방법 비교

	지리정보시스템 기반 다기준 의사결정 분석 (GIS-MCDA)	최소 비용 경로 / 비용 거리 분석	로케이션-할당 모델
분야	공간분석	공간분석	공간분석
계열	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	2006	1994	1963
창시자	Jacek Malczewski (GIS-MCDA synthesis)	Edsger Dijkstra (shortest path); GIS cost-surface adaptation	Leon Cooper; S. L. Hakimi
유형	Spatial multi-criteria suitability/decision analysis	Raster cost-surface routing	Spatial facility-location optimization
원전	Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726.	Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271.	Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.
별칭	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	cost-distance analysis, accumulated cost surface, least-cost corridor, en düşük maliyetli yol	facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri
관련	4	3	4
요약	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.	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.	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.