方法对比

	Markov Land-Use Model	Spatial Microsimulation
领域	Human Geography	Human Geography
方法族	Process / pipeline	Process / pipeline
起源年份	1994	2016
提出者	Mark R. Muller & John Middleton	Developed in the IPF/microsimulation tradition; synthesized for geography by Lovelace & Dumont
类型	Stochastic projection of land-use/land-cover areas using a transition probability matrix	Method for generating and analysing synthetic individual-level populations within small areas
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.	Lovelace, R., & Dumont, M. (2016). Spatial Microsimulation with R. Chapman and Hall/CRC, Boca Raton. ISBN: 9781498711548
别名	Markov Chain Land-Cover Model, LULC Transition Matrix Model, CA-Markov Model, Markovian Land Change Model	Small-Area Population Synthesis, Synthetic Population Generation, Geographical Microsimulation, Spatial Microdata Estimation
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.	Spatial microsimulation is a family of techniques for generating realistic synthetic populations of individuals within small geographic areas, by combining detailed but geographically coarse survey microdata with geographically fine but aggregate census tables. It estimates, for every neighbourhood, a population of individuals whose collective characteristics match the published margins — the right number of each age, sex, income, and tenure group — even though no survey directly samples individuals at that fine scale. Synthesized for the geographic community in Robin Lovelace and Morgane Dumont's 2016 book, it bridges the gap between rich individual data and small-area aggregates so that policy and behaviour can be modelled where people actually live.