Spatial Logic and the Distribution of Open and Green Public Spaces in Hanoi: Planning in a Dense and Rapidly Changing City
Bibliographic record
Abstract
Vietnam recently started to recognise the multiple benefits brought by open and green spaces to urban population and environment. In this paper, we analyse the provision of open and green spaces (parks, public gardens and lakeshores) in Hanoi. Using a model proposed by Talen (2010), we examine the spatial evolution of these spaces between 2000 and 2010, their level of proximity to residential units, and the extent to which their distribution matches social needs (defined in terms of population density). We find that while the absolute number and surface area of parks and public gardens has increased significantly in Hanoi, these new public spaces are mainly built on the city’s newly urbanised periphery. As a result, in 2010, only 15% of Hanoi’s residential blocks had access to a park or public garden within a reasonable walking (1000m) or biking distance (2500m). Moreover, the city’s densest residential areas have only access to relatively small gardens and parks, resulting in overcrowding. Lakeshores, however, represent an opportunity to enhance access to open and green spaces in Hanoi due to their spatial distribution. We conclude by advocating for the integration of spatial measures of proximity and needs into Hanoi’s public space planning policy framework.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".