A GIS-based analytical framework for evaluating the effect of COVID-19 on the restaurant industry with big data

Siqin Wang, Ruomei Wang, Xiao Huang, Zhenlong Li, Shuming Bao

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

COVID-19 cripples the restaurant industry as a crucial socioeconomic sector that contributes immensely to the global economy. However, what the current literature less explored is to quantify the effect of COVID-19 on restaurant visitation and revenue at different spatial scales, as well as its relationship with the neighborhood characteristics of customers’ origins. Based on the Point of Interest (POI) measures derived from SafeGraph data providing mobility records of 45 million cell phone users in the US, our study takes Lower Manhattan, New York City, as the pilot study, and aims to examine 1) the change of restaurant visitations and revenue in the period prior to and after the COVID-19 outbreak, 2) the areas where restaurant customers live, and 3) the association between the neighborhood characteristics of these areas and lost customers. By doing so, we provide a geographic information system-based analytical framework integrating the big data mining, web crawling techniques, and spatial-economic modelling. Our analytical framework can be implemented to estimate the broader effect of COVID-19 on other industries and can be augmented in a financially monitoring manner in response to future pandemics or public emergencies.

Original languageEnglish (US)
Pages (from-to)47-68
Number of pages22
JournalBig Earth Data
Volume7
Issue number1
DOIs
StatePublished - 2023

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computers in Earth Sciences

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