Exploring the Impacts of Neighborhood Size and Type Variations on GIS-Based Cellular Automata Model: A Sensitivity Analysis Approach
Bibliographic record
Abstract
Cellular Automata (CA) approach is based on complexity theory and is widely used in geospatial modeling. A reason for the increasing attention given to CA models is that they can easily be integrated with raster-based GIS environment. However, the behavior of the CA models is affected by uncertainties arising from the interaction between model elements, structures, and the quality of data sources used as model input. The objective of this study is to examine the impacts of model elements on the generated outputs of a GIS-based CA land- use growth model using sensitivity analysis (SA) approach. The proposed SA method consists of cross-tabulation maps, KAPPA index with coincidence matrices, and different spatial metrics. The neighborhood surface is kept constant when different neighborhood size and type configurations are used. The variations of the model simulation outputs were examined and the results suggest that CA model is sensitive on the variation of the neighborhoods elements.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".