Relationship Between the Landscape Structure of Urban Green Spaces and Residents’ Satisfaction: The Case of a Central District in Hanoi (Vietnam)
Bibliographic record
Abstract
In urban planning, it is crucial to develop our understanding of human preferences for green spaces in order to maintain and develop them more efficiently and effectively. However, the research available on this issueis limited to developing and tropical countries. In this study, we investigated the relationships between residents’ satisfaction with green spaces in their neighbourhoods and the landscape structure of green spaces in Hanoi, where intensive transformations in built environment are threatening the existence of green spaces and hence the quality of life. Data on the satisfaction levelsof residents were obtained from a governmental survey. Vegetation classes were identified from a QuickBirdimage by applying object-oriented classification. We then computed landscape metrics for street-side trees and all trees. The results confirmed that people were more satisfied in areas where 1) all trees were more abundant, well-connected and of variable sizes; and 2) street-side trees were of considerable size and complex canopy shape.These findings are consistent with similar studies in Western countries, at an even higher degree, and underscore the urgent need to plantmore trees along the streets of the Old Quarter in Hanoi and along the Red River banks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".