Boba Maps of NYC¶

Best NYC Neighborhoods for Boba Lovers?¶

### Imports for Google Colab Session

# # imports for Google Colab Sessions
# !apt install gdal-bin python-gdal python3-gdal 
# # Install rtree - Geopandas requirment
# !apt install python3-rtree 
# # Install Geopandas
# !pip install git+git://github.com/geopandas/geopandas.git
# # Install descartes - Geopandas requirment
# !pip install descartes 

import os
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt

C:\Users\datal\anaconda3\envs\boba-nyc\lib\site-packages\pyproj\__init__.py:89: UserWarning: pyproj unable to set database path.
  _pyproj_global_context_initialize()

url = 'https://raw.githubusercontent.com/mebauer/boba-nyc/master/teabook/boba-nyc.csv'
df = pd.read_csv(url)
df.head()

	id	alias	name	image_url	is_closed	url	review_count	categories	rating	coordinates	transactions	price	location	phone	display_phone	distance	latitude	longitude	geometry
0	Q3fmTHT7zilDWtfzLK9lMA	truedan-new-york-3	Truedan	https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...	False	https://www.yelp.com/biz/truedan-new-york-3?ad...	99	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]	4.5	{'latitude': 40.7191742, 'longitude': -73.9962...	['pickup', 'delivery']	$$	{'address1': '208 Grand St', 'address2': None,...	1.646559e+10	(646) 559-2886	1535.241614	40.719174	-73.996220	POINT (-73.9962197 40.7191742)
1	7-bx74TooPuZKZDNW4WFcQ	chun-yang-tea-new-york	Chun Yang Tea	https://s3-media1.fl.yelpcdn.com/bphoto/JrGwrZ...	False	https://www.yelp.com/biz/chun-yang-tea-new-yor...	91	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]	4.5	{'latitude': 40.71617, 'longitude': -73.9971}	['pickup', 'delivery']	$$	{'address1': '26B Elizabeth St', 'address2': '...	1.212420e+10	(212) 420-0123	1212.877370	40.716170	-73.997100	POINT (-73.9971 40.71617)
2	3aypSFXLfkAL4dhHVFobKg	lazy-sundaes-new-york-6	Lazy Sundaes	https://s3-media3.fl.yelpcdn.com/bphoto/kEfCYC...	False	https://www.yelp.com/biz/lazy-sundaes-new-york...	32	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}...	4.5	{'latitude': 40.720597, 'longitude': -73.984539}	['pickup', 'delivery']	$$	{'address1': '23 Clinton St', 'address2': None...	NaN	NaN	1877.016204	40.720597	-73.984539	POINT (-73.984539 40.720597)
3	bVJQEeRNi34-3XN_F1AZEg	xing-fu-tang-new-york	Xing Fu Tang	https://s3-media2.fl.yelpcdn.com/bphoto/cyFHFE...	False	https://www.yelp.com/biz/xing-fu-tang-new-york...	91	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]	4.5	{'latitude': 40.7288, 'longitude': -73.98775}	['pickup', 'delivery']	NaN	{'address1': '133 2nd Ave', 'address2': '', 'a...	NaN	NaN	2656.450427	40.728800	-73.987750	POINT (-73.98775000000001 40.7288)
4	zupVwJAFYkHDwrsQd2ktXA	fiftylan-union-square-new-york-3	FIFTYLAN Union Square	https://s3-media3.fl.yelpcdn.com/bphoto/UIwR20...	False	https://www.yelp.com/biz/fiftylan-union-square...	60	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]	4.0	{'latitude': 40.735516, 'longitude': -73.989191}	['pickup', 'delivery']	NaN	{'address1': '32 Union Square E', 'address2': ...	1.646767e+10	(646) 767-0085	3367.900728	40.735516	-73.989191	POINT (-73.98919100000001 40.735516)

df.describe()

	review_count	rating	phone	distance	latitude	longitude
count	414.000000	414.000000	3.620000e+02	414.000000	414.000000	414.000000
mean	89.722222	3.985507	1.613962e+10	8980.359386	40.716170	-73.941997
std	116.765312	0.608481	2.375761e+09	5684.076763	0.058255	0.084129
min	1.000000	1.000000	1.201432e+10	953.875131	40.537609	-74.166080
25%	16.250000	3.500000	1.347732e+10	3954.891025	40.689505	-73.994920
50%	49.000000	4.000000	1.718286e+10	8254.243639	40.731610	-73.975096
75%	109.000000	4.500000	1.718886e+10	13019.588280	40.757300	-73.880179
max	740.000000	5.000000	1.934235e+10	25132.157979	40.886959	-73.702410

