Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, United States of America.
Department of Computational and Data Sciences, George Mason University, Fairfax, VA, United States of America.
PLoS One. 2021 Nov 2;16(11):e0259031. doi: 10.1371/journal.pone.0259031. eCollection 2021.
With the onset of COVID-19 and the resulting shelter in place guidelines combined with remote working practices, human mobility in 2020 has been dramatically impacted. Existing studies typically examine whether mobility in specific localities increases or decreases at specific points in time and relate these changes to certain pandemic and policy events. However, a more comprehensive analysis of mobility change over time is needed. In this paper, we study mobility change in the US through a five-step process using mobility footprint data. (Step 1) Propose the Delta Time Spent in Public Places (ΔTSPP) as a measure to quantify daily changes in mobility for each US county from 2019-2020. (Step 2) Conduct Principal Component Analysis (PCA) to reduce the ΔTSPP time series of each county to lower-dimensional latent components of change in mobility. (Step 3) Conduct clustering analysis to find counties that exhibit similar latent components. (Step 4) Investigate local and global spatial autocorrelation for each component. (Step 5) Conduct correlation analysis to investigate how various population characteristics and behavior correlate with mobility patterns. Results show that by describing each county as a linear combination of the three latent components, we can explain 59% of the variation in mobility trends across all US counties. Specifically, change in mobility in 2020 for US counties can be explained as a combination of three latent components: 1) long-term reduction in mobility, 2) no change in mobility, and 3) short-term reduction in mobility. Furthermore, we find that US counties that are geographically close are more likely to exhibit a similar change in mobility. Finally, we observe significant correlations between the three latent components of mobility change and various population characteristics, including political leaning, population, COVID-19 cases and deaths, and unemployment. We find that our analysis provides a comprehensive understanding of mobility change in response to the COVID-19 pandemic.
随着 COVID-19 的爆发和随之而来的就地避难准则以及远程工作实践,2020 年人类流动性受到了巨大影响。现有研究通常考察特定地点的流动性在特定时间点是增加还是减少,并将这些变化与某些大流行和政策事件联系起来。然而,需要对随时间推移的流动性变化进行更全面的分析。在本文中,我们使用移动足迹数据通过五个步骤研究了美国的流动性变化。(步骤 1)提出“公共场所在时间花费的变化量”(ΔTSPP)作为衡量美国每个县从 2019 年到 2020 年流动性日常变化的指标。(步骤 2)进行主成分分析(PCA),以将每个县的ΔTSPP 时间序列降低到流动性变化的低维潜在成分。(步骤 3)进行聚类分析以找到具有相似潜在成分的县。(步骤 4)研究每个组件的局部和全局空间自相关。(步骤 5)进行相关分析以调查各种人口特征和行为与流动模式的相关性。结果表明,通过将每个县描述为三个潜在成分的线性组合,我们可以解释全美所有县的流动趋势变化的 59%。具体来说,2020 年美国各县的流动变化可以解释为三个潜在成分的组合:1)流动性的长期减少,2)流动性没有变化,3)流动性的短期减少。此外,我们发现地理位置相近的美国县更有可能表现出相似的流动性变化。最后,我们观察到流动变化的三个潜在成分与各种人口特征,包括政治倾向、人口、COVID-19 病例和死亡以及失业之间存在显著相关性。我们发现,我们的分析提供了对 COVID-19 大流行背景下流动性变化的全面理解。