Raster-based MCDA land suitability workflows with explainable analytics and uncertainty.
PlanX Suitability Lab provides an end-to-end raster MCDA pipeline in QGIS: data harmonization, criterion generation, AHP/PCA/Entropy weighting, WLC/OWA suitability composition, Monte Carlo uncertainty, and explainability outputs for planning-grade decision support. Developed with feedback from educational workflows at Dokuz Eylul University, Department of City and Regional Planning.
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