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Staying at Home Is a Privilege

Evidence from Fine-Grained Mobile Phone Location Data in the United States during the Covid-19 Pandemic

Bibliographic Data

ID3133121
AuthorsXiao Huang (0000-0002-4323-382X, University of Arkansas at Fayetteville), Junyu Lu (0000-0002-0992-5662, Arizona State University), Song Gao (0000-0003-4359-6302, University of Wisconsin–Madison), Sicheng Wang (0000-0002-2143-767X, Rutgers, the State University of New Jersey), Zhewei Liu (0000-0002-4023-9142, Hong Kong Polytechnic University), Hanxue Wei (0000-0001-6657-4048, Cornell University)
Year2022
Volume112
Issue1
Pages286-305
Publication date2022-01-02
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueAnnals of the American Association of Geographers (JOURNAL)
Journal identifiersISSN: 2469-4452 • E-ISSN: 2469-4460
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/24694452.2021.1904819
OpenAlexW3164766304
LanguageEN
Citations received61
References cited43

The coronavirus disease 2019 (COVID-19) has exposed and, to some degree, exacerbated social inequity in the United States. This study reveals the correlation between demographic and socioeconomic variables and home-dwelling time records derived from large-scale mobile phone location tracking data at the U.S. census block group (CBG) level in the twelve most populated Metropolitan Statistical Areas (MSAs) and further investigates the contribution of these variables to the disparity in home-dwelling time that reflects the compliance with stay-at-home orders via machine learning approaches. We find statistically significant correlations between the increase in home-dwelling time (∇HDT) and variables that describe economic status in all MSAs, which is further confirmed by the optimized random forest models, because median household income and percentage of high income are the two most important variables in predicting ∇HDT. The partial dependence between median household income and ∇HDT reveals that the contribution of income to ∇HDT is place dependent, nonlinear, and different given varying income intervals. Our study reveals the luxury nature of stay-at-home orders with which lower income groups cannot afford to comply. Such disparity in responses under stay-at-home orders reflects the long-standing social inequity issues in the United States, potentially causing unequal exposure to COVID-19 that disproportionately affects vulnerable populations. We must confront systemic social inequity issues and call for a high-priority assessment of the long-term impact of COVID-19 on geographically and socially disadvantaged groups

Census · Demographic economics · Disadvantaged · Economic growth · Economics · Geography · Household income · Metropolitan area · Socioeconomic status · Socioeconomics · Sociology · COVID-19 epidemiological studies · Demography · Human Mobility and Location-Based Analysis · Urban, Neighborhood, and Segregation Studies

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Unique citing works61
Citations per year12,2
Citation span2021 - 2026 (6)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 46
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