Multi-Resolution Analyses of Neighborhood Correlates of Crime: Smaller is not Better.

Population analyses of the correlates of neighborhood crime implicitly assume that a single spatial unit can be used to assess neighborhood effects. However, no single spatial unit may be suitable for analyses of the many social determinants of crime. Instead, effects may appear at multiple spatial resolutions with some determinants acting broadly, others locally, and yet others as some function of both global and local conditions. We provide a multi-resolution spatial analysis that simultaneously examines Census block, block group, and tract effects of alcohol outlets and drug markets on violent crimes in Oakland, California, incorporating spatial lag effects at the two smaller spatial resolutions. Using call data from the Oakland Police Department from 2010-2015, we examine associations between assaults, burglaries, and robberies with multiple resolutions of alcohol outlet types, and compare the performance of single (block-level) vs. multi-resolution models. Multi-resolution models performed better than the block models, reflected in improved Deviance and Watanabe-Akaike Information Criteria and well-supported multi-resolution associations. By considering multiple spatial scales and spatial lags in a Bayesian framework, researchers can explore multi-resolution processes, providing more detailed tests of expectations from theoretical models and leading the way to more effective intervention efforts.