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

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.

Zhou Huang

Associate professor

Associate professor of GIScience

Yi Bao

Postdoctoral Researcher
2023 - present
PhD Student
2018 - 2023

My research interests include Geographical Information Systems, Remote Sensing, Urban Data Mining, Deep Learning

Han Wang

Grad Student
2020 - 2023

Interested in all things cool.

Ganmin Yin

PhD Student
2020 - present

My research interests include Human Mobility, Transportation, Urban Data Mining, Social Sensing and GeoAI.

Houji Qi

PhD Student
2019 - present

My research interests include High-perforance Geocomputation, Geograpghic Data Mining, Deep Learning, GeoAI.

Yu Liu

Professor
1997 - present

Professor of GIScience