Objective - This study aims to analyze crime dynamics in Chicago by examining the predictive relationship between crime categories and arrest frequencies using regression analysis.

Methodology - A quantitative research design was applied using secondary data from the Chicago Data Portal. Regression analysis was performed to evaluate how crime categories predict arrest patterns.

Findings - The results reveal a strong, positive, and statistically significant relationship between crime categories and arrest frequencies (R² = 0.613, β = 0.783, p < .001). Each unit increase in crime category is associated with a 0.813 increase in arrest frequency. Findings indicate that targeted law enforcement strategies based on crime categories could significantly improve resource allocation and intervention effectiveness. The study supports the implementation of robust crime forecasting systems to assist police departments in identifying potential criminal activity, enabling preemptive intervention and thereby enhancing societal well-being. These predictive technologies could help bridge the technological gap between sophisticated criminal operations and law enforcement capabilities, thereby improving crime-control effectiveness across Chicago's diverse communities.

Novelty - This study integrates criminological theory with statistical modeling to address limitations in prior predictive policing models, thereby providing a more theory-driven framework for urban crime prediction.

Type of Paper - Empirical

Keywords: regression analysis; crime; Chicago; prediction model; crime prevention.

JEL Classification: C12, C35, C55, C80, Z39, Z19

URI: https://gatrenterprise.com/GATRJournals/JBER/vol10.4_3.html

DOI: https://doi.org/10.35609/jber.2026.10.4(3)

Pages 17–24