Actionable Insights on Philadelphia Crime Hot-Spots: Clustering and Statistical Analysis to Inform Future Crime Legislation
Ishan S. Khare, Tarun K. Martheswaran, Rahul K. Thomas, Aditya Bora

TL;DR
This study analyzes Philadelphia's crime data from 2012-2022 using clustering and statistical methods to identify systemic districts and inform targeted crime mitigation strategies.
Contribution
The paper introduces a novel Non-Systemic Index (NSI) and applies clustering to Philadelphia crime data, revealing district heterogeneity and associations with HOLC grades for better policy guidance.
Findings
High clusterability of crimes within districts
NSI effectively differentiates systemic and non-systemic districts
Significant correlation between HOLC grades and systemic districts
Abstract
Philadelphia's problem with high crime rates continues to be exacerbated as Philadelphia's residents, community leaders, and law enforcement officials struggle to address the root causes of the problem and make the city safer for all. In this work, we deeply understand crime in Philadelphia and offer novel insights for crime mitigation within the city. Open source crime data from 2012-2022 was obtained from OpenDataPhilly. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) was used to cluster geographic locations of crimes. Clustering of crimes within each of 21 police districts was performed, and temporal changes in cluster distributions were analyzed to develop a Non-Systemic Index (NSI). Home Owners' Loan Corporation (HOLC) grades were tested for associations with clusters in police districts labeled `systemic.' Crimes within each district were highly clusterable,…
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Taxonomy
TopicsCrime Patterns and Interventions · Data-Driven Disease Surveillance · Traffic and Road Safety
