Modelling Land Use Changes at the Peri-Urban Areas using Geographic Information Systems and Cellular Automata Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.001 | 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 | 0.001 |
| 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 it