Comparison of GIS‐Based Logic Scoring of Preference and Multicriteria Evaluation Methods: Urban Land Use Suitability
Bibliographic record
Abstract
Multi‐criteria evaluation (MCE) methods are useful tools to evaluate the land suitability for various uses and assist in the effective management of available land. Many common GIS‐based MCE methods, such as analytical hierarchy process (AHP), ordered weighted averaging (OWA), and a combination of AHP and OWA methods (AHP–OWA) are not able to fully represent all the logic that constitute a wide range of human decision‐making reasoning. Consequently, improved GIS‐based MCE methods such as Logic Scoring of Preference (LSP) method are needed. The main objectives of this study are to: (1) implement the GIS‐based LSP method for land suitability evaluation and (2) compare qualitatively and quantitatively the suitability maps generated by LSP and three GIS‐based MCE methods. This study was implemented with data sets from Boulder County, Colorado, USA for the case study of the urban land suitability evaluation. The qualitative properties of MCE methods and the Receiver Operating Characteristic (ROC) statistics were used as comparison metrics. The results indicate that soft computing methods and particularly LSP performed the best among GIS‐based MCE methods for the urban land use application.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".