Modelling Land Use Changes at the Peri-Urban Areas using Geographic Information Systems and Cellular Automata Model
Bibliographic record
Abstract
In many cities, urban area expansion encroaches into agriculture area especially at the peri-urban region. While it helps to minimize commuting duration and distance between and in and out of cities, peri-urban area, however, experiences land loss due to housing needs, economic transformation from agricultural activities, environmental degradation, and decline of agricultural land without any replacement by alternative economic activity. Land use changes at peri-urban areas is a complex and dynamic process which involves both natural and human systems. In monitoring and evaluating these dynamic changes, GIS can effectively be used to detect trends of urban expansion and predict future growth pattern. This paper discusses the study undertaken in Seberang Perai region of Penang State which experience significant land use transformation since the 1970s. GIS was integrated with Markov Cellular Automata Model to evaluate land use changes and forecast land use pattern until the year 2020. It was found that significant changes have occurred since 1990s and the same urban growth pattern will continue. Major concentration of urban development will grow towards the southern districts. The constraint used, namely valuable paddy fields, manage to control urban development in the northern district. The findings provide invaluable information for planners and decision makers in managing and planning urban growth.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".