df.describe(include=['O']).T

	count	unique	top	freq
id	414	414	Q3fmTHT7zilDWtfzLK9lMA	1
alias	414	414	truedan-new-york-3	1
name	414	259	Kung Fu Tea	26
image_url	409	409	https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...	1
url	414	414	https://www.yelp.com/biz/truedan-new-york-3?ad...	1
categories	414	177	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]	115
coordinates	414	409	{'latitude': 40.71697, 'longitude': -73.99471}	2
transactions	414	5	['pickup', 'delivery']	229
price	258	3	$	166
location	414	411	{'address1': '90-15 Queens Blvd', 'address2': ...	2
display_phone	362	357	(718) 395-8297	2
geometry	414	409	POINT (-73.99471 40.71697)	2

Map Boba Data for NYC Neighborhoods¶

# read neighborhood data as geodataframe
url = 'https://data.cityofnewyork.us/api/geospatial/cpf4-rkhq?method=export&format=Shapefile'
neighborhoods = gpd.read_file(url)
neighborhoods.head()

	boro_code	boro_name	county_fip	ntacode	ntaname	shape_area	shape_leng	geometry
0	4.0	Queens	081	QN51	Murray Hill	5.248828e+07	33266.904856	POLYGON ((-73.80379 40.77561, -73.80099 40.775...
1	4.0	Queens	081	QN27	East Elmhurst	1.972685e+07	19816.711894	POLYGON ((-73.86110 40.76366, -73.85993 40.762...
2	4.0	Queens	081	QN41	Fresh Meadows-Utopia	2.777485e+07	22106.431272	POLYGON ((-73.77758 40.73019, -73.77849 40.729...
3	1.0	Manhattan	061	MN17	Midtown-Midtown South	3.019153e+07	27032.700375	POLYGON ((-73.97301 40.76428, -73.97141 40.763...
4	2.0	Bronx	005	BX09	Soundview-Castle Hill-Clason Point-Harding Park	5.198380e+07	67340.977626	MULTIPOLYGON (((-73.88064 40.81852, -73.88098 ...

# re-project neighborhood data
neighborhoods = neighborhoods.to_crs('EPSG:4326')

# open the boba dataframe as geo data frame using the lat-long info in the data
gdf = gpd.GeoDataFrame(df, crs=neighborhoods.crs, geometry=gpd.points_from_xy(df.longitude, df.latitude))

print(neighborhoods.crs)
print(gdf.crs)

---------------------------------------------------------------------------
CRSError                                  Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_26748\3558824444.py in <module>
      1 # re-project neighborhood data
----> 2 neighborhoods = neighborhoods.to_crs('EPSG:4326')
      3 
      4 # open the boba dataframe as geo data frame using the lat-long info in the data
      5 gdf = gpd.GeoDataFrame(df, crs=neighborhoods.crs, geometry=gpd.points_from_xy(df.longitude, df.latitude))

~\anaconda3\envs\boba-nyc\lib\site-packages\geopandas\geodataframe.py in to_crs(self, crs, epsg, inplace)
   1273         else:
   1274             df = self.copy()
-> 1275         geom = df.geometry.to_crs(crs=crs, epsg=epsg)
   1276         df.geometry = geom
   1277         df.crs = geom.crs

~\anaconda3\envs\boba-nyc\lib\site-packages\geopandas\geoseries.py in to_crs(self, crs, epsg)
   1118         """
   1119         return GeoSeries(
-> 1120             self.values.to_crs(crs=crs, epsg=epsg), index=self.index, name=self.name
   1121         )
   1122 

~\anaconda3\envs\boba-nyc\lib\site-packages\geopandas\array.py in to_crs(self, crs, epsg)
    766             )
    767         if crs is not None:
--> 768             crs = CRS.from_user_input(crs)
    769         elif epsg is not None:
    770             crs = CRS.from_epsg(epsg)

~\anaconda3\envs\boba-nyc\lib\site-packages\pyproj\crs\crs.py in from_user_input(cls, value, **kwargs)
    477         if isinstance(value, cls):
    478             return value
--> 479         return cls(value, **kwargs)
    480 
    481     def get_geod(self) -> Optional[Geod]:

~\anaconda3\envs\boba-nyc\lib\site-packages\pyproj\crs\crs.py in __init__(self, projparams, **kwargs)
    324             self._local.crs = projparams
    325         else:
--> 326             self._local.crs = _CRS(self.srs)
    327 
    328     @property

pyproj\_crs.pyx in pyproj._crs._CRS.__init__()

CRSError: Invalid projection: EPSG:4326: (Internal Proj Error: proj_create: no database context specified)

