COVID-19 in Toronto: Investigating the Spatial Impact of Retailers in the Food Retail and Food Service Sector
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".