Võrdle meetodeid

	Vähimalkulude tee / kuludistantsi analüüs	GIS-põhine mitmekriteeriumiline otsustusanalyys (GIS-MCDA)	Asukohavaliku mudelid
Valdkond	Ruumianalüüs	Ruumianalüüs	Ruumianalüüs
Perekond	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	1994	2006	1963
Looja	Edsger Dijkstra (shortest path); GIS cost-surface adaptation	Jacek Malczewski (GIS-MCDA synthesis)	Leon Cooper; S. L. Hakimi
Tüüp	Raster cost-surface routing	Spatial multi-criteria suitability/decision analysis	Spatial facility-location optimization
Algallikas	Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271.	Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726.	Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.
Rööpnimetused	cost-distance analysis, accumulated cost surface, least-cost corridor, en düşük maliyetli yol	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri
Seotud	3	4	4
Kokkuvõte	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.	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.	Location-allocation models decide where to place a set of facilities and simultaneously assign demand points to them so as to optimize an objective such as total travel cost, worst-case distance, or population covered. Rooted in the operations-research work of Cooper (1963) and Hakimi (1964) and central to network GIS, they answer questions like where to site warehouses, hospitals, fire stations, or schools to best serve a spatially distributed population.