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Integrating Multi-Source Urban Data with Interpretable Machine Learning for Uncovering the Multidimensional Drivers of Urban Vitality

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  • Yuchen Xie

    (College of Architecture and Design, Nanchang University, Nanchang 330000, China
    The Department of Geography, The University of Hong Kong, Hong Kong SAR, China)

  • Jiaxin Zhang

    (College of Architecture and Design, Nanchang University, Nanchang 330000, China
    Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, Osaka 565-0871, Japan)

  • Yunqin Li

    (College of Architecture and Design, Nanchang University, Nanchang 330000, China)

  • Zehong Zhu

    (College of Architecture and Design, Nanchang University, Nanchang 330000, China)

  • Junye Deng

    (College of Architecture and Design, Nanchang University, Nanchang 330000, China)

  • Zhixiu Li

    (College of Architecture and Design, Nanchang University, Nanchang 330000, China)

Abstract

The complexity of urban street vitality is reflected in the interaction of multiple factors. A deep understanding of the multi-dimensional driving mechanisms behind it is crucial to enhancing urban street vitality. However, existing studies lack comprehensive interpretative analyses of urban multi-source data, making it difficult to uncover these drivers’ nonlinear relationships and interaction effects fully. This study introduces an interpretable machine learning framework, using Nanchang, China as a case study. It utilizes urban multi-source data to explore how these variables influence different dimensions of street vitality. This study’s innovation lies in employing an integrated measurement approach which reveals the complex nonlinearities and interaction effects between data, providing a more comprehensive explanation. The results not only demonstrate the strong explanatory power of the measurement approach but also reveal that (1) built environment indicators play a key role in influencing street vitality, showing significant spatial positive correlations; (2) different dimensions of street vitality exhibit nonlinear characteristics, with transit station density being the most influential one; and (3) cluster analysis revealed distinct built environment and socioeconomic characteristics across various street vitality types. This study provides urban planners with a data-driven quantitative tool to help formulate more effective strategies for enhancing street vitality.

Suggested Citation

  • Yuchen Xie & Jiaxin Zhang & Yunqin Li & Zehong Zhu & Junye Deng & Zhixiu Li, 2024. "Integrating Multi-Source Urban Data with Interpretable Machine Learning for Uncovering the Multidimensional Drivers of Urban Vitality," Land, MDPI, vol. 13(12), pages 1-24, November.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:12:p:2028-:d:1531106
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    References listed on IDEAS

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    1. Aibo Jin & Yunyu Ge & Shiyang Zhang, 2024. "Spatial Characteristics of Multidimensional Urban Vitality and Its Impact Mechanisms by the Built Environment," Land, MDPI, vol. 13(7), pages 1-22, July.
    2. Yun Han & Chunpeng Qin & Longzhu Xiao & Yu Ye, 2024. "The nonlinear relationships between built environment features and urban street vitality: A data-driven exploration," Environment and Planning B, , vol. 51(1), pages 195-215, January.
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    Cited by:

    1. Dong Li & Houzeng Han & Jian Wang & Xingxing Xiao, 2025. "Explaining Urban Vitality Through Interpretable Machine Learning: A Big Data Approach Using Street View Images and Environmental Factors," Sustainability, MDPI, vol. 17(11), pages 1-21, May.

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