Predictive policing using hotspot analysis

Policing approaches to patrolling and response to incidents can be more effective if predictive policing is used to make decisions. Predictive policing uses information such as historical crime data to predict crime patterns and response demand. This information can then be used to direct resources more efficiently. Historical crime data is used to identify high crime areas through kernel density estimation. It is also used to anticipate the levels of response demand. Both of these factors are used to determine how to direct police patrols. This study looks at identifying high crime areas which require an officer presence and predicting the possible response demand to increase the efficiency of response officer patrols. This study shows how kernel density estimation is used for crime mapping and a maximum coverage location problem is formulated to maximize demand coverage. This is then solved using exhaustive search and tabu search. Tabu search gave sub-optimal results but gave significant reductions in computational time.