Confronta i metodi

	Markov Land-Use Model	Land-Use Change Modeling
Campo	Human Geography	Human Geography
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1994	2002
Ideatore	Mark R. Muller & John Middleton	Peter H. Verburg and colleagues (CLUE-S); broader land-change-science community
Tipo	Stochastic projection of land-use/land-cover areas using a transition probability matrix	Family of spatially explicit models simulating land-use and land-cover change
Fonte seminale	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.	Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S. S. A. (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental Management, 30(3), 391–405.
Alias	Markov Chain Land-Cover Model, LULC Transition Matrix Model, CA-Markov Model, Markovian Land Change Model	Land Change Modeling, LUCC Simulation, Spatial Land-Use Allocation Modeling, Land-Use Scenario Modeling
Correlati	4	4
Sintesi	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.	Land-use change modeling is the umbrella family of methods that simulate how the land surface is converted between uses — forest to farmland, farmland to city — by combining where change is likely with how much change is demanded. A typical model statistically relates observed change to spatial drivers such as slope, roads, and population, sets future demand for each land-use class from scenarios, and then allocates that demand across space to the most suitable cells, iterating until supply meets demand. The CLUE-S model of Verburg and colleagues, alongside the Land Change Modeler and SLEUTH, exemplifies this demand-plus-allocation architecture that underpins much of land-change science.