# join the neighborhood information to each boba shop
join_df = gpd.sjoin(gdf, neighborhoods, how="left")
print(join_df.shape)
join_df.head()

(414, 27)

	id	alias	name	image_url	is_closed	url	review_count	categories	rating	coordinates	...	longitude	geometry	index_right	boro_code	boro_name	county_fip	ntacode	ntaname	shape_area	shape_leng
0	Q3fmTHT7zilDWtfzLK9lMA	truedan-new-york-3	Truedan	https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...	False	https://www.yelp.com/biz/truedan-new-york-3?ad...	99	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]	4.5	{'latitude': 40.7191742, 'longitude': -73.9962...	...	-73.996220	POINT (-73.99622 40.71917)	94	1.0	Manhattan	061	MN24	SoHo-TriBeCa-Civic Center-Little Italy	2.503268e+07	26855.031527
1	7-bx74TooPuZKZDNW4WFcQ	chun-yang-tea-new-york	Chun Yang Tea	https://s3-media1.fl.yelpcdn.com/bphoto/JrGwrZ...	False	https://www.yelp.com/biz/chun-yang-tea-new-yor...	91	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]	4.5	{'latitude': 40.71617, 'longitude': -73.9971}	...	-73.997100	POINT (-73.99710 40.71617)	88	1.0	Manhattan	061	MN27	Chinatown	1.450188e+07	20786.254332
2	3aypSFXLfkAL4dhHVFobKg	lazy-sundaes-new-york-6	Lazy Sundaes	https://s3-media3.fl.yelpcdn.com/bphoto/kEfCYC...	False	https://www.yelp.com/biz/lazy-sundaes-new-york...	32	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}...	4.5	{'latitude': 40.720597, 'longitude': -73.984539}	...	-73.984539	POINT (-73.98454 40.72060)	88	1.0	Manhattan	061	MN27	Chinatown	1.450188e+07	20786.254332
3	bVJQEeRNi34-3XN_F1AZEg	xing-fu-tang-new-york	Xing Fu Tang	https://s3-media2.fl.yelpcdn.com/bphoto/cyFHFE...	False	https://www.yelp.com/biz/xing-fu-tang-new-york...	91	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]	4.5	{'latitude': 40.7288, 'longitude': -73.98775}	...	-73.987750	POINT (-73.98775 40.72880)	139	1.0	Manhattan	061	MN22	East Village	1.089424e+07	13555.232094
4	zupVwJAFYkHDwrsQd2ktXA	fiftylan-union-square-new-york-3	FIFTYLAN Union Square	https://s3-media3.fl.yelpcdn.com/bphoto/UIwR20...	False	https://www.yelp.com/biz/fiftylan-union-square...	60	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]	4.0	{'latitude': 40.735516, 'longitude': -73.989191}	...	-73.989191	POINT (-73.98919 40.73552)	32	1.0	Manhattan	061	MN21	Gramercy	7.526916e+06	12096.891148

5 rows × 27 columns

# get the counts of boba shops groupped by neighborhood names
nt_count = join_df.groupby(by='ntaname')['id'].count().sort_values(ascending=True)
nt_count = nt_count.reset_index()
nt_count.columns = ['ntaname', 'counts']
nt_count.describe()

	counts
count	93.000000
mean	4.451613
std	6.289253
min	1.000000
25%	1.000000
50%	2.000000
75%	5.000000
max	40.000000

# get the average ratings of boba shops groupped by neighborhood names
nt_rating = join_df.groupby(by='ntaname')['rating'].mean().sort_values(ascending=True)
nt_rating = nt_rating.reset_index()
nt_rating.columns = ['ntaname','rating']
nt_rating.describe()

	rating
count	93.000000
mean	4.018089
std	0.472595
min	2.000000
25%	3.833333
50%	4.000000
75%	4.333333
max	5.000000

# get the average ratings of boba shops groupped by neighborhood names
nt_revcount = join_df.groupby(by='ntaname')['review_count'].sum().sort_values(ascending=True)
nt_revcount = nt_revcount.reset_index()
nt_revcount.columns = ['ntaname','review_count']
nt_revcount.describe()

	review_count
count	93.000000
mean	399.408602
std	812.981820
min	1.000000
25%	32.000000
50%	114.000000
75%	384.000000
max	5093.000000

