RAPID: Pinpointing Expected Covid-19 Related Voter Turnout Problems

投票是一项基本的民主活动。然而，这个过程很容易受到干扰。飓风等自然灾害，以及911恐怖袭击等人为造成的灾害，都严重阻碍了公民的投票能力。例如，飓风桑迪在选举期间影响了20个州。当灾害在选举季节发生时，必须对受灾地区进行特别投资，以确保选民能够投票。这类投资可以是简单的，比如针对特定领域开展宣传活动，也可以是更实际的，比如提供辅助运输供应。然而，所有这些措施都需要资金、志愿者和政府的承诺，因此需要提前通知。新冠病毒是威胁选举的最新灾难。与飓风一样，美国一些地区受到的影响比其他地区更大。类似的投票障碍可能会出现：投票站可能需要移动，投票时间可能需要改变，与灾难有关的经济损害可能会降低人们的积极性。该项目创建了一个数据驱动的早期预警系统，可以发现受灾害影响的地区的选民投票率大幅下降。该工具旨在帮助所有利益相关者采取局部行动，及时缓解问题。该系统将持续监测谷歌搜索、全国州议会会议（NCSL）数据和社交媒体趋势等多种在线资源。虽然每个信号源本身可能不是完全可靠的，但它们的复合信号将帮助系统确定正在制造困难的地方。这项工作的结果最初旨在支持2020年的选举。但这个工具将为未来的选举服务。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Voting is a fundamental democratic activity. And yet, the process is vulnerable to disruption. Natural disasters such as hurricanes, and those created by humans, such as the 9/11 terrorist attack pose serious impediments to citizens' ability to vote. Hurricane Sandy, for example, affected twenty States during an election. When disasters occur during voting seasons, special investments must be directed into affected areas to ensure that voters can cast their ballots. Such investments may be as simple as information campaigns focused on particular areas or more hands-on such as providing an auxiliary transportation supply. However, all these measures require monetary, volunteer, and government commitments, and thereby advance notice.The Covid virus is the latest disaster that threatens an election. As with hurricanes, some US regions are more affected than others. Similar obstacles to voting may be expected: Polling places may need to be moved, voting hours may need to change, and disaster related economic harm to the population may lower motivation. Thisproject creates a data driven early warning system that discovers regions threatened by disaster related voter turnout collapse. The tool aims to help all stake holders take localized action towards mitigating problems in time. The system will continuously monitor many diverse online sources such as Google searches, National Conference of State Legislatures (NCSL) data, and social media trends. While each source by itself may not be wholly reliable, their composite signals will help the system decide where difficulties are in the making. The results of this work are initially aimed to support the 2020 election. But the tool will serve for elections in the future.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.