def main():
api = PoliceAPI()
# Force
print(api.get_forces()) # All forces
force = Force(api, id='leicestershire') # Leicestershire Police
print(force.description) # description of Leicestershire Police
# all the Neighbourhood Policing Teams in this Leicestershire Police area
print(force.neighbourhoods)
print(force.telephone) # The force’s main switchboard number
# Neighbourhood
neighbourhood = Neighbourhood(api, force=force,
id='NW02') # neighbourhood with NW02 id
print(neighbourhood.name) # Name of neighbourhood (Abbey)
print(neighbourhood.officers) # All Abbey officers
print(neighbourhood.events) # Events in Abbey
# Crimes
anti_social_crimes = PoliceAPI().get_crimes_area(
neighbourhood.boundary, category='anti-social-behaviour')
print(anti_social_crimes) # All crimes in Abbey with anti-social behaviour
# All crimes in Abbey in October 2008
crimes = api.get_crimes_area(neighbourhood.boundary, date='2018-10')
crime = crimes[30]
print(crime.location.name) # location of crime
print(crime.category) # category of crime
print(crime.outcomes) # crime outcomes
def create_crimes(f_name, neighbourhoods, Ward_array):
"""
Adds crimes to Ward objects.
(str,Array,Array) -> None
"""
api = PoliceAPI()
i = 0
csv_arr = Array()
for neighbourhood in neighbourhoods:
try:
wrd = Ward_array.get_element(Ward(neighbourhood.item.name))
mounthes = [str(i) for i in range(1, 13)]
for m in mounthes:
crimes = api.get_crimes_area(neighbourhood.item.boundary,
date='2018-' + m)
wrd.add_number_crimes(len(crimes))
element = {
'Ward_name': wrd.get_name(),
'Price': wrd.get_price(),
'Schools_number': wrd.get_number_of_schools(),
'Crimes_number': wrd.get_number_of_crimes()
}
wrte_csv(f_name, element, i)
i += 1
print(neighbourhood.item.name, end=' ')
print(i, end=' out of ')
print(len(neighbourhoods))
except:
pass
def filter_crimes(Ward_array):
"""
Creates an Array of Neighbourhood objects.
(Array) -> Array
"""
api = PoliceAPI()
neighbourhoods = Array()
for force in api.get_forces():
for neighbourhood in force.neighbourhoods:
if Ward(neighbourhood.name) in Ward_array:
neighbourhoods.add(neighbourhood)
return neighbourhoods
def list_recent_crime_categories_and_resolutions_in_order(postcode):
api = PoliceAPI()
hood = api.locate_neighbourhood(*get_lat_long_of_postcode(postcode))
recent_crimes = api.get_crimes_area(hood.boundary)
all_outcomes_of_recent_crimes = []
for crime in recent_crimes:
if str(crime.outcome_status) not in all_outcomes_of_recent_crimes:
all_outcomes_of_recent_crimes.append(str(crime.outcome_status))
recent_crimes_by_category = {}
# TODO: it could be richer to have non exclusive categories (tagging vs one category)
# Build up categories
for crime in recent_crimes:
recent_crimes_by_category[crime.category.name] = []
for crime in recent_crimes:
if is_ongoing(crime) is False:
recent_crimes_by_category[crime.category.name].append(crime)
def get_outcome_breakdown(crime_category_string):
crime_category = recent_crimes_by_category[crime_category_string]
crime_outcomes = {} # Build category specific crime outcomes
for crime in crime_category:
status = str(crime.outcome_status)
crime_outcomes[status] = []
for crime in crime_category:
status = str(crime.outcome_status)
crime_outcomes[status].append(crime)
counted_crime_outcomes = {}
for category in crime_outcomes:
counted_crime_outcomes[category] = len(crime_outcomes[category])
counted_crime_outcomes_ordered = get_ordered_dict_sorted_by_number_of_definitions(counted_crime_outcomes)
return counted_crime_outcomes_ordered
for category in recent_crimes_by_category.keys():
print(category)
print(get_outcome_breakdown(category))
print()
def main(t_current):
# Call the police API
api = PoliceAPI()
# Define tables
crime_categories = pd.DataFrame({'id': [], 'description': []})
outcome_categories = pd.DataFrame({'id': [], 'description': []})
streets = pd.DataFrame({'id': [], 'name': []})
crimes = pd.DataFrame({
'persistent_id': [],
'category': [],
'street': [],
'city': [],
'latitude': [],
'longitude': [],
'date': [],
'context': []
})
outcomes = pd.DataFrame({
'crime': [],
'category': [],
'date': [],
'person_id': []
})
# Transform dates into pandas Series for better manipulation
dates = pd.Series(api.get_dates())
# Get Forces
forces = api.get_forces()
# Get neighbourhoods
neighbourhoods = [f.neighbourhoods for f in forces]
