Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA

Abstract

One approach to delaying the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. Understanding the actual human mobility response to such policies remains a challenge owing to the lack of an observed and large-scale dataset describing human mobility during the pandemic. This study uses an integrated dataset, consisting of anonymized and privacy-protected location data from over 150 million monthly active samples in the USA, COVID-19 case data and census population information, to uncover mobility changes during COVID-19 and under the stay-at-home state orders in the USA. The study successfully quantifies human mobility responses with three important metrics: daily average number of trips per person; daily average person-miles travelled; and daily percentage of residents staying at home. The data analytics reveal a spontaneous mobility reduction that occurred regardless of government actions and a ‘floor’ phenomenon, where human mobility reached a lower bound and stopped decreasing soon after each state announced the stay-at-home order. A set of longitudinal models is then developed and confirms that the states' stay-at-home policies have only led to about a 5% reduction in average daily human mobility. Lessons learned from the data analytics and longitudinal models offer valuable insights for government actions in preparation for another COVID-19 surge or another virus outbreak in the future.

Spatial association of mobility and COVID-19 infection rate in the USA: A county-level study using mobile phone location data

“…With the availability of recent data, there is an emerging number of studies with more extended periods. Xiong et al (2020a) analyzed the inflow mobility in US counties from March 1 to June 9. He found a positive relationship between the COVID cases and inflow in counties.…”

“…Firstly, in modeling practice, accounting for spatial autocorrelation is less commonly seen in contemporary works. Xiong et al (2020a) also recommended addressing this issue in future studies. The second issue is about the data.…”

A data-driven travel mode share estimation framework based on mobile device location data

“…In addition, the proposed framework can also be applied to other realms, such as business development and public health. For instance, during the COVID-19 pandemic, a handful of research utilized the MDLDs to derive the travel statistics, i.e., trip rate and inflow, and study their correlation with the new COVID-19 infections (Xiong et al 2020a , b ; Pan et al 2020 ). By applying our proposed framework to the MDLDs, the correlation between multimodal travel and the spread of COVID-19 can be studied to support governments decision makings on transit or airline operations.…”

“…Lots of applications have been developed using the LBS data. For instance, a recent smartphone-enhanced travel survey conducted in the U.S. used a mobile application, rMove developed by Resource Systems Group (RSG), to collect high-frequency location data and let respondents recall their trips by showing the trajectories in rMove (RSG 2014 , 2015a , b , 2017 ); Airsage leveraged LBS data to develop a traffic platform that can estimate traffic flow, speed, congestion and road user sociodemographic for every road and time of day (AirSage 2020 ); Maryland Transportation Institute (MTI) at the University of Maryland (UMD) developed the COVID-19 Impact Analysis Platform ( data.covid.umd.edu ) to provide insight on COVID-19’s impact on mobility, health, economy and society across the U.S. (Zhang et al 2020 ; Xiong et al 2020a , b ).…”

Lockdowns Lose One Third of Their Impact on Mobility in a Month

“…Our work contributes to a growing body of research analyzing the impact of government policies on factors such as mobility. 12,13 Prior research has found that government policies as well as pandemic severity impacts social distancing that is practiced within communities, and that less social distancing is practiced after observing local mitigation. 8 Researchers have also shown that social distancing and lower population density may be associated with decreased spread of the coronavirus.…”