COVID-19与收入结构：美国社区在疫情早期如何应对出行限制
COVID-19 and income profile: How communities in the United States responded to mobility restrictions in the pandemic's early stages
AI总结：
本研究基于美国大量匿名设备位置数据，分析不同收入社区在疫情早期的社交距离表现差异，发现高收入社区表现更优，并指出低收入社区面临的相关现实问题，为疫情防控政策讨论提供数据支撑。
摘要：
社区层面的出行干预在疫情早期遏制传播中发挥着关键作用。保持社交距离的实际实践能为政策制定者提供启发，助力其推行更高效、更具成效的防控措施。目前缺乏基于真实世界观测的相关研究，这正是本文开展研究的动因。我们分析了美国3142个县共66149个人口普查 tract（普查小区）的社交距离执行情况，重点聚焦收入结构维度。研究基于每月超1亿台匿名设备的位置数据，为每个普查小区计算了6项日常出行指标，包括社交距离指数、居家百分比、人均出行英里数、出行率、工作出行率及非工作出行率。研究进一步从三个维度对每项出行指标的社交距离表现进行了统计：“最佳表现”“努力程度”和“一致性”。我们发现，在全部18项指标上，高收入社区的社交距离表现都更优。本文详细呈现了不同收入水平社区之间的此类差异。跨场景的比较还凸显了低收入社区面临的其他问题，如就业状况、工作条件以及基本生活需求的可及性。本文列出了一系列从真实世界数据中提取的事实，为未来的相关讨论提供了有说服力的视角。
英文摘要：
Mobility interventions in communities play a critical role in containing a pandemic at an early stage. The real-world practice of social distancing can enlighten policymakers and help them implement more efficient and effective control measures. A lack of such research using real-world observations initiates this article. We analyzed the social distancing performance of 66,149 census tracts from 3,142 counties in the United States with a specific focus on income profile. Six daily mobility metrics, including a social distancing index, stay-at-home percentage, miles traveled per person, trip rate, work trip rate, and non-work trip rate, were produced for each census tract using the location data from over 100 million anonymous devices on a monthly basis. Each mobility metric was further tabulated by three perspectives of social distancing performance: "best performance", "effort", and "consistency". We found that for all 18 indicators, high-income communities demonstrated better social distancing performance. Such disparities between communities of different income levels are presented in detail in this article. The comparisons across scenarios also raise other concerns for low-income communities, such as employment status, working conditions, and accessibility to basic needs. This article lays out a series of facts extracted from real-world data and offers compelling perspectives for future discussions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
