The Spatial Dimensions of Multi-Criteria Evaluation : Case Study of a Home Buyer’s Spatial Decision Support System
Bibliographic record
Abstract
This paper explores the spatial aspects of GIS-based multi-criteria evaluation. We provide a systematic account of geographically defined decision criteria based on three classes of spatial relations: location, proximity, and direction. We also discuss whether the evaluation score of a decision alternative should be directly influenced by neighbouring scores and outline a methodology for distance-based adjustment of evaluation scores. A home buyer case study is employed to demonstrate how spatial criteria can be included in a spatial decision support system and to investigate the effect of geographically adjusting the evaluation scores of decision alternatives. The case study demonstrates how spatial criteria can be presented to decision-makers and their effects be observed in the decision outcome. Further, the spatial adjustment of evaluation scores using the performance of neighbouring properties smoothes the distribution of scores across the study area and allows decision-makers to consider a location’s environment. Keywords: Multi-Criteria Evaluation, Residential Real-Estate Choice, Spatial Decision Support Systems, Spatial Relations.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".