AtlasPilotPuppy · October 28, 2017 02:53 · Oct 5, 2014 · Oct 5, 2014
diff --git a/visualizing_crime.py b/visualizing_crime.py
@@ -1,4 +1,5 @@
 # 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

diff --git a/visualizing_crime.py b/visualizing_crime.py
@@ -0,0 +1,60 @@
+# data can be found at https://data.sfgov.org/api/views/tmnf-yvry/rows.csv?accessType=DOWNLOAD
+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()
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