Supplementary Appendix: How to Measure Legislative District Compactness If You Only Know it When You See it

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. Verification Materials: The data and materials required to verify the computational reproducibility of the results, procedures and analyses in this article are available on the American Journal of Political Science Dataverse within the Harvard Dataverse Network, at Kaufman et al. (2021) at https://doi.org/10.7910/DVN/FA8FVF. Compactness is treated in the law as an important legal bulwark against gerrymandering. The Apportionment Act of 1901, many court decisions, and 18 state constitutions require compactness for U.S. House districts, and 37 states require their legislative districts to be compact (see j.mp/aRED). Compactness is also required in federal law as one of the “traditional redistricting principles,” which, when followed, can “defeat a claim that a district has been gerrymandered...” on the basis of race (Shaw v. Reno, 509 U.S. 630, 647 (1993)) or political party (Davis v. Bandemer, 478 U.S. 173, 2815 (1986)).1 Compactness is also important for the academic literature, where scholars seek to help the redistricting and litigation processes, and also to study venerable political science questions such as the causes, consequences, and normative implications of compact districts over American history (e.g., Ansolabehere and Palmer 2016; Ansolabehere and Snyder Jr 2012; Forgette and Platt 2005). Compactness intuitively refers to both how close a legislative district’s boundaries are to its geographic center and how “regular” in shape a district appears to be. But upon deeper study, scholars have shown that in fact compactness is a complicated multidimensional Aaron R. Kaufman is Assistant Professor, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi, UAE (AaronrKaufman.com; aaronkaufman@nyu.edu). Gary King is Albert J. Weatherhead III University Professor, Institute for Quantitative Social Science, 1737 Cambridge Street, Harvard University, Cambridge, MA 02138 (GaryKing.org; King@Harvard.edu). Mayya Komisarchik is Assistant Professor, University of Rochester, Harkness Hall, 333 Hutchinson Road, Rochester, NY 14627 (www.mayyakomisarchik.com; mayya.komisarchik@rochester.edu). Winner of the 2018 Robert H. Durr Award from the Midwest Political Science Association. Our thanks to Steve Ansolabehere, Fred Boehmke, Ryan Enos, Dan Gilbert, Jim Griener, Bernie Grofman, Andrew Ho, Dan Ho, James Honaker, Justin Levitt, Luke Miratrix, Max Palmer, Stephen Pettigrew, Jamie Saxon, Steve Shavell, Anton Strezhnev, Wendy Tam, Rocio Titiunik, Larry Tribe, Robert Ward, participants in “A Causal Lab,” and the audiences at the Society for Political Methodology Meetings, Nuffield College at Oxford University, the Harvard Applied Statistics Workshop, and the ICPSR Summer Program for helpful data or suggestions, and to Stacy Bogan, the Center for Geographic Analysis, and the Institute for Quantitative Social Science at Harvard University for research assistance and support. Claims about most other types of unfairness in redistricting all also seem to depend on a legal finding of noncompactness (Davis v. Bandemer, 478 U.S. 165; Justice Powell in Vieth v. Jubilerer, 541 U.S. 267 (2004) 176–177; Kirkpatrick v. Preisler, supra, at 394 U. S. 526, 538). American Journal of Political Science, Vol. 65, No. 3, July 2021, Pp. 533–550 ©2021, Midwest Political Science Association DOI: 10.1111/ajps.12603

[1]  L. Thurstone The method of paired comparisons for social values , 1927 .

[2]  E. Cox A method of assigning numerical and percentage values to the degree of roundness of sand grains , 1927 .

[3]  P. Moran On the method of paired comparisons. , 1947, Biometrika.

[4]  Ernest C. Reock join A Note: Measuring Compactness as a Requirement of Legislative Apportionment , 1961 .

[5]  C. C. Harris,et al.  A scientific method of districting. , 1964, Behavioral science.

[6]  R. R. Boyce,et al.  The Concept of Shape in Geography , 1964 .

[7]  D. Stoddart,et al.  The shape of atolls , 1965 .

[8]  Gustav Theodor Fechner,et al.  Elements of psychophysics , 1966 .

[9]  Henry F. Kaiser,et al.  An Objective Method for Establishing Legislative Districts , 1966 .

[10]  M. Polanyi The Logic of Tacit Inference , 1966, Philosophy.

[11]  Mei-Ling Hsu,et al.  The fidelity of isopleth maps : an experimental study , 1970 .

[12]  David R. Lee,et al.  A Method of Measuring Shape , 1970 .

[13]  Bruce E. Cain,et al.  The Reapportionment Puzzle , 1984 .

[14]  Alan M. MacEachren,et al.  Compactness of Geographic Shape: Comparison and Evaluation of Measures , 1985 .

[15]  S. Presser,et al.  Survey Questions: Handcrafting the Standardized Questionnaire. , 1988 .

[16]  Robert Browning,et al.  Democratic Representation and Partisan Bias in Congressional Elections , 1987, American Political Science Review.

[17]  H. Young Measuring the Compactness of Legislative Districts , 1988 .

