পদ্ধতির তুলনা করুন

	সর্বনিম্ন-ব্যয় পথ / ব্যয়-দূরত্ব বিশ্লেষণ	CA-Markov ভূমি-ব্যবহার পরিবর্তন মডেল
ক্ষেত্র	স্থানিক বিশ্লেষণ	স্থানিক বিশ্লেষণ
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1994	1997
প্রবর্তক	Edsger Dijkstra (shortest path); GIS cost-surface adaptation	Cellular automata (Clarke) + Markov chain (Muller & Middleton)
ধরন	Raster cost-surface routing	Spatio-temporal land-use change simulation
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	3	3
সারসংক্ষেপ	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.