A crime simulation model based on social networks and swarm intelligence

Abstract

Experience in the domain of criminology has shown that spatial data distribution of crime in urban centers follows a Zipf law in which few places concentrate most of the crimes while several other places have few crimes. In order to reproduce and better understand the nuances of such a crime distribution profile, we introduce in this paper a novel multi-agent-based crime simulation model that is directly inspired by the swarm intelligence paradigm. In this model, criminals are regarded as distributed entities endowed with the capability to pursue self-organizing behavior by considering their individual (local) activities as well as the influence of other criminals. 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. At the same time, our experiments reveal that the spatial distribution of crime achieved by experimenting with the simulation model closely follows the real crime data distribution as expected.

Dynamics of crime activities in the network of city community areas

“…In contrast, in the top 19 highest crime communities the lowest number of crime incidents is 1992, while the highest is 7342, so nearly fifteen times higher than in the 19 lowest crime community areas. As expected based on (Furtado et al 2007), the distribution of the number of crime incidents in community areas decays in the Zipf 's Law type manner. One consequence of this is that more than half of the crime incidents that happened in 2017 in Chicago were located in just the top 16 highest crime community areas, while the remaining less than half incidents happened in 61 communities.…”

“…In addition, the levels of crimes at community level have been shown to be strongly correlated with demographic features Alves et al 2018;da Cunha and Gonçalves 2018). Moreover, certain urban characteristics such as importance of community size, has been recognized as triggers for the spatial spreading of criminal activities (Furtado et al 2007). Also, it has been demonstrated here and in (Almanie et al 2015) that incorporating into the crime predictive models both demographic and spatial information increases their predictive capabilities.…”