A look at Starbucks Location Distribution

When looking at block data, I noticed that very few geographic units have more than one Starbucks.



In [36]:

    
%pylab inline
import pandas as pd
import json

def summarize(fname, title):
    d = pd.DataFrame(json.loads(l) for l in open(fname))
    counts = pd.DataFrame(d.GISJOIN.value_counts())
    counts.columns = ['counts']
    ax = counts['counts'].value_counts().plot(kind='barh')
    ax.set_title(title)
    ax.set_xscale('log')
    ax.set_xlabel('number of geographic units')
    ax.set_ylabel('Starbucks per unit')
    
summarize('sb_block.json', 'Frequency of starbucks counts for census blocks')









    



Populating the interactive namespace from numpy and matplotlib



In [37]:

    
summarize('sb_track.json', 'Frequency of starbucks counts for census tracks')

For county data, there's a much greater distribution of starbucks counts.



In [38]:

    
summarize('sb_county.json', 'Frequency of starbucks counts for census counties')
import pylab
pylab.gcf().set_size_inches(6., 16.)