Using predictability to improve matching of urban locations in Philadelphia

Abstract

Motivated by theories in urban planning and criminology, we use high-resolution data to investigate the relationship between crime and the built environment in the City of Philadelphia. We develop a novel and flexible matching framework that uses the predictability of the treatment variable within matched pairs to empirically inform both the differential weighting of covariates in the matching as well as the selection of the number of matched pairs to create. We use this matching framework for a series of comparisons, each involving matched pairs of Philadelphia intersections that are highly similar on a set of covariates but restricted to differ on a single aspect of the built environment. Our predictability-based matching framework includes data-driven decisions about differential weighting of covariates and the number of matched pairs to create, which is beneficial in our setting as our urban comparisons involve a large number of potential intersections and a large set of covariates to be balanced. In these comparisons we find substantial heterogeneity in the relationships between crime and different aspects of the built environment as well as some empirical support for historical theories.