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Evaluating Spatiotemporal Distribution of Residential Sprawl and Influencing Factors Based on Multi-Dimensional Measurement and GeoDetector Modelling

Author

Listed:
  • Linlin Zhang

    (School of Tourism and Urban-Rural Planning, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Guanghui Qiao

    (School of Tourism and Urban-Rural Planning, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Huiling Huang

    (School of Architecture and Civil Engineer, Heilongjiang University of Science and Technology, Harbin 150022, China)

  • Yang Chen

    (Law School, Ningbo University, Ningbo 315211, China)

  • Jiaojiao Luo

    (School of Economics, Zhejiang University of Finance & Economics, Hangzhou 310018, China)

Abstract

Residential sprawl constitutes a main part of urban sprawl which poses a threat to the inhabitant environment and public health. The purpose of this article is to measure the residential sprawl at a micro-scale using a case study of Hangzhou city. An integrated sprawl index on each 1 km × 1 km residential land cell was calculated based on multi-dimensional indices of morphology, population density, land-use composition, and accessibility, followed by a dynamic assessment of residential sprawl. Furthermore, the method of GeoDetector modeling was applied to investigate the potential effects of location, urbanization, land market, and planning policy on the spatial variation of residential sprawl. The results revealed a positive correlation between CO 2 emissions and residential sprawl in Hangzhou. There has been a remarkable increase of sprawl index on residential land cells across the inner suburb and outer suburb, and more than three-fifths of the residential growth during 2000–2010 were evaluated as dynamic sprawl. The rapid development of the land market and urbanization were noted to impact the spatiotemporal distribution of residential sprawl, as q -statistic values of population growth and land price ranked highest. Most notably, the increasing q -statistic values of urban planning and its significant interactions with other factors highlighted the effects of incremental planning policies. The study derived the policy implication that it is necessary to transform the traditional theory and methods of incremental planning.

Suggested Citation

  • Linlin Zhang & Guanghui Qiao & Huiling Huang & Yang Chen & Jiaojiao Luo, 2021. "Evaluating Spatiotemporal Distribution of Residential Sprawl and Influencing Factors Based on Multi-Dimensional Measurement and GeoDetector Modelling," IJERPH, MDPI, vol. 18(16), pages 1-18, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8619-:d:614777
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    References listed on IDEAS

    as
    1. Jie Shen & Fulong Wu, 2013. "Moving to the Suburbs: Demand-Side Driving Forces of Suburban Growth in China," Environment and Planning A, , vol. 45(8), pages 1823-1844, August.
    2. Kamyar Fuladlu & Müge Riza & Mustafa Ilkan, 2021. "Monitoring Urban Sprawl Using Time-Series Data: Famagusta Region of Northern Cyprus," SAGE Open, , vol. 11(2), pages 21582440211, April.
    3. Liu, Yong & Fan, Peilei & Yue, Wenze & Song, Yan, 2018. "Impacts of land finance on urban sprawl in China: The case of Chongqing," Land Use Policy, Elsevier, vol. 72(C), pages 420-432.
    4. Chen Zeng & Sanwei He & Jiaxing Cui, 2014. "A Multi-Level and Multi-Dimensional Measuring on Urban Sprawl: A Case Study in Wuhan Metropolitan Area, Central China," Sustainability, MDPI, vol. 6(6), pages 1-28, June.
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    1. Lina Tang & Alimujiang Kasimu & Haitao Ma & Mamattursun Eziz, 2023. "Monitoring Multi-Scale Ecological Change and Its Potential Drivers in the Economic Zone of the Tianshan Mountains’ Northern Slopes, Xinjiang, China," IJERPH, MDPI, vol. 20(4), pages 1-20, February.

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