We chose to address the issue of misinformation with the general public when it comes to the crime and its patterns in the Hartford region. Dataset which we chose for this is from the publicly available Police Incidents registered in Hartford area from 2005 till date with the time stamp. Pattern of the crime can be analyzed like e.g. Robbery incidents mainly happen during holiday season. Using the API we built a live tableau dashboard and also forecasted the drug offenses as per neighborhood.

1. Hartford Crime Analysis
TEAM: STORY TELLERS
ASHWIN CHADAGA
POOJA SANKHE
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2. Table of Contents
 Project Objectives
 Methodology
 Insights
 Scope
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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
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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
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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
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6. Methodology
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7. Methodology
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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
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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
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10. Insights
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11. Insights
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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
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13. Appendix

14. References
 https://public.tableau.com/profile/publish/Hartford_crime/Dashboard1#!/pu
blish-confirm
 https://data.hartford.gov/Public-Safety/Police-Incidents-01012005-to-
Current/889t-nwfu
 https://www.census.gov/cgi-bin/geo/shapefiles/index.php