Proximity-Adjusted WLC — spatially explicit weighted linear combination
PROXIMITY-WLC (Proximity-Adjusted WLC — spatially explicit weighted linear combination) is a ranking multi-criteria decision-making (MCDM) method introduced by Rinner, C.; Heppleston, A. in 2006. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
Key highlights
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
Intuition
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How it works
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When to use it
V(A^p_i) reflects both criterion performance and spatial proximity to the reference location. An alternative far from the reference will have low proximity-adjusted weights — its criterion values count for very little in the aggregation even if they are high. This is by design: the method explicitly encodes a spatial preference bias toward the reference location. Always inspect the proximity-adjusted weight matrix (output G.proximity_adjusted_weights) to understand how spatial position is affecting the result.
Strengths & limitations
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
- May exhibit rank reversal when alternatives are added to or removed from the set.
- Assumes full compensation — a strong score on one criterion can offset a weak score on another.
Common pitfalls
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Sources
- 1.Rinner, C., Heppleston, A. (2006). The spatial dimensions of multi-criteria evaluation — case study of a home buyer's spatial decision support system. Lecture Notes in Computer Science (GIScience 2006)
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ScholarGate. (2026, June 2). PROXIMITY-WLC. ScholarGate. https://scholargate.app/decision-making/proximity-wlc