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Fast Identification of Urban Sprawl Based on K-Means Clustering with Population Density and Local Spatial Entropy

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  • Lingbo Liu

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Zhenghong Peng

    (Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Hao Wu

    (Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Hongzan Jiao

    (Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Yang Yu

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Jie Zhao

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China)

Abstract

As urban sprawl is proven to jeopardize the sustainability system of cities, the identification of urban sprawl is essential for urban studies. Compared with previous related studies which tend to utilize more and more complicated variables to recognize urban sprawl while still retaining an element of uncertainty, this paper instead proposes a simplified model to identify urban sprawl patterns. This is a working theory which is based on a diagram interpretation of the classic urban spatial structure patterns of the Chicago School. The method used in our study is K-means clustering with gridded population density and local spatial entropy. The results and comparison with open population data and mobile phone data verify the assumption and furthermore indicate that the accuracy of source population data will limit the precision of output identification. This article concludes that urban sprawl is mainly dominated by population and surrounding unevenness. Moreover, the Floating Catchment Area (FCA) local spatial entropy method presented in this research brings about an integration of Shannon entropy, Tobler’s first law of geography and the Moore neighborhood, improving the spatial homogeneity and locality of Batty’s Spatial Entropy model which can only be used in a general scope.

Suggested Citation

  • Lingbo Liu & Zhenghong Peng & Hao Wu & Hongzan Jiao & Yang Yu & Jie Zhao, 2018. "Fast Identification of Urban Sprawl Based on K-Means Clustering with Population Density and Local Spatial Entropy," Sustainability, MDPI, vol. 10(8), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2683-:d:161014
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    References listed on IDEAS

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    6. Yiming Wang & Pengcheng Xiang, 2018. "Urban Sprawl Sustainability of Mountainous Cities in the Context of Climate Change Adaptability Using a Coupled Coordination Model: A Case Study of Chongqing, China," Sustainability, MDPI, vol. 11(1), pages 1-20, December.
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    8. Yanyan Chen & Hanqiang Qian & Yang Wang, 2020. "Analysis of Beijing’s Working Population Based on Geographically Weighted Regression Model," Sustainability, MDPI, vol. 12(12), pages 1-16, June.

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