Compară metode

	Analiza Căii cu Cost Minim (Least-Cost Path) / Analiza Cost-Distanță	Modelul CA-Markov pentru schimbarea utilizării terenurilor	Analiza Decizională Multi-Criterială Bazată pe GIS (GIS-MCDA)
Domeniu	Analiză spațială	Analiză spațială	Analiză spațială
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Anul apariției	1994	1997	2006
Autorul original	Edsger Dijkstra (shortest path); GIS cost-surface adaptation	Cellular automata (Clarke) + Markov chain (Muller & Middleton)	Jacek Malczewski (GIS-MCDA synthesis)
Tip	Raster cost-surface routing	Spatio-temporal land-use change simulation	Spatial multi-criteria suitability/decision analysis
Sursa seminală	Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271.	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.
Denumiri alternative	cost-distance analysis, accumulated cost surface, least-cost corridor, en düşük maliyetli yol	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
Înrudite	3	3	4
Rezumat	Least-cost path analysis finds the route between two locations that minimizes accumulated travel cost across a landscape, rather than minimizing straight-line distance. By encoding terrain, slope, land cover, and other frictions into a cost surface and accumulating cost outward from a source, it identifies optimal corridors for roads, pipelines, trails, power lines, and wildlife movement — a core raster-GIS technique built on Dijkstra's shortest-path logic.	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.