An Integrated Software Environment for Object-Based Cellular Automata: An Application to the Study of Land-Use Changes
Bibliographic record
Abstract
Cellular automata (CA) is a well-established modelling approach used to study patterns and dynamics of land-use/land-cover (LULC) systems and to predict their evolution. Increased computer performance, along with the need to improve how geographic space is represented have resulted in the recent development of object-based CA models. Their main advantage over conventional cell-based models is that they allow for the representation of meaningful, real-world entities. However, their use remains limited due to issues with data model inconsistencies between calibration and simulation, simple neighborhood configurations and driving factors, overlooking spatial and temporal scaling effects on simulated results, increased computation time required to handle vector geometrical transformations and topology, and lack of an integrated framework that encompasses the functionalities required for calibration and simulation. The objective of this research is to describe the architecture and functionality of a novel, object-based CA model that were tested in two study areas in the Elbow River watershed in southern Alberta at 5 m and 60 m resolution. A change detection analysis is first performed on a series of historical LULC maps in vector format to identify the trends and speed of change in LULC and the driving factors responsible for these changes. This information is stored in a spatial database accessible from the software environment. Calibration is conducted with several neighborhood configurations and drivers using the multi-class weight of evidence method to calculate the transition probabilities. Simulation is performed by allowing for the change of state and geometry of each object over time. Time-consuming vector-handling operations are optimized or parallelized to increase the speed of execution. The final model results indicate a positive agreement with an independent map used for comparison. The model reproduces realistic urbanization patterns along the main roads and the river. Also, it is apparent that there is a substantial improvement in computation time. This model represents a powerful exploration and application tool that will enable a large community of users to exploit the potential of CA modelling for understanding the dynamics of LULC systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".