# merge the counts, ratings, review counts by neighborhood dataframe to the neighborhood geodataframe, which contains spatial information of the neighborhoods
group_nt_gdf = neighborhoods.merge(nt_count, how="left", left_on='ntaname', right_on='ntaname')
group_nt_gdf = group_nt_gdf.merge(nt_rating, how="left", left_on='ntaname', right_on='ntaname')
group_nt_gdf = group_nt_gdf.merge(nt_revcount, how="left", left_on='ntaname', right_on='ntaname')

group_nt_gdf.head()

	boro_code	boro_name	county_fip	ntacode	ntaname	shape_area	shape_leng	geometry	counts	rating	review_count
0	4.0	Queens	081	QN51	Murray Hill	5.248828e+07	33266.904856	POLYGON ((-73.80379 40.77561, -73.80099 40.775...	4.0	4.250000	56.0
1	4.0	Queens	081	QN27	East Elmhurst	1.972685e+07	19816.711894	POLYGON ((-73.86110 40.76366, -73.85993 40.762...	NaN	NaN	NaN
2	4.0	Queens	081	QN41	Fresh Meadows-Utopia	2.777485e+07	22106.431272	POLYGON ((-73.77758 40.73019, -73.77849 40.729...	5.0	4.000000	338.0
3	1.0	Manhattan	061	MN17	Midtown-Midtown South	3.019153e+07	27032.700375	POLYGON ((-73.97301 40.76428, -73.97141 40.763...	15.0	3.833333	2564.0
4	2.0	Bronx	005	BX09	Soundview-Castle Hill-Clason Point-Harding Park	5.198380e+07	67340.977626	MULTIPOLYGON (((-73.88064 40.81852, -73.88098 ...	NaN	NaN	NaN

# get the lat-long of the centroid of each neighborhood ("label_geometry") for labeling
group_nt_gdf['label_geometry'] = group_nt_gdf['geometry'].centroid
group_nt_gdf.sort_values('counts', ascending=True)
group_nt_gdf.head()

C:\Users\datal\AppData\Local\Temp\ipykernel_14736\2953681496.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  group_nt_gdf['label_geometry'] = group_nt_gdf['geometry'].centroid

	boro_code	boro_name	county_fip	ntacode	ntaname	shape_area	shape_leng	geometry	counts	rating	review_count	label_geometry
0	4.0	Queens	081	QN51	Murray Hill	5.248828e+07	33266.904856	POLYGON ((-73.80379 40.77561, -73.80099 40.775...	4.0	4.250000	56.0	POINT (-73.80955 40.76835)
1	4.0	Queens	081	QN27	East Elmhurst	1.972685e+07	19816.711894	POLYGON ((-73.86110 40.76366, -73.85993 40.762...	NaN	NaN	NaN	POINT (-73.86840 40.76335)
2	4.0	Queens	081	QN41	Fresh Meadows-Utopia	2.777485e+07	22106.431272	POLYGON ((-73.77758 40.73019, -73.77849 40.729...	5.0	4.000000	338.0	POINT (-73.78372 40.73489)
3	1.0	Manhattan	061	MN17	Midtown-Midtown South	3.019153e+07	27032.700375	POLYGON ((-73.97301 40.76428, -73.97141 40.763...	15.0	3.833333	2564.0	POINT (-73.98350 40.75573)
4	2.0	Bronx	005	BX09	Soundview-Castle Hill-Clason Point-Harding Park	5.198380e+07	67340.977626	MULTIPOLYGON (((-73.88064 40.81852, -73.88098 ...	NaN	NaN	NaN	POINT (-73.85619 40.81805)

# create a choropleth map of boba shop counts in NYC neighborhoods

fig, ax = plt.subplots(figsize=(20, 12))
missing_kwds={"color": "lightgrey","edgecolor": "lightgrey","label": "Missing values"}
group_nt_gdf.plot(ax=ax, column='counts', cmap='OrRd', edgecolor="none", linewidth=1, legend=True, legend_kwds = {'label': "Counts of Boba Tea Shops in NYC Neighborhoods"}, missing_kwds=missing_kwds)

# lebel the top 3 neighborhoods
top_counts = group_nt_gdf.sort_values("counts", ascending=False)[0:3]
for x, y, label in zip(top_counts.label_geometry.x, top_counts.label_geometry.y, top_counts.ntaname):
    ax.annotate(label, xy=(x, y))

ax.set_axis_off()

# create a choropleth map of average boba shop ratings in NYC neighborhoods

fig, ax = plt.subplots(figsize=(20, 12))
missing_kwds={"color": "lightgrey","edgecolor": "lightgrey","label": "Missing values"}
group_nt_gdf.plot(
    ax=ax, column='rating', cmap='BuPu', edgecolor="none", linewidth=1, legend=True,
    legend_kwds = {'label': "Rating of Boba Tea Shops in NYC Neighborhoods"}, missing_kwds=missing_kwds)

# lebel the top 3 neighborhoods
top_rated = group_nt_gdf.sort_values("rating", ascending=False)[0:3]
for x, y, label in zip(top_rated.label_geometry.x, top_rated.label_geometry.y, top_rated.ntaname):
    ax.annotate(label, xy=(x, y))
ax.set_axis_off()

