Adaptive Spatial Mining Algorithm for Crime Prediction

Abstract

Crime prediction and hotspot detection are critical components of modern urban safety management. Traditional spatial mining algorithms often rely on fixed parameters and static assumptions, limiting their effectiveness in capturing the dynamic and complex nature of crime patterns. This research explores the development and application of adaptive and context-aware spatial mining algorithms that dynamically adjust their behaviour based on local crime density, environmental context, and data quality. By integrating real-world crime datasets with adaptive clustering techniques, this study demonstrates improved accuracy and robustness in identifying emerging crime hotspots. The adaptive approach enables timely updates and more precise delineation of high-risk areas, facilitating proactive law enforcement and resource allocation. The proposed methodology also highlights the importance of incorporating multi-source contextual information and handling data uncertainties in spatial crime analysis. Results from experiments on Chicago crime data showcase the potential of adaptive spatial mining to enhance predictive policing and urban safety strategies.