EXACERBATING LAND USE PATTERNS IN URBAN AREAS: A SPATIO - TEMPORAL STUDY OF URBANIZATION IN PATAN CITY, GUJARAT
Bibliographic record
Abstract
This study examines the urbanisation and alterations in land use in Patan, a city in Gujarat. Rural areas are transformed into urban areas through the process of urbanisation. The study investigates the effects of this procedure on Patan's land use. Satellite images from 2012 and 2022 are used in the study to map and track changes in the study area. The study examines the many land classifications in the region, including water bodies, built-up areas, vegetation, agriculture, agricultural fallow land, riverbeds, and barren land. It does so using tools like QGIS and ArcGIS. The study's conclusions show that Patan's land use has changed significantly during the previous ten years. While agriculture and agricultural fallow have reduced, the built-up area has expanded. These changes are attributable to the urbanisation and expanding population of the region. This study underlines the requirement for efficient land use laws and rules that support Patan's sustainable urban development. Urbanization can have detrimental effects on the ecology, the loss of agricultural land, and other factors if it is not planned for properly. The study emphasises the significance of efficiently managing urban areas through the use of GIS and remote sensing technology. These technologies can offer insightful information about how land use is changing and how urbanisation affects the environment. This study sheds light on Patan's urbanisation and shifting land use patterns and emphasises the necessity of sustainable urban planning and management.
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 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".