Spatial-temporal Analysis of COVID-19's Impact on Human Mobility: the Case of the United States

Abstract

COVID-19 has been affecting every aspect of societal life including human mobility since December, 2019. In this paper, we study the impact of COVID-19 on human mobility patterns at the state level within the United States. From the temporal perspective, we find that the change of mobility patterns does not necessarily correlate with government policies and guidelines, but is more related to people's awareness of the pandemic, which is reflected by the search data from Google Trends. Our results show that it takes on average 14 days for the mobility patterns to adjust to the new situation. From the spatial perspective, we conduct a state-level network analysis and clustering using the mobility data from Multiscale Dynamic Human Mobility Flow Dataset. As a result, we find that 1) states in the same cluster have shorter geographical distances; 2) a 14-day delay again is found between the time when the largest number of clusters appears and the peak of Coronavirus-related search queries on Google Trends; and 3) a major reduction in other network flow properties, namely degree, closeness, and betweenness, of all states from the week of March 2 to the week of April 6 (the week of the largest number of clusters).

Accepted to the 20th and 21st Joint COTA International Conference of Transportation Professionals

References in corpus (7)

• Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic
• Effects of the COVID-19 Pandemic on Population Mobility under Mild Policies: Causal Evidence from Sweden
• Observed mobility behavior data reveal social distancing inertia
• The COVID-19 Pandemic, Community Mobility and the Effectiveness of Non-pharmaceutical Interventions: The United States of America, February to May 2020
• How different age groups responded to the COVID-19 pandemic in terms of mobility behaviors: a case study of the United States
• Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates
• A New Dynamic Model to Predict the Effects of Governmental Decisions on the Progress of the CoViD-19 Epidemic

Cited by in corpus (3)

• Assessing Road Traffic Safety During COVID-19: Inequality, Irregularity, and Severity
• Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models
• Analyzing Effects of The COVID-19 Pandemic on Road Traffic Safety: The Cases of New York City, Los Angeles, and Boston