GIS‐Based Multiple‐Criteria Decision Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Important and complex spatial decisions, such as allocating land to development or conservation‐oriented goals, require information and tools to aid in understanding the inherent tradeoffs. They also require mechanisms for incorporating and documenting the value judgements of interest groups and decision makers. Multiple‐criteria decision analysis (MCDA) is a family of techniques that aid decision makers in formally structuring multi‐faceted decisions and evaluating the alternatives. It has been used for about two decades with geographic information systems (GIS) to analyse spatial problems. However, the variety and complexity of MCDA methods, with their varying terminologies, means that this rich set of tools is not easily accessible to the untrained. This paper provides background for GIS users, analysts and researchers to quickly get up to speed on MCDA, supporting the ultimate goal of making it more accessible to decision makers. A number of factors for describing MCDA problems and selecting methods are outlined then simplified into a decision tree, which organises an introduction of key methods. Approaches range from mathematical programming and heuristic algorithms for simultaneously optimising multiple goals, to more common single‐objective techniques based on weighted addition of criteria values, attainment of criteria thresholds, or outranking of alternatives. There is substantial research that demonstrates ways to couple GIS with multi‐criteria methods, and to adapt MCDA for use in spatially continuous problems. Increasing the accessibility of GIS‐based MCDA provides new opportunities for researchers and practitioners, including web‐based participation and advanced visualisation of decision processes.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.017 | 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