GeoCrime Analytic Framework (GCAF): A Comprehensive Framework for Dynamic Spatial Temporal Crime Analysis

Crime can drastically alter a neighborhood's social and economic fabric, creating fear and instability that disrupt communal harmony and daily life. To address the growing impact of crime and the need for efficient analysis and predictions, we introduce the Geographic Crime Analysis Framework (G.C.A.F.). This new framework enhances the capability to analyze and predict urban crime activities through advanced spatial-temporal geographic crime analysis techniques. The G.C.A.F. addresses gaps in existing methods by comprehensively evaluating various approaches, discussing their advantages and disadvantages, and determining their suitability for specific situations. Additionally, it introduces criteria for evaluating the efficiency of different geographic crime prediction methods, aiding in selecting the most appropriate approach for given objectives. The framework concludes with a comparison of the effectiveness of each method in crime prediction and analysis. By implementing the G.C.A.F., law enforcement, and policymakers can more effectively combat crime, thereby improving public safety.