Crime in Philadelphia: Bayesian Clustering with Particle Optimization

Understand

Accurate estimation of the change in crime over time is a critical first step towards better understanding of public safety in large urban environments.

• Bayesian hierarchical modeling is a natural way to study spatial variation in urban crime dynamics at the neighborhood level, since it facilitates principled ``sharing of information'' between spatially adjacent neighborhoods.
• Typically, however, cities contain many physical and social boundaries that may manifest as spatial discontinuities in crime patterns.
• In this situation, standard prior choices often yield overly-smooth parameter estimates, which can ultimately produce mis-calibrated forecasts.