Spatial Ordered Weighted Averaging
SOWA (Spatial Ordered Weighted Averaging) is a ranking multi-criteria decision-making (MCDM) method introduced by Makropoulos, C. K.; Butler, D. in 2003/2006. 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^s_i) reflects both criterion performance and the spatial risk attitude of the zone the alternative belongs to. An alternative with extreme values (very high on its best criterion, very low on its worst) will be strongly penalised if it lies in a pessimistic zone (low ORness) and strongly rewarded if it lies in an optimistic zone (high ORness). Always inspect the ORness map (output G.orness_per_zone) and the rank-shift chart (H3) to understand how zone assignments are driving the result. The zone_order_weights assignment is the key modelling decision — it embeds a spatial theory of risk.
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.
Common pitfalls
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Sources
- 1.Makropoulos, C. K., Butler, D. (2006). Spatial ordered weighted averaging: incorporating spatially variable attitude towards risk in spatial multi-criteria decision-making. Environmental Modelling & Software
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ScholarGate. (2026, June 2). SOWA. ScholarGate. https://scholargate.app/decision-making/sowa