A bio-inspired crime simulation model

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.

[1]  P. Richerson,et al.  The Origin and Evolution of Cultures , 2005 .

[2]  George Kingsley Zipf,et al.  Human behavior and the principle of least effort , 1949 .

[3]  Hsinchun Chen,et al.  Topological analysis of criminal activity networks in multiple jurisdictions , 2005, DG.O.

[4]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[5]  J. Wyatt.  Decision support systems. , 2000, Journal of the Royal Society of Medicine.

[6]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[7]  Lawrence E. Cohen,et al.  Social Change and Crime Rate Trends: A Routine Activity Approach , 1979 .

[8]  A. Baddeley,et al.  Non‐ and semi‐parametric estimation of interaction in inhomogeneous point patterns , 2000 .

[9]  Tassni Eunice Miguel Lopes Cancado.  Alocação e despacho de recursos para combate à criminalidade , 2005 .

[10]  Alain Pétrowski,et al.  A clearing procedure as a niching method for genetic algorithms , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[11]  Chris Baerveldt,et al.  Just like normal: A social network study of the relation between petty crime and the intimacy of adolescent friendships. , 1999 .

[12]  Yves Zenou,et al.  Working Paper No . 601 , 2003 Social Networks and Crime Decisions : The Role of Social Structure in Facilitating Delinquent Behavior , 2003 .

[13]  Donald E. Brown,et al.  Spatial analysis with preference specification of latent decision makers for criminal event prediction , 2006, Decis. Support Syst..

[14]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[15]  John E. Eck,et al.  Simulating Crime Events and Crime Patterns in a RA/CA Model , 2005 .

[16]  David W. Pentico,et al.  Assignment problems: A golden anniversary survey , 2007, Eur. J. Oper. Res..

[17]  D. Cressey,et al.  Principles of Criminology. , 1947 .

[18]  A. Drogoul,et al.  Multi-Agent Simulation as a Tool for Modeling Societies: Application to Social Differentiation in Ant Colonies , 1992, MAAMAW.

[19]  Lin Liu.  Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems , 2008 .

[20]  André L. V. Coelho,et al.  GAPatrol: An Evolutionary Multiagent Approach for the Automatic Definition of Hotspots and Patrol Routes , 2006, IBERAMIA-SBIA.

[21]  Z. Neda,et al.  Measuring preferential attachment in evolving networks , 2001, cond-mat/0104131.

[22]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[23]  Vasco Furtado,et al.  A Multiagent Simulator for Teaching Police Allocation , 2005, AI Mag..

[24]  Michael Guntsch,et al.  Applying Population Based ACO to Dynamic Optimization Problems , 2002, Ant Algorithms.

[25]  Lawrence W. Sherman,et al.  General deterrent effects of police patrol in crime “hot spots”: A randomized, controlled trial , 1995 .

[26]  Hsinchun Chen,et al.  Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks , 2004, Decis. Support Syst..

[27]  Subhasish Dasgupta,et al.  Geospatial information utility: an estimation of the relevance of geospatial information to users , 2004, Decis. Support Syst..

[28]  P. Brantingham,et al.  Environment, Routine, and Situation: Toward a Pattern Theory of Crime (1993) , 2010 .

[29]  L. Anselin.  Local Indicators of Spatial Association—LISA , 2010 .

[30]  Ronaldo Menezes,et al.  Simulating Crime Against Properties Using Swarm Intelligence and Social Networks , 2008 .

[31]  Julie A. Adams,et al.  Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence , 2001, AI Mag..

[32]  B. Ripley.  Statistical inference for spatial processes , 1990 .

[33]  N. Gilbert,et al.  Artificial Societies: The Computer Simulation of Social Life , 1995 .

[34]  L. Lanza-Kaduce,et al.  Social learning and deviant behavior: a specific test of a general theory. , 1979, American sociological review.

[35]  Patrick R. Gartin,et al.  Hot Spots of Predatory Crime: Routine Activities and the Criminology of Place , 1989 .

[36]  中嶋 和久,et al.  環境 Environment について , 1992 .

[37]  E. Glaeser,et al.  Crime and Social Interactions , 1995 .

[38]  Hsinchun Chen,et al.  Intelligence and security informatics: information systems perspective , 2006, Decis. Support Syst..

[39]  Joao Antonio Pereira,et al.  Linked: The new science of networks , 2002 .

[40]  John Scott.  Social Network Analysis , 1988 .

[41]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[42]  Terry L King.  A Guide to Chi-Squared Testing , 1997 .

[43]  Richard Block,et al.  The Journey to Crime: Victims and Offenders Converge in Violent Index Offences in Chicago , 2007 .