Detecting Criminal Behaviour Patterns in Spain and Italy Using Formal Concept Analysis
Automatic number plate reading systems (NPRS) collect considerable amount of information from roads: number of vehicles, movements, legal status, etc. An immense quantity of information does not represent an answer to a problem if we cannot define what we are looking for and cannot extract knowledge from this information. Formal concept analysis is not recommended for big data, but it has interesting tools to extract knowledge from information stored in databases. Pruning consists in reducing initial information, done by discarding a selectable number of data that we consider not relevant. If pruned properly, the size of the database is reduced but interesting information are retained. Considerable resources are required to assess specific criminal behaviour profiles and research can help to determine which profiles we are interested in. In this paper, we focus on observed behaviour patterns in criminal activities committed in Southern Spain to reduce information provided by NPRS on Italian roads. With this reduced information we conclude that a consensus on appropriate data analysis could be reached if we focus on specific profiles.
Author want to thank Police forces in Costa del Sol in Southern Spain for their support in this study. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
- 1.Florido, E., O. Castaño, A. Troncoso, and F. Martínez-Álvarez. 2015. Data mining for predicting traffic congestion and its application to Spanish data, pp. 341–351. Cham: Springer International Publishing.Google Scholar
- 2.Ganter, B. 1984. Two basic algorithms in concept analysis. Darmstadt: Technische Hochschule.Google Scholar
- 3.Guigues, J.L., and V. Duquenne. 1986. Familles minimales d implications informatives resultant d un tableau de donnees binaires. Mathematiques et Sciences Sociales 95: 5–18.Google Scholar
- 4.Lou, Xinyan, Yang Liu, and Xiaohui Yu. 2013. Traffic session identification based on statistical language model, pp. 264–275. Berlin: Springer.Google Scholar
- 5.Polizia di Stato. 2017. Dataset from traffic cameras.Google Scholar
- 6.Rodríguez-Jiménez, J.M., P. Cordero, M. Enciso, and A. Mora. 2014. A generalized framework to consider positive and negative attributes in formal concept analysis. In CLA, pp. 267–278.Google Scholar
- 8.Rodríguez-Jiménez, J.M., P. Cordero, M. Enciso, and A. Mora. 2016. Analyzing criminal networks using formal concept analysis with negative attributes. In Proceedings of the International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE.Google Scholar
- 11.R. Wille. 1982. Restructuring lattice theory: an approach based on hierarchies of concepts. In Ordered Sets, ed. I. Rival, pp. 445–470. Boston.Google Scholar