nb_flat = [n for sublist in neighbourhoods for n in sublist]
s_nb_flat = pd.Series(nb_flat).unique()
first_job(api, dates, t_current)
t_last_update = api.get_latest_date()
second_job(api, dates, t_last_update, t_current)
last_job(api, t_current)
def latlng():
lat = request.form.get('lat')
lng = request.form.get('lng')
api = PoliceAPI()
n = api.locate_neighbourhood(lat, lng)
ncrimes = len(api.get_crimes_area(n.boundary, date='2020-01'))
population = n.population
highRisk = False
if population > 0:
cpp = ncrimes / population
noMonths = 1 / cpp
noMonths = round(noMonths)
Mth = str(noMonths)
else:
minLat = min(latlong[0] for latlong in n.boundary)
maxLat = max(latlong[0] for latlong in n.boundary)
minLong = min(latlong[1] for latlong in n.boundary)
maxLong = max(latlong[1] for latlong in n.boundary)
totalLat = maxLat - minLat
totalLong = maxLong - minLong
latMeters = totalLat * 111.32
longCos = math.cos(totalLong)
longMeters = (longCos / 360) * 40075
totalArea = latMeters * longMeters
ukArea = 242495
numberPeople = 66440000
ppa = numberPeople / ukArea
ppn = totalArea * ppa
ppn = round(ppn)
cpp = ncrimes / ppn
noMonths = 1 / cpp
noMonths = round(noMonths)
Mth = str(noMonths)
population = ppn
if cpp > 0.02:
highRisk = True
#as = Antisocial behaviour
#t = theft
#b = burglary
#cda = criminal damage and arson
#d = drugs
#pw = possession of weapons
#po = public order
#r = robbery
#s = shoplifting
#vc = vehicle crime
#vso = violent and sexual offences
#o = other crime
asTally = 0
tTally = 0
bTally = 0
cdaTally = 0
dTally = 0
pwTally = 0
poTally = 0
rTally = 0
sTally = 0
vcTally = 0
vsoTally = 0
oTally = 0
crimes = api.get_crimes_area(n.boundary, date='2020-01')
for x in crimes:
type = str(x.category)
if type == '<CrimeCategory> Anti-social behaviour':
asTally += 1
elif type == '<CrimeCategory> Bicycle theft' or type == '<CrimeCategory> Other theft' or type == '<CrimeCategory> Theft from the person':
tTally += 1
elif type == '<CrimeCategory> Burglary':
bTally += 1
elif type == '<CrimeCategory> Criminal damage and arson':
cdaTally += 1
elif type == '<CrimeCategory> Drugs':
dTally += 1
elif type == '<CrimeCategory> Possession of weapons':
pwTally += 1
elif type == '<CrimeCategory> Public order':
poTally += 1
elif type == '<CrimeCategory> Robbery':
rTally += 1
elif type == '<CrimeCategory> Shoplifting':
sTally += 1
elif type == '<CrimeCategory> Vehicle crime':
vcTally += 1
elif type == '<CrimeCategory> Violence and sexual offences':
vsoTally += 1
elif type == '<CrimeCategory> Other crime':
oTally += 1
array = [
asTally, tTally, bTally, cdaTally, dTally, pwTally, poTally,
rTally, sTally, vcTally, vsoTally, oTally
]
array.sort(reverse=True)
mostCrimeNumber = int(array[0])
if array[0] == bTally:
mostCrime = 'Burglary'
elif array[0] == cdaTally:
mostCrime = 'Criminal damage and arson'
elif array[0] == dTally:
mostCrime = 'Drugs'
elif array[0] == pwTally:
mostCrime = 'Possession of weapons'
elif array[0] == poTally:
mostCrime = 'Public order'
elif array[0] == rTally:
mostCrime = 'Robbery'
elif array[0] == sTally:
mostCrime = 'Shoplifting'
elif array[0] == vcTally:
mostCrime = 'Vehicle crime'
elif array[0] == vsoTally:
mostCrime = 'Violence and sexual offences'
elif array[0] == oTally:
mostCrime = 'Other crime'
elif array[0] == asTally:
mostCrime = 'Antisocial Behaviour'
#these are the percentages that the clock needs:
asTotal = (asTally / ncrimes) * 100
tTotal = (tTally / ncrimes) * 100
bTotal = (bTally / ncrimes) * 100
cdaTotal = (cdaTally / ncrimes) * 100
dTotal = (dTally / ncrimes) * 100
pwTotal = (pwTally / ncrimes) * 100
poTotal = (poTally / ncrimes) * 100
rTotal = (rTally / ncrimes) * 100
sTotal = (sTally / ncrimes) * 100
vcTotal = (vcTally / ncrimes) * 100
vsoTotal = (vsoTally / ncrimes) * 100
oTotal = (oTally / ncrimes) * 100
return render_template(
'clock.html',
lng=lng,
lat=lat,
ncrimes=ncrimes,
n=n,
mostCrime=mostCrime,
mostCrimeNumber=mostCrimeNumber,
Mth=Mth,
highRisk=highRisk,
population=population,
asTotal=asTotal,
tTotal=tTotal,
bTotal=bTotal,
cdaTotal=cdaTotal,
dTotal=dTotal,
pwTotal=pwTotal,
poTotal=poTotal,
rTotal=rTotal,
sTotal=sTotal,
vcTotal=vcTotal,
vsoTotal=vsoTotal,
oTotal=oTotal,
)
#!/usr/bin/env python2
import csv
import json
from police_api import PoliceAPI
from police_api.forces import Force
api = PoliceAPI()
forces = api.get_forces()
