COVID-19 in Toronto: Investigating the Spatial Impact of Retailers in the Food Retail and Food Service Sector

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.

Urban habitats and food insecurity: Lessons learned throughout a pandemic

Background: An increasing amount of literature raises the issue of food deserts and urban heterogeneity in larger metropolitan cores throughout North America. Specific to Canadian cities, the disparity between access to health, education, and affordable food is of growing concern. Recently, these drivers seem to be significantly linked to the propagation of COVID-19. This paper explores the spatially-explicit dynamics of food deserts in Toronto, by integrating Geographic Information Systems and machine learning to understand the clusters of food deserts. The integration of spatial analysis with self-organizing maps (SOM) offers insights on the relation between neighborhoods, geodemographic profiles and urban characteristics, and whether one might expect consequences of food insecurity given COVID-19. Methods: The paper starts out with developing a machine learning algorithm based on SOM to define meaningful clusters within the hedonic dataset. Further to this, an exploratory regression was built per cluster as to allow an exploratory spatial analysis to derive an explanatory framework for the key characteristics of socio-economic profiles within the Greater Toronto Area and impacts of SARS-CoV-2. Results: The findings suggest that there are clear spatial profiles within the urban core of Toronto in regards to food deserts, showing a direct relation between socioeconomic characteristics and the results on environmental injustice and livability. These profiles are strongly linked with the areas of COVID-19 occurrence, and share a very similar socio-demographic profile, particularly in regards to young and lower income families. Conclusion: There are several food deserts currently in Toronto, Ontario. The integration of policies that involve public health and spatial decision-support, particularly when linked to machine learning to aggregate characteristics of big data, establish a multi-functional understanding of the complexity of food security. This has a direct relation with diet, environment, and the opportunity to enhance subjective well-being in city cores. © 2023 Elsevier Ltd