Diverged landscape of restaurant recovery from the COVID-19 pandemic in the United States

Siqin Wang; Xiao Huang; Bing She; Zhenlong Li

doi:10.1016/j.isci.2023.106811

Diverged landscape of restaurant recovery from the COVID-19 pandemic in the United States

iScience. 2023 May 4;26(6):106811. doi: 10.1016/j.isci.2023.106811. eCollection 2023 Jun 16.

Authors

Siqin Wang^{1

2

3}, Xiao Huang⁴, Bing She⁵, Zhenlong Li⁶

Affiliations

¹ School of Earth and Environmental Sciences, University of Queensland, Brisbane, QLD, Australia.
² Graduate School of Interdisciplinary Information Studies, University of Tokyo, Tokyo, Japan.
³ School of Science, Royal Melbourne Institute of Technology (RMIT), Melbourne, VIC, Australia.
⁴ Department of Geosciences, University of Arkansas, Fayetteville, AR, USA.
⁵ Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
⁶ Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, USA.

Abstract

The COVID-19 pandemic has imposed catastrophic impacts on the restaurant industry as a crucial socioeconomic sector that contributes to the global economy. However, the understanding of how the restaurant industry was recovered from COVID-19 remains underexplored. This study constructs a spatially explicit evaluation of the effect of COVID-19 on the restaurant industry in the US, drawing on the attributes of +200,000 restaurants from Yelp and +600 million individual-level restaurant visitations provided by SafeGraph from 1st January 2019 to 31st December 2021. We produce quantitative evidence of lost restaurant visitations and revenue amid the pandemic, the changes in the customers' origins, and the retained visitation law of human mobility-the number of restaurant visitations decreases as the inverse square of their travel distances-though such a distance-decay effect becomes marginal at the later pandemic. Our findings support policy makers to monitor economic relief and design place-based policies for economic recovery.

Keywords: Artificial intelligence; Geographical information science; Geography.