Grid, Patch, or Multi‐Scale Integration? A Comparative Analysis for Cellular Automata‐Based Urban Growth Simulations
Bibliographic record
Abstract
ABSTRACT Capturing patch features can significantly enhance the performance of cellular automata (CA)‐based land use modeling. However, current research has yet to comprehensively explore the definition of patch‐based CA simulation rules and their integration with grid‐based rules. This study proposes a generalized urban CA framework that integrates grid‐ and patch‐based rules across scales. Using Beijing's urban growth from 2000 to 2020 as a case study, we evaluated the simulation performance of CA under different rule‐integration modes. The results demonstrate that patch‐level assessment of urban growth potential improves the model's temporal generalizability, robustness, and accuracy. However, using patches as cells for local interactions reduces simulation performance and efficiency, whereas grid‐based neighborhoods produce better results by more closely resembling complex boundary buffer neighborhoods. Furthermore, treating patches as basic units for urban expansion control enhances simulated urban morphology and accuracy. Integrating these optimal rules across scales within the proposed framework yields the best‐performing CA model. This study offers a methodological reference for grid‐patch integration in land use modeling, which can facilitate pre‐assessing urban growth‐induced land degradation risks and achieving reasonable spatial planning, supporting sustainable urban development.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".