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Abstract Numerous machine learning-based land change models have been presented by researchers over the last two decades. To date, however, far less have simulated multiple land use classes and specific land use subclasses at the same time. In some areas of the world, it is important to simulate both of these dynamics to understand fully the drivers and consequences of land change. One important example is the process of urbanization in China, as urban policies have been developed that guide urban expansion, rural protections, and urban subclass development. This paper presents a new model integrating geographic information systems (GIS) with artificial neural networks (ANNs) to predict multiple transitions among land use types and urban subclasses in the Zhanggong District of Ganzhou city in China. We show that the model produces satisfactory goodness of fit values-based on location, quantity and spatial configuration-between simulated and observed land use maps for 2015. Our simulated future maps produced by the model for 2020 and 2025 demonstrate that transitions from farmland and forest to urban will remain the main pathway of urbanization although we predict that the rate will slow after 2025. The goals of urban planning should be aligned with land use planning according to "Combining multiple laws and regulations" in China. Taking into account the current and future land use transitions will enhance the accuracy and timeliness of land use policy making and urban land planning. For the sustainable land use in Zhanggong District, we argue for a strengthened regulation of the land market by government and believe that planning officials should guide the spatial distribution of land supply actively. Furthermore, improving the production, living and ecological functions of land resources are the key points to optimize urban land use functions and the allocation of land resources. We discuss how our model can be adapted to other areas to benefit land use management and urban planning in China. Highlights • GIS and ANNs were used to predict multiple land use transitions and urban subclasses. • A case study demonstrates that the model performs relatively well validated with several measures for goodness of fit. • The simulated maps suggest that farmland and forest are the most common candidates for urban expansion. • The methodology can be adapted to other study areas to benefit land use management and urban planning in China. [ABSTRACT FROM AUTHOR]

In the recent past Beijing has experienced rapid development. This growth has been accompanied by many problems including traffic congestion and air pollution. Understanding what stimulates urban growth is important for sustainable development in the coming years. In this paper, we first estimate a binary auto-logistic model of land use change, using physical and socioeconomic characteristics of the location and its access to major centers within the city as predictors. We find that variables determining regional access, like time distance to the city center, the Central Business District (CBD), industrial centers, employment centers, and the transportation system, significantly impact urban land conversion. By using measures of access to predict land use change we believe that we can better understand the planning implications of urban growth not only in Beijing but other rapidly developing cities. [ABSTRACT FROM AUTHOR]

The Chinese government is a promoting sustainable urbanization process to develop future cities, and various indicator systems have been introduced for guiding the sustainable urbanization practice. This paper examines the effectiveness of these existing indicator systems introduced in China. Nine sample indicator systems were collected and compared. Principal criteria selected to analyze the effectiveness of the nine samples was based on a literature review. This study reveals that the existing indicator systems have limitations for guiding sustainable urbanization practice and the differences between the various indicator systems present barriers for their effective application. It is found that no official department in China supervises the application of these indicators. The lack of a systematic method for establishing indicators is considered as a major reason for contributing to the limited effectiveness of these established indicators. There is a need to develop an effective method to supervise decision makers to establish and select effective indicators. [ABSTRACT FROM AUTHOR]

Urban and rural areas are two different yet coexisting systems. Developing countries such as China have been undergoing a rapid urbanization process. However, urbanization often causes competition for land resources between urban and rural areas, and such competition can result in conflicts between the two systems. To identify the risk factors of urban-rural conflict, this paper utilizes a research framework including content analysis, brainstorming and semi-structured interviews. The results indicated that the main risk factors between urban and rural areas comprise conflicts in property, resources and development. Suggestions are proposed to reduce the conflicts occurring between urban and rural areas during the urbanization process. [ABSTRACT FROM AUTHOR]