Multi-Criteria Decision Analysis (MCDA)

Standardize

xrspatial.mcda.standardize.standardize(agg)

Standardize a criterion raster to 0-1 suitability scale.

Weights

xrspatial.mcda.weights.ahp_weights(criteria, ...)	Derive criterion weights from pairwise comparisons using AHP.
xrspatial.mcda.weights.rank_weights(ranking)	Derive weights from a rank ordering of criteria.

Combination

xrspatial.mcda.combine.wlc(criteria, weights)	Weighted Linear Combination.
xrspatial.mcda.combine.wpm(criteria, weights)	Weighted Product Model.
xrspatial.mcda.combine.owa(criteria, ...[, name])	Ordered Weighted Averaging.
xrspatial.mcda.combine.fuzzy_overlay(criteria)	Combine criteria using fuzzy set operators.
xrspatial.mcda.combine.boolean_overlay(criteria)	Combine binary (boolean) criterion masks.

Constraints

xrspatial.mcda.constrain.constrain(...[, ...])

Mask out areas that are categorically unsuitable.

Sensitivity

xrspatial.mcda.sensitivity.sensitivity(...)

Assess how weight changes affect the suitability surface.