Machine Learning for Criteria Weighting in GIS-Based Multi-Criteria Evaluation: A Case Study of Urban Suitability Analysis
Bibliographic record
Abstract
Geographic Information System-based Multi-Criteria Evaluation (GIS-MCE) methods are designed to assist in various spatial decision-making problems using spatial data. Deriving criteria weights is an important component of GIS-MCE, typically relying on stakeholders’ opinions or mathematical methods. These approaches can be costly, time-consuming, and prone to subjectivity or bias. Therefore, the main objective of this study is to investigate the use of Machine Learning (ML) techniques to support criteria weight derivation within GIS-MCE. The proposed ML-MCE method is explored in a case study of urban development suitability analysis of the City of Kelowna, Canada. Feature importance values drawn from three ML techniques–Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM)–are used to derive criteria weights. The suitability scores obtained using the ML-MCE methodology are compared with Equal-Weights (EW) and the Analytical Hierarchy Process (AHP) approach for criteria weighting. The results indicate that ML-derived criteria weights can be used in GIS-MCE, where RF and XGB techniques provide more similar values for criteria weights than those derived from SVM. The similarities and differences are confirmed with Kappa indices obtained from comparing pairs of suitability maps. The proposed new ML-MCE methodology can support various decision-making processes in urban land-use planning.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".