An ecological analysis of walkability and housing affordability in Canada: Moderation by city size and neighbourhood property type composition
Bibliographic record
Abstract
The neighbourhood built environment can support the physical activity of adults regardless of their individual-level socioeconomic status. However, physical activity supportive (walkable) neighbourhoods may not be accessible to those with lower incomes if homes in walkable neighbourhoods are too expensive. The objectives of this study were: 1) to estimate the associations between neighbourhood walkability and home values in Canadian cities, and 2) to test whether these associations differ by city size and residential property type composition within neighbourhoods. We linked built environment data from the 2016 Canadian Active Living Environments (Can-ALE) index with neighbourhood-level structural home characteristics and sociodemographic data from the 2016 Canadian census for 33,026 neighbourhoods across 31 Census Metropolitan Areas. We used multilevel linear regression models to estimate covariate-adjusted associations between neighbourhood walkability and natural-log median home values and tested city size and neighbourhood property type composition as moderators. There were no statistically significant associations between walkability and home values overall. The associations between neighbourhood walkability and home values were jointly moderated by city size and property type composition. For small and medium sized cities, within neighbourhoods containing a high proportion of detached homes, walkability was negatively associated with home values (b = -0.05, 95% CI: -0.10, -0.01; and, b = -0.04, 95% CI: -0.06, -0.02, for small and medium cities, respectively). However, for extra-large cities, within neighbourhoods containing a high proportion of detached homes, walkability was positively associated with home values (b = 0.06, 95% CI: 0.01, 0.10). Our findings suggest that, based on housing affordability, higher walkable neighbourhoods are likely accessible to lower income households that are situated in small and medium Canadian cities. In larger cities, however, municipal interventions (e.g., inclusionary zoning or targeted development of subsidized or social housing) may be needed to ensure equitable access to walkable neighbourhoods for lower income households.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".