Assessing the Number of Criteria in <scp>GIS</scp>‐Based Multicriteria Evaluation: A Machine Learning Approach
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT The analytical hierarchy process (AHP) is a widely used approach and a decision rule to derive criteria weights in geographic information system‐based multi‐criteria evaluation (GIS‐MCE). However, one limitation of the AHP method is that it constrains the number of criteria that can be meaningfully weighted to typically seven to nine criteria. Recently, machine learning (ML) techniques have emerged as a compelling alternative for deriving criteria weights. This research aims to assess the capabilities of ML‐MCE in handling a larger number of criteria and is specifically applied to a case study of urban suitability analysis. The random forest (RF) ML technique is used to evaluate the ability of the MCE method to handle up to 27 criteria. Geospatial data from the Metro Vancouver Region, Canada, are used, with the criteria subdivided into 11 groups starting with the most basic seven criteria and incrementally adding two new criteria per group. The results indicate the RF‐ML approach can manage a larger number of criteria compared to the traditional AHP approach, with 15 criteria providing a meaningful upper threshold, demonstrating its potential to accommodate a wider range of stakeholder preferences for complex urban suitability analysis contexts.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.007 |
| 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) | 0.002 | 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 it