Story Tellers: Hartford Crime Analysis

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

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Hartford Crime AnalysisTeam: Story tellers Ashwin ChadagaPooja Sankhe

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Table of ContentsProject ObjectivesMethodologyInsights Scope

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Crime has patterns and can be analysed Predicting those patterns based on the geographical characteristics and time will help us in segregating crime zonesThis 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 periodForecasting the crime rate for HartfordProject Objective

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Chose the publicly available Police Record for Hartford data set Data available is from January 2005 till October 2015Data consists of different categories of crimes across various neighbourhood within HartfordSteps takenBuilt a dynamic dashboardBuilt a model which forecasts the drug offenses

Methodology

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MethodologyConverted 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 formatDynamic data from API was used to create live dashboard in Tableau For forecasting, used the subset(drug offenses) of the complete dataConverted categorical variables into continuous to forecast

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Methodology

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Methodology

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InsightsFrom 2010, we see a drastic drop in the drug offenses in HartfordFrom 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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Very important: Put forecast

Slides showing trend from 2010-2015 8

Methodology

Training Data 2005 to 2013Hold Out Data Year 2014Built 2 ARIMA models and 3 ETS models Chose the model which gave us the best RMSE & ME valueForecasted 3 months crime rate

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Insights

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Slides showing trend from 2010-2015

Put forecasting10

Insights

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Slides showing trend from 2010-2015

Put forecasting11

ScopeWe can built models for different categories of crime Based on crime rate, we can predict the valuation of a propertyIf we have the right infrastructure we can fetch the entire API data and will be able to project it on live dashboardBased on the forecast, better resource management is possible

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Appendix

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

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