Mapping county-level mobility pattern changes in the United States in response to COVID-19
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Abstract (arXiv)
To contain the Coronavirus disease (COVID-19) pandemic, one of the non-pharmacological epidemic control measures in response to the COVID-19 outbreak is reducing the transmission rate of SARS-COV-2 in the population through (physical) social distancing. An interactive web-based mapping platform that provides timely quantitative information on how people in different counties and states reacted to the social distancing guidelines was developed with the support of the National Science Foundation (NSF). It integrates geographic information systems (GIS) and daily updated human mobility statistical patterns derived from large-scale anonymized and aggregated smartphone location big data at the county-level in the United States, and aims to increase risk awareness of the public, support governmental decision-making, and help enhance community responses to the COVID-19 outbreak.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Apr 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 1.
8 papers.
- 2024Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasauruscited by 8
- 2022The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood levelcited by 4
- 2021Change of human mobility during COVID-19: A United States case studycited by 3
- 2023STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19cited by 2
- 2025Infected Smallville: How Disease Threat Shapes Sociality in LLM Agentscited by 2
- 2026Enhancing Ride-Hailing Forecasting at DiDi with Multi-View Geospatial Representation Learning from the Webcited by 2
- 2021Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposurescited by 1
- 2021Causal Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemiccited by 1
- 2026Enhancing Ride-Hailing Forecasting at DiDi with Multi-View Geospatial Representation Learning from the Web11 Feb 2026
- 2025Infected Smallville: How Disease Threat Shapes Sociality in LLM Agents10 Jun 2025
- 2024Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasaurus20 Sep 2024
- 2023STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-1920 Jan 2023
- 2022The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood level26 Feb 2022
- 2021Causal Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic24 Nov 2021
- 2021Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposures24 Oct 2021
- 2021Change of human mobility during COVID-19: A United States case study18 Sep 2021