# creating new csv file with structure.
with open("forces.csv", "w") as file:
writer = csv.writer(file)
writer.writerow([
"force id",
"facebook page",
])
for dep in forces:
# opening forces.csv in append mode.
f = open("forces.csv", "a")
force = Force(api, id=dep.id)
# getting engagements methods
json_engagements = force.engagement_methods
for data in json_engagements:
# searching for those who has facebook type of engagement
if data["type"] == "Facebook":
force_facebook = data["url"]
csv_data = [dep.id, force_facebook]
# appending to csv file.
with f:
writer = csv.writer(f)
"""
form = cgi.FieldStorage()
Userpc = form["command"].value
if command != "" :
print "<p> Finding Data For Postcode" + Userpc + "</p>"
pcr = pc.get(Userpc)
pcr2 = pcr["geo"]
UserLat = pcr2["lat"]
UserLong = pcr2["lng"]
location = g_m.search(location=Userpc)
#locate = location.first()
#print locate.city
print location
from police_api import PoliceAPI
papi = PoliceAPI()
locate = papi.locate_neighbourhood(UserLat, UserLong)
print "Police Data for" + Userpc
#print "<h2>" + locate + "</h2>"
print "<h2> Force </h2<"
print "<h2> Contact details </h2> "
print
print "<h3>" + locate.force + "</h3>"
print "<h3>" + papi.get.crimes.area + "</h3>"
#OSM = OsmApi()
#print " "
#print " "
#print "OSM Data"
#print OSM.NodeGet(123)
'Burglary': 'Red',
'Violence and sexual offences': 'Magenta',
'Drugs': 'Blue',
'Bicycle theft': 'Green',
'Criminal damage and arson': 'light yellow',
'Other theft': 'light green',
'Possession of weapons': 'cyan',
'Public order': 'white',
'Shoplifting': 'grey',
'Theft from the person': 'light orange',
'Vehicle crime': 'brown',
'Other crime': 'light blue',
'Robbery': 'Yellow'
}
police = PoliceAPI()
# Get the date range of data
dt_range = police.get_dates()
def format_date_range(date_range):
month_dict = {
1: 'Jan',
2: 'Feb',
3: 'Mar',
4: 'Apr',
5: 'May',
6: 'Jun',
7: 'Jul',
8: 'Aug',
9: 'Sep',
def get_crime_counts(coord):
api = PoliceAPI()
crimes = api.get_crimes_point(*coord)
catgories = pd.Series([c.category.id for c in crimes])
counts = catgories.value_counts().to_dict()
return counts
#!/usr/bin/env python
# -*- coding: utf-8 -*-
""" Collect the infromation from the https://data.police.uk/docs/method/crime-street/ API
Using https://github.com/rkhleics/police-api-client-python/
"""
from police_api import PoliceAPI
from matchingClosestPoint import closest
from scipy.stats import zscore
import numpy as np
api = PoliceAPI()
# forces = api.get_forces()
def get_closest_street_id(streets_location, closest_street):
"""
"""
for k in streets_location:
if float(streets_location[k]['lat']) == float(closest_street['lat']):
if streets_location[k]['lon'] == closest_street['lon']:
return k
def aggregate_crime_per_street(crime_location):
"""
"""
crime_count = dict()
streets_location = dict()
for crime in crime_location: