Urban Green Quarter - Strategies for assessing green spaces of high-density low-rise residential areas in Ho Chi Minh City, Viet Nam.
Bibliographic record
Abstract
The Urban Green Quarter is a proposed model for the integration of low-rise high-density housing with green spaces in Ho Chi Minh City – the economic centre of Vietnam. According to statistics from 2019, the population density was around 290 inhabitants per square kilometre, whereas the average population density of the whole world was only 59 people per square kilometre (Nguyen, 2021). As a result, in Viet Nam, there is a constant shortage of housing, especially in Ho Chi Minh City with a metropolitan population of 8,993,082 in 2019 (General Statistics Office, 2020). The city’s population increases by 200,000 to 400,000 annually (Huy Sơn, 2018) and is still growing rapidly. In order to cope with the rising population and prioritise economic development; more tube houses, apartments, and high-rise projects are being developed focusing mainly on creating more affordable dwellings to accommodate as many people as possible and reduce the green spaces. In fact, the urban development in Ho Chi Minh City only provides two square metres of public green space per capita (Ministry of Natural Resources and Environment, 2016), compared to the nine square metre minimum recommended by the World Health Organisation (WHO) (Russo & Cirella, 2018); which adversely affects the residents’ well-being. This is the primary reason this proposal is undertaken, seeking to improve the well-being of the inhabitants by incorporating green spaces into indoor and outdoor designs. Green spaces have a critical impact on the quality of the living environment. However, the rapidly increasing population and urban expansion have resulted in changes to the natural environment, resulting in a noticeable reduction in public and private green spaces in urban settings. Consequently, people’s health and well-being are being put at risk. Numerous studies have demonstrated that people who are able to interact with green spaces in everyday life receive multiple benefits for both physical and mental health (Barton & Rogerson, 2017). Residing in areas with enhanced public and private green spaces helps reduce stress and anxiety and improve spiritual health. Therefore, it is fair to say that existing green spaces in urban planning and dwelling design are considered essential ingredients for the inhabitants’ health and well-being. This research follows a design-led, methodology, and typological research; and takes into consideration the adaptation of traditional Vietnamese tube houses and low-rise apartments in high-density urban development. This study has three objectives. Firstly, it addresses the problems of the current living environment in urban areas, and green spaces in dwelling design. Secondly, it studies the housing typology in Vietnam throughout history. Lastly, it proposes a prototype and design strategies for the urban green quarter, which aims at preserving the majority of existing green spaces and maximising the quantity of green spaces in the design. A guideline and strategy for maximising green spaces while also delivering accommodations for the residents in this green urban design are concluded through an examination of a case study of Phu Nhuan District – a central district of Ho Chi Minh City. The district is chosen because it represents a standard layout of Ho Chi Minh City landscape and is one of the most economic areas in Ho Chi Minh City. The outcome of this research can be used as a reference for designing the proposed Green Urban Quarter; as well as a reminder/emphasis of the significant value of green spaces in urban housing 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".