Assessing the Validity of <scp>OpenStreetMap</scp> for Food Environment Research
Bibliographic record
Abstract
ABSTRACT This study assessed agreement between food environment measures derived from OpenStreetMap (OSM) data, a commercial dataset, and an administrative dataset (the Canadian Food Environment Dataset, Can‐FED) to better understand the suitability of OSM food‐related data for food environment research. We calculated Spearman's correlations between continuous retail food environment measures in Can‐FED and those derived from OSM and DMTI Spatial. Additionally, using Can‐FED as the reference, we assessed the accuracy of categorical food environment variables derived from OSM and DMTI data. OSM consistently reported fewer food retailers than Can‐FED, but correlations between density and proportion measures from OSM, DMTI, and Can‐FED were moderate to very strong. OSM and DMTI reliably identified areas with low proportions of healthier food retailers and fast‐food outlets, though accuracy was lower in areas with higher proportions. In metropolitan areas, where categorized variables from OSM differed from Can‐FED, proportions of healthier retailers and fast‐food outlets were often underestimated. This study highlights OSM's limitations, such as missing data and error in accurately classifying neighborhood food environments, yet suggests that OSM may be useful for capturing general trends or measuring food environments in low‐density areas when higher quality administrative data is not accessible.
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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 | 0.078 | 0.319 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".