Spatiotemporal Variability of Urban Greenspace and Surface Temperature in Dhaka City: A Public Health Aspect
Bibliographic record
Abstract
Abstract Urban greenspaces can affect the physical and mental health of city residents and they can also contribute to improving urban environmental quality in ways that can benefit human health. Dhaka, a megacity with over 22.4 million residents, has progressively lost its greenspace over the past decade as the city has grown and urbanised. This study evaluates the availability and accessibility of greenspace considering its population and residential areas, as well as assessing the changes to greenspace in the last 30 years throughout the city. We utilized secondary data from the 2011 Census and areal imagery to perform the analysis for city wards, the smallest administrative unit, using ArcGIS software. We generated geospatial maps of greenspace distribution and accessibility as well as vegetation, land surface temperature and humidity in different years. Accessibility to greenspace was measured with 100-meter and 300-meter buffer zones, and a total of 56.5 square kilometers area of 77.47 square kilometers of residential area fell under these territories. Changes in vegetation were obtained using Normalized Difference Vegetation Index (NDVI) for the years 1990, 2000, 2010, and 2020, and a high level of loss in vegetation was observed. Land Surface Temperature (LST) and Normalized Difference Moisture Index (NDMI) were used to assess the temperature and humidity for the same years. We measured that Dhaka has 2.24% greenspace coverage and only 2 wards out of 110 have greater than 20% greenspace coverage. A highest estimate of 0.003207 square meter per capita greenspace was found at ward-46, which does not even meet the minimum health standard. Increased temperature and decreased humidity were observed in Dhaka city from 1990 to 2020, in a level that may adversely impact on the city population’s public health. We found a high correlation between NDVI with LST and NDMI. In 49% of wards, vegetation and humidity decreased, whereas temperature increased. This study provides noteworthy information on the lack of greenspace throughout Dhaka city. The spatial distribution of greenspace provided in the study has the potential to be useful in taking measures for improving sustainable greenery management in the city area and the health of Dhaka’s growing population.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".