Urban development is a complex dynamic process involving various actors with different patterns of behaviour. Modelling urban development patterns is a prerequisite to understanding the process. This paper presents a preliminary multiscale perspective for such modelling based on spatial hierarchical theory and uses it for the analysis of a rapidly developing city. This framework starts with a conceptual model, which aims at linking planning hierarchy, analysis hierarchy, and data hierarchy. Analysis hierarchy is the focus of this paper. It is divided into three scales: probability of change (macro), density of change (meso), and intensity of change (micro). The multiscale analysis seeks to distinguish spatial determinants on each of the three scales, which are able to provide deeper insights into urban growth patterns shaped by spontaneous and self-organised spatial processes. A methodology is also presented to implement the framework, based on exploratory data analysis and spatial logistic regression. The combination of both is proven to have strong capacity of interpretation. This framework is tested by a case study of Wuhan City, China. The scale-dependent and scale-independent determinants are found significantly on two scales.
The Effects of Portland's Urban Growth Boundary on Urban Development Patterns and Commuting
