Developing an Open Source Plugin for Spatial Multi-Criteria Decision Analysis: Ordered Weighted Averaging in QGIS
Bibliographic record
Abstract
Multi-Criteria Decision Analysis (MCDA) is a technique commonly used in GIS for decision support. In the QGIS environment, few pre-existing plugins exist to help calculate vector-based MCDA techniques such as Ordered Weighted Averaging (OWA). This research paper creates a set of two new Python 3-based plugins: The “Field Standardizer” plugin for quick standardization of vector data, and the “WLC/OWA Tool” plugin for an intuitive calculator to carry out both Weighted Linear Combination (WLC) and OWA to expand MCDA functionalities in QGIS. The plugins are then illustrated in an investigation of socio-economic status of neighbourhoods in the City of Toronto. The investigation also analyzes the mapped results of different OWA weight options tested, such as MAX, MIN, WLC, and other customized weights.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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".