方法对比

	Markov Land-Use Model	Cellular Automata Urban Model
领域	Human Geography	Human Geography
方法族	Process / pipeline	Process / pipeline
起源年份	1994	1993
提出者	Mark R. Muller & John Middleton	Roger White & Guy Engelen
类型	Stochastic projection of land-use/land-cover areas using a transition probability matrix	Spatially explicit simulation of urban land-use change on a cell grid
开创性文献	Muller, M. R., & Middleton, J. (1994). A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2), 151–157.	White, R., & Engelen, G. (1993). Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environment and Planning A, 25(8), 1175–1199.
别名	Markov Chain Land-Cover Model, LULC Transition Matrix Model, CA-Markov Model, Markovian Land Change Model	Urban Cellular Automata, CA Urban Growth Model, Constrained Cellular Automata, White-Engelen CA Model
相关	4	4
摘要	A Markov land-use model treats land-use and land-cover change as a stochastic process in which the area in each class evolves according to fixed probabilities of transitioning from one class to another between time steps. Estimated from two dated maps as a transition probability matrix, it projects how much of the landscape will convert from, say, forest to cropland or cropland to urban, assuming the future obeys the same transition tendencies as the recent past. Introduced to landscape ecology by Muller and Middleton in 1994, it is most powerful when coupled with a cellular automaton — the CA-Markov framework — that decides where, not just how much, change occurs.	A cellular automata (CA) urban model simulates the growth and transformation of cities by dividing space into a grid of cells, each holding a land-use state, and letting those states evolve through local transition rules that depend on the states of neighbouring cells. Introduced for urban form by Roger White and Guy Engelen in 1993 and popularized in Michael Batty's work on cities as complex systems, the approach reproduces realistic, fractal urban patterns from simple bottom-up rules rather than top-down equations. It has become a workhorse for exploring how compact or sprawling settlement patterns emerge from neighbourhood-scale interactions under regional land demand.