CrimeTracker: A Frequent Pattern-based Crime Knowledge Support for Strategic Community Policing

Policing in South Africa (SA) faces numerous challenges, including limited resources, high rates of crime and inadequate safety methods. This research developed a crime knowledge support system (coined CrimeTracker), to help identify crime hotspots and patterns using the frequent pattern (FP) growth model. The goal is to improve community policing and inform the public of crime trends, enabling them to avoid dangerous areas. The study used data from Stats SA, which was filtered by province (Western Cape) and location (Bellville). HTML, CSS, Python, and JavaScript are used for the development of the system. CrimeTracker shows promise in assisting the police with strategic decision-making and crime prevention. This is the first exploration of Stats SA data using the FP-Growth model, and the system shows promise in assisting the community and crime agencies by providing information that can help to improve crime-reduction strategies, resource allocation, and communication among stakeholders.