Investigating Human Mobility During Pandemic: An Integrated Social Media Approach

Authors: Chenxiao (Atlas) Guo*, University of Wisconsin, Qunying Huang, University of Wisconsin-Madison, Song Gao, University of Wisconsin-Madison
Topics: Geographic Information Science and Systems, Hazards, Risks, and Disasters, Cartography
Keywords: Human mobility, covid-19, pandemic, social media, twitter
Session Type: Virtual Paper
Day: 4/11/2021
Start / End Time: 8:00 AM / 9:15 AM
Room: Virtual 9
Presentation File: No File Uploaded

The United States has witnessed a significant shift in human mobility patterns due to the coronavirus pandemic since March. The trends of remote working and virtual gathering reduce the amount of commuting, and the closures of various businesses also lower people’s willingness for long-distance traveling. Besides, various orders of quarantine and lockdown from local governments were issued directly minimizing the unnecessary movement. Therefore, it is of great importance to visualize and investigate the change of mobility pattern and how it interacts with the pandemics in a spatiotemporal context, providing more evidence for relevant policy-making agencies. To examine the spatiotemporal pattern of mobility, we utilize the twitter dataset to generate trajectories representing the movement. By comparing the estimated local movements with the corresponding pandemic level, a national picture will be figured in answering whether and how human mobility is associated with the development of pandemics spatiotemporally. Furthermore, we investigate the social links from Facebook county-level connectivity dataset and see how it impacts human mobility. Finally, we will look into the socioeconomic characteristics, and explore the potential associations with the mobility pattern. With integrated use of social media, pandemic and socioeconomic datasets, this study generates a series of visual analytics in contiguous U.S., drawing a comprehensive picture of human mobility during covid-19 pandemics.

Abstract Information

This abstract is already part of a session. View the session here.

To access contact information login