Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina
Not on the map. Why?
Abstract (arXiv)
Voter suppression and associated racial disparities in access to voting are long-standing civil rights concerns in the United States. Barriers to voting have taken many forms over the decades. A history of violent explicit discouragement has shifted to more subtle access limitations that can include long lines and wait times, long travel times to reach a polling station, and other logistical barriers to voting. Our focus in this work is on quantifying disparities in voting access pertaining to the overall time-to-vote, and how they could be remedied via a better choice of polling location or provisioning more sites where voters can cast ballots. However, appropriately calibrating access disparities is difficult because of the need to account for factors such as population density and different community expectations for reasonable travel times. In this paper, we quantify access to polling locations, developing a methodology for the calibrated measurement of racial disparities in polling location "load" and distance to polling locations. We apply this methodology to a study of real-world data from Florida and North Carolina to identify disparities in voting access from the 2020 election. We also introduce algorithms, with modifications to handle scale, that can reduce these disparities by suggesting new polling locations from a given list of identified public locations (including schools and libraries). Applying these algorithms on the 2020 election location data also helps to expose and explore tradeoffs between the cost of allocating more polling locations and the potential impact on access disparities. The developed voting access measurement methodology and algorithmic remediation technique is a first step in better polling location assignment.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 54 references that are in the map. First posted on arXiv 30 May 2022.
Builds on
16 of 54 references.
References · 16
- 2016Equality of Opportunity in Supervised Learningcited by 2,007
- 2017Counterfactual Fairnesscited by 804
- 2014Certifying and removing disparate impactcited by 715
- 2018Fair Clustering Through Fairletscited by 176
- 2019Guarantees for Spectral Clustering with Fairness Constraintscited by 72
- 2019Proportionally Fair Clusteringcited by 69
- 2019Fair k-Center Clustering for Data Summarizationcited by 65
- 2019Clustering without Over-Representationcited by 50
- 2020Fair clustering via equitable group representationscited by 43
- 2021Improved Approximation Algorithms for Individually Fair Clusteringcited by 33
- 2021Better Algorithms for Individually Fair k-Clusteringcited by 28
- 2019Fairness in representation: quantifying stereotyping as a representational harmcited by 26
4 more
- 2021Approximation Algorithms for Socially Fair Clusteringcited by 25
- 2019Fairness in Clustering with Multiple Sensitive Attributescited by 21
- 2021Approximating Fair Clustering with Cascaded Norm Objectivescited by 13
- 2019Minimizing Margin of Victory for Fair Political and Educational Districtingcited by 2
Built on by
Citing papers per year: 2024: 1.
1 paper.
- 2024Fair Clustering: Critique, Caveats, and Future Directions22 Jun 2024