Modelling land-use changes using a novel vector-based geographic cellular automata
Bibliographic record
Abstract
Cellular automata (CA) models have been increasingly used to simulate land-use changes due to their computational simplicity and their explicit representation of space and time. Typically, these models use the raster model, as defined in Geographic Information Systems, to represent geographic space. However, recent studies have demonstrated that raster-based CA are sensitive to spatial scale, i.e. cell size and neighborhood configuration. To overcome this limitation, a novel Vector-based Geographic Cellular Automata (VecGCA) model has been developed in which space is represented as a collection of geographic objects corresponding to meaningful entities of irregular shape and size composing a landscape. This paper presents a land-use change model using this new approach, tested on two study areas of different spatial complexity, in southern Quebec and in the Calgary region, respectively. The results obtained are compared to the patterns produced by a conventional raster-based CA and with land-use maps in each study area. They reveal that VecGCA generates an adequate evolution of the geometry of the objects composing the landscape and produces spatial patterns that are more similar to the land-use maps in each region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".