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Unraveling Street Configuration Impacts on Urban Vibrancy: A GeoXAI Approach

Author

Listed:
  • Longzhu Xiao

    (School of Architecture and Civil Engineering, Xiamen University, Xiamen 361000, China)

  • Minyi Wu

    (School of Architecture and Civil Engineering, Xiamen University, Xiamen 361000, China)

  • Qingqing Weng

    (CALB Technology (Shenzhen) Co., Ltd., Shenzhen 518000, China)

  • Yufei Li

    (School of Architecture and Civil Engineering, Xiamen University, Xiamen 361000, China)

Abstract

As a catalyst for sustainable urbanization, urban vibrancy drives human interactions, economic agglomeration, and resilient development through its spatial manifestation of diverse activities. While previous studies have emphasized the connection between built environment features—especially street network centrality—and urban vibrancy, the broader mechanisms through which the full spectrum of street configuration dimensions shape vibrancy patterns remain insufficiently examined. To address this gap, this study applies a GeoXAI approach that synergizes random forest modeling and GeoShapley interpretation to reveal the influence of street configuration on urban vibrancy. Leveraging multi-source geospatial data from Xiamen Island, China, we operationalize urban vibrancy through a composite index derived from three-dimensional proxies: life service review density, social media check-in intensity, and mobile device user concentration. Street configuration is quantified through a tripartite measurement system encompassing network centrality, detour ratio, and shape index. Our findings indicate that (1) street network centrality and shape index, as well as their interactions with location, emerge as the dominant influencing factors; (2) The relationships between street configuration and urban vibrancy are predominantly nonlinear, exhibiting clear threshold effects; (3) The impact of street configuration is spatially heterogeneous, as evidenced by geographically varying coefficients. The findings can enlighten urban planning and design by providing a basis for the development of nuanced criteria and context-sensitive interventions to foster vibrant urban environments.

Suggested Citation

  • Longzhu Xiao & Minyi Wu & Qingqing Weng & Yufei Li, 2025. "Unraveling Street Configuration Impacts on Urban Vibrancy: A GeoXAI Approach," Land, MDPI, vol. 14(7), pages 1-23, July.
  • Handle: RePEc:gam:jlands:v:14:y:2025:i:7:p:1422-:d:1696297
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    References listed on IDEAS

    as
    1. Longzhu Xiao & Siuming Lo & Jiangping Zhou & Jixiang Liu & Linchuan Yang, 2021. "Predicting vibrancy of metro station areas considering spatial relationships through graph convolutional neural networks: The case of Shenzhen, China," Environment and Planning B, , vol. 48(8), pages 2363-2384, October.
    2. Bo Huang & Yulun Zhou & Zhigang Li & Yimeng Song & Jixuan Cai & Wei Tu, 2020. "Evaluating and characterizing urban vibrancy using spatial big data: Shanghai as a case study," Environment and Planning B, , vol. 47(9), pages 1543-1559, November.
    3. 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.
    4. Wang, Xiaoxi & Zhang, Yaojun & Yu, Danlin & Qi, Jinghan & Li, Shujing, 2022. "Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China," Land Use Policy, Elsevier, vol. 119(C).
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