Methoden vergleichen

	Cellular Automata Urban Model	Land-Use Change Modeling
Fachgebiet	Human Geography	Human Geography
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1993	2002
Urheber	Roger White & Guy Engelen	Peter H. Verburg and colleagues (CLUE-S); broader land-change-science community
Typ	Spatially explicit simulation of urban land-use change on a cell grid	Family of spatially explicit models simulating land-use and land-cover change
Wegweisende Quelle	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.	Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S. S. A. (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental Management, 30(3), 391–405.
Aliasnamen	Urban Cellular Automata, CA Urban Growth Model, Constrained Cellular Automata, White-Engelen CA Model	Land Change Modeling, LUCC Simulation, Spatial Land-Use Allocation Modeling, Land-Use Scenario Modeling
Verwandt	4	4
Zusammenfassung	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.	Land-use change modeling is the umbrella family of methods that simulate how the land surface is converted between uses — forest to farmland, farmland to city — by combining where change is likely with how much change is demanded. A typical model statistically relates observed change to spatial drivers such as slope, roads, and population, sets future demand for each land-use class from scenarios, and then allocates that demand across space to the most suitable cells, iterating until supply meets demand. The CLUE-S model of Verburg and colleagues, alongside the Land Change Modeler and SLEUTH, exemplifies this demand-plus-allocation architecture that underpins much of land-change science.