Towards Holistic Crime Management Through Descriptive Analysis and Predictive Forecasting

Throughout the evolution of humankind, devising effective crime prevention strategies has been a concern in fostering safer communities. Although achieving a zero-crime rate is not possible yet, using data management methods can contribute to preventing and minimizing crimes. This article presents an integrated approach to proactive crime prevention. Data insights derived from analysis and visualization provide a comprehensive overview of historical trends and scenarios, aiding understanding of past and present situations. The forecasting of crime rates extends this comprehension to the future, spotlighting the severity of the potential threat to the safety of the community. Three distinct prediction models are presented to predict the occurrence of violent/non-violent crimes, the most probable crime type, and the most probable crime location in a spatial-temporal context, facilitating optimal resource allocation of police patrolling. Combining these three analytical components, an application is designed to present this holistic solution for effective crime prevention, with a 73% accuracy in predicting violent crimes. To achieve higher accuracy in predicting the most probable crime types and locations, it is essential to go beyond spatiotemporal context and augment data on other factors that affect the occurrence of crimes.