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.
翻译:在投票方面,选民的压制和相关的种族差异是美国长期存在的民权问题。几十年来,投票的障碍表现为多种形式。暴力明显的阻遏历史已经演变为更微妙的准入限制,包括长线和等待时间、前往投票站的长途旅行时间,以及投票的其他后勤障碍。我们这项工作的重点是量化与整个投票时间和投票地点有关的投票准入差异,以及如何通过更好地选择投票地点或提供更多选民可以投票的地点来纠正这些差异。然而,由于需要考虑到人口密度和社区对合理旅行时间的不同期望等因素,因此很难适当校正准入差异。在本文件中,我们量化投票地点的准入,制定衡量投票地点“负荷”和距离投票地点之间种族差异的校准方法。我们采用这种方法来研究佛罗里达和北卡罗来的真实世界数据,以找出2020年第一次选举的投票准入差距。我们还引入了算法,调整了规模,这样可以减少这些差异,因为需要将新的投票地点从既定的投票地点的投票地点列表中提出新的投票地点和2020年选举地点的升级影响。此外,还利用了2020年选举方法(包括学校和图书馆)所有可能的投票费用。