Ian Masser

Urban growth pattern modeling: a case study of Wuhan city, PR China

Abstract Urban expansion has been a hot topic not only in the management of sustainable development but also in the fields of remote sensing and geographic information science (GIS). After land reform initiated in 1987, Chinese cities are facing a new development wave, which is the mixture of urban expansion and redevelopment. Local urban planners are also facing a huge challenge to require the understanding of complex urban growth process, which involves various actors with different patterns of behavior. Modeling an urban development pattern is the prerequisite to understanding the process. This paper presents a spatial data analysis method to seek and model major determinants of urban growth in the period 1993–2000 by a case study of Wuhan City in PR China. The method comprises exploratory data analysis and spatial logistic regression technique. The former is able to visually explore the spatial impacts of each explanatory variable. The latter can provide a systematic confirmatory approach to comparing the variables. The study shows that the major determinants are urban road infrastructure and developed area, and master planning is losing its role in the specific period.

Modelling urban growth patterns: a multiscale perspective

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.

Cellular Automata Based Temporal Process Understanding of Urban Growth

Understanding of urban growth process is highly crucial in making development plan and sustainable growth management policy. As the process involves multi-actors, multi-behavior and various policies, it is endowed with unpredictable spatial and temporal complexities, it requires the occurrence of new simulation approach, which is process-oriented and has stronger capacities of interpretation. In this paper, A cellular automata-based model is designed for understanding the temporal process of urban growth by incorporating dynamic weighting concept and project-based approach. We argue that this methodology is able to interpret and visualize the dynamic process more temporally and transparently.

Qualitative monitoring of information infrastructures: A case study of INSPIRE

This paper considers the experience of the implementation of the Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE) as a case study of qualitative monitoring in building information infrastructures. It considers the nature of information infrastructures and possible approaches to qualitative monitoring in situations of this kind and describes the outcomes of two rounds of qualitative country reports prepared by the European Union national Member States in 2010 and 2013. The findings of the analysis highlight the great diversity of approaches developed by the participating countries and the complexity of the tasks involved as well as pointing to a number of areas of potential research on the implementation of information infrastructures.

Managing our urban future

Abstract Urban growth is inevitable over the next two decades. The bulk of this growth will take place in less developed countries. This presents a formidable challenge for urban planers and managers. With this in mind this paper considers some of the ways urban planners can respond to this challenge. The discussion is divided into four sections. The first of these considers the nature of the tasks involved. The second examines the potential of remote sensing and geographic information systems to assist in these tasks in general terms. The third section presents some of the findings of three case studies which give some insights as to how these tools can be used to respond to this challenge while the final section sets out a vision of sustainable urban development and its implementation at the local level.

Understanding Urban Growth: a Conceptual Model

Understanding the urban growth system is a prerequisite for modelling and forecasting future trends of urban land use/cover change and its ecological impacts. As urban growth involves various actors with different patterns of behaviour, we argue that scientific understanding must be based on elaborated complexity theory and a multidisciplinary framework. The theoretical analysis can provide a guideline for selecting modelling methods currently available in complexity modelling and in remote sensing and GIS environments. This paper first proposes a conceptual model for defining urban growth and its complexity, in which spatial, temporal and decision-making complexity are distinguished as separate domains. Second, this paper links the conceptual model with the major current methods of modem urban modelling, such as cellular automata, fractals, neural networks, multi-agent, spatial statistics etc. This confrontation enables the possibilities of various modelling methods to understand urban growth complexity to be indicated. Third, this paper evaluates the operational implementation of representative methods based on criteria such as interpretability, data need and GIS embedded ness. Finally, two case studies are used to test the conceptual model.