This document analyzes crime patterns in Hartford, Connecticut. It uses publicly available police records from 2005 to 2015 to identify patterns by geography and time. Models are built to forecast future drug offenses, with insights showing reductions since 2010 and the most impacted area being Frog Hollow. The analysis is presented through a Tableau dashboard, with the scope discussed to expand modeling to other crime categories and use forecasts to aid resource management.

1. Hartford Crime Analysis
TEAM: STORY TELLERS
ASHWIN CHADAGA
POOJA SANKHE
1

2. Table of Contents
 Project Objectives
 Methodology
 Insights
 Scope
2

3.  Crime has patterns and can be analysed
 Predicting those patterns based on the geographical characteristics and time
will help us in segregating crime zones
 This makes people aware of the neighborhood
Data tells us about
Police incidents in
Hartford area
A pattern can be
identified for a region
and for a time period
Forecasting the
crime rate for
Hartford
Project Objective
3

4.  Chose the publicly available ‘Police Record for Hartford’ data set
 Data available is from January 2005 till October 2015
 Data consists of different categories of crimes across various neighbourhood
within Hartford
 Steps taken
 Built a dynamic dashboard
 Built a model which forecasts the drug offenses
Methodology
4

5. Methodology
 Converted API data from JSON format into table format by using tools
available in Alteryx
 US ZIP code data was mapped with the dynamic data to get the data in spatial
format
 Dynamic data from API was used to create live dashboard in Tableau
 For forecasting, used the subset(drug offenses) of the complete data
 Converted categorical variables into continuous to forecast
5

6. Methodology
6

7. Methodology
7

8. Insights
 From 2010, we see a drastic drop
in the drug offenses in Hartford
 From 2010, overall crime rate has
been reduced
 ‘Frog Hollow’ is the most impacted
region (Drug related crimes)
 Further insights can be interpreted
though live dashboard
8

9. Methodology
 Training Data – 2005 to 2013
 Hold Out Data – Year 2014
 Built 2 ARIMA models and 3 ETS
models
 Chose the model which gave us
the best RMSE & ME value
 Forecasted 3 month’s crime rate
9

10. Insights
10

11. Insights
11

12. Scope
 We can built models for different categories of crime
 Based on crime rate, we can predict the valuation of a property
 If we have the right infrastructure we can fetch the entire API data and will
be able to project it on live dashboard
 Based on the forecast, better resource management is possible
12

13. Appendix

14. References
 https://public.tableau.com/profile/ashwin2184#!/vizhome/Hartford_crime/D
ashboard1
 https://data.hartford.gov/Public-Safety/Police-Incidents-01012005-to-
Current/889t-nwfu
 https://www.census.gov/cgi-bin/geo/shapefiles/index.php