| Resumen |
Tools that perform pattern recognition analysis of crimes, comprising at the same time forecasting, clustering, and recommendations on real data such as patrolling routes, are not fully integrated; modules are developed separately, and thus, a single workflow providing all the steps necessary to perform this analysis has not been reported. In this paper, we propose forecasting criminal activity in a particular region by using supervised classification; then, to use this information to automatically cluster and find important hot spots; and finally, to optimize patrolling routes for personnel working in public security. The proposed forecasting model (CR-Ω+) is based on the family of Kora-Ω Logical-Combinatorial algorithms operating on large data volumes from several heterogeneous sources using an inductive learning process. We perform two analyses: punctual prediction and tendency analysis, which show that it is possible to punctually predict one out of four crimes to be perpetrated (crime family, in a specific space and time), and two out of three times the place of crime, despite of the noise of the dataset. The forecasted crimes are then clustered using a density-based clustering algorithm, and finally route patrolling routes were crafted using an ant-colony optimization algorithm. For three different patrolling requirements, we were always able to find optimal routes in shorter time compared to commonly used random walk algorithms. We present a case study based on real crime data from the municipality of Cuautitlán Izcalli, in Mexico |
