Management of urban land expansion in China through intensity assessment: A big data perspective

Rapid urbanization and widespread urban sprawl have induced a new era of urban resource management that focuses on efficiency, particularly in megacities in China. Big data is a platform for multi-source data fusion that helps to create spatially explicit decisions in regulating urban land expansion. In this study, we use big data to assess the intensity of urban land use in the metropolitan areas of China. OpenStreetMap and point-of-interest data are used to infer the urban function of each established parcel. Geographical weighted regression (GWR) is used to generate input output matchups and to formulate integrated urban land use intensity values. To incorporate spatial relations among cities into a final assessment, spatial networks derived from check-in data of the social media platform, Weibo, are used to rank through the technique for order preference by similarity to the ideal solution (TOPSIS). Results show that Guangzhou has the most efficient urban land use system, followed by Shanghai and Shenzhen, and that Suzhou has the lowest urban land intensity. It is also revealed that the megalopolises in the Pearl River Delta and the Yangtze River Delta are superior in urban land use in general, whereas urban land use in the northern and western areas of China are less efficient. The megacities have strengths and weaknesses with respect to urban land use efficiency, and they advance at different stages when characteristic input output relationships are identified. This advancement is largely attributed to their unique political, economic, and cultural roles in China. Further improvements in each land use function will be proposed in the future and the profound networked big data from each city will be utilized to improve urban resource management. (C) 2016 Elsevier Ltd. All rights reserved.