# Put the maps side to side..
# create a choropleth map of boba shop ratings in NYC neighborhoods
# and proportional symbol maps of boba shop counts and boba shop review counts in NYC neighborhoods

fig, ax = plt.subplots(2, 2, figsize=(30, 25))


# draw average rating of boba tea shops
ax[0][0].title.set_text('Average Ratings of Boba Shops in NYC')
missing_kwds={"color": "lightgrey","edgecolor": "lightgrey","label": "Missing values"}
group_nt_gdf.plot(ax=ax[0][0], 
    column='rating',
    cmap='BuPu',
    edgecolor="none", 
    linewidth=1,
    legend=True,
    legend_kwds = {
        'label': "Rating of Boba Tea Shops in NYC Neighborhoods"
    }, 
    missing_kwds = missing_kwds
)

# To create proportional symbol maps,
# create a copy of the geodataframe using the centroids coordinate as the geometry
group_nt_gdf_pt = group_nt_gdf.copy()
group_nt_gdf_pt['geometry'] = group_nt_gdf_pt['label_geometry']
group_nt_gdf_pt.head()


# draw counts of boba tea shops
ax[0][1].title.set_text('Counts of Boba Shops in NYC')
neighborhoods.plot(ax=ax[0][1], color="#ebd7b8", edgecolor="white",linewidth=0.5, alpha=1)
group_nt_gdf_pt.plot(
    ax=ax[0][1], color="#2e2622", markersize=group_nt_gdf_pt.counts * 50, alpha=0.5, edgecolor='#2e2622', linewidth=0.4, legend=True, label="Counts of Boba Tea Shops in NYC Neighborhoods")


# draw counts of reviews
ax[1][0].title.set_text('Counts of Boba Shops Reviews in NYC')
neighborhoods.plot(ax=ax[1][0], color="#b8bd88", edgecolor="whitesmoke",linewidth=0.5, alpha=1)
group_nt_gdf_pt.plot(
    ax=ax[1][0], color="#2e2622", markersize=group_nt_gdf_pt.review_count / 2, alpha=0.4, edgecolor='#2e2622', linewidth=0.4, legend=True, label="Counts of Boba Shops Reviews in NYC Neighborhoods")

ax[0][0].set_axis_off()
ax[0][1].set_axis_off()
ax[1][0].set_axis_off()
ax[1][1].set_axis_off()

ax[0][1].legend(loc='lower right', markerscale=0.7, frameon=False)
ax[1][0].legend(loc='lower right', markerscale=0.7, frameon=False)

''

The top-rated neighborhoods seem to have low counts of boba shops and count of reviews.¶

fewer boba shops…less competition?
fewer counts of review…less representative rating?

group_nt_gdf[['rating', 'counts', 'review_count']].describe()

	rating	counts	review_count
count	93.000000	93.000000	93.000000
mean	4.018089	4.451613	399.408602
std	0.472595	6.289253	812.981820
min	2.000000	1.000000	1.000000
25%	3.833333	1.000000	32.000000
50%	4.000000	2.000000	114.000000
75%	4.333333	5.000000	384.000000
max	5.000000	40.000000	5093.000000

group_nt_gdf.sort_values("rating", ascending=False).dropna()[0:10]

