RAPID: Pinpointing Expected Covid-19 Related Voter Turnout Problems

RAPID：查明与 Covid-19 相关的预期选民投票率问题

• 批准号: 2031398
• 负责人: Jeffrey Hancock
• 金额: $ 19.93万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031398&HistoricalAwards=false
• 关键词: RAPID; Pinpointing; Expected; Covid; 19

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.

投票是一项基本的民主活动。然而，这一进程很容易受到破坏。飓风等自然灾害和9/11恐怖袭击等人为灾害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。当选举季节发生灾害时，必须向受灾地区提供特别投资，以确保选民能够投票。这种投资可以简单到只针对特定领域开展宣传活动，也可以更实际地进行，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：投票地点可能需要搬迁，投票时间可能需要改变，与灾害有关的经济损失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，发现受灾害相关选民投票率崩溃威胁的地区。该工具旨在帮助所有利益攸关方采取本地化行动，及时缓解问题。该系统将持续监测许多不同的在线来源，如谷歌搜索、全国州议会会议（NCSL）数据和社交媒体趋势。虽然每个信号源本身可能并不完全可靠，但它们的复合信号将有助于系统确定困难在哪里。这项工作的结果最初旨在支持2020年的选举。该奖项反映了NSF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。