Порівняння методів

	Urban Growth Boundary Analysis	Markov Land-Use Model
Галузь	Urban Studies	Human Geography
Родина	Process / pipeline	Process / pipeline
Рік появи	1997	1994
Автор методу	Cellular-automata urban growth lineage (Clarke et al., SLEUTH); UGB policy from Oregon land-use planning	Mark R. Muller & John Middleton
Тип	Scenario simulation and evaluation of urban containment policies	Stochastic projection of land-use/land-cover areas using a transition probability matrix
Основоположне джерело	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: Planning and Design, 24(2), 247–261.	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.
Інші назви	UGB Analysis, Urban Containment Modelling, Growth Boundary Scenario Simulation, Urban Containment Policy Evaluation	Markov Chain Land-Cover Model, LULC Transition Matrix Model, CA-Markov Model, Markovian Land Change Model
Пов'язані	4	4
Підсумок	Urban growth boundary (UGB) analysis uses spatial simulation to design and evaluate containment lines that separate land where urban development is allowed from land to be kept rural. Built on the cellular-automata urban-growth tradition exemplified by Clarke, Hoppen, and Gaydos's self-modifying SLEUTH model, it calibrates how a region urbanizes, then imposes candidate boundaries as hard or soft constraints and simulates land conversion forward in time. By comparing scenarios with and without a boundary, the method estimates how much farmland and open space a UGB would protect, how much it would densify the interior, and whether it would push leapfrog development beyond the line.	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.