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Simulating Crime Against Properties Using Swarm Intelligence and Social Networks

Simulating Crime Against Properties Using Swarm Intelligence and Social Networks

Vasco Furtado, Adriano Melo, Andre L.V. Coelho, Ronaldo Menezes
Copyright: © 2008 |Pages: 19
ISBN13: 9781599045917|ISBN10: 1599045915|ISBN13 Softcover: 9781616927288|EISBN13: 9781599045931
DOI: 10.4018/978-1-59904-591-7.ch015
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MLA

Furtado, Vasco, et al. "Simulating Crime Against Properties Using Swarm Intelligence and Social Networks." Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, edited by Lin Liu and John Eck, IGI Global, 2008, pp. 300-318. https://doi.org/10.4018/978-1-59904-591-7.ch015

APA

Furtado, V., Melo, A., Coelho, A. L., & Menezes, R. (2008). Simulating Crime Against Properties Using Swarm Intelligence and Social Networks. In L. Liu & J. Eck (Eds.), Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems (pp. 300-318). IGI Global. https://doi.org/10.4018/978-1-59904-591-7.ch015

Chicago

Furtado, Vasco, et al. "Simulating Crime Against Properties Using Swarm Intelligence and Social Networks." In Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems, edited by Lin Liu and John Eck, 300-318. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-591-7.ch015

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Abstract

Experience in the domain of criminology has shown that the spatial distribution of some types of crimes in urban centers follows Zipf’s Law in which most of the crime events are concentrated in a few places while other places have few crimes. Moreover, the temporal distribution of these crime events follows an exponential law. In order to reproduce and better understand the nuances of such crime distribution profile, we introduce in this chapter a novel multi-agent-based crime simulation model that is directly inspired by the swarm intelligence paradigm. In this model, criminals are regarded as agents endowed with the capability to pursue self-organizing behavior by considering their individual (local) activities as well as the influence of other criminals pertaining to their social networks. Through controlled experiments with the simulation model, we could indeed observe that self-organization phenomena (i.e., criminal behavior toward crime) emerge as the result of both individual and social learning factors. As expected, our experiments reveal that the spatial distribution of crime occurrences achieved with the simulation model provides a good approximation of the real-crime data distribution. A detailed analysis of the social aspect is also conducted here as this factor is shown to be instrumental for the accurate reproduction of the spatial pattern of crime occurrences.

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