mcda-suitability-analysis

# MCDA & Suitability Analysis Purpose: produce suitability maps whose weights, scales, and assumptions are explicit, consistent, and stress-tested. A suitability map without a sensitivity analysis is an opinion with a legend. ## Workflow 1. **Structure**: goal → criteria (factors) → constraints. Constraints are binary masks (legal exclusions, water bodies, slope > threshold) applied at the END by multiplication; factors are continuous and weighted. Keep them apart — encoding a constraint as a heavily-weighted factor is a classic error that lets forbidden areas score "acceptable". 2. **Criteria layers**: each factor as a raster on a COMMON grid (same CRS, extent, cell size, snap). Resample categorical layers with nearest, continuous with bilinear; document each. 3. **Standardization** to a common suitability scale (0-1 or 0-255): - Linear min-max for monotonic "more is better/worse". - Fuzzy membership (sigmoid/linear with control points) when suitability saturates — justify control points from domain knowledge. - Categorical layers: explicit reclass table, shown to the user. Direction check: confirm for EVERY layer whether high raw value means high or low suitability (slope: low=good; distance-to-road: usually low=good). Direction bugs survive to the final map invisibly. 4. **Weights** (AHP below, or direct/ranked methods with rationale). 5. **Aggregation**: weighted linear combination (WLC) default; OWA when the decision-maker's...