Comparer des méthodes

	Modèle CA-Markov de changement d'utilisation des terres	L'analyse multicritère (AMC) basée sur SIG (AMC-SIG)
Domaine	Analyse spatiale	Analyse spatiale
Famille	Process / pipeline	Process / pipeline
Année d'origine	1997	2006
Auteur d'origine	Cellular automata (Clarke) + Markov chain (Muller & Middleton)	Jacek Malczewski (GIS-MCDA synthesis)
Type	Spatio-temporal land-use change simulation	Spatial multi-criteria suitability/decision analysis
Source fondatrice	Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24(2), 247–261.	Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726.
Alias	CA-Markov model, cellular automata Markov, land-use change simulation, CA-Markov arazi kullanımı modeli	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability
Apparentées	3	4
Résumé	CA-Markov is a hybrid spatio-temporal model that projects land-use and land-cover change by combining a Markov chain — which predicts how much of each class will change — with cellular automata, which decide where that change happens. Widely used for urban-growth and land-cover forecasting, it answers both the quantity and the location of change, something neither component does well alone.	GIS-MCDA combines the map layers of a geographic information system with multi-criteria decision analysis to produce suitability or priority maps — ranking locations by how well they satisfy several weighted criteria at once. It is the standard framework for spatial decisions such as siting hospitals, solar farms, landfills, or evacuation areas, integrating methods like AHP, TOPSIS, and weighted overlay with spatial data.