Bridging the Form and Function Gap in Urban Green Space Design through Environmental Systems Modeling
Bibliographic record
Abstract
Using a case study approach from past projects in Singapore, Australia, Cambodia, Thailand and Vietnam, we examine the benefits, but also some of the challenges, to implementing green space in urban design. Green space can have multiple physical and psychological wellbeing benefits, as well as environmental benefits, including urban runoff quantity and quality management, urban heat island abatement, air quality improvement, and noise reduction. Water sensitive urban design (WSUD) can be an important element of green space design and here we explore how modeling of ecosystem services and dynamic modeling of WSUD can help to facilitate sound planning and management decision making in support of green space implementation. As we illustrate with examples for Australia, Singapore and Cambodia, we believe that application of an urban ecosystem services modeling approach can elucidate environmental benefits of urban green space that otherwise may not be considered. Engineers may include dynamic modeling of WSUD in support of an urban master plan, or urban redevelopment, but generally urban planners are less conversant in applying models. We discuss some of the challenges to integrating multidisciplinary visioning and modeling of green space design and performance evaluation through our experience with a stormwater and wastewater design study for Cha Am, Thailand, that included landscape architecture and engineering classes at Thammasat University, Mahidol University, and AIT. Through a case study of Phnom Penh, we illustrate how modeling and 3D visualization can be used to effectively explore the benefits of green space. We conclude that a user-friendly decision support system is needed to integrate modeling and visualization tools and thereby bridge the gap between form and function in urban green space design.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".