How to Measure Legislative District Compactness If You Only Know it When You See it (Department of Biomedical Informatics, Harvard Medical School)

Presentation Date: 

Tuesday, March 16, 2021


Department of Biomedical Informatics, Harvard Medical School

Presentation Slides: 

To deter gerrymandering, many state constitutions require legislative districts to be "compact." Yet, the law offers few precise definitions other than "you know it when you see it," which effectively implies a common understanding of the concept. In contrast, academics have shown that compactness has multiple dimensions and have generated many conflicting measures. We hypothesize that both are correct -- that compactness is complex and multidimensional, but a common understanding exists across people. We develop a survey to elicit this understanding, with high reliability (in data where the standard paired comparisons approach fails). We then create a statistical model that predicts, with high accuracy, solely from the geometric features of the district, compactness evaluations by judges and public officials responsible for redistricting, among others. We also discuss our complex computational requirements, a perspective on research computing from the social sciences, and software to compute our compactness measure from any district. 

Based on joint work with Aaron Kaufman and Mayya Komisarchik. Winner of the Robert Durr Award from the MPSA. Forthcoming in the American Journal of Political Science: See

The talk will also touch on this paper: Jonathan N. Katz, Gary King, and Elizabeth Rosenblatt. Forthcoming. “Theoretical Foundations and Empirical Evaluations of Partisan Fairness in District-Based Democracies.” Forthcoming in the American Political Science Review.