Visualizing Crime data from SFPD using Matplotlib, Pandas

visualizing_crime.py

# data can be found at https://data.sfgov.org/api/views/tmnf-yvry/rows.csv?accessType=DOWNLOAD
# or https://data.sfgov.org/Public-Safety/SFPD-Incidents-Previous-Three-Months/tmnf-yvry
import time
import matplotlib.colors as colors
import matplotlib.cm as cmx
from matplotlib import pyplot as plt
from matplotlib.patches import Patch
import numpy as np
import pandas

def classify_day_part(row):
    day_time = time.strptime(data['Time'][row], '%H:%M')
    hour = day_time.tm_hour
    if hour > 5 and hour < 8:
        return 'early morning'
    if hour >= 8 and hour < 12:
        return 'late morning'
    if hour >= 12 and hour <= 15:
        return 'early afternoon'
    if hour <= 16 and hour <= 17:
        return 'late afternoon'
    if hour >=17 and hour <= 19:
        return 'evening'
    if hour >= 20 and hour < 23:
        return 'night'
    if hour >= 23 or hour <= 5:
        return 'late night'

data = pandas.read_csv('SFPD_Incidents_-_Previous_Three_Months.csv')

by_area = data.groupby('PdDistrict')
crimes_by_area = by_area.aggregate({'Category': np.count_nonzero})
unresolved_crimes = data.loc[data['Resolution'] == 'NONE']
unresolved_by_area = unresolved_crimes.groupby('PdDistrict').aggregate({'Category': np.count_nonzero})
unresolved_by_area_percent = [(float(unsolved[0])/total[0]) * 100 for unsolved, total in
                              zip(unresolved_by_area.values, crimes_by_area.values)]
#getting colors                                                                                                                                                                                                                                                            
jet = cm = plt.get_cmap('hot')
cNorm  = colors.Normalize(vmin=0, vmax=len(unresolved_by_area))
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
colors = [scalarMap.to_rgba(i) for i in range(len(unresolved_by_area))]
color_legend = [Patch(color=c[0], label=c[1]) for c in zip(colors, unresolved_by_area.index)]

plt.subplot(2, 2, 1)
plt.scatter(crimes_by_area, unresolved_by_area_percent,s=80, c=colors)
plt.legend(handles=color_legend, loc=4)
plt.title('Total vs unresolved crimes by district')

unresolved_shares = [float(area)/len(unresolved_crimes)* 100 for area in unresolved_by_area.values]

plt.subplot(2, 2, 2)
plt.pie(unresolved_shares, labels= unresolved_by_area.index, colors=colors, autopct='%1.1f%%')
plt.title('Shares of unresolved crimes')

plt.subplot(2, 2, 3)
crime_part_day = data.groupby(classify_day_part)
crime_part_day_agg = crime_part_day.agg({'Category': np.count_nonzero})
crime_day_part_percent = [float(part)/len(data) for part in crime_part_day_agg.values]
plt.pie(crime_day_part_percent, labels= crime_part_day_agg.index, autopct='%1.1f%%')
plt.title('Crimes by part of day')
plt.show()

Looks like a great start to working with the dataset! 👍

Would love to see a map version with color coded neighborhoods depending on the crimes - and then with a slider letting the user select different times of day. As for the scatterplot, the unlabeled axes make it hard to tell which is which variable.

Also, in case you didn't know - [there's a civic hacking group in the SF] area(http://www.meetup.com/Code-for-San-Francisco-Civic-Hack-Night/) who might give you more ideas on working with open data.

@katychuang thanks a lot for your comments. I will make some improvements and get back. I dont live in the SF bay area unfortunately so i am missing out but i would like to check it out if I am in SF