Geographies of grocery shopping in major Canadian cities: Evidence from large-scale mobile app data
Bibliographic record
Abstract
Socioeconomic and place-based factors contribute to grocery shopping patterns which may be important for diet and health. Big data provide the opportunity to explore behaviours at the population level. We used data collected from Flipp, a free all-in-one savings and deals content app, to identify visitation to grocery stores and estimate home-to-store distances, monthly frequencies and number of unique stores visited in eight Canadian cities during 2020. Grocery shopping outcomes and associations with income, population density and percentage of car commuters were explored using data aggregated at the Aggregate Dissemination Area level in which app users lived. Changes in patterns of grocery shopping following restrictions implemented in response to the COVID-19 pandemic were also investigated. The median of average home-to-store distances ranged from 4 to 5 km across all cities throughout 2020. Shorter distances for grocery shopping were shown consistently for shoppers living in lower income, densely populated and low car-commuting ADAs. A maximum of three unique supermarkets were visited on average each month. Decreases in the frequency and variability of grocery store visits were shown across all cities in April 2020 following the implementation of restrictions in response to COVID-19, and pre-pandemic levels of shopping were rarely achieved by the end of the year. Ultimately, these results provide much needed information regarding the characteristics of grocery shopping trips in a high-income country, as well as how food shopping was impacted by the onset of the COVID-19 pandemic. This information will be useful for a range of future studies seeking to characterise access to food retail.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.021 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".