Assessing the equity of urban public green space visitation for cooling off from extreme heat: A public participation GIS (PPGIS) survey
Bibliographic record
Abstract
Public green spaces (PGS) in urban areas can both reduce ambient temperatures and provide a place for residents to visit and cool off on hot days while enjoying a range of health and biodiversity co-benefits. High indoor temperatures and social vulnerability (e.g., being elderly or infirm) increase the potential reliance on PGS visitation and their cooling features. As the severity of urban heat increases due to climate change and cities recognize the need for ensuring availability, access, and quality of PGS, research is needed to support their planning and equitable benefits across social groups and space. We use an online public participation GIS (PPGIS) survey to explore the degree of (spatial) equity in PGS visitation for cooling off during extreme heat in Bochum, Germany. Our study also aims to determine the degree of pubic reliance on cooling as an ecosystem service, what biophysical features shape preferences for visitation, and the distributive environmental justice of neighborhood green space. We find that residents are concerned about extreme heat and visiting PGS for cooling off is common. Their current use is relatively equitable in the study area but more focus is needed on aged and low-income groups as well as the provision of large trees, shade, and water features. Residents travel on average over 10 km from home to visit the most valued PGS for cooling, likely due to poor availability and quality in the urban core. Additionally, our research provides a methodological template enabling equitable spatial planning for climate change adaptation.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".