	boro_code	boro_name	county_fip	ntacode	ntaname	shape_area	shape_leng	geometry	counts	rating	review_count	label_geometry
64	2.0	Bronx	005	BX06	Belmont	1.355635e+07	17104.422131	POLYGON ((-73.88309 40.86660, -73.88298 40.866...	1.0	5.0	5.0	POINT (-73.88587 40.85778)
103	4.0	Queens	081	QN23	College Point	5.055398e+07	59454.750110	MULTIPOLYGON (((-73.86788 40.78736, -73.86803 ...	1.0	5.0	2.0	POINT (-73.84281 40.78092)
145	3.0	Brooklyn	047	BK73	North Side-South Side	2.886227e+07	28155.614043	POLYGON ((-73.95814 40.72440, -73.95772 40.724...	5.0	4.6	434.0	POINT (-73.95862 40.71492)
122	5.0	Staten Island	085	SI07	Westerleigh	6.325658e+07	41655.704816	POLYGON ((-74.13047 40.63089, -74.13014 40.629...	3.0	4.5	68.0	POINT (-74.12535 40.61688)
81	4.0	Queens	081	QN71	Old Astoria	1.562046e+07	19367.656081	POLYGON ((-73.92828 40.77690, -73.92785 40.776...	1.0	4.5	124.0	POINT (-73.92833 40.77157)
63	3.0	Brooklyn	047	BK35	Stuyvesant Heights	3.140694e+07	28239.081099	POLYGON ((-73.91805 40.68721, -73.91800 40.686...	1.0	4.5	49.0	POINT (-73.93189 40.68817)
97	1.0	Manhattan	061	MN28	Lower East Side	2.329937e+07	26518.511656	MULTIPOLYGON (((-73.98237 40.70554, -73.98246 ...	1.0	4.5	8.0	POINT (-73.98091 40.71784)
100	4.0	Queens	081	QN68	Queensbridge-Ravenswood-Long Island City	2.331294e+07	27228.382705	POLYGON ((-73.93453 40.77118, -73.93425 40.771...	1.0	4.5	27.0	POINT (-73.93875 40.75756)
109	1.0	Manhattan	061	MN06	Manhattanville	1.073480e+07	17357.246731	POLYGON ((-73.94608 40.82126, -73.94640 40.820...	1.0	4.5	90.0	POINT (-73.95382 40.81802)
52	2.0	Bronx	005	BX29	Spuyten Duyvil-Kingsbridge	2.321411e+07	27990.757219	POLYGON ((-73.89788 40.89028, -73.89827 40.889...	1.0	4.5	19.0	POINT (-73.91067 40.88241)

NYC Neighborhood Boba Index¶

that take into account counts of reviews and counts of boba shops in the neighborhood?¶

# make a copy of the boba dataframe
join_df2 = join_df.copy()

# make a copy of the neighborhoods geodataframe 
group_nt_gdf2 = group_nt_gdf[['boro_name', 'ntaname', 'geometry', 'label_geometry']].copy()
group_nt_gdf2.head()

	boro_name	ntaname	geometry	label_geometry
0	Queens	Murray Hill	POLYGON ((-73.80379 40.77561, -73.80099 40.775...	POINT (-73.80955 40.76835)
1	Queens	East Elmhurst	POLYGON ((-73.86110 40.76366, -73.85993 40.762...	POINT (-73.86840 40.76335)
2	Queens	Fresh Meadows-Utopia	POLYGON ((-73.77758 40.73019, -73.77849 40.729...	POINT (-73.78372 40.73489)
3	Manhattan	Midtown-Midtown South	POLYGON ((-73.97301 40.76428, -73.97141 40.763...	POINT (-73.98350 40.75573)
4	Bronx	Soundview-Castle Hill-Clason Point-Harding Park	MULTIPOLYGON (((-73.88064 40.81852, -73.88098 ...	POINT (-73.85619 40.81805)

join_df2['rate_rev'] = join_df2['rating'] * join_df2['review_count']
join_df2.head()

	id	alias	name	image_url	is_closed	url	review_count	categories	rating	coordinates	...	geometry	index_right	boro_code	boro_name	county_fip	ntacode	ntaname	shape_area	shape_leng	rate_rev
0	Q3fmTHT7zilDWtfzLK9lMA	truedan-new-york-3	Truedan	https://s3-media2.fl.yelpcdn.com/bphoto/BmUZXO...	False	https://www.yelp.com/biz/truedan-new-york-3?ad...	99	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]	4.5	{'latitude': 40.7191742, 'longitude': -73.9962...	...	POINT (-73.99622 40.71917)	94	1.0	Manhattan	061	MN24	SoHo-TriBeCa-Civic Center-Little Italy	2.503268e+07	26855.031527	445.5
1	7-bx74TooPuZKZDNW4WFcQ	chun-yang-tea-new-york	Chun Yang Tea	https://s3-media1.fl.yelpcdn.com/bphoto/JrGwrZ...	False	https://www.yelp.com/biz/chun-yang-tea-new-yor...	91	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]	4.5	{'latitude': 40.71617, 'longitude': -73.9971}	...	POINT (-73.99710 40.71617)	88	1.0	Manhattan	061	MN27	Chinatown	1.450188e+07	20786.254332	409.5
2	3aypSFXLfkAL4dhHVFobKg	lazy-sundaes-new-york-6	Lazy Sundaes	https://s3-media3.fl.yelpcdn.com/bphoto/kEfCYC...	False	https://www.yelp.com/biz/lazy-sundaes-new-york...	32	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}...	4.5	{'latitude': 40.720597, 'longitude': -73.984539}	...	POINT (-73.98454 40.72060)	88	1.0	Manhattan	061	MN27	Chinatown	1.450188e+07	20786.254332	144.0
3	bVJQEeRNi34-3XN_F1AZEg	xing-fu-tang-new-york	Xing Fu Tang	https://s3-media2.fl.yelpcdn.com/bphoto/cyFHFE...	False	https://www.yelp.com/biz/xing-fu-tang-new-york...	91	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]	4.5	{'latitude': 40.7288, 'longitude': -73.98775}	...	POINT (-73.98775 40.72880)	139	1.0	Manhattan	061	MN22	East Village	1.089424e+07	13555.232094	409.5
4	zupVwJAFYkHDwrsQd2ktXA	fiftylan-union-square-new-york-3	FIFTYLAN Union Square	https://s3-media3.fl.yelpcdn.com/bphoto/UIwR20...	False	https://www.yelp.com/biz/fiftylan-union-square...	60	[{'alias': 'bubbletea', 'title': 'Bubble Tea'}]	4.0	{'latitude': 40.735516, 'longitude': -73.989191}	...	POINT (-73.98919 40.73552)	32	1.0	Manhattan	061	MN21	Gramercy	7.526916e+06	12096.891148	240.0

