Theorizing globally, but analyzing locally: the importance of geographically weighted regression in crime analysis

Abstract

Theoretical relationships with crime across cities are explicitly or implicitly assumed to be the same in all places: a one-unit change in X leads to a β change in Y. But why would we assume the impact of unemployment, for example, is the same in wealthy and impoverished neighborhoods? We use a local statistical technique, geographically weighted regression, to identify local relationships with property crime. We find that theoretical relationships vary across the city, most often only being statistically significant in less than half of the city. This is important for the development of criminal justice policy and crime prevention, because these initiatives most often work in particular places potentially leading to a misallocation of scarce public resources.