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

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.

Automated summary

COVID-19 has had a significant impact on the restaurant industry, and this study focuses on quantifying the effects of the pandemic on restaurant visitation and revenue at different scales, as well as the relationship with customers' neighborhood characteristics. Using data from 45 million cell phone users in the US, the study examines changes in restaurant visitation and revenue in Lower Manhattan, New York City, and analyzes the areas where restaurant customers live and the association with neighborhood characteristics. The study provides an analytical framework that integrates big data mining, web crawling techniques, and spatial-economic modeling, which can be applied to assess the broader effects of COVID-19 on other industries and as a means of financial monitoring during future pandemics or public emergencies.