Using anonymized mobility data to reduce inequality in the availability and use of urban parks
Bibliographic record
Abstract
Abstract Parks are an integral component of cities. Ensuring city residents have equitable and easy access to parks is crucial for human well‐being. In temperate climates, park accessibility is particularly important in the summer months when these green spaces provide an area to recreate, exercise and escape indoor temperatures and heat emanating from paved and built surfaces. However, there are well‐known disparities in park accessibility in cities globally that may threaten the health of city residents, especially with global warming. We examined some of the largest city parks (>50 ha) in Toronto, Canada, by comparing park activity, housing demographics and daily weather patterns. We found that parks that provided more green space area per resident were situated in neighbourhoods that had higher proportions of single‐detached housing, higher automobile use and fewer multistorey apartments. We also found a strong correlation between park activity with population density and the number of amenities in the park. Surprisingly, we found no relationship between park activity and daily weather patterns, although park use was higher on weekends and holidays. These results suggest denser communities are at a disadvantage because they have proportionately less park area within walking distance in addition to having no private green spaces (e.g. backyards). We recommend revising municipal zoning around certain parks and the creation of new green spaces as methods to balance park provisioning in the city. Our findings suggest that designing and maintaining accessible, amenity‐rich parks is an important strategy for promoting health and well‐being in urban populations. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".