Designing Urban Green Space (UGS) to Enhance Health: A Methodology
Bibliographic record
Abstract
Policymakers and urban designers strive to implement the increasing evidence about the positive association between urban green space (UGS) and health in policy. In Almere, The Netherlands, the Regenboogbuurt ("Rainbow Quarter") neighbourhood is currently being revitalized. The research team was asked to deliver design principles for the improvement of UGS in this neighbourhood to benefit the health of its residents. However, robust studies that demonstrate what UGS criteria offer what particular benefit for what target group are scarce. This paper contributes to the need for more evidence-based UGS design by presenting the approach we used to develop UGS design principles for Regenboogbuurt. Demographic information, health statistics, residents' opinions, and data about the current use of UGS were analysed to choose target groups and to formulate health benefit goals. We also developed a model for assessing the health benefits of UGS. For two age groups (those aged 10-24 and 40-60), stimulating physical health and social cohesion, respectively, were determined to be the goals of improving UGS. UGS design principles were then assessed based on the existing literature. These principles will be taken into account when this area is revitalized in 2021. Thus, there will be an opportunity to measure whether these design principles did indeed contribute to residents' health.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".