[18]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[19]  H. A. David,et al.  The Method of Paired Comparisons (2nd ed.). , 1989 .

[20]  B. Grofman,et al.  Measuring Compactness and the Role of a Compactness Standard in a Test for Partisan and Racial Gerrymandering , 1990, The Journal of Politics.

[21]  Hazel Everett,et al.  The Graham scan triangulates simple polygons , 1990, Pattern Recognit. Lett..

[22]  D. Polsby,et al.  The Third Criterion: Compactness as a Procedural Safeguard Against Partisan Gerrymandering , 1991 .

[23]  P. Eason,et al.  Optimization of territory shape in heterogeneous habitats : a field study of the red-capped cardinal (Paroaria gularis) , 1992 .

[24]  Richard G. Niemi,et al.  Expressive Harms, "Bizarre Districts," and Voting Rights: Evaluating Election-District Appearances After Shaw v. Reno , 1993 .

[25]  W. Prinzmetal,et al.  Vertical-horizontal illusion: One eye is better than two , 1993, Perception & psychophysics.

[26]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Gary King,et al.  Enhancing Democracy Through Legislative Redistricting , 1994, American Political Science Review.

[28]  Timothy D. Wilson,et al.  Mental contamination and mental correction: unwanted influences on judgments and evaluations. , 1994, Psychological bulletin.

[29]  Gary King,et al.  A Unified Method of Evaluating Electoral Systems and Redistricting Plans , 1994 .

[30]  P. Peterson Classifying by Race , 1995 .

[31]  Racial Fairness in Legislative Redistricting , 1996 .

[32]  Micah Altman,et al.  Modeling the effect of mandatory district compactness on partisan gerrymanders , 1998 .

[33]  J. Krosnick,et al.  Survey research. , 1999, Annual review of psychology.

[34]  D. Maurer,et al.  The many faces of configural processing , 2002, Trends in Cognitive Sciences.

[35]  Redistricting Principles and Racial Representation , 2004, State Politics & Policy Quarterly.

[36]  THE NATIONAL HISTORICAL GEOGRAPHIC INFORMATION SYSTEM ( NHGIS ) , 2005 .

[37]  Yu-hsin Tsai Quantifying Urban Form: Compactness versus 'Sprawl' , 2005 .

[38]  Richard G. Forgette,et al.  Redistricting principles and incumbency protection in the U.S. Congress , 2005 .

[39]  Gary King,et al.  The Dangers of Extreme Counterfactuals , 2006, Political Analysis.

[40]  Frank Nielsen,et al.  On the Smallest Enclosing Information Disk , 2008, CCCG.

[41]  Y. Kwan,et al.  Modification and simplification of thurstone scaling method, and its demonstration with a crime seriousness assessment , 2007 .

[42]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[43]  B. Grofman,et al.  The Future of Partisan Symmetry as a Judicial Test for Partisan Gerrymandering after LULAC v. Perry , 2007 .

[44]  A. Adler,et al.  Configural Processing , 2009, Encyclopedia of Biometrics.

[45]  Non-Compactness and Voter Exchange; Towards a Constitutional Cure for Gerrymandering , 2010 .

[46]  Ioannis Mitliagkas,et al.  User rankings from comparisons: Learning permutations in high dimensions , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[47]  D. Kahneman Thinking, Fast and Slow , 2011 .

[48]  J. Blasius Comparing Ranking Techniques in Web Surveys , 2012 .

[49]  S. Ansolabehere,et al.  The Effects of Redistricting on Incumbents , 2012 .

[50]  L. Gideon,et al.  The Art of Question Phrasing , 2012 .

[51]  L. Gideon Handbook of Survey Methodology for the Social Sciences , 2012 .

[52]  Redistricting Principles for the Twenty-First Century , 2012 .

[53]  Paulo J. G. Lisboa,et al.  Making machine learning models interpretable , 2012, ESANN.

[54]  Jonathan Rodden,et al.  Unintentional Gerrymandering: Political Geography and Electoral Bias in Legislatures , 2013 .

[55]  Richard L. Church,et al.  UC Office of the President Recent Work Title An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems Permalink , 2013 .

[56]  Luigi Fabbris,et al.  Measurement Scales for Scoring or Ranking Sets of Interrelated Items , 2013 .

[57]  Steven O. Kimbrough,et al.  On Empirical Validation of Compactness Measures for Electoral Redistricting and Its Significance for Application of Models in the Social Sciences , 2014 .

[58]  N. McGlynn Thinking fast and slow. , 2014, Australian veterinary journal.

[59]  Cynthia Rudin,et al.  Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model , 2015, ArXiv.

[60]  Maxwell Palmer,et al.  A Two Hundred-Year Statistical History of the Gerrymander , 2016 .

[61]  Peter Kraft,et al.  Improving Supreme Court Forecasting Using Boosted Decision Trees , 2019, Political Analysis.

[62]  J. E. Schwartzberg Reapportionment, Gerrymanders, and the Notion of Compactness , 2019 .

[63]  A. Kaufman Implementing novel, flexible, and powerful survey designs in R Shiny , 2020, PloS one.