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Aggregated & analyzed NYPD crime data which can aid NYPD to have better hot-spot policing, judicious staff deployment across boroughs, and narrow down plausible root-causes of crime trends

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geekyspartan/nyc-crime-data-analysis

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CSE544-Probability and Statistics-Final Project

Abstract:


In this project, we have analyzed the NYPD Complaint Data from NYC Open Data. \cite{nycod} Our objective is to empower NYPD with exploratory data analysis and predictions so that they utilize their time and energy in the most effective way. That would result in better hot-spot policing(surveillance of crime prone zones), judicious staff deployment across boroughs, time-dependant vigilance and narrow down plausible root-causes in crime trends.

Introduction:


New York City or The Big Apple is the most densely populated city in United States as of 2017. Manhattan (a NYC Borough) as we all know is the heart of the 'Big Apple'. It is one of the world's largest commercial, financial , cultural and entertainment centers. The list of eminent buildings and places of interest is endless, therefore its protection is of supreme importance to the nation. Now that makes the job of the NYPD (New York Police Department) astoundingly challenging. This motivates us to do exploratory crime/complaint data analysis and empower these policemen with effective information to combat crime.

Topics Covered:


Topic 1:

Predict the number of crimes in NYC boroughs in the next 2 weeks by the hour so that NYPD can deploy staff judiciously across boroughs for the next 2 weeks.

Topic 2:

Does opening a liquor store in an area impact the Spatial Crime Rate?. Regulating Police patrolling, and identifying a safe(r) place to buy liquor.

Topic 3:

Are number of hourly crimes happening in 2017, Normally Distributed? This would help NYPD can be more vigilant at peak hours.

Topic 4:

Is there a relation between crime rate and poverty? To debunk the view that poor people commit more crimes.

Team:


Anurag Arora

Bhavesh Goyal

Renu Rani

Shayan Ray

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Aggregated & analyzed NYPD crime data which can aid NYPD to have better hot-spot policing, judicious staff deployment across boroughs, and narrow down plausible root-causes of crime trends

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