A bio-inspired crime simulation model

a r t i c l e i n f o In this paper we describe a multiagent crime simulation model that resorts to concepts of self-organizing bio-inspired systems, in particular, of the Ant Colony Optimization algorithm. As the matching between simulated and real crime data distributions depends upon the tuning of some control parameters of the simulation model (in particular, of the initial places where criminals start out), we have modeled the calibration of the simulation as an optimization problem. The solution for the allocation of criminals into gateways is also undertaken by a bio-inspired method, namely, a customized Genetic Algorithm. We show that this approach allows for the automatic discovery of gateway configurations that, when employed in the simulation, produce crime distributions that are statistically close to those observed in real data. Recently, an extensive analysis conducted over real crime data related to a large Brazilian metropolis [11] demonstrated that the spatial distribution of crimes such as robberies, thefts, and burglaries follows a power law, more specifically, a Zipfian distribution [40]. This means that the frequency of crime occurrences related to a specific geographic area, when considered per type of crime, tends to scale according to a power-law distribution, yielding the formation of hot spots [34]. In the same work [11], an analysis over the temporal aspect reveals that these crime events follow an exponential distribution per period of analysis. Although knowing the crime distribution profile for a given moment may be necessary to better conduct some of the police decision-making activities, it is not enough to help one gain further insights into crime in its totality. This is because crime is a dynamic phenomenon, and the decision of protecting a frequently-attacked target at a given point in time eventually leads to the exposure of other potential targets in the future, due to a range of restrictions in terms of resources availability (e.g., human resources). In this sense, we advocate that a better understanding of the trends of criminal activities and the types of reactions criminals might potentially undertake is a crucial task to be pursued. In this context, the goal of the research we have been conducting in the last years [18,19,31] is to produce a crime simulation system that reproduces crime phenomena as realistically as possible. Our ultimate goal is to uncover strategies for police patrolling (more precisely, police patrol routes) that could cope well with …