Utilising GIS for studying urban entropy, population dynamics, and ventilation disparity: A case study of changing land use, land cover, and socially vulnerable hotspots in Hyderabad, India
Bibliographic record
Abstract
Citywide ventilation studies are challenging because they involve complicated interactions between various variables, including anthropogenic variables, housing conditions, and socio-economic considerations. The present paper aims to clarify the complex interactions between these three vital variables and the restricted ventilation zones. The ventilation patterns from 1990 to 2020 are meticulously examined in the first half of this research. This study observed the expansion of built-up areas, which increased from 13.7% in 1990 to 51.8% in 2020. The remainder of the paper explores population dynamics in locations with poor ventilation. In severely ventilated areas, the population increased from 10.7% in 1990 to 16.6% in 2020. The percentage of severely ventilated areas increased from 0.7% in 1990 to 31.6% in 2020, highlighting the urgent need to address ventilation inequities in urban planning. The research uses Spearman correlation analysis, which shows that industrial areas have a substantial positive association with ventilation disparities among anthropogenic factors, indicating the effect of industrial zones on decreased ventilation. The absence of toilets is a powerful and substantial housing variable contributing to poor ventilation. Additionally, the existence of the Total Scheduled Caste population within the socio-economic variables exhibits a strong and significant association, indicating that social issues are intricately linked to ventilation disparities. This research provides critical insights that can inform urban planning and policy decisions to improve ventilation and living conditions in densely populated cities. • The study explores the population dynamics in locations with poor ventilation. • The severely ventilated areas increased from 0.7% in 1990 to 31.6% in 2020. • Impact of industrial zones on decreased ventilation is highlighted. • Critical insights highlighted to improve ventilation and living conditions in densely populated city.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".