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Spatiotemporal evolution of urbanization and its implications to urban planning of the megacity, Shanghai, China

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Abstract

Context

Urbanization has profoundly changed urban landscape patterns and morphologies. Understanding the spatiotemporal evolution of these changes and their driving forces is vital to decision making for urban planning and sustainable urban development.

Objectives

This study aims to quantify the spatiotemporal pattern of urban growth in Shanghai, China for testing urban growth hypotheses, to identify its driving factors, and to provide insights for sustainable urban planning.

Methods

We fitted a nonlinear curve to the urbanization pattern, employed landscape expansion indices to quantify the spatiotemporal evolution of urbanization, utilized partial least square regression to differentiate contribution of main socioeconomic driving factors.

Results

Urbanized land in Shanghai exhibited a logistic growth pattern from 1985 to 2015. The annual growth rate of urban area showed a wave-like pattern and peaked in 2000–2005. Urban growth modes of leapfrog, edge expansion, and infilling were identified, and these patterns alternates in dominance over time. The urbanization process in Shanghai followed the spiraling diffusion-coalescence hypothesis. The net increase of year-end residential population, urban infrastructure investment, and the total investment in fixed assets were the dominant driving factors to urban growth.

Conclusion

A logistic curve well quantified the temporal pattern of urbanization in Shanghai. Urbanization has slowed down, approaching the plateau of the curve, implying that urban growth driven by population increase and investment should switch to sustainable urban renewal and ecological constructions. Investment to urban green and blue infrastructures could help achieve “negative growth” targeted by the Shanghai Master Plan (2017–2035) for the overall developed land.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We are grateful to Dr. Xiaoping Liu in the School of Geography and Planning, Sun Yat-Sen University for providing the new updated GAUD dataset for reference. We also thank the two anonymous reviewers for their comments and constructive suggestions.

Funding

This work was partly supported by the National Key R&D Program of China (Grant No. 2017YFC0505701 to J. Li), the Natural Science Foundation of China (Grant No. 31870453 to J. Li, Grant No. 32001162 to C. Wu, Grant No. 31971485 to C. Wang, and Grant No. 31528004 to C. Song), Guangdong Foundation for Program of Science and Technology Research (Grant No. 2020B1212060048, and Grant No. 2019B121201004 to C. Li), China Postdoctoral Science Foundation 2021M702131 to C. Wu, and the Joint-PhD project of Shanghai Jiao Tong University and The University of Melbourne to J. Li and A. Hahs.

Author information

Author notes

  1. Caiyan Wu and Cheng Li are co-first authors who are equally contribute to the paper.

Authors and Affiliations

  1. Department of Landscape Architecture, School of Design, Shanghai Jiao Tong University, Shanghai, 200240, China

    Caiyan Wu, Huirong Xiao, Jiong Wu, Minghao Zhuang, Xing Bi & Junxiang Li

  2. National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and Management, Institute of Eco-Environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou, 510650, China

    Cheng Li

  3. Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China

    Linke Ouyang

  4. Shanghai Municipal Center for Disease Control and Prevention, Shanghai, 200336, China

    Chunfang Wang

  5. Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA

    Conghe Song

  6. Department of Ecosystem Science and Management, Pennsylvania State University, University Park, PA, 16802, USA

    Tong Qiu

  7. Department of Geography, Humboldt-Universität zu Berlin, 10117, Berlin, Germany

    Dagmar Haase

  8. Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research-UFZ, 04318, Leipzig, Germany

    Dagmar Haase

  9. School of Ecosystem and Forest Sciences, Faculty of Science, The University of Melbourne, Burnley Campus, 500 Yarra Blvd, Richmond, VIC, 3121, Australia

    Amy Hahs

  10. SPECTRA Centre of Excellence EU, Slovak University of Technology in Bratislava (STU), Vazovova 5, 81243, Bratislava, Slovak Republic

    Maroš Finka

Authors
  1. Caiyan Wu
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  2. Cheng Li
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  3. Linke Ouyang
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  4. Huirong Xiao
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  5. Jiong Wu
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  6. Minghao Zhuang
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  7. Xing Bi
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  8. Junxiang Li
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  9. Chunfang Wang
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  10. Conghe Song
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  11. Tong Qiu
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  12. Dagmar Haase
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  13. Amy Hahs
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  14. Maroš Finka
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Contributions

Conceptualization: JL, Data preparation: CWU, LO, HX, JW, MZ and XB, Methodology: CWU and CL, Writing: CWU, Editing: JL, CS, TQ, CWA, DH, AH and MF.

Corresponding authors

Correspondence to Junxiang Li or Chunfang Wang.

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Wu, C., Li, C., Ouyang, L. et al. Spatiotemporal evolution of urbanization and its implications to urban planning of the megacity, Shanghai, China. Landsc Ecol 38, 1105–1124 (2023). https://doi.org/10.1007/s10980-022-01578-7

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