An agent-based model for simulating urban crime with improved daily routines

Abstract

We propose a scalable agent-based crime simulation model based on the routine activity theory. It uses census data and time geography to create a synthetic population with residences and job locations, commuting schedules, and daily routines constrained by disposable time that are more representative than existing models. The time and location of crime incidents in this model are determined by random encounters between vulnerable targets and motivated offenders when they travel or carry out scheduled activities on a road network. This model is applied to simulate robberies in January 2011 for Baton Rouge, Louisiana. Three scenarios are simulated to demonstrate its value for crime theory development and shed light on modeling issues in need of improvement before it can reliably assist policymaking or inform the public. Major findings from this study include the model's ability to replicate prominent robbery hotspots in the study area with various degrees of success and the consistent effects of target definition and offender strategy on model performance. The study also suggests missing model components to effectively constrain the displacement of crime opportunities under hotspot policing, which can be the key to resolving contradiction between our simulation results and other empirical researches and crime simulations.