Multi-Criteria Decision Analysis (MCDA)

Multi-Criteria Decision Analysis (MCDA) in a GISGISGeographic Information Systems (GIS) enable users to analyze and visualize spatial data to uncover patterns, relation... context provides a systematic approach for combining diverse spatial information with decision-maker preferences to rank, score, or classify geographic alternatives. It addresses decisions where multiple factors must be balanced simultaneously, such as selecting the best site for a facility, prioritizing areas for conservation, or allocating resources across competing demands.

Several MCDA methods are commonly integrated with GISGISGeographic Information Systems (GIS) enable users to analyze and visualize spatial data to uncover patterns, relation.... The Analytical Hierarchy Process (AHP) uses pairwise comparisons to derive consistent weights from expert judgments. Weighted Linear Combination (WLC) multiplies each standardized criterion map by its weight and sums the results. Ordered Weighted Averaging (OWA) introduces a second set of order weights that control the degree of trade-off between criteria. Outranking methods like ELECTRE and PROMETHEE compare alternatives pairwise across criteria rather than aggregating into a single score. The choice of method depends on the decision context, number of criteria, and stakeholder engagement requirements.

MCDA is applied in environmental management for prioritizing habitat restoration, in public health for locating medical facilities, in urban planningUrban PlanningUrban Planning is the systematic process of designing and managing the development of cities and communities. It inte... for comparing development scenarios, and in disaster management for identifying vulnerable populations and optimal evacuation routes. Renewable energy sitingRenewable Energy SitingRenewable Energy Siting uses geospatial analysis to identify optimal locations for solar, wind, and other clean energ... uses MCDA to balance technical potential, environmental impact, and social acceptability.

MCDA makes decision logic explicit and auditable, facilitates stakeholder participation, and supports sensitivity analysis to test how changes in weights affect outcomes. However, results are inherently dependent on subjective weight assignments, and poorly structured criteria or inconsistent preferences can undermine the validity of the analysis.