Crime, neighborhoods, and units of analysis: Putting space in its place

Abstract

Research has long established that crime is not randomly distributed, and spatial regression models of crime have clearly demonstrated that crime patterns cannot be explained merely by the socio-economic characteristics of a particular place. These findings are a reminder that space matters and that neighborhoods are not analytically independent units. Modeling the clustering of crime through spatial regression requires two important decisions. First, one must choose a unit of analysis that is consistent with the social processes believed to be driving the observed patterns. Second, one must consider the relationships among these units such that the model captures the influence the activities in other areas have on outcomes in the neighborhood. Within criminology, this second feature has been given insufficient consideration. Instead, the connectedness of spatial units has been taken as given and modeled solely through adjacency or a distance decay function. This chapter critiques such inductive approaches used to model and explain the spatial distribution of crime. Drawing upon the modeling of network autocorrelation within the social influence literature, we describe a deductive approach wherein specific social processes are posited, measured and modeled a priori. An empirical example using gang violence demonstrates this deductive approach and we find that the spatial distribution of violence is influenced by neighbors defined by the socio-spatial dimensions of gang rivalries rather than simply by geographically contiguous neighbors. We emphasize that a complete discussion of the appropriate unit of analysis must also consider the spatial dimensions of the social phenomena thought to be responsible for the spatial patterning. © 2009 Springer New York.