Introduction

Human mobility has been a central issue in the discussion from the beginning of COVID-19. While the body of literature on the relationship of COVID transmission and mobility is large, studies mostly captured a relatively short timeframe. Moreover, spatial non-stationarity has garnered less attention in these explorative models. Therefore, the major concern of this study is to see the relationship of mobility and COVID on a broader temporal scale and after mitigating this methodological gap.

Objective

In response to this concern, this study first explores the spatiotemporal pattern of mobility indicators. Secondly, it attempts to understand how mobility is related to COVID infection rate and how this relationship has been changed over time and space after controlling several sociodemographic characteristics, spatial heterogeneity, and policy-related changes during different phases of Coronavirus.

Data and method

This study uses GPS-based mobility data for a wider time frame of six months (March 20-August’20) divided into four tiers and carries analysis for all the US counties (N = 3142). Space-time cube is used to generate the spatiotemporal pattern. For the second objective, Ordinary Least Square (OLS), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR) were used.

Result

The spatial-temporal pattern suggests that the trip rate, out-of-county trip rate, and miles/person traveled were mostly plummeted till the first wave reached its peak, and subsequently, all of these mobility matrices started to rise. From spatial models, infection rates were found negatively correlated with miles traveled and out-of-county trips. Highly COVID infected areas mostly had more people working from home, low percentages of aged people and educated people, and high percentages of poor people.

Conclusion

This study, with necessary policy implications, provides a comprehensive understanding of the shifting pattern of mobility and COVID. Spatial models outperform OLS with better fits and non-clustered residuals.

中文翻译：

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美国流动性和 COVID-19 感染率的空间关联：一项使用手机位置数据的县级研究

介绍

从 COVID-19 开始，人员流动性一直是讨论的核心问题。虽然关于 COVID 传播和流动性关系的文献很多，但研究大多是在相对较短的时间范围内进行的。此外，空间非平稳性在这些探索模型中引起的关注较少。因此，本研究的主要关注点是在更广泛的时间范围内并在缩小这种方法论差距之后，了解流动性和 COVID 之间的关系。

客观的

针对这一担忧，本研究首先探讨了流动性指标的时空格局。其次，它试图了解流动性如何与 COVID 感染率相关，以及在控制冠状病毒不同阶段的几个社会人口特征、空间异质性和政策相关变化后，这种关系如何随时间和空间发生变化。

数据与方法

本研究使用基于 GPS 的移动数据，时间跨度为六个月（3 月 20 日至 20 年 8 月），分为四个层级，并对美国所有县 (N = 3142) 进行分析。时空立方体用于生成时空模式。对于第二个目标，使用了普通最小二乘法 (OLS)、空间误差模型 (SEM) 和地理加权回归 (GWR)。

结果

时空模式表明，出行率、县外出行率和人均出行里程在第一波达到顶峰之前大部分都直线下降，随后，所有这些流动性矩阵都开始上升。从空间模型中，发现感染率与行驶里程和县外旅行呈负相关。COVID 高发地区大多有更多的人在家工作，老年人和受过教育的人所占比例较低，而穷人所占比例较高。

结论

这项具有必要政策影响的研究提供了对流动性和 COVID 的转变模式的全面理解。空间模型优于 OLS，具有更好的拟合度和非聚类残差。