地理大数据揭示中国城市间及城市内部建成环境存量的空间模式

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

工程(英文) ›› 2024, Vol. 34 ›› Issue (3) : 143-153.

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工程(英文) ›› 2024, Vol. 34 ›› Issue (3) : 143-153. DOI: 10.1016/j.eng.2023.05.015
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地理大数据揭示中国城市间及城市内部建成环境存量的空间模式

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Big Geodata Reveals Spatial Patterns of Built Environment Stocks Across and Within Cities in China

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Abstract

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.

Keywords

Urban system engineering / Built environment stock / Spatial pattern / Urban sustainability / Big geodata

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Zhou Huang, Yi Bao, Ruichang Mao. . Engineering. 2024, 34(3): 143-153 https://doi.org/10.1016/j.eng.2023.05.015

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