Introduction

Dataset:

Boston Crime: https://www.kaggle.com/AnalyzeBoston/crimes-in-boston

District in Boston: https://bpdnews.com/districts

Software: Python

Packages :

• Data Cleaning : Pandas, Numpy

• Data Visualization : Matplotlib, Seaborn, folium

Goal

• Identify time pattern of incidents in boston area

• Uncover pattern of district incidents in boston area

• Specific incidents pattern in time and location

Data Cleaning

• Get rid of unmeaningful columns : incident number, shooting (large missing value )

• Exclude 2015 and 2020 in time (not full data)

• Exclude external district (not clear)

• Change Distriction Code into name

Location Pattern

• Except Mattapan, Downtown, South boston, Hyde Park, West Roxbury and Charlestown, other districts had decrease in the incidents number.

• South End, Roxbury, Mattapan, Downtown and Dorchester are over the mean level.

More incidents happened in Roxbury, Dorchester and SouthEnd.

We can divide areas into five specific areas according to incidents :

Business Area

From map, we can see shoplifting is serve in the backbay.

For store in there, they get better surveillance camera in the store to keep safe.

Including South End, Downtown and South Boston.

Bad Transportation Area

Having incidents includes towed

Including Charles town and Brighton, notely that downtown and south end have less towed incidents than other areas

Historical Area:

Having incidents like vandalism

Including east Boston

Mostly because there are more historical site in these districts.

Incidents Pattern

Motor Vehicle Accident Response- Personal Injury Location distribution

Code

Boston Crime Analysis