Exploring Human Mobility Patterns during COVID-19 using Social Media and Land Use Data

Authors: Tao Hu, Center for Geographic Analysis, Harvard University, Regina Liu, Department of Biology, Mercer University, Scott Blender, Department of Economics, Temple University, Kelly Ly, Department of Computer Science, University of Massachusetts Lowell, Bing She*, University of Michigan
Topics: Geographic Information Science and Systems
Keywords: land use, mobility, covid-19, social media, twitter
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

Mobile device location data are becoming increasingly popular and have proved to be an invaluable source for studying human mobility behaviors. During the COVID-19 pandemic, accurate estimates of population flow not only help decision making but also helps quantify the influence of social distancing policies. Many companies such as Google, Apple, Foursquare, and Safegraph, have provided community mobility reports which aim to provide insights into what has changed in response to public health policies. Social media location-based services, offered by Twitter, Facebook, Flicker, and Instagram, give another solution to timely monitor human mobility behavior. By integrating the land use data and social media data, this paper aims to answer the following questions: (1) how to comprehensively understand human mobility trends across different land-use types, and (2) What are the human mobility transitions across different land-use types before and during COVID-19 pandemic. The results show the different patterns in various land-use types: including recreation, industry, nature, commercial, public, residential, transport, etc. This paper introduces a new perspective on human mobility patterns and transitions using big data sources in the context of a pandemic.

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