A novel HP-Growth model for enhancing community policing in South Africa

Abstract South Africa (SA) grapples with a rising crime rate, which poses challenges to the safety and economic growth of the country. Despite the limited literature on pattern-based models in SA, frequent pattern-based models, particularly Frequent Pattern Growth (FP-Growth) and Hyper Structure Mining (Hmine), have demonstrated utility in various research fields. This paper introduces a novel model, Hybrid Pattern-Growth (HP-Growth), which combines the strengths of FP-Growth and Hmine. A comparative analysis of the South African crime statistics (Stats SA crime) dataset’s computational time complexity, scalability, and memory usage revealed that HP-Growth and Hmine outperform FP-Growth. This study establishes association rule thresholds and emphasizes the importance of selecting the most appropriate pattern-based model for generating crime patterns. According to the study, HP-Growth outperformed the other two models on sparse datasets, whereas Hmine excelled on dense datasets. FP-Growth uses more memory and has a greater time complexity than HP-Growth and Hmine. The most suitable model was then integrated into the developed crime support system. The research outcome has practical implications for law enforcement in aiding strategic resource allocation and proactive policing for crime reduction and control.