Salīdzināt metodes

	CA-Markov zemes lietojuma pārmaiņu modelis	Multi-kritēriju lēmumu analīze, kas balstīta uz ĢIS (GIS-MCDA)
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1997	2006
Autors	Cellular automata (Clarke) + Markov chain (Muller & Middleton)	Jacek Malczewski (GIS-MCDA synthesis)
Tips	Spatio-temporal land-use change simulation	Spatial multi-criteria suitability/decision analysis
Pirmavots	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.
Citi nosaukumi	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
Saistītās	3	4
Kopsavilkums	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.