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Article type: Research Article
Authors: Calvo, Hiram* | Godoy-Calderón, Salvador | Moreno-Armendáriz, Marco A. | Martínez-Hernández, Víctor M.
Affiliations: Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City, Mexico
Correspondence: [*] Corresponding author: Hiram Calvo,
Abstract: 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.
Keywords: Forecasting models for crime analysis, public security, patrolling routes optimization, ant-colony systems, Spatio-temporal similarity function, pattern recognition, supervised classification, clustering
DOI: 10.3233/IDA-170883
Journal: Intelligent Data Analysis, vol. 21, no. 3, pp. 697-720, 2017
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