Crime prediction and prevention using police patrolling data: challenges and prospects

Abstract

Spatiotemporal crime analysis and prediction aim at identifying criminal patterns in space and time. In previous work, crime prediction has been performed by identifying hotspots from data, which means areas of high criminal activity on the streets. By focusing efforts on such sites, police patrolling is expected to be more efficient, thus reducing criminal activity. However, not many studies focus on investigating how police patrolling affects crime, and whether it can be a predictor of crime activity. In this paper we discuss the main challenges of this problem, and describe some work in progress towards developing a robust methodology to represent, visually analyze, and build predictors for criminal activity, considering both criminal and police patrolling spatiotemporal data. As a case study, we use real datasets from the Military Police of the state of Alagoas, Brazil (PM-AL).