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.011111 0.00 0.000000 0.000000 0.000000 0.011111 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.055556 0.00 0.00 0.000000 0.000000 0.011111 0.000000 0.00 0.022222 0.00 0.00 0.00000 0.011111 0.00 0.011111 0.022222 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.022222 0.000000 0.00 0.000000 0.000000 0.011111 0.000000 0.00 0.00 0.000000 0.022222 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.000000 0.000000 0.011111 0.000000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.00 0.00 0.00 0.000000 0.000000 0.044444 0.022222 0.011111 0.00 0.00 0.000000 0.011111 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.011111 0.011111 0.00 0.000000 0.011111 0.00 0.00 0.011111 0.00000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.011111 0.00 0.000000 0.000000 0.022222 0.00 0.00 0.00 0.000000 0.000000 0.011111 0.011111 0.00 0.011111 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.022222 0.00 0.00 0.000000 0.033333 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.022222 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.011111 0.022222 0.00 0.000000 0.000000 0.00 0.000000 0.011111 0.0000 0.011111 0.011111 0.00 0.00 0.00 0.00 0.00 0.022222 0.000000 0.011111 0.00 0.011111 0.00 0.000000 0.000000 0.011111 0.00 0.00 0.00 0.011111 0.000000 0.00 0.011111 0.011111 0.00 0.000000 0.00 0.00 0.00 0.011111 0.000000 0.022222 0.00 0.011111 0.000000 0.00 0.011111 0.000000 0.011111 0.011111 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.000000 0.000000 0.022222 0.000000 0.00 0.011111 0.00 0.00 0.000000 0.000000 0.00000 0.00 0.022222 0.022222 0.011111 0.00 0.000000 0.000000 0.00 0.00 0.022222 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.00 0.00 0.00 0.00 0.000000 0.011111 0.00 0.000000 0.00 0.000000 0.00 0.00 0.011111 0.022222 0.000000 0.011111 0.000000
38 West Village 0.010000 0.00 0.00 0.000000 0.050000 0.00 0.00 0.000000 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.01 0.000000 0.00 0.000000 0.030000 0.000000 0.000000 0.0000 0.00 0.00 0.010000 0.000000 0.00 0.00 0.00 0.00 0.00000 0.010000 0.01 0.000000 0.010000 0.010000 0.000000 0.01 0.010000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.020000 0.00 0.00 0.00000 0.000000 0.00 0.040000 0.030000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.050000 0.00 0.00 0.00000 0.00 0.000000 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.010000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.01000 0.000000 0.00 0.000000 0.0000 0.020000 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.00000 0.010000 0.01 0.00 0.00 0.00 0.00 0.00 0.020000 0.000000 0.000000 0.010000 0.000000 0.00 0.00 0.000000 0.010000 0.000000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.030000 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.090000 0.00000 0.020000 0.030000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.010000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.060000 0.01 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.01 0.000000 0.00 0.000000 0.000000 0.040000 0.00 0.000000 