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	Algebra map	Analiza Decyzyjna Wielokryterialna oparta na GIS (GIS-MCDA)	Ścieżka najmniejszego kosztu / Analiza kosztu-dystansu
Dziedzina	Analiza przestrzenna	Analiza przestrzenna	Analiza przestrzenna
Rodzina	Process / pipeline	Process / pipeline	Process / pipeline
Rok powstania	1990	2006	1994
Twórca	Dana Tomlin	Jacek Malczewski (GIS-MCDA synthesis)	Edsger Dijkstra (shortest path); GIS cost-surface adaptation
Typ	Raster spatial analysis framework	Spatial multi-criteria suitability/decision analysis	Raster cost-surface routing
Źródło pierwotne	Tomlin, C. D. (1990). Geographic Information Systems and Cartographic Modeling. Prentice Hall. ISBN: 978-0-13-350927-4	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.
Inne nazwy	Cartographic Modeling, Raster Algebra, Grid Algebra, Harita Cebiri	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	cost-distance analysis, accumulated cost surface, least-cost corridor, en düşük maliyetli yol
Pokrewne	3	4	3
Podsumowanie	Map Algebra is a rule-based language and computational framework for deriving new raster layers from existing ones by applying arithmetic, logical, or statistical operations cell by cell or across neighborhoods. Formalized by Dana Tomlin in 1990, it is the foundational algebraic system underlying raster GIS analysis and is widely used in environmental science, urban planning, hydrology, and land-use modeling whenever spatially explicit calculations on gridded data are required.	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.