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Unveiling the Spatial Heterogeneity of Urban Vitality Using Machine Learning Methods: A Case Study of Tianjin, China

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  • Fengshuo Sun

    (Jangho Architecture College, Northeastern University, Shenyang 110169, China
    Liaoning Key Laboratory of Urban and Architectural Digital Technology, Shenyang 110169, China)

  • Enxu Wang

    (Jangho Architecture College, Northeastern University, Shenyang 110169, China
    Liaoning Key Laboratory of Urban and Architectural Digital Technology, Shenyang 110169, China)

Abstract

The impact of the built environment (BE) on urban vitality (UV) has become a key issue in the field of urban planning. However, few studies have explored the impact of the BE on UV from the perspective of urban function zones (UFZs). Taking the central urban area of Tianjin as an example, this paper explores the nonlinear influences and threshold effects of the BE on UV using machine learning methods. It also reveals the spatiotemporal variations in UV across different UFZs during the daytime and nighttime on weekdays and weekends. The results show the following: (1) Education and culture zones showed the highest UV during weekday daytime, while commercial zones dominated at other times. Industrial zones remained the least active throughout. Residential zones demonstrated higher nighttime UV than daytime UV on weekdays, with the opposite pattern observed on weekends. Public service zones maintained a comparable level of UV between the daytime and nighttime on weekdays. Other function zones generally displayed higher daytime UV. During the daytime on weekends, all function zones except industrial zones demonstrated higher UV compared to other time periods. (2) In commercial zones, the floor area ratio (FAR) exerted the strongest influence, displaying distinct threshold effects. Residential zones showed dual sensitivity to building height (BH) and the FAR. Public service zones were predominantly influenced by Road Density (RD) and Bus Station Density (BSD). RD exhibited higher marginal utility for enhancing UV during the nighttime. Education and culture zones were significantly influenced by the FAR, RD, and POI Density (POID).

Suggested Citation

  • Fengshuo Sun & Enxu Wang, 2025. "Unveiling the Spatial Heterogeneity of Urban Vitality Using Machine Learning Methods: A Case Study of Tianjin, China," Land, MDPI, vol. 14(7), pages 1-25, June.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:7:p:1316-:d:1683974
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    References listed on IDEAS

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