Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic

Abstract

Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.

Intra-county modeling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age and race

“…To enhance privacy, SafeGraph excludes census block group (CBG) information if fewer than five devices visited a place in a month from a given CBG. For each POI, the records of aggregated visitor patterns record the number of unique visitors and the number of total visits to each venue during a specified time window (i.e., hourly, weekly, and monthly); this allows us to estimate the foot-traffic of each venue and the origin-to-destination (O-D) spatial interaction flow patterns during the the study period 44 . We further aggregate the O-D flow matrices to the census-tract level to match the COVID-19 testing data.…”

Impact of different control policies for COVID-19 outbreak on the air transportation industry: A comparison between China, the U.S. and Singapore

“…As it has come to a global consensus based on the experiences from previous epidemic control that human mobility is the key to the disease transmission, studying mobility patterns during the COVID-19 epidemic has become of utmost importance. Wang et al [5] found a 14-day delay between people's awareness of the pandemic and change in mobility patterns of the U.S. Kang et al [6] proposed that the most effective factor that affects population mobility is the governmental non-pharmaceutical intervention strategies. Lee et al [7] has modeled spatial-temporal heterogeneity of human mobility in early stages of the pandemic in the U.S. and confirmed the effectiveness of the staying home order.…”

Making Space in Geographical Analysis

“…During the time of the COVID-19 pandemic, increased attention has been given to changes in mobility including a major reduction in both local and long-distance travel, as well as the role of mobility in controlling the spread of this highly infectious disease on the one hand (Zhou et al 2020), or contributing to spread on the other (Chang et al 2020;Giles et al 2021). From personal social interactions and daily movements, to movements over larger spatial extents including travel between countries, researchers have examined mobility trends during major events such as the pandemic (Kang et al, 2020;Lee et al, 2020;Fan and Stewart, 2020). This holds, of course, not only for COVID-19, but for other infectious diseases, such as malaria, where patterns of movement have been studied particularly with respect to risk of transmission (Wesolowski et al, 2012;Sinha et al, 2020).…”