Gerrymandering and Compactness: Implementation Flexibility and Abuse

The shape of an electoral district may suggest whether it was drawn with political motivations, or gerrymandered. For this reason, quantifying the shape of districts, in particular their compactness, is a key task in politics and civil rights. A growing body of literature suggests and analyzes compactness measures mathematically, but little consideration has been given to how these scores should be calculated in practice. Here, we consider the effects of a number of decisions that must be made in interpreting and implementing a set of popular compactness scores. We show that the choices made in quantifying compactness may themselves become political tools, with seemingly innocuous decisions leading to disparate scores. We show that when the full range of implementation flexibility is used, it can be abused to make clearly gerrymandered districts appear quantitatively reasonable. This complicates using compactness as a legislative or judicial standard to counteract unfair redistricting practices. This paper accompanies the release of packages in C++, Python, and R which correctly, efficiently, and reproducibly calculate a variety of compactness scores.

[1]  Moon Duchin,et al.  A formula goes to court: Partisan gerrymandering and the efficiency gap , 2017, 1705.10812.

[2]  RamachandranGowri,et al.  Using Outlier Analysis to Detect Partisan Gerrymanders: A Survey of Current Approaches and Future Directions , 2018 .

[3]  Nicholas Stephanopoulos,et al.  Partisan Gerrymandering and the Efficiency Gap , 2014 .

[4]  Oscar S. Adams,et al.  Elements of Map Projection: With Applications to Map and Chart Construction , 1945 .

[5]  J. Ioannidis.  Why Most Published Research Findings Are False , 2005, PLoS medicine.

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

[7]  J. Jenness.  Calculating landscape surface area from digital elevation models , 2004 .

[8]  Christopher P. Chambers,et al.  A Measure of Bizarreness , 2007 .

[9]  Nick Barnes.  Publish your computer code: it is good enough , 2010, Nature.

[10]  Z. Merali.  Computational science: ...Error , 2010, Nature.

[11]  Nancy Wilkins-Diehr,et al.  XSEDE: Accelerating Scientific Discovery , 2014, Computing in Science & Engineering.

[12]  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.

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

[14]  Bernd Gärtner,et al.  Fast and Robust Smallest Enclosing Balls , 1999, ESA.

[15]  Ellen Veomett,et al.  Efficiency Gap, Voter Turnout, and the Efficiency Principle , 2018, Election Law Journal: Rules, Politics, and Policy.

[16]  John P. Snyder,et al.  A Low-Error Conformal Map Projection for the 50 States , 1984 .

[17]  Micah Altman,et al.  BARD: Better Automated Redistricting , 2011 .

[18]  Dustin G. Mixon,et al.  An Impossibility Theorem for Gerrymandering , 2017, Am. Math. Mon..

[19]  Mark A. Fonstad,et al.  Kansas Is Flatter Than a Pancake , 2003 .

[20]  Darrel C. Ince,et al.  The case for open computer programs , 2012, Nature.

[21]  Y LiuYan,et al.  Toward a Talismanic Redistricting Tool: A Computational Method for Identifying Extreme Redistricting Plans , 2016 .

[22]  Yan Y. Liu,et al.  Toward a Talismanic Redistricting Tool: A Computational Method for Identifying Extreme Redistricting Plans , 2016 .