In [1]:

    

%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


    

In [2]:

    

df = pd.read_csv('Utah_County_Sheriff_s_Office.csv', 
                 parse_dates=["incident_datetime"],
                 infer_datetime_format=True, usecols=["incident_type_primary","city","latitude","longitude","incident_datetime","parent_incident_type"])
eagle = df[df['city']=="EAGLE MOUNTAIN"]
eagle.dtypes


    

    
Out[2]:





incident_datetime        datetime64[ns]
incident_type_primary            object
city                             object
latitude                        float64
longitude                       float64
parent_incident_type             object
dtype: object

In [3]:

    

eagle.sort_values("incident_datetime").head()


    

    
Out[3]:






  

    

      

      
incident_datetime
      
incident_type_primary
      
city
      
latitude
      
longitude
      
parent_incident_type
    
  
  

    

      
12709
      
2008-03-29
      
Community Policing
      
EAGLE MOUNTAIN
      
40.30744
      
-112.00943
      
Proactive Policing
    
    

      
12710
      
2008-03-29
      
Community Policing
      
EAGLE MOUNTAIN
      
40.33309
      
-112.01421
      
Proactive Policing
    
    

      
12712
      
2008-03-29
      
Traffic
      
EAGLE MOUNTAIN
      
40.38468
      
-111.94985
      
Traffic
    
    

      
12713
      
2008-03-29
      
Community Policing
      
EAGLE MOUNTAIN
      
40.30543
      
-112.00576
      
Proactive Policing
    
    

      
12721
      
2008-03-29
      
Traffic
      
EAGLE MOUNTAIN
      
40.37757
      
-112.00396
      
Traffic
    
  

In [4]:

    

eagle.parent_incident_type.value_counts()


    

    
Out[4]:





Community Policing            5936
Proactive Policing            3800
Other                         2837
Traffic                       2176
Alarm                         1652
Theft                         1480
Family Offense                 902
Assault                        547
Property Crime                 380
Drugs                          282
Missing Person                 203
Quality of Life                186
Other Sexual Offense           177
Breaking & Entering            165
Disorder                       156
Liquor                         131
Property Crime Residential     127
Sexual Offense                  52
Weapons Offense                 22
Sexual Assault                  15
Assault with Deadly Weapon      13
Death                           12
Homicide                         1
Name: parent_incident_type, dtype: int64

In [5]:

    

theft_times = eagle[eagle.parent_incident_type=='Property Crime'].incident_datetime.values
thefts = pd.Series([1]*len(theft_times), index=theft_times)
thefts_by_mo = thefts.resample('3M').sum()
thefts_by_mo.plot()


    

    
Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x110ea3650>






    

In [6]:

    

theft_times = df[df.parent_incident_type=='Property Crime'].incident_datetime.values
thefts = pd.Series([1]*len(theft_times), index=theft_times)
thefts_by_mo = thefts.resample('3M').sum()
thefts_by_mo.plot()


    

    
Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x110d3c0d0>






    

In [7]:

    

eagle.incident_type_primary.value_counts()


    

    
Out[7]:





Community Policing                3800
CITIZEN ASSIST                    2275
ANIMAL PROBLEM                    1921
ALARM                             1652
Other                              840
Alarm                              817
Traffic                            586
FAMILY PROBLEM-NO VIOLENCE         525
TRAFFIC ACCIDENT-REPORTABLE        524
Theft                              454
TRAFFIC OFFENSE                    451
SUSPICIOUS                         448
CRIMINAL MISCHIEF                  443
Assault                            417
Family Offense                     367
THEFT-PROPERTY-OTHER               346
ANIMAL STRAY                       322
AGENCY ASSIST                      243
THEFT-PROPERTY-FROM MOTOR VEHI     224
TRAFFIC ACCIDENT-NON REPORTABL     218
FRAUD                              215
DRIVING UNDER INFLUENCE            211
MISSING PERSON                     203
ANIMAL BITE                        157
DISORDERLY CONDUCT                 156
PROPERTY-FOUND                     154
CS-POSS-MARIJUANA                  137
DOMESTIC VIOLENCE WITH ARREST      129
ANIMAL LIVESTOCK                   129
PROPERTY DAMAGE-NON VANDALISM      127
                                  ... 
THEFT-IDENTITY                       7
ASSAULT-POLICE-SIMPLE                6
OVERDOSE-CS                          5
ATTEMPT TO LOCATE                    5
PROTECTIVE ORDER                     5
ASSAULT WITH OTHER WEAPON            4
CS-POSS-COCAINE                      4
DEAD BODY                            4
STALKING                             4
ANIMAL CRUELTY                       3
FORGERY                              3
PROPERTY-RECOVERED STOLEN            3
SUICIDE                              3
SEARCH WARRANT                       3
PROPERTY-RECEIVED-POSS-STOLEN        3
ADULT ABUSE OR NEGLECT               3
CS-SALE-MANUF-OTHER                  2
CS-POSS-HALLUCINOGEN                 2
OBSTRUCTION-FALSE INFO               2
Arson                                2
CS-POSS-SYNTHETIC                    2
OBSTRUCTION-RESISTING                2
OBSTRUCTION-OTHER                    1
COMMUNICATIONS OFFENSE               1
SEX OFFENDER - NOT REGISTERED        1
BURGLARY-NON-RES-ATT FORCED EN       1
THEFT-PROPERTY-DECEPTION             1
Homicide                             1
AIRCRAFT PROBLEM                     1
Sexual Assault                       1
Name: incident_type_primary, dtype: int64

In [8]:

    

eagle[eagle.incident_type_primary == "THEFT-PROPERTY-FROM MOTOR VEHI"].to_csv("thefts_from_vehicles.csv", index=False, columns=["incident_datetime","latitude","longitude"])


    

In [9]:

    

theft_times = df[df.incident_type_primary=="THEFT-PROPERTY-FROM MOTOR VEHI"].incident_datetime.values
thefts = pd.Series([1]*len(theft_times), index=theft_times)
thefts_by_mo = thefts.resample('1M').sum()
thefts_by_mo.plot()


    

    
Out[9]:





<matplotlib.axes._subplots.AxesSubplot at 0x118ec7450>






    

In [19]:

    

from scipy import stats


    

In [64]:

    

em_thefts = df[df.incident_type_primary=="THEFT-PROPERTY-FROM MOTOR VEHI"][['latitude', 'longitude']]
et = em_thefts[(np.abs(stats.zscore(em_thefts)) < .12501).all(axis=1)]
x = et.latitude.values
y = et.longitude.values
heatmap, xedges, yedges = np.histogram2d(y, x, bins=100)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]

plt.clf()
plt.imshow(heatmap.T, extent=extent, origin='lower')
plt.show()


    

In [77]:

    

i, j = np.unravel_index(heatmap.argmax(), heatmap.shape)
yedges[j:j+1]
#"https://www.google.com/maps?daddr=%s,%s" % (np.mean([yedges[j:j+1]]), np.mean([xedges[i:i+1]])


    

    
Out[77]:





array([ 40.36444825])

In [ ]: