ICAR Research Data Repository for Knowledge Management
Replication Data for: How to Measure Legislative District Compactness If You Only Know It When You See It
Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)
View Archive Info| Field | Value | |
| Title |
Replication Data for: How to Measure Legislative District Compactness If You Only Know It When You See It
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| Identifier |
https://doi.org/10.7910/DVN/FA8FVF
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| Creator |
Kaufman, Aaron R.
King, Gary Komisarchik, Mayya |
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| Publisher |
Harvard Dataverse
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| Description |
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 17,896 state legislative and congressional districts, as well as software to compute this measure from any district. |
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| Subject |
Social Sciences
Redistricting Measurement Machine learning |
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| Contributor |
Kaufman, Aaron R.
Mayya Komisarchik |
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