Cellular automata belong to a family of discrete, connectionist techniques being used to investigate fundamental principles of dynamics, evolution, and self-organization. In this paper, a cellular automaton is developed to model the spatial structure of urban land use over time. For realistic parameter values, the model produces fractal or bifractal land-use structures for the urbanized area and for each individual land-use type. Data for a set of US cities show that they have very similar fractal dimensions. The cellular approach makes it possible to achieve a high level of spatial detail and realism and to link the results directly to general theories of structural evolution.

A cellular automaton is specified to give a spatially detailed representation of the evolution of urban land-use patterns. Cell states represent land uses, and transition rules express the likelihood of a change from one state to another as a function both of existing land use in the 113-cell neighbourhood of the cell and of the inherent suitability of the cell for each possible use. The model is used to simulate the land-use pattern of Cincinnati, Ohio. The simulation results are realistic and sensitivity analysis shows that the predictions of the model are relatively accurate and reproducible, thus suggesting that cellular ast;automata-based models may be useful in a planning context.

We present an integrated model of regional spatial dynamics consisting of a cellular automaton-based model of land use linked both to a geographic information system (GIS) and to standard nonspatial models of regional economics and demographics, as well as to a simple model of environmental change. The operation of the model is illustrated with an application to the island of St Lucia developed for the purpose of providing insights into the possible socioeconomic consequences for the island of global climate change. On the basis of results from this and other applications of the model, we conclude that cellular automata not only permit a detailed modelling and realistic prediction of land-use patterns, but they also provide a means of introducing the effects of spatially localized environmental factors, as represented in the GIS, into the operation of standard economic and demographic models, which are otherwise unconstrained.