Agent-based simulation of offender mobility: integrating activity nodes from location-based social networks

Abstract

In recent years, simulation techniques have been applied to investigate the spatio-temporal dynamics of crime. Researchers have instantiated mobile offenders in agent-based simulations for theory testing, experimenting with prevention strategies, and crime prediction purposes, despite facing challenges due to the complex dynamics of crime and the lack of detailed information about offender mobility. This paper presents an agent-based model to explore offender mobility, focusing on the interplay between the agent’s awareness space and activity nodes. To instantiate a realistic urban environment, we use open data to simulate the urban structure and location-based social networks data to represent activity nodes as proxy for human activity. 18 mobility strategies have been tested, combining search distance strategies (e.g. Lévy flight, inspired by insights in human dynamics literature) and destination selection strategies (enriched with Foursquare data). We analyze and compare the different mobility strategies, and show the impact of using activity nodes extracted from social networks to simulate offender mobility. This agent-based model provides a basis for comparing offender mobility in crime simulations by inferring offender mobility in urban areas from real world data.