# Spatial Modeling of Trends in Crime over Time in Philadelphia

## TL;DR

This paper develops Bayesian spatial models to analyze and predict crime trends over time in Philadelphia, incorporating neighborhood predictors and spatial discontinuities for improved accuracy.

## Contribution

It introduces novel Bayesian approaches for spatial sharing of information in crime trend modeling, including handling spatial discontinuities.

## Key findings

- Conditional autoregressive models improve predictive accuracy.
- Spatial discontinuities may indicate natural barriers or built environment effects.
- Incorporating economic and demographic predictors enhances model insights.

## Abstract

Understanding the relationship between change in crime over time and the geography of urban areas is an important problem for urban planning. Accurate estimation of changing crime rates throughout a city would aid law enforcement as well as enable studies of the association between crime and the built environment. Bayesian modeling is a promising direction since areal data require principled sharing of information to address spatial autocorrelation between proximal neighborhoods. We develop several Bayesian approaches to spatial sharing of information between neighborhoods while modeling trends in crime counts over time. We apply our methodology to estimate changes in crime throughout Philadelphia over the 2006-15 period, while also incorporating spatially-varying economic and demographic predictors. We find that the local shrinkage imposed by a conditional autoregressive model has substantial benefits in terms of out-of-sample predictive accuracy of crime. We also explore the possibility of spatial discontinuities between neighborhoods that could represent natural barriers or aspects of the built environment.