COVID-19 in Toronto: Investigating the Spatial Impact of Retailers in the Food Retail and Food Service Sector
Advances in Geographic Information Science
; : 35-64, 2023.
Article
Dans Anglais
| Scopus | ID: covidwho-2304731
ABSTRACT
COVID-19 has had a significant impact on the global economy. The retailing sector, which relies heavily on high levels of human interaction, has experienced the worst impact. This study aimed to assess the spatial distribution of COVID-19 in Toronto and its impact on business locations from the food retail and food service sectors by investigating four retailers Starbucks, McDonald's, Shoppers Drug Mart, and Loblaws. Kernel density estimation revealed that the spatial distribution of COVID-19 incidences in the City of Toronto is uneven, with a high density of cases present in the Downtown core. Spatial autocorrelation was performed at the global and local levels to assess the spatial pattern of Starbucks, McDonald's, Shoppers Drug Mart, and Loblaws locations. The findings revealed that retailers spatially clustered in a COVID-19 hotspot are the most impacted. Further to this analysis, a geographically weighted regression model was generated, which indicated a strong correlation between COVID-19 and low socio-economic status. This allows for a better understanding of the characteristics associated with the retail locations at risk from COVID-19, enabling retailers to make strategic adjustments to respond to a rapidly changing marketplace. © 2023, Springer Nature Switzerland AG.
COVID-19; Food retail; Geographically weighted regression; Spatial analysis; Spatial autocorrelation; Toronto; Autocorrelation; Economics; Location; Regression analysis; Sales; Spatial distribution; Spatial variables measurement; Foodservices; McDonald's; Retail services; Service sectors; Spatial autocorrelations; Starbucks
Texte intégral:
Disponible
Collection:
Bases de données des oragnisations internationales
Base de données:
Scopus
Type d'étude:
Études expérimentales
langue:
Anglais
Revue:
Advances in Geographic Information Science
Année:
2023
Type de document:
Article
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