方法对比

	Cellular Automata Urban Model	Spatial Microsimulation
领域	Human Geography	Human Geography
方法族	Process / pipeline	Process / pipeline
起源年份	1993	2016
提出者	Roger White & Guy Engelen	Developed in the IPF/microsimulation tradition; synthesized for geography by Lovelace & Dumont
类型	Spatially explicit simulation of urban land-use change on a cell grid	Method for generating and analysing synthetic individual-level populations within small areas
开创性文献	White, R., & Engelen, G. (1993). Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environment and Planning A, 25(8), 1175–1199.	Lovelace, R., & Dumont, M. (2016). Spatial Microsimulation with R. Chapman and Hall/CRC, Boca Raton. ISBN: 9781498711548
别名	Urban Cellular Automata, CA Urban Growth Model, Constrained Cellular Automata, White-Engelen CA Model	Small-Area Population Synthesis, Synthetic Population Generation, Geographical Microsimulation, Spatial Microdata Estimation
相关	4	4
摘要	A cellular automata (CA) urban model simulates the growth and transformation of cities by dividing space into a grid of cells, each holding a land-use state, and letting those states evolve through local transition rules that depend on the states of neighbouring cells. Introduced for urban form by Roger White and Guy Engelen in 1993 and popularized in Michael Batty's work on cities as complex systems, the approach reproduces realistic, fractal urban patterns from simple bottom-up rules rather than top-down equations. It has become a workhorse for exploring how compact or sprawling settlement patterns emerge from neighbourhood-scale interactions under regional land demand.	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.