Spatial Distribution and Coverage of Urban Green Spaces: Public Parks in Pune
Bibliographic record
Abstract
Abstract. With the rapid urbanisation, Green Spaces (GS) are shrinking steeply in urban areas. Parks not only provide green spaces in an urban area, but play a pivotal role in the life of individuals. Apart from being the lungs of the city, they help to reduce the effect of urban heat islands. The current study aims at mapping all public parks sourced and integrated from Open Street Map, Google Earth and Pune Municipal Corporation (PMC) database in PMC using Geographic Information Systems (GIS). As per the integrated database, there are 216 public parks in the PMC area. We have analysed the spatial distribution and coverage of green spaces in the PMC. The spatial distribution reveals that the public parks are located in all 15 administrative wards of Pune. However, the analysis indicates a clustered pattern; most of the parks are concentrated in the central part of the city and as the city limits expand, there is a noticeable drop in the coverage of public parks. Cluster boundaries of the existing public parks collectively indicate that many residential areas of PMC are deprived of this GS. In the future, it will be useful to study the existing infrastructure and the usage of existing public parks. Also, an additional important aspect is studying the proximity and access to GS from densely packed informal settlements in the PMC area.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".