The influence of neighbourhood walkability and bikeability on park visits using mobility data in Canada
Bibliographic record
Abstract
Parks afford the opportunity to engage with nature and have physical, social and psychological benefits. It is important to understand whether neighbourhood design can influence park visits. This study’s objective was to examine the association between neighbourhood walkability/bikeability and park visits in Canada. Between January 2019 and October 2021, park visits from 215 municipal and dog parks were linked to neighbourhood walkability and bikeability. Negative binomial regressions estimated associations between walkability, bikeability, and visits controlling for year, park type, median neighbourhood income, median age of residents, and urban/rural location. Neighbourhood walkability and bikeability were moderately correlated and explored separately (r = 0.375, p < .001). Compared to the lowest, parks with the highest level of neighbourhood walkability (RR = 11.64, p < .001) and bikeability (RR = 2.29, p < .001) had significantly more visits. The results suggest that the walkability/bikeability surrounding parks may impact visits. Future studies would benefit from exploring the ways in which neighbourhood characteristics can promote park use. • Higher neighbourhood walkability was associated with more municipal park visits. • Higher neighbourhood bikeability was associated with more municipal park visits. • Neighbourhood design supportive of walking and cycling may promote park use.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".