Jianquan Cheng

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.

Measuring Sustainable Accessibility

Transport is one of the most significant sources of unsustainability in urban regional areas. This challenge is stimulating urban planners and decision makers to incorporate the concept of sustainability into their policy design at various levels. Despite its successful implementation in several sectors and wide recognition in academic and professional debate, sustainability is still not that evident in day-to-day regional planning practice. Interpretable measures integrating accessibility and sustainability and linking them with policy-making practice are relatively scarce. This paper aims to take some steps toward measuring sustainable accessibility, which in turn is intended to help regional planners define the potential problems and design possible alternatives at the strategic planning level for a sustainable regional transport and land use system. The proposed methodology for such measurement consists of the concept of conflicts in the planning process, job opportunity modeling, and sustainability and spatial conflict analysis. The Amsterdam urban region in the Netherlands is taken as a case study. Sustainable accessibility is measured in an integrated geographic information system environment, followed by corresponding policy implications for strategy design. The experimental study demonstrates that the indicator of sustainable accessibility can be incorporated into the process of strategic policy design.

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.

Exploring urban morphology using multi-temporal urban growth data: a case study of Wuhan, China

Understanding temporal urban growth process is crucial to the interpretation of urban morphology and a key challenge for the study of rapid urbanization in contemporary China. As an evolutionary process of urban form or landscape, urban morphology helps us track the trend of urban form development, which is characterized by multi-temporality in terms of data. The increasingly improved multi-temporal data availability, as pushed by the massive advances in geospatial technology, particularly remotely sensed imagery, offers great opportunity for measuring and describing urban morphology. Previous studies have reported the measurement of urban morphology using spatial methods including fractals, landscape matrix and density gradient. However these methods are subject to poor interpretation of urban morphology and low-level understandability to local planners. This paper demonstrates an innovative application of a machine learning method – Maxent for analyzing the urban morphology of a fast growing city – Wuhan, China. Multi-temporal data sets for 1955, 1965, 1993 and 2000 were processed from remotely sensed imagery. The model results facilitate local planners better to track and interpret the urban form development process of Wuhan city in the past half-century. The paper also illustrates that Maxent is an effective exploratory method and tool for analyzing urban morphology.

Using User-Generated Content to Explore the Temporal Heterogeneity in Tourist Mobility:

In tourism studies, new means of data collection are opening up opportunities for disclosing hidden mobility patterns. This paper aims to analyze and model the tourist flow networks for different lengths of trip on urban scale, using user generated content (UGC) data collated from an open tourism web service. The textual UGC data, with high spatial and temporal resolution, is utilized to construct three tourist flow networks in response to length of trips. Social network analysis and a revised spatial interaction model are deployed for exploring the temporal heterogeneity in the tourist movements. This empirical study from Nanjing City has further confirmed the power law of distance decay in intraurban tourist mobility. Furthermore, the research reveals temporal variations with length of trip. The paper highlights the role of time in the tourism study through incorporating a temporal dimension into the analyses and taking advantage of the availability of new data.

Beyond Space: Spatial (Re)production and Middle Class Remaking Driven by Jiaoyufication in Nanjing City, China

As an extension of gentrification, high quality education-driven jiaoyufication not only displaces previous lower class as jiaoyufiers, but also replaces former jiaoyufiers with newcomers, as well as blenching former blue collar neighborhoods. New middle class communities are emerging as spatially limited education-apartment zones attract social groups who attempt to occupy these spaces to facilitate social mobility and consolidation, causing tension between them. Consequently, jiaoyufication has narrowed down opportunities for intra-generation-based social mobility and exacerbated social polarization, gradually replacing traditional social hierarchies with an intergeneration-based neoliberal stratification.

Location patterns of urban industry in Shanghai and implications for sustainability

China’s economy has undergone rapid transition and industrial restructuring. The term “urban industry” describes a particular type of industry within Chinese cities experiencing restructuring. Given the high percentage of industrial firms that have either closed or relocated from city centres to the urban fringe and beyond, emergent global cities such as Shanghai, are implementing strategies for local economic and urban development, which involve urban industrial upgrading numerous firms in the city centre and urban fringe. This study aims to analyze the location patterns of seven urban industrial sectors within the Shanghai urban region using 2008 micro-geography data. To avoid Modifiable Areal Unit Problem (MAUP) issue, four distance-based measures including nearest neighbourhood analysis, Kernel density estimation, K-function and co-location quotient have been extensively applied to analyze and compare the concentration and co-location between the seven sectors. The results reveal disparate patterns varying with distance and interesting co-location as well. The results are as follows: the city centre and the urban fringe have the highest intensity of urban industrial firms, but the zones with 20–30 km from the city centre is a watershed for most categories; the degree of concentration varies with distance, weaker at shorter distance, increasing up to the maximum distance of 30 km and then decreasing until 50 km; for all urban industries, there are three types of patterns, mixture of clustered, random and dispersed distribution at a varied range of distances. Consequently, this paper argues that the location pattern of urban industry reflects the stage-specific industrial restructuring and spatial transformation, conditioned by sustainability objectives.

Urban Growth in a Rapidly Urbanized Mega City: Wuhan

Wuhan, the largest mega city in central China, is not only a historic and cultural city but also a regional economic, transportation and educational centre. In the past six decades, Wuhan has witnessed massive changes in national and local policies of urbanization and urban development. Its spatial and temporal growth has shaped a representative urban morphology, which is interpretable from socio-economic and spatial processes in each corresponding period. Since 2000, Wuhan has entered a new era of economic boom and started the construction of metro system. However, such rapid urban development has addressed great challenges to local urban planning. After reviewing the process of its urban growth and evaluating the roles of urban planning, this paper aims to analyze the challenging issues in transport, population ageing, migration and environment and ecology. We argue the current local data infrastructure is insufficient to support comprehensive or integrated planning in Wuhan.

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.