手法を比較

	GISベース多基準意思決定分析 (GIS-MCDA)	最小費用経路 / 費用距離解析	線形計画法
分野	空間分析	空間分析	最適化
系統	Process / pipeline	Process / pipeline	Process / pipeline
提唱年	2006	1994	1947
提唱者	Jacek Malczewski (GIS-MCDA synthesis)	Edsger Dijkstra (shortest path); GIS cost-surface adaptation	George B. Dantzig
種類	Spatial multi-criteria suitability/decision analysis	Raster cost-surface routing	Mathematical programming / continuous optimization
原典	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.	Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press. ISBN: 9780691059136
別名	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	cost-distance analysis, accumulated cost surface, least-cost corridor, en düşük maliyetli yol	LP, linear optimization, Doğrusal Programlama (LP)
関連	4	3	4
概要	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.	Linear programming (LP), pioneered by George B. Dantzig in 1947, is a mathematical method for finding the best value of a linear objective function — such as minimum cost or maximum profit — subject to a set of linear inequality and equality constraints. It is the foundational technique in operations research and underlies production planning, resource allocation, logistics, diet problems, and countless other decision-making scenarios across engineering, economics, and the natural sciences.