Neighbourhood Walkability in Montreal Comparing Observational and GIS-based Measures of the Built Environment for Physical Activity
Bibliographic record
Abstract
As the North American obesity epidemic intensifies, the relationship between urban form and health is of growing interest to researchers, health care professionals, policymakers, and various publics. The onset of type-2 diabetes (T2D) represents one consequence of current physical activity trends, which are in turn influenced by the built environment. This project uses both GIS-based and observational measures to quantify the “walkability” of 201 neighbourhoods in the greater Montreal area. The GIS-derived measure is a composite index of z-scores for land-use mix, net residential density, and street connectivity, approximated by three types of 500-m buffers, measured radially and along the street network starting from the centroid of 200 T2D patients' home postal codes. A fourth index adds retail/service density to the radial buffer using enhanced points of interest. To 'ground-truth' these GIS-based results, two undergraduate researchers conducted an observational walkability audit of 21 variables (assessing building and sidewalk characteristics and maintenance, public space/amenities, and safety indicators) of five randomly selected street segments in each of the 201 neighbourhoods, using Kappa tests to assess inter-rater reliability. Median household income is used as a proxy for socio-economic status, and compared to both GIS-based and observational measures for walkability. However, linear regression models show that none of the walkability indices explain the variation in individual steps taken per day, suggesting factors other than the built environment affect the physical activity patterns of T2D patients living in and around Montreal.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".