方法对比

	CA-马尔可夫土地利用变化模型	基于主体的建模（ABM）	最小成本路径 / 成本距离分析
领域	空间分析	仿真	空间分析
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1997	1970s–1990s (formalized as a field)	1994
提出者	Cellular automata (Clarke) + Markov chain (Muller & Middleton)	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)	Edsger Dijkstra (shortest path); GIS cost-surface adaptation
类型	Spatio-temporal land-use change simulation	Computational simulation method	Raster cost-surface routing
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.	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.	Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271.
别名	CA-Markov model, cellular automata Markov, land-use change simulation, CA-Markov arazi kullanımı modeli	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling	cost-distance analysis, accumulated cost surface, least-cost corridor, en düşük maliyetli yol
相关	3	5	3
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.	Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior of complex systems by specifying and running autonomous agents — individuals, firms, cells, or any bounded entity — whose local interactions with each other and with their environment collectively produce global, system-level patterns that could not be predicted from any single agent's rules alone.	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.