Methoden vergleichen

	Markov Land-Use Model	Zelluläre Automaten
Fachgebiet	Human Geography	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1994	1940s–1950s (formalized); 1970 (Conway's Game of Life); 2002 (Wolfram's systematic classification)
Urheber	Mark R. Muller & John Middleton	John von Neumann and Stanislaw Ulam (1940s–1950s); popularized by John Conway (1970) and Stephen Wolfram (1980s–2002)
Typ	Stochastic projection of land-use/land-cover areas using a transition probability matrix	Grid-based computational simulation model
Wegweisende Quelle	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.	Wolfram, S. (2002). A New Kind of Science. Wolfram Media. ISBN: 978-1579550080
Aliasnamen	Markov Chain Land-Cover Model, LULC Transition Matrix Model, CA-Markov Model, Markovian Land Change Model	CA, Hücresel Otomat (Cellular Automata), lattice model, grid-based simulation
Verwandt	4	5
Zusammenfassung	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.	Cellular automata (CA) is a grid-based computational simulation model, first formalized by John von Neumann and Stanislaw Ulam in the 1940s–1950s and brought to wide attention by John Conway's Game of Life (1970) and Stephen Wolfram's systematic classification (2002), in which a lattice of cells — each holding a finite discrete state — evolves in discrete time steps according to local neighborhood interaction rules, causing complex global patterns to emerge from simple local specifications.