Temporal and Spatial Analysis of Crime Patterns in New York City: A Statistical Investigation of NYPD Complaint Data (1963-2025)

TL;DR

This paper analyzes 47 years of NYPD complaint data to uncover spatial and temporal crime patterns in NYC, revealing significant associations and variations useful for law enforcement planning.

Contribution

It provides a comprehensive statistical investigation of NYC crime data over decades, highlighting key spatial and temporal crime trends and their implications.

Findings

Brooklyn has the highest crime volume

Petit-larceny is the most common offense

Crime peaks during weekday evenings, especially Fridays

Abstract

This study presents a comprehensive statistical analysis of criminal complaint data from the New York City Police Department (NYPD) spanning 47 years (1963-2025) [1]. Using a dataset of 438,556 complaint records, we employed exploratory data analysis (EDA), descriptive statistics, and multiple statistical hypothesis tests to investigate the spatial, temporal, and categorical patterns of urban crimes. Our findings revealed significant associations between crime types and geographic locations, temporal variations in criminal activity, and differences in crime severity across time. The results demonstrate that Brooklyn experiences the highest crime volume, petit-larceny constitutes the most common offense, and criminal activity peaks during the evening hours on weekdays, particularly Fridays. Statistical tests, including chi-square tests, Kruskal-Wallis H-test, and Mann-Whitney U test, confirmed highly significant relationships (p < 0.001) across all examined dimensions, providing evidence-based insights for law enforcement resource allocation and urban safety policy development.