5 rows × 28 columns

# sum up the (rating * review_count) by neighborhood
nt_raterev2 = join_df2.groupby(by='ntaname')['rate_rev'].sum().sort_values(ascending=True)
nt_raterev2 = nt_raterev2.reset_index()
nt_raterev2.columns = ['ntaname','rate_rev']
nt_raterev2.head()

	ntaname	rate_rev
0	park-cemetery-etc-Queens	2.0
1	College Point	10.0
2	Fort Greene	10.5
3	Belmont	25.0
4	Georgetown-Marine Park-Bergen Beach-Mill Basin	28.0

# merge to the neighborhood geodataframe
group_nt_gdf2 = group_nt_gdf2.merge(nt_count, how='left', left_on='ntaname', right_on='ntaname')
group_nt_gdf2 = group_nt_gdf2.merge(nt_rating, how='left', left_on='ntaname', right_on='ntaname')
group_nt_gdf2 = group_nt_gdf2.merge(nt_revcount, how='left', left_on='ntaname', right_on='ntaname')

group_nt_gdf2 = group_nt_gdf2.merge(nt_raterev2, how='left', left_on='ntaname', right_on='ntaname')

group_nt_gdf2.head()

	boro_name	ntaname	geometry	label_geometry	counts	rating	review_count	rate_rev
0	Queens	Murray Hill	POLYGON ((-73.80379 40.77561, -73.80099 40.775...	POINT (-73.80955 40.76835)	4.0	4.250000	56.0	245.0
1	Queens	East Elmhurst	POLYGON ((-73.86110 40.76366, -73.85993 40.762...	POINT (-73.86840 40.76335)	NaN	NaN	NaN	NaN
2	Queens	Fresh Meadows-Utopia	POLYGON ((-73.77758 40.73019, -73.77849 40.729...	POINT (-73.78372 40.73489)	5.0	4.000000	338.0	1230.5
3	Manhattan	Midtown-Midtown South	POLYGON ((-73.97301 40.76428, -73.97141 40.763...	POINT (-73.98350 40.75573)	15.0	3.833333	2564.0	9598.5
4	Bronx	Soundview-Castle Hill-Clason Point-Harding Park	MULTIPOLYGON (((-73.88064 40.81852, -73.88098 ...	POINT (-73.85619 40.81805)	NaN	NaN	NaN	NaN

The Creation of Boba Index…¶

group_nt_gdf2['boba_index'] = (

    # review counts-weighted rating...
    group_nt_gdf2['rate_rev']                        # sum of (rating * review counts) in neighborhood
    / group_nt_gdf2['review_count']                  # / (the total review counts in neighborhood)

    # consider competition...
    * np.sqrt(group_nt_gdf2['counts'])               # * (square root of total boba shop count in neighborhood)
    / (np.sqrt(group_nt_gdf2['counts'])).mean()      # / (mean of square root boba shop count in whole NYC)

)

group_nt_gdf2.head()

	boro_name	ntaname	geometry	label_geometry	counts	rating	review_count	rate_rev	boba_index
0	Queens	Murray Hill	POLYGON ((-73.80379 40.77561, -73.80099 40.775...	POINT (-73.80955 40.76835)	4.0	4.250000	56.0	245.0	4.826066
1	Queens	East Elmhurst	POLYGON ((-73.86110 40.76366, -73.85993 40.762...	POINT (-73.86840 40.76335)	NaN	NaN	NaN	NaN	NaN
2	Queens	Fresh Meadows-Utopia	POLYGON ((-73.77758 40.73019, -73.77849 40.729...	POINT (-73.78372 40.73489)	5.0	4.000000	338.0	1230.5	4.489884
3	Manhattan	Midtown-Midtown South	POLYGON ((-73.97301 40.76428, -73.97141 40.763...	POINT (-73.98350 40.75573)	15.0	3.833333	2564.0	9598.5	7.996799
4	Bronx	Soundview-Castle Hill-Clason Point-Harding Park	MULTIPOLYGON (((-73.88064 40.81852, -73.88098 ...	POINT (-73.85619 40.81805)	NaN	NaN	NaN	NaN	NaN

