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Automated Congressional Redistricting

Published: 25 April 2019 Publication History

Abstract

Every 10 years, when states are forced to redraw their congressional districts, the process is intensely partisan, and the outcome is rarely fair and democratic. In the past few decades, the growing capabilities of computers have offered the promise of objective, computerized redistricting. Unfortunately, the redistricting problem can be shown to be NP-Complete, but there are a number of heuristics that are effective. We specifically define the redistricting problem and analyze several variations of a new divide and conquer algorithm, comparing the compactness and population deviation of our new algorithm to existing algorithms and the actual districts. We offer a comparative component-based analysis that demonstrates the strengths and weaknesses of each algorithm component and the type of input. The comparative analysis shows that there are several ways to produce valid redistricting plans, but each approach has benefits and consequences.
Our new algorithm produces valid results to the redistricting problem in almost every state that undergoes congressional redistricting, offering a new solution to this challenging real-world problem. In one version, the algorithm produces plans with the optimal population deviation in 42 out of 43 multi-district states, which is better than most algorithms in the literature. While compactness scores vary, this approach offers new opportunities to improve population deviation. Our output files comply with the accepted format at most government hearings and redistricting competitions, so the results would be compatible with most public participation efforts in 2020.

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Published In

cover image ACM Journal of Experimental Algorithmics
ACM Journal of Experimental Algorithmics  Volume 24, Issue
Special Issue ESA 2016, Regular Papers and Special Issue SEA 2018
2019
622 pages
ISSN:1084-6654
EISSN:1084-6654
DOI:10.1145/3310279
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 April 2019
Accepted: 01 February 2019
Revised: 01 April 2018
Received: 01 February 2017
Published in JEA Volume 24

Author Tags

  1. Computational geometry
  2. clustering
  3. experimental algorithms
  4. gerrymandering
  5. redistricting

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