0.010000 0.00 0.000000 0.000000 0.0000 0.000000 0.000000 0.00 0.00 0.01 0.00 0.00 0.020000 0.000000 0.000000 0.00 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 0.00 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.010000 0.00 0.010000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 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 0.000000 0.00000 0.00 0.010000 0.000000 0.010000 0.00 0.000000 0.010000 0.00 0.00 0.000000 0.00 0.01 0.000000 0.000000 0.000000 0.030000 0.00 0.00000 0.00 0.00 0.00 0.010000 0.000000 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 0.040000 0.010000 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 0.000000 0.00 0.00 0.000000 0.00 0.020000 0.010000 0.010000 0.050000 0.0000 0.00 0.00 0.000000 0.000000 0.01 0.00 0.00 0.00 0.00000 0.010000 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.000000 0.00 0.000000 0.000000 0.000000 0.010000 0.000000 0.000000 0.000000 0.000000 0.01 0.000000 0.010000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.010000 0.00 0.00 0.00000 0.000000 0.00 0.000000 0.060000 0.00 0.00 0.00 0.00000 0.00 0.00 0.000000 0.00 0.00 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.00000 0.00 0.000000 0.000000 0.00 0.000000 0.01 0.040000 0.000000 0.00 0.010000 0.000000 0.030000 0.000000 0.00 0.00 0.010000 0.000000 0.00 0.000000 0.00000 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.010000 0.000000 0.000000 0.00 0.00 0.000000 0.00000 0.000000 0.00 0.000000 0.0000 0.010000 0.000000 0.000000 0.000000 0.00 0.000000 0.00 0.00000 0.000000 0.00 0.00 0.00 0.01 0.00 0.00 0.010000 0.000000 0.000000 0.060000 0.010000 0.00 0.01 0.000000 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.00 0.000000 0.000000 0.01 0.000000 0.000000 0.01 0.000000 0.000000 0.00 0.020000 0.010000 0.00 0.000000 0.000000 0.00 0.00 0.070000 0.00000 0.030000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.000000 0.000000 0.010000 0.00 0.00 0.00 0.000000 0.000000 0.010000 0.010000 0.00 0.000000 0.00 0.00 0.00 0.010000 0.000000 0.000000 0.000000 0.030000 0.00 0.00 0.000000 0.000000 0.00 0.00 0.010000 0.00 0.000000 0.00 0.000000 0.000000 0.00 0.010000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.00 0.00 0.00 0.00 0.00000 0.00 0.000000 0.00 0.000000 0.000000 0.020000 0.00 0.000000 0.000000 0.00 0.010000 0.000000 0.0000 0.000000 0.010000 0.00 0.00 0.00 0.00 0.00 0.040000 0.000000 0.000000 0.00 0.010000 0.00 0.010000 0.000000 0.000000 0.00 0.00 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.00 0.000000 0.00 0.00 0.00 0.000000 0.010000 0.020000 0.00 0.000000 0.000000 0.00 0.000000 0.000000 0.000000 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.00 0.00 0.000000 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 0.000000 0.00000 0.00 0.000000 0.000000 0.040000 0.00 0.000000 0.000000 0.00 0.00 0.000000 0.00 0.01 0.000000 0.000000 0.010000 0.000000 0.00 0.00000 0.00 0.00 0.00 0.000000 0.000000 0.01 0.00 0.00 0.00 0.00 0.000000 0.000000 0.01 0.020000 0.00 0.000000 0.00 0.00 0.010000 0.030000 0.000000 0.000000 0.000000
# confirm the new size
manhattan_grouped.shape
(40, 344)

