From cellular automata to urban models: new principles for model development and implementation

Integration with geographic information systems (GIS) has helped move cellular automata (CA)-based urban and regional models from the realm of instructive metaphors to that of potentially useful qualitative forecasting tools. Such models can now be fully interactive for exploratory purposes and they can be based on actual data. New problems, however, arise as the formal integrity of the original CA framework is lost through successive relaxations of the assumptions and as the resulting complicated models become increasingly difficult to implement and understand. In this paper I propose that the theoretical problem can find a satisfactory answer in the notion of <i>proximal space </i>and the practical one can be handled successfully within the formalism of <i>geo-algebra</i>, a mathematical expression of proximal space which builds a bridge between GIS data and operations, on the one hand, and traditional robust classes of urban and regional models, on the other.