Baltimore Crime Pattern Forecasting
An ARIMA-based forecasting system that analyses historical crime data to predict temporal patterns and trends, supporting more efficient allocation of policing resources across districts and time periods.
The Challenge
SARIMA forecasting quantified seasonal crime trends and significant district-level geographic variation in Baltimore incident data, turning historical patterns into forward-looking projections for proactive patrol deployment.
Law enforcement agencies allocate patrol and investigative resources based on historical patterns and operational experience, but these approaches struggle with changing trends, seasonal variations, and emerging hotspots.
Without predictive capability, resource allocation is inherently reactive, responding to crime after it occurs rather than positioning resources where they are most likely to be needed. Statistical forecasting can bridge this gap.
Approach
Results
The forecasting models successfully captured seasonal crime patterns and longer-term trends, providing a statistical basis for forward-looking resource allocation. The district-level analysis revealed significant geographic variation in crime patterns, enabling more targeted deployment recommendations.