方法对比

	CA-马尔可夫土地利用变化模型	基于地理信息系统的多准则决策分析 (GIS-MCDA)	区位-分配模型
领域	空间分析	空间分析	空间分析
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1997	2006	1963
提出者	Cellular automata (Clarke) + Markov chain (Muller & Middleton)	Jacek Malczewski (GIS-MCDA synthesis)	Leon Cooper; S. L. Hakimi
类型	Spatio-temporal land-use change simulation	Spatial multi-criteria suitability/decision analysis	Spatial facility-location optimization
开创性文献	Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24(2), 247–261.	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.
别名	CA-Markov model, cellular automata Markov, land-use change simulation, CA-Markov arazi kullanımı modeli	GIS-MCDM, spatial multi-criteria analysis, GIS-AHP, weighted overlay suitability	facility location, p-median problem, maximal covering location problem, yer-tahsis modelleri
相关	3	4	4
摘要	CA-Markov is a hybrid spatio-temporal model that projects land-use and land-cover change by combining a Markov chain — which predicts how much of each class will change — with cellular automata, which decide where that change happens. Widely used for urban-growth and land-cover forecasting, it answers both the quantity and the location of change, something neither component does well alone.	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.