Implications of Distance over Redistricting Maps: Central and Outlier Maps

• URL: http://arxiv.org/abs/2203.00872v4
• Date: Tue, 30 May 2023 23:08:01 GMT
• Title: Implications of Distance over Redistricting Maps: Central and Outlier Maps
• Authors: Seyed A. Esmaeili, Darshan Chakrabarti, Hayley Grape, Brian Brubach
• Abstract summary: In representative democracy, a redistricting map is chosen to partition an electorate into a collection of districts each of which elects a representative. A valid redistricting map must satisfy a collection of constraints such as being compact, contiguous, and of almost equal population. This fact introduces a difficulty in drawing redistricting maps and it also enables a partisan legislature to possibly gerrymander by choosing a map which unfairly favors it.
• Score: 6.757783454836096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In representative democracy, a redistricting map is chosen to partition an electorate into a collection of districts each of which elects a representative. A valid redistricting map must satisfy a collection of constraints such as being compact, contiguous, and of almost equal population. However, these imposed constraints are still loose enough to enable an enormous ensemble of valid redistricting maps. This fact introduces a difficulty in drawing redistricting maps and it also enables a partisan legislature to possibly gerrymander by choosing a map which unfairly favors it. In this paper, we introduce an interpretable and tractable distance measure over redistricting maps which does not use election results and study its implications over the ensemble of redistricting maps. Specifically, we define a central map which may be considered as being "most typical" and give a rigorous justification for it by showing that it mirrors the Kemeny ranking in a scenario where we have a committee voting over a collection of redistricting maps to be drawn. We include run-time and sample complexity analysis for our algorithms, including some negative results which hold using any algorithm. We further study outlier detection based on this distance measure. More precisely, we show gerrymandered maps that lie very far away from our central maps in comparison to a large ensemble of valid redistricting maps. Since our distance measure does not rely on election results, this gives a significant advantage in gerrymandering detection which is lacking in all previous methods.

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