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Exploring Nonlinear Threshold Effects and Interactions Between Built Environment and Urban Vitality at the Block Level Using Machine Learning

Author

Listed:
  • Cong Li

    (College of Landscape Architecture and Arts, Northwest A&F University, Yangling 712100, China)

  • Yajuan Zhou

    (College of Landscape Architecture and Arts, Northwest A&F University, Yangling 712100, China)

  • Manfei Wu

    (College of Landscape Architecture and Arts, Northwest A&F University, Yangling 712100, China)

  • Jiayue Xu

    (College of Landscape Architecture and Arts, Northwest A&F University, Yangling 712100, China)

  • Xin Fu

    (College of Landscape Architecture and Arts, Northwest A&F University, Yangling 712100, China)

Abstract

Urban vitality is a critical indicator of both urban sustainability and quality of life. However, comprehensive studies examining the threshold effects and interaction mechanisms of built environment factors on urban vitality at the block level remain limited. This study proposed to develop a comprehensive framework for urban vitality by incorporating multi-source data, and the central urban area of Xi’an, China, was selected as the study area. Four machine learning models, LightGBM, XGBoost, GBDT, and random forest, were employed to identify the most fitted model for analyzing threshold effects and interactions among built environment factors on shaping urban vitality. The results showed the following: (1) Xi’an’s urban vitality exhibited a distinct gradient, with the highest vitality concentrated in the Yanta District; (2) life service facility density was the most significant determinant of vitality (19.91%), followed by air quality (9.01%) and functional diversity (6.49%); and (3) significant interactions among built environment factors were observed. In particular, streets characterized by both high POI diversity (greater than 0.8) and low PM 2.5 concentrations (below 48.5 μg/m 3 ) exhibited notably enhanced vitality scores. The findings of this study provide key insights into strategies for boosting urban vitality, offering actionable insights for improving land use allocations and enhancing quality of life.

Suggested Citation

  • Cong Li & Yajuan Zhou & Manfei Wu & Jiayue Xu & Xin Fu, 2025. "Exploring Nonlinear Threshold Effects and Interactions Between Built Environment and Urban Vitality at the Block Level Using Machine Learning," Land, MDPI, vol. 14(6), pages 1-25, June.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:6:p:1232-:d:1674017
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    References listed on IDEAS

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    1. Jiaqi Zhang & Zhen He & Weijing Wang & Ziwen Sun, 2025. "Spatial Drivers of Urban Industrial Agglomeration Using Street View Imagery and Remote Sensing: A Case Study of Shanghai," Land, MDPI, vol. 14(8), pages 1-26, August.

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