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	Vacancy Chain Analysis	Markov Land-Use Model
Ämnesområde	Human Geography	Human Geography
Familj	Process / pipeline	Process / pipeline
Ursprungsår	1970	1994
Upphovsperson	Harrison C. White	Mark R. Muller & John Middleton
Typ	System model of mobility driven by the movement of vacancies through linked units	Stochastic projection of land-use/land-cover areas using a transition probability matrix
Ursprungskälla	White, H. C. (1970). Chains of Opportunity: System Models of Mobility in Organizations. Harvard University Press, Cambridge, MA. ISBN: 9780674080652	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.
Alias	Vacancy Chain Model, Chains of Opportunity, Vacancy Transfer Analysis, Vacancy Chain Mobility Model	Markov Chain Land-Cover Model, LULC Transition Matrix Model, CA-Markov Model, Markovian Land Change Model
Närliggande	4	4
Sammanfattning	Vacancy chain analysis is a system model of mobility, introduced by Harrison White in his 1970 book Chains of Opportunity, that follows opportunities rather than people. When a unit such as a house or a job is freed and filled by someone who in turn vacates another unit, a chain of moves cascades through the system until it ends with a new entrant or a unit leaving the stock. By treating vacancies as the things that move — through an absorbing Markov chain — the framework explains how a single new dwelling or retirement can ripple into many household relocations or promotions.	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.