Towards Simulating Criminal Offender Movement Based on Insights from Human Dynamics and Location-Based Social Networks

Interest in data-driven crime simulations has been growing in recent years, confirming its potential to advance crime prevention and prediction. Especially, the use of new data sources in crime simulation models can contribute towards safer and smarter cities. Previous work on agent-based models for crime simulations have intended to simulate offender behavior in a geographical environment, relying exclusively on a small sample of offender homes and crime locations. The complex dynamics of crime and the lack of information on criminal offender’s movement patterns challenge the design of offender movement in simulations. At the same time, the availability of big, GPS-based user data samples (mobile data, social media data, etc.) already allowed researchers to determine the laws governing human mobility patterns, which, we argue, could inform offender movement. In this paper, we explore: (1) the use of location-based venue data from Foursquare in New York City (NYC), and (2) human dynamics insights from previous studies to simulate offender movement. We study 9 offender mobility designs in an agent-based model, combining search distances strategies (static, uniform distributed, and Levy-flight approximation) and target selection algorithms (random intersection, random Foursquare venues, and popular Foursquare venues). The offender behavior performance is measured using the ratio of crime locations passed vs average distance traveled by each offender. Our initial results show that agents moving between POI perform best, while the performance of the three search distance strategies is similar. This work provides a step forward towards more realistic crime simulations.