Machine Learning Methodologies for Crime Predictive Analytics and Hotspot Mapping: An In-depth Review

Abstract

Increasing crime incidents in cities and towns have raised the demand for smart systems that help the police take proactive action against crime. This study reviews machine learning techniques applied to the domains of crime prediction and hotspot mapping. The paper has systematically drawn on existing literature, pointing out various algorithms that include decision trees, support vector machines, neural networks, and clustering methods employed toward analyzing historical crime data. It also discusses spatial and temporal data in determining criminal-prone areas, usually referred to as crime hotspots. Major issues faced, including data quality, model interpretability, and associated ethical concerns about predictive policing, have been analyzed. Finally, the study discusses the use of geographic information systems (GIS) integrated with machine learning models during the last years, with the goal of enhancing the accuracy and reliability of the forecast of crime incidents. Therefore, synthesizing current research trends, this paper tries to provide a comprehensive understanding of the capabilities and limitations of machine learning in criminal pattern analysis, coupled with directions for future research in developing robust, ethical, and effective systems for crime prediction.