Rationale: Crime is shaped by both spatial-temporal dynamics and social structures, yet most models treat these dimensions separately. This project bridges this gap by integrating mathematical modeling, simulation, and network analysis to understand how co-offending networks influence crime patterns in space and time.

Objective: Model crime dynamics by linking social networks to spatial and temporal distributions.

Approach: Combining computational methods, such as agent-based models and reaction-diffusion partial differential equations, with graph-theoretic methods, such as centrality and community detection analysis, to model offender behavior across space, time, and networks. Validate with real-world data.

Impact: Reveal interactions between social and spatiotemporal crime mechanisms, test interventions, and support evidence-based policing through simulations.