What We Can Learn from Small Units of Analysis

Abstract

This article provides motivation for examining small geographic units of analysis based on a causal logic framework. Local, spatial, and contextual effects are confounded when using larger units of analysis, as well as treatment effect heterogeneity. I relate these types of confounds to all types of aggregation problems, including temporal aggregation, and aggregation of dependent or explanatory variables. Unlike prior literature critiquing the use of aggregate level data, examples are provided where aggregation is unlikely to hinder the goals of the particular research design, and how heterogeneity of measures in smaller units of analysis is not a sufficient motivation to examine small geographic units. Examples of these confounds are presented using simulation with a dataset of crime at micro place street units (i.e. street segments and intersections) in Washington, D.C.