import pandas as pd
import folium
import math
from folium.plugins import MarkerCluster,HeatMap
import plotly.graph_objects as go
import plotly.express as px
import datetime
from plotly.subplots import make_subplots
import seaborn as sns

df=pd.read_csv('https://raw.githubusercontent.com/VinitaSilaparasetty/coursera-spatial-data-analysis/master/boston-crime%202.csv')
df.head(3)

	INCIDENT_NUMBER	OFFENSE_CODE	OFFENSE_CODE_GROUP	OFFENSE_DESCRIPTION	DISTRICT	REPORTING_AREA	SHOOTING	OCCURRED_ON_DATE	YEAR	MONTH	DAY_OF_WEEK	HOUR	UCR_PART	STREET	Lat	Long	Location	Date	Time
0	I192082859	724	Auto Theft	AUTO THEFT	E18	519	NaN	2019-10-13 09:28:24	2019	10	Sunday	9	Part One	LINCOLN ST	42.259518	-71.121563	(42.25951765, -71.12156299)	2019-10-13	09:28:24
1	I192082751	724	Auto Theft	AUTO THEFT	E18	493	NaN	2019-10-12 20:11:26	2019	10	Saturday	20	Part One	METROPOLITAN AVE	42.262092	-71.116710	(42.26209214, -71.11670964)	2019-10-12	20:11:26
2	I192082680	727	Auto Theft	AUTO THEFT - LEASED/RENTED VEHICLE	D14	794	NaN	2019-10-12 15:12:43	2019	10	Saturday	15	Part One	ALLSTON ST	42.352375	-71.135096	(42.35237455, -71.13509584)	2019-10-12	15:12:43

Base Map

boston = (42.358443,-71.05977)
m= folium.Map(location = boston, tile = 'stamen terrain', zoom_start =12)
m

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Cluster Map (Boston)

mc = MarkerCluster()
for idx,row in df.iterrows():
    if not math.isnan(row['Long']) and not math.isnan(row['Lat']):
        mc.add_child(folium.Marker([row['Lat'],row['Long']]))
m.add_child(mc)

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crime = df.groupby(['DISTRICT','STREET','REPORTING_AREA','Lat','Long']).sum().reset_index()

crime.update(crime['DISTRICT'].map('District:{}'.format))
crime.update(crime['REPORTING_AREA'].map('Reports:{}'.format))

Crime Heat Map (Boston)

m2 = folium.Map(location=boston,tiles='stamentoner', zoom_start =12)
HeatMap(data = crime[['Lat','Long']],radius=15).add_to(m2)
def plotDot(point):
    folium.CircleMarker(location=[point.Lat,point.Long],
                                  radius=5,
                                  weight=2,
                                  popup=[point.DISTRICT, point.REPORTING_AREA],
                                  fill_color='#000000').add_to(m2)
crime.apply(plotDot,axis=1)
m2.fit_bounds(m2.get_bounds())
m2   

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Medical Assistance (Boston)

med = df.loc[df.OFFENSE_CODE_GROUP == 'Medical Assistance'][['Lat','Long']]
med.Lat.fillna(0, inplace = True)
med.Long.fillna(0, inplace = True)
m6 = folium.Map(location = boston, tiles='openstreetmap', zoom_start = 11)
HeatMap(data = med, radius = 16).add_to(m6)
m6

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Crime Count

sns.catplot(y= 'OFFENSE_CODE_GROUP',
           kind= 'count',
           height = 8,
           aspect = 1.5,
           order = df.OFFENSE_CODE_GROUP.value_counts().index,
           data = df)

<seaborn.axisgrid.FacetGrid at 0x1c9431c07c0>

Motor Vehicle Accidents

mv = df.loc[df.OFFENSE_CODE_GROUP == 'Motor Vehicle Accident Response'][['Lat','Long']]
mv.Lat.fillna(0, inplace = True)
mv.Long.fillna(0, inplace = True)
m4 = folium.Map(location = boston, tiles='openstreetmap', zoom_start = 11)
HeatMap(data = mv, radius = 16).add_to(m4)
m4

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Larency

lar = df.loc[df.OFFENSE_CODE_GROUP == 'Larceny'][['Lat','Long']]
lar.Lat.fillna(0, inplace = True)
lar.Long.fillna(0, inplace = True)
m5 = folium.Map(location = boston, tiles='openstreetmap', zoom_start = 11)
HeatMap(data = lar, radius = 16).add_to(m5)
m5

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