GIS.LSP: A soft computing logic method and tool for geospatial suitability analysis
Bibliographic record
Abstract
Abstract This research study extends the Logic Scoring of Preference (LSP) as a general multicriteria evaluation (MCE) method by presenting and evaluating a new GIS.LSP method and software tool implemented within the geographic information systems (GIS) environment. For the evaluation and validation of the method and software tool, we describe a case study of urban densification suitability analysis using geospatial data for the Metro Vancouver Region, Canada. The criteria, LSP structures, and aggregators groups were developed from the perspective of urban developers who are key stakeholders in the densification process. We compare two group of aggregators and perform sensitivity and cost–suitability analysis of the LSP method by variation of input suitability scores, input attributes, and aggregators. The results indicate the GIS.LSP method is effective in providing a flexible and sensitive workflow to create realistic and justifiable outcomes from complex criteria that are bounded by stakeholders' goals and requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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 teacher head, 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".