Fighting the Pandemic with Big Movement Data: Our Approaches

Authors: Zhenlong LI*, University of South Carolina
Topics: Spatial Analysis & Modeling, Geographic Information Science and Systems
Keywords: human mobility, social media, big data, covid-19
Session Type: Virtual Paper
Day: 4/9/2021
Start / End Time: 1:30 PM / 2:45 PM
Room: Virtual 24
Presentation File: No File Uploaded

Human movement is among the essential forces that drive spatial spread of infectious diseases. To date, reducing and tracking human movement during the pandemic have proven effective in limiting the spread of COVID-19. Prediction and control of the spread of COVID-19 benefits greatly from our growing computing capacity to quantify fine-scale human movement. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and dollar bills tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement from different spatial scales (from local to global). Big social media data such as geotagged tweets have been widely used in human mobility studies, yet more research are needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (e.g., from local to global) in the context of global infectious disease transmission. In this presentation, I will introduce our approaches of using big social media data coupled with other mobility data sources such as SafeGraph's foot traffic data and Google Mobility report in fighting COVID-19. The approaches cover different perspectives including extracting population flows from geotagged tweets, developing indices to quantify human mobility, and understanding socioeconomic disparities in mobility patterns, and human mobility data visualization and sharing.

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