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 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 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)
#!/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)
for d in date_range:
a = d.split('-')
dt = month_dict[int(a[1])] + ' ' + a[0]
new_date_range.append((dt))
return new_date_range
new_dt_range = format_date_range((dt_range))
date_range = dict(zip(new_dt_range, dt_range))
date_dropdown = [{
'label': str(k),
'value': str(v)
} for k, v in date_range.items()]
# Get the police force
police_forces = police.get_forces()
police_force_list = [{'label': p.name, 'value': p.name} for p in police_forces]
def get_police_force_id(police_name):
"""A function to return the police id in str data format."""
police_id = [p.id for p in police_forces if p.name == police_name]
if police_id != []:
return police_id[0]
else:
return None
# Get neighbourhood boundary for finding crime
@cache.memoize(10)
def get_neighbourhood_id(police_name, neighbourhood_name):