Let’s print the top 5 most common venues for each neighborhood.

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  0.07
1    Coffee Shop  0.06
2          Hotel  0.05
3  Shopping Mall  0.03
4  Women's Store  0.03


----Carnegie Hill----
                  venue  freq
0           Coffee Shop  0.06
1           Pizza Place  0.06
2                  Café  0.05
3  Gym / Fitness Center  0.03
4        Cosmetics Shop  0.03


----Central Harlem----
                venue  freq
0  African Restaurant  0.07
1                 Bar  0.04
2  Seafood Restaurant  0.04
3   French Restaurant  0.04
4      Cosmetics Shop  0.04


----Chelsea----
                venue  freq
0         Coffee Shop  0.06
1  Italian Restaurant  0.05
2              Bakery  0.05
3               Hotel  0.03
4             Theater  0.03


----Chinatown----
                   venue  freq
0     Chinese Restaurant  0.09
1           Cocktail Bar  0.05
2  Vietnamese Restaurant  0.04
3    American Restaurant  0.04
4                 Bakery  0.03


----Civic Center----
                  venue  freq
0           Coffee Shop  0.06
1  Gym / Fitness Center  0.05
2                 Hotel  0.04
3     French Restaurant  0.04
4           Yoga Studio  0.03


----Clinton----
                  venue  freq
0               Theater  0.09
1    Italian Restaurant  0.05
2  Gym / Fitness Center  0.05
3   American Restaurant  0.04
4           Coffee Shop  0.04


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


----East Village----
                venue  freq
0                 Bar  0.07
1      Ice Cream Shop  0.05
2            Wine Bar  0.05
3  Mexican Restaurant  0.04
4        Cocktail Bar  0.04


----Financial District----
                 venue  freq
0          Coffee Shop  0.09
1                  Bar  0.05
2  American Restaurant  0.05
3          Pizza Place  0.04
4                  Gym  0.04


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


----Gramercy----
                venue  freq
0                 Bar  0.05
1  Italian Restaurant  0.05
2          Bagel Shop  0.04
3  Mexican Restaurant  0.04
4         Pizza Place  0.04


----Greenwich Village----
                venue  freq
0  Italian Restaurant  0.13
1      Clothing Store  0.06
2                Café  0.04
3    Sushi Restaurant  0.04
4   French Restaurant  0.03


----Hamilton Heights----
                venue  freq
0         Pizza Place  0.10
1       Deli / Bodega  0.07
2         Coffee Shop  0.07
3                Café  0.07
4  Mexican Restaurant  0.05


----Hudson Yards----
                  venue  freq
0   American Restaurant  0.06
1  Gym / Fitness Center  0.05
2           Coffee Shop  0.05
3                 Hotel  0.05
4    Italian Restaurant  0.05


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


----Lenox Hill----
                venue  freq
0  Italian Restaurant  0.07
1         Coffee Shop  0.06
2    Sushi Restaurant  0.05
3         Pizza Place  0.05
4        Cocktail Bar  0.04


----Lincoln Square----
                   venue  freq
0                  Plaza  0.06
1                   Café  0.05
2                Theater  0.05
3     Italian Restaurant  0.05
4  Performing Arts Venue  0.04


----Little Italy----
                      venue  freq
0                      Café  0.06
1                    Bakery  0.05
2           Bubble Tea Shop  0.04
3  Mediterranean Restaurant  0.03
4        Italian Restaurant  0.03


----Lower East Side----
                 venue  freq
0          Pizza Place  0.05
1          Coffee Shop  0.05
2          Art Gallery  0.05
3                 Café  0.05
4  Japanese Restaurant  0.04


----Manhattan Valley----
               venue  freq
0        Pizza Place  0.06
1                Bar  0.06
2  Indian Restaurant  0.06
3         Playground  0.04
4    Thai Restaurant  0.04


----Manhattanville----
                venue  freq
0         Coffee Shop  0.07
1                Park  0.05
2  Mexican Restaurant  0.05
3       Deli / Bodega  0.05
4  Seafood Restaurant  0.05


----Marble Hill----
                venue  freq
0                 Gym  0.08
1      Sandwich Place  0.08
2         Coffee Shop  0.08
3         Yoga Studio  0.04
4  Seafood Restaurant  0.04


----Midtown----
                 venue  freq
0                Hotel  0.07
1          Coffee Shop  0.04
2       Clothing Store  0.04
3         Cocktail Bar  0.03
4  Sporting Goods Shop  0.03


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


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


----Murray Hill----
                 venue  freq
0          Coffee Shop  0.05
1       Sandwich Place  0.05
2  American Restaurant  0.04
3  Japanese Restaurant  0.04
4                Hotel  0.04


----Noho----
                venue  freq
0   French Restaurant  0.05
1  Italian Restaurant  0.05
2        Cocktail Bar  0.04
3               Hotel  0.04
4           Rock Club  0.03


----Roosevelt Island----
                                      venue  freq
0                               Coffee Shop  0.07
1                            Sandwich Place  0.07
2                                      Park  0.07
3  Residential Building (Apartment / Condo)  0.03
4                                       Gym  0.03


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


----Stuyvesant Town----
                  venue  freq
0                   Bar  0.12
1                  Park  0.12
2        Farmers Market  0.06
3  Gym / Fitness Center  0.06
4              Heliport  0.06


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


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


----Tudor City----
                venue  freq
0                Park  0.06
1                Café  0.06
2  Mexican Restaurant  0.06
3         Coffee Shop  0.04
4               Diner  0.04


----Turtle Bay----
                venue  freq
0  Italian Restaurant  0.07
1    Sushi Restaurant  0.05
2         Coffee Shop  0.05
3          Steakhouse  0.05
4            Wine Bar  0.04


----Upper East Side----
                venue  freq
0             Exhibit  0.06
1         Art Gallery  0.06
2  Italian Restaurant  0.06
3         Coffee Shop  0.05
4              Bakery  0.05


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


----Washington Heights----
               venue  freq
0               Café  0.06
1      Grocery Store  0.04
2             Bakery  0.04
3  Mobile Phone Shop  0.03
4    Supplement Shop  0.02


----West Village----
                     venue  freq
0       Italian Restaurant  0.09
1  New American Restaurant  0.06
2           Cosmetics Shop  0.05
3      American Restaurant  0.05
4                 Wine Bar  0.04


----Yorkville----
                venue  freq
0  Italian Restaurant  0.07
1         Coffee Shop  0.06
2                 Gym  0.06
3                 Bar  0.05
4         Pizza Place  0.04

Let’s put that into a pandas dataframe.

# funciton to sort the venues in descending order
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]

Let’s create the new dataframe and display the top 10 venues for each neighborhood.

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()
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 Women's Store Memorial Site Shopping Mall Gym Wine Shop Boat or Ferry Food Court
1 Carnegie Hill Pizza Place Coffee Shop Café Bookstore Cosmetics Shop Grocery Store Gym Gym / Fitness Center Bakery Japanese Restaurant
2 Central Harlem African Restaurant Fried Chicken Joint French Restaurant American Restaurant Chinese Restaurant Cosmetics Shop Seafood Restaurant Bar Metro Station Southern / Soul Food Restaurant
3 Chelsea Coffee Shop Italian Restaurant Bakery Wine Shop American Restaurant Ice Cream Shop Theater Hotel Sushi Restaurant Cupcake Shop
4 Chinatown Chinese Restaurant Cocktail Bar American Restaurant Vietnamese Restaurant Hotpot Restaurant Optical Shop Spa Bakery Salon / Barbershop Bar

Cluster the neighborhoods

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

# set the 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]
array([1, 0, 1, 1, 1, 1, 1, 3, 0, 0], dtype=int32)

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

# add clustering labels
neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)

manhattan_merged = manhattan_data

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

manhattan_merged.head()
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 Coffee Shop Gym Sandwich Place Yoga Studio Tennis Stadium Supplement Shop Donut Shop Miscellaneous Shop Steakhouse Discount Store
1 Manhattan Chinatown 40.715618 -73.994279 1 Chinese Restaurant Cocktail Bar American Restaurant Vietnamese Restaurant Hotpot Restaurant Optical Shop Spa Bakery Salon / Barbershop Bar
2 Manhattan Washington Heights 40.851903 -73.936900 3 Café Grocery Store Bakery Mobile Phone Shop Pizza Place Chinese Restaurant Tapas Restaurant Mexican Restaurant Coffee Shop Supplement Shop
3 Manhattan Inwood 40.867684 -73.921210 3 Mexican Restaurant Lounge Pizza Place Restaurant Café Spanish Restaurant Bakery Park Chinese Restaurant Frozen Yogurt Shop
4 Manhattan Hamilton Heights 40.823604 -73.949688 3 Pizza Place Café Coffee Shop Deli / Bodega Mexican Restaurant Yoga Studio Sushi Restaurant Caribbean Restaurant Chinese Restaurant School

Let’s visualize the clusters.

# 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

Examine the clusters

You can examine and determine the discriminating venue categories that distinguish each cluster. Based on the defining categories, you can assign a name to each cluster.

Cluster 1

manhattan_merged.loc[manhattan_merged['Cluster Labels'] == 0, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]
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
9 Yorkville Italian Restaurant Coffee Shop Gym Bar Pizza Place Deli / Bodega Sushi Restaurant Wine Shop Mexican Restaurant Japanese Restaurant
10 Lenox Hill Italian Restaurant Coffee Shop Sushi Restaurant Pizza Place Cocktail Bar Burger Joint Gym / Fitness Center Café Gym Sporting Goods Shop
12 Upper West Side Italian Restaurant Wine Bar Bar Café Coffee Shop Mediterranean Restaurant Bakery Indian Restaurant Yoga Studio Dessert Shop
13 Lincoln Square Plaza Café Theater Italian Restaurant Concert Hall Performing Arts Venue Indie Movie Theater American Restaurant Gym / Fitness Center Park
18 Greenwich Village Italian Restaurant Clothing Store Sushi Restaurant Café Seafood Restaurant Indian Restaurant French Restaurant Cosmetics Shop Gourmet Shop Boutique
19 East Village Bar Wine Bar Ice Cream Shop Pizza Place Mexican Restaurant Chinese Restaurant Cocktail Bar Ramen Restaurant Italian Restaurant Japanese Restaurant
25 Manhattan Valley Pizza Place Indian Restaurant Bar Yoga Studio Coffee Shop Mexican Restaurant Deli / Bodega Thai Restaurant Playground Park
29 Financial District Coffee Shop American Restaurant Bar Gym Pizza Place Gym / Fitness Center Hotel Steakhouse Food Truck Event Space
30 Carnegie Hill Pizza Place Coffee Shop Café Bookstore Cosmetics Shop Grocery Store Gym Gym / Fitness Center Bakery Japanese Restaurant
33 Midtown South Korean Restaurant Hotel Hotel Bar Japanese Restaurant Dessert Shop Gym / Fitness Center Coffee Shop Cosmetics Shop American Restaurant Fried Chicken Joint
34 Sutton Place Gym / Fitness Center Furniture / Home Store Italian Restaurant Gym Coffee Shop Indian Restaurant Yoga Studio Chinese Restaurant Sushi Restaurant Pilates Studio
35 Turtle Bay Italian Restaurant Coffee Shop Sushi Restaurant Steakhouse Wine Bar Park Ramen Restaurant Hotel Indian Restaurant Café
38 Flatiron Yoga Studio American Restaurant Gym / Fitness Center Café Japanese Restaurant Spa Mediterranean Restaurant Cycle Studio Cosmetics Shop New American Restaurant
39 Hudson Yards American Restaurant Hotel Gym / Fitness Center Coffee Shop Italian Restaurant Café Thai Restaurant Park Art Gallery Spanish Restaurant

Cluster 2

manhattan_merged.loc[manhattan_merged['Cluster Labels'] == 1, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]
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 Hotpot Restaurant Optical Shop Spa Bakery Salon / Barbershop Bar
6 Central Harlem African Restaurant Fried Chicken Joint French Restaurant American Restaurant Chinese Restaurant Cosmetics Shop Seafood Restaurant Bar Metro Station Southern / Soul Food Restaurant
8 Upper East Side Art Gallery Italian Restaurant Exhibit Coffee Shop Bakery Juice Bar Gym / Fitness Center French Restaurant Hotel Pizza Place
14 Clinton Theater Italian Restaurant Gym / Fitness Center American Restaurant Coffee Shop Sandwich Place Wine Shop Spa Hotel Lounge
15 Midtown Hotel Coffee Shop Clothing Store Bakery Steakhouse French Restaurant Café Theater Sporting Goods Shop Cocktail Bar
16 Murray Hill Coffee Shop Sandwich Place Japanese Restaurant American Restaurant Hotel Gym Italian Restaurant Gym / Fitness Center Mediterranean Restaurant Bagel Shop
17 Chelsea Coffee Shop Italian Restaurant Bakery Wine Shop American Restaurant Ice Cream Shop Theater Hotel Sushi Restaurant Cupcake Shop
20 Lower East Side Pizza Place Coffee Shop Café Art Gallery Japanese Restaurant Ramen Restaurant Chinese Restaurant Cocktail Bar Bakery Filipino Restaurant
21 Tribeca Park American Restaurant Italian Restaurant Spa Café Wine Shop Wine Bar Boutique Coffee Shop Greek Restaurant
22 Little Italy Café Bakery Bubble Tea Shop Sandwich Place Salon / Barbershop Italian Restaurant Mediterranean Restaurant Cocktail Bar Yoga Studio Women's Store
23 Soho Clothing Store Boutique Art Gallery Shoe Store Women's Store Bakery Furniture / Home Store Sporting Goods Shop Mediterranean Restaurant Men's Store
24 West Village Italian Restaurant New American Restaurant American Restaurant Cosmetics Shop Park Wine Bar Cocktail Bar Coffee Shop Theater Bakery
27 Gramercy Bar Italian Restaurant Mexican Restaurant Pizza Place Bagel Shop Thai Restaurant Thrift / Vintage Store Coffee Shop Diner Cocktail Bar
28 Battery Park City Park Coffee Shop Hotel Women's Store Memorial Site Shopping Mall Gym Wine Shop Boat or Ferry Food Court
31 Noho Italian Restaurant French Restaurant Cocktail Bar Hotel Grocery Store Art Gallery Pizza Place Coffee Shop Mexican Restaurant American Restaurant
32 Civic Center Coffee Shop Gym / Fitness Center Hotel French Restaurant Yoga Studio Spa Park American Restaurant Bakery Sandwich Place

Cluster 3

manhattan_merged.loc[manhattan_merged['Cluster Labels'] == 2, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]
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 Park Bar Baseball Field Pet Service Gas Station Boat or Ferry Farmers Market Gym / Fitness Center Cocktail Bar Harbor / Marina

Cluster 4

manhattan_merged.loc[manhattan_merged['Cluster Labels'] == 3, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]
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
2 Washington Heights Café Grocery Store Bakery Mobile Phone Shop Pizza Place Chinese Restaurant Tapas Restaurant Mexican Restaurant Coffee Shop Supplement Shop
3 Inwood Mexican Restaurant Lounge Pizza Place Restaurant Café Spanish Restaurant Bakery Park Chinese Restaurant Frozen Yogurt Shop
4 Hamilton Heights Pizza Place Café Coffee Shop Deli / Bodega Mexican Restaurant Yoga Studio Sushi Restaurant Caribbean Restaurant Chinese Restaurant School
5 Manhattanville Coffee Shop Deli / Bodega Italian Restaurant Mexican Restaurant Seafood Restaurant Park Indian Restaurant Supermarket Boutique Spanish Restaurant
7 East Harlem Mexican Restaurant Bakery Latin American Restaurant Thai Restaurant Deli / Bodega French Restaurant Spa Liquor Store Taco Place Gas Station
11 Roosevelt Island Park Coffee Shop Sandwich Place Kosher Restaurant Gym / Fitness Center Gym Greek Restaurant Dry Cleaner Outdoors & Recreation Playground
26 Morningside Heights Bookstore Park American Restaurant Coffee Shop Burger Joint Sandwich Place Deli / Bodega Café Seafood Restaurant Salad Place
36 Tudor City Park Mexican Restaurant Café Deli / Bodega Diner Pizza Place Coffee Shop Greek Restaurant Dog Run Thai Restaurant

Cluster 5

manhattan_merged.loc[manhattan_merged['Cluster Labels'] == 4, manhattan_merged.columns[[1] + list(range(5, manhattan_merged.shape[1]))]]
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 Coffee Shop Gym Sandwich Place Yoga Studio Tennis Stadium Supplement Shop Donut Shop Miscellaneous Shop Steakhouse Discount Store

Cluster 2 is the biggest. Cluster 3 and 5 have only one neighborhood each.