Identifying Food Deserts in Mississauga: A Comparative Analysis of Socioeconomic Indicators
Bibliographic record
Abstract
A lack of access to healthy food has been a problem for low-income residents in many developed urban areas. Due to travel time and additional transportation costs, these residents often opt for unhealthy food rather than nutritious alternatives. This study examines the spatial distribution of food deserts in Mississauga—one of Canada’s most populous cities and a city with one of the highest diabetes rates in the Province of Ontario. Network analysis was employed to map the geographic inaccessibility to essential nutritious food, defined as residential areas that are beyond a 15-min walking distance from grocery stores. Socioeconomic indicators were integrated to identify and compare the regions that are socioeconomically disadvantaged and, therefore, most affected by food inaccessibility. The results reveal the presence of several food deserts spatially dispersed in Mississauga. The implications of these findings are discussed, with a focus on the relationship between food desert locations and the socioeconomic conditions of the affected residents. This study provides a practical, replicable approach for identifying food deserts that can be easily applied in other regions. The model developed offers valuable tools for policymakers and urban planners to address food desert issues, improving access to healthy food and positively impacting the health and well-being of affected populations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".