Big Geodata Reveals Spatial Patterns of Built Environment Stocks Across and Within Cities in China
Zhou Huang,
Yi Bao,
Ruichang Mao,
Han Wang,
Ganmin Yin,
Lin Wan,
Houji Qi,
Qiaoxuan Li,
Hongzhao Tang,
Qiance Liu,
Linna Li,
Bailang Yu,
Qinghua Guo,
Yu Liu,
Huadong Guo,
Gang Liu
Affiliations
Zhou Huang
Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China; International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Corresponding authors.
Yi Bao
Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China; International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Ruichang Mao
SDU Life Cycle Engineering, Department of Green Technology, University of Southern Denmark, Odense 5230, Denmark; Department of Construction Management, School of Civil Engineering, Tsinghua University, Beijing 100084, China
Han Wang
Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Ganmin Yin
Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Lin Wan
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Houji Qi
Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Qiaoxuan Li
School of Geographical Sciences, East China Normal University, Shanghai 200062, China
Hongzhao Tang
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China
Qiance Liu
SDU Life Cycle Engineering, Department of Green Technology, University of Southern Denmark, Odense 5230, Denmark; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Linna Li
Department of Geography, California State University, Long Beach, CA 90840, USA
Bailang Yu
School of Geographical Sciences, East China Normal University, Shanghai 200062, China
Qinghua Guo
Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Yu Liu
Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Huadong Guo
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; Corresponding authors.
Gang Liu
College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; Corresponding authors.
The patterns of material accumulation in buildings and infrastructure accompanied by rapid urbanization offer an important, yet hitherto largely missing stock perspective for facilitating urban system engineering and informing urban resources, waste, and climate strategies. However, our existing knowledge on the patterns of built environment stocks across and particularly within cities is limited, largely owing to the lack of sufficient high spatial resolution data. This study leveraged multi-source big geodata, machine learning, and bottom-up stock accounting to characterize the built environment stocks of 50 cities in China at 500 m fine-grained levels. The per capita built environment stock of many cities (261 tonnes per capita on average) is close to that in western cities, despite considerable disparities across cities owing to their varying socioeconomic, geomorphology, and urban form characteristics. This is mainly owing to the construction boom and the building and infrastructure-driven economy of China in the past decades. China’s urban expansion tends to be more “vertical” (with high-rise buildings) than “horizontal” (with expanded road networks). It trades skylines for space, and reflects a concentration–dispersion–concentration pathway for spatialized built environment stocks development within cities in China. These results shed light on future urbanization in developing cities, inform spatial planning, and support circular and low-carbon transitions in cities.