research-article

Mapping county-level mobility pattern changes in the United States in response to COVID-19

Authors:

Jake KruseAuthors Info & Claims

SIGSPATIAL Special, Volume 12, Issue 1

Pages 16 - 26

https://doi.org/10.1145/3404820.3404824

Published: 08 July 2020 Publication History

Abstract

To contain the COVID-19 epidemic, one of the non-pharmacological epidemic control measures is reducing the transmission rate of SARS-COV-2 in the population through 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 by the GeoDS Lab @UW-Madison with the support of the National Science Foundation RAPID program. The web portal integrates geographic information systems (GIS) and daily updated human mobility statistical patterns (median travel distance and stay-at-home dwell time) 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 data-driven public health and governmental decision-making, and help enhance community responses to the COVID-19 pandemic.

References

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About 95% of Americans have been ordered to stay at home. This map shows which cities and states are under lockdown, available at https://www.businessinsider.com/us-map-stay-at-home-orders-lockdowns-2020-3.

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Cited By

Zhu WLi YJiang HZhang XHuang YWang P(2025)Regionally differentiated responses of chlorophyll-a concentrations to reduced human activity during COVID-19 lockdown in the San Francisco Bay areaJournal of Environmental Management10.1016/j.jenvman.2024.123801373(123801)Online publication date: Jan-2025
https://doi.org/10.1016/j.jenvman.2024.123801
Zhang ZLi DZhang ZDuffield N(2024)Mining Spatiotemporal Mobility Patterns Using Improved Deep Time Series ClusteringISPRS International Journal of Geo-Information10.3390/ijgi1311037413:11(374)Online publication date: 24-Oct-2024
https://doi.org/10.3390/ijgi13110374
Knoblauch SGroß SLautenbach Sde Aragão Rocha AGonzález MResch BArifi DJänisch TMorales IZipf A(2024)Long-term validation of inner-urban mobility metrics derived from Twitter/XEnvironment and Planning B: Urban Analytics and City Science10.1177/23998083241278275Online publication date: 14-Oct-2024
https://doi.org/10.1177/23998083241278275
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Mapping county-level mobility pattern changes in the United States in response to COVID-19
1. Information systems
  1. Information systems applications

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Published In

cover image SIGSPATIAL Special

SIGSPATIAL Special Volume 12, Issue 1

March 2020

61 pages

EISSN:1946-7729

DOI:10.1145/3404820

Editor:
Andreas Züfle
George Mason University

Issue’s Table of Contents

Copyright © 2020 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 08 July 2020

Published in SIGSPATIAL Volume 12, Issue 1

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Cited By

Zhu WLi YJiang HZhang XHuang YWang P(2025)Regionally differentiated responses of chlorophyll-a concentrations to reduced human activity during COVID-19 lockdown in the San Francisco Bay areaJournal of Environmental Management10.1016/j.jenvman.2024.123801373(123801)Online publication date: Jan-2025
https://doi.org/10.1016/j.jenvman.2024.123801
Zhang ZLi DZhang ZDuffield N(2024)Mining Spatiotemporal Mobility Patterns Using Improved Deep Time Series ClusteringISPRS International Journal of Geo-Information10.3390/ijgi1311037413:11(374)Online publication date: 24-Oct-2024
https://doi.org/10.3390/ijgi13110374
Knoblauch SGroß SLautenbach Sde Aragão Rocha AGonzález MResch BArifi DJänisch TMorales IZipf A(2024)Long-term validation of inner-urban mobility metrics derived from Twitter/XEnvironment and Planning B: Urban Analytics and City Science10.1177/23998083241278275Online publication date: 14-Oct-2024
https://doi.org/10.1177/23998083241278275
Han SKim JJiang YKang JPark JHan CMichels AWang SKim JAnderson TLau MRoess AZüfle A(2024)CyberGIS-Vis for Democratizing Access to Scalable Spatiotemporal Geovisual Analytics: A Case Study of COVID-19Proceedings of the 5th ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology10.1145/3681777.3698474(19-22)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681777.3698474
Züfle ASalim FAnderson TScotch MXiong LSokol KXue HKong RHeslop DPaik HMacIntyre C(2024)Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease SpreadACM Transactions on Spatial Algorithms and Systems10.1145/366063110:2(1-22)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3660631
Liu BChen SZhang YWang ZXu YWang L(2024)Changes of nighttime lights in the Guanzhong region of China during the COVID-19 pandemicSecond International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023)10.1117/12.3023999(8)Online publication date: 21-Feb-2024
https://doi.org/10.1117/12.3023999
Wang PYuan Y(2024) Analyzing Key Factors Influencing Human Mobility Before and During COVID ‐19 With Explainable Machine Learning Transactions in GIS10.1111/tgis.1327129:1Online publication date: 20-Nov-2024
https://doi.org/10.1111/tgis.13271
Kubinec RCarvalho LBarceló JCheng CMesserschmidt LCottrell M(2024)A Bayesian latent variable model for the optimal identification of disease incidence rates given information constraintsJournal of the Royal Statistical Society Series A: Statistics in Society10.1093/jrsssa/qnae040188:1(287-312)Online publication date: 7-May-2024
https://doi.org/10.1093/jrsssa/qnae040
Li XQiang YCervone G(2024)Using human mobility data to detect evacuation patterns in hurricane IanAnnals of GIS10.1080/19475683.2024.2341703(1-19)Online publication date: 14-Apr-2024
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Lin BDai YZou LNing N(2024)Modeling the impacts of governmental and human responses on COVID-19 spread using statistical machine learningInternational Journal of Digital Earth10.1080/17538947.2024.243465117:1Online publication date: 9-Dec-2024
https://doi.org/10.1080/17538947.2024.2434651
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