Multi-Scenario Land Use and Land Cover (LULC) Change Projection Framework Using Markov Chain and PLUS Integrated Model
Bibliographic record
Abstract
The spatial distribution of urban land use has undergone significant transformations due to rapid urbanization. Assessing the dynamic and complex interactions of land use and land cover (LULC) can help planners and policymakers understand the extent and effects of those changes. This study proposes a novel framework for land use and land cover (LULC) change through the integration of patch-generating land use simulation (PLUS) and Markov Chain (MC) model under different scenarios. Various simulations have been conducted for the island of Montreal, Quebec, Canada using regional land use types under the five shared socioeconomic pathways (SSPs) for the year of 2028. In addition, a comparative study was conducted between three major cities in Canada: Toronto, Ottawa, and Montreal, in which global land use types were used to project LULC change in 2030 based on historical trends. Different accuracy measures were calculated to validate our model and compared to the accuracy of other models reported in the literature. Our findings show that our model achieved a higher figure of merit (FoM) than other models and was able to simulate LULC change without the need for expert knowledge in the field. The results of this multi-scenario simulation and ecological, environmental effect study can be used as a reference for future regional territorial spatial planning and policy formulation. The integration of the PLUS and Markov Chain models is shown to be quite applicable to the projection and assessment of urban spatial land use patterns.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".