Please join the Goergen Institute for Data Science for How to Measure Legislative District Compactness If You Only Know it When You See It, a research seminar with Mayya Komisarchik, Assistant Professor in the Department of Political Science at the University of Rochester.

Abstract: 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 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 offer compactness data from our validated measure for 20,160 state legislative and congressional districts, as well as open source software to compute this measure from any district.

Bio: Mayya Komisarchik is an Assistant Professor in the Department of Political Science at the University of Rochester. She received her Ph.D. in Government from Harvard University. She studies American politics and quantitative methodology. Her substantive research interests include race and ethnic politics, representation, voting rights, policing, immigration, and political incorporation. 

For more information about Mayya's research, please see the paper her talk is based on, linked below.

https://gking.harvard.edu/files/gking/files/compact.pdf

To view a recording of this talk, click on the link below:

https://rochester.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=826fbe97-cdf3-4f74-b6c2-ac5900f09edd

Event Details

  • Francisco, Sylvia
  • JEN-LI CHEN

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