Understanding Spatial and Temporal Processes of Urban Growth: Cellular Automata Modelling

Abstract

An understanding of the dynamic process of urban growth is a prerequisite to the prediction of land-cover change and the support of urban development planning and sustainable growth management. The spatial and temporal complexity inherent in urban growth requires the development of a new simulation approach, which should be process-oriented and have a strong interpretive element. In this paper the authors present an innovative methodology for understanding spatial processes and their temporal dynamics on two interrelated scales—the municipality and project scale—by means of a multistage framework and a dynamic weighting concept. The multistage framework is aimed at modelling local spatial processes and global temporal dynamics by the incorporation of explicit decisionmaking processes. It is divided into four stages: project planning, site selection, local growth, and temporal control. These four stages represent the interactions between top-down and bottom-up decisionmaking involved in land development in large-scale projects. Project-based cellular automata modelling is developed for interpreting the spatial and temporal logic between various projects that form the whole of urban growth. Use of dynamic weighting is an attempt to model local temporal dynamics at the project level as an extension of the local growth stage. As nonlinear function of temporal land development, dynamic weighting can link spatial processes and temporal patterns. The methodology is tested with reference to the urban growth of a fast growing city—Wuhan, in the People's Republic of China—from 1993 to 2000. The findings from this research suggest that this methodology can be used to interpret and visualise the dynamic process of urban growth temporally and transparently, globally and locally.