Factors influencing subjective walkability: Results from built environment audit data
Bibliographic record
Abstract
Subjective walkability is a measure of the perceived friendliness of walking in an area. Though subjective walkability is less commonly assessed than objective measurements, the latter often fail to reflect the experience of walking. This study aims to better understand subjective walkability and how it varies between travel and leisure walking by investigating its relationship with the built environment and land-use characteristics. Data is collected from 848 street segments in Montreal, Canada, using the MAPS-mini audit tool, external measurements including Walkscore as well as synthetic subjective walkability scores. Mixed effect multilevel models are then generated using travel and leisure subjective walkability scores as dependent variables and built environment features as independent variables. Statistically significant positive predictors of perceived walkability differ between walking for travel and walking for leisure. Walkscore is found to have a weak but significant effect on perceived walkability for travel but no effect at all for leisure. Based on this research, a multi-scalar approach both at the street and neighborhood level making use of a combination of objective and subjective walkability measures should be employed to study predictors of walking behavior. Lastly, distinctions of walking behaviors based on trip purpose should be integrated in future research.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".