# create a choropleth map of Baba Index in NYC neighborhoods

fig, ax = plt.subplots(figsize=(20, 12), sharey=False)

ax.title.set_text('Boba Index of NYC Neighborhoods')
missing_kwds={"color": "lightgrey","edgecolor": "lightgrey","label": "Missing values"}
group_nt_gdf2.plot(
    ax=ax, column='boba_index', cmap='BuPu', edgecolor="none", linewidth=1, legend=True,
    legend_kwds = {'label': "Boba Index"}, missing_kwds=missing_kwds)

top_rated = group_nt_gdf2.sort_values("boba_index", ascending=False)[0:3]
for x, y, label in zip(top_rated.label_geometry.x, top_rated.label_geometry.y, top_rated.ntaname):
    ax.annotate(label, xy=(x, y))

ax.set_axis_off()

group_nt_gdf2.sort_values("boba_index", ascending=False).dropna()[0:10]

	boro_name	ntaname	geometry	label_geometry	counts	rating	review_count	rate_rev	boba_index
155	Queens	Flushing	POLYGON ((-73.82331 40.77958, -73.82355 40.779...	POINT (-73.82887 40.76109)	40.0	3.687500	5093.0	19257.0	13.189555
88	Manhattan	Chinatown	POLYGON ((-73.98382 40.72147, -73.98386 40.721...	POINT (-73.99463 40.71561)	28.0	4.000000	4803.0	19208.0	11.671691
166	Queens	Hunters Point-Sunnyside-West Maspeth	POLYGON ((-73.92563 40.75214, -73.92442 40.752...	POINT (-73.93121 40.73977)	19.0	4.289474	970.0	4076.0	10.102397
150	Brooklyn	Sunset Park East	POLYGON ((-73.98906 40.64412, -73.98978 40.643...	POINT (-74.00465 40.64189)	23.0	3.434783	1183.0	4148.5	9.275888
144	Queens	Elmhurst	POLYGON ((-73.86791 40.74493, -73.86788 40.744...	POINT (-73.87712 40.73949)	17.0	4.088235	1048.0	4059.5	8.808885
140	Manhattan	West Village	POLYGON ((-73.98990 40.73443, -73.98987 40.733...	POINT (-74.00155 40.73305)	13.0	4.269231	1232.0	5169.0	8.343585
118	Brooklyn	Park Slope-Gowanus	POLYGON ((-73.97376 40.68305, -73.97375 40.682...	POINT (-73.98390 40.67123)	13.0	4.269231	1764.0	7257.0	8.181170
3	Manhattan	Midtown-Midtown South	POLYGON ((-73.97301 40.76428, -73.97141 40.763...	POINT (-73.98350 40.75573)	15.0	3.833333	2564.0	9598.5	7.996799
106	Brooklyn	Bensonhurst West	POLYGON ((-73.97964 40.61556, -73.98022 40.615...	POINT (-73.99526 40.61222)	15.0	3.833333	845.0	3097.5	7.830427
139	Manhattan	East Village	POLYGON ((-73.98378 40.72154, -73.98382 40.721...	POINT (-73.98593 40.72762)	12.0	4.041667	1652.0	6668.5	7.712478

group_nt_gdf2.describe()

	counts	rating	review_count	rate_rev	boba_index
count	93.000000	93.000000	93.000000	93.000000	93.000000
mean	4.451613	4.018089	399.408602	1576.779570	3.959228
std	6.289253	0.472595	812.981820	3165.047052	2.340969
min	1.000000	2.000000	1.000000	2.000000	1.103101
25%	1.000000	3.833333	32.000000	124.000000	2.481977
50%	2.000000	4.000000	114.000000	477.500000	3.150041
75%	5.000000	4.333333	384.000000	1470.500000	4.826066
max	40.000000	5.000000	5093.000000	19257.000000	13.189555

Searching for Boba

Boba Maps of NYC

Contents

Boba Maps of NYC¶

Best NYC Neighborhoods for Boba Lovers?¶

Map Boba Data for NYC Neighborhoods¶

The top-rated neighborhoods seem to have low counts of boba shops and count of reviews.¶

NYC Neighborhood Boba Index¶

that take into account counts of reviews and counts of boba shops in the neighborhood?¶

The Creation of Boba Index…¶