CitiSafe: Adaptive Spatial Pattern Knowledge Using Fp-Growth Algorithm for Crime Situation Recognition

Crime prevention and control are issues of great concern to the government and public safety agencies. If these issues are not well controlled and managed, they could drastically affect the economy of a country negatively over time, since more emigration is naturally induced. With the existence of crime databases in the society, data mining techniques are viable tools for crime situation recognition and control. However, most existing techniques often fall short to consider salient features during the mining process. In this paper, we experimented on various crime categories across 32 locations within the western cape province of the republic of South Africa. Our research focus is to create a flexible and effective, yet simple, solution to crime situation recognition. A successful mining mechanism, adopting a batch-merge paradigm, that builds upon the Fp-Growth algorithm, referred to as CitiSafe algorithm is presented, followed by concluding remarks. The visualization techniques presenting the crime patterns, through our algorithm, would assist law enforcement agencies and public safety organisations to channelize their resources accordingly to achieve a focused and effective crime prevention strategy.