مقایسهٔ روش‌ها

	تحلیل تصمیم چندمعیاره مبتنی بر سامانه اطلاعات جغرافیایی (GIS-MCDA)	شاخص‌های الگوی منظر (Landscape Pattern Metrics)	تحلیل مسیر کم‌هزینه / تحلیل هزینه-فاصله
حوزه	تحلیل فضایی	تحلیل فضایی	تحلیل فضایی
خانواده	Process / pipeline	Process / pipeline	Process / pipeline
سال پیدایش	2006	1988	1994
پدیدآور	Jacek Malczewski (GIS-MCDA synthesis)	R. V. O'Neill et al.; McGarigal & Marks (FRAGSTATS)	Edsger Dijkstra (shortest path); GIS cost-surface adaptation
نوع	Spatial multi-criteria suitability/decision analysis	Quantitative landscape pattern description	Raster cost-surface routing
منبع بنیادین	Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726.	O'Neill, R. V., et al. (1988). Indices of landscape pattern. Landscape Ecology, 1(3), 153–162.	Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271.
نام‌های دیگر	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	landscape pattern indices, FRAGSTATS metrics, fragmentation indices, peyzaj metrikleri	cost-distance analysis, accumulated cost surface, least-cost corridor, en düşük maliyetli yol
مرتبط	4	3	3
خلاصه	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.	Landscape metrics are quantitative indices that describe the composition and spatial configuration of a categorical map — typically land cover — at the patch, class, and whole-landscape levels. Developed in landscape ecology (O'Neill and colleagues, 1988) and made widely usable by the FRAGSTATS software, they turn maps into numbers like patch density, edge density, fragmentation, diversity, and connectivity for ecological, planning, and change analysis.	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.