Compare methods

	Cellular Automata Urban Model	Cellular Automata
Field	Human Geography	Simulation
Family	Process / pipeline	Process / pipeline
Year of origin	1993	1940s–1950s (formalized); 1970 (Conway's Game of Life); 2002 (Wolfram's systematic classification)
Originator	Roger White & Guy Engelen	John von Neumann and Stanislaw Ulam (1940s–1950s); popularized by John Conway (1970) and Stephen Wolfram (1980s–2002)
Type	Spatially explicit simulation of urban land-use change on a cell grid	Grid-based computational simulation model
Seminal source	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.	Wolfram, S. (2002). A New Kind of Science. Wolfram Media. ISBN: 978-1579550080
Aliases	Urban Cellular Automata, CA Urban Growth Model, Constrained Cellular Automata, White-Engelen CA Model	CA, Hücresel Otomat (Cellular Automata), lattice model, grid-based simulation
Related	4	5
Summary	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.	Cellular automata (CA) is a grid-based computational simulation model, first formalized by John von Neumann and Stanislaw Ulam in the 1940s–1950s and brought to wide attention by John Conway's Game of Life (1970) and Stephen Wolfram's systematic classification (2002), in which a lattice of cells — each holding a finite discrete state — evolves in discrete time steps according to local neighborhood interaction rules, causing complex global patterns to emerge from simple local specifications.