Revealing the Impact of the Built Environment on the Temporal Heterogeneity of Urban Vitality Using Ensemble Machine Learning

Xuyang Chen, Junyan Yang (), Jingjing Mai, Ao Cui and Xinyue Gu
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Xuyang Chen: School of Architecture, Southeast University, 2nd Sipailou Street, Nanjing 210096, China
Junyan Yang: School of Architecture, Southeast University, 2nd Sipailou Street, Nanjing 210096, China
Jingjing Mai: School of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
Ao Cui: School of Architecture, Southeast University, 2nd Sipailou Street, Nanjing 210096, China
Xinyue Gu: Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China

Land, 2025, vol. 14, issue 11, 1-24

Abstract: The multidimensional urban built environment (BE) in high-density cities has been shown to be closely related to the urban vitality (UV) of residents’ travelling. However, existing research lacks consideration of the differences in this relationship over a week, so this paper proposes an ensemble machine learning approach that simultaneously considers different time periods of the week. This study reveals the impacts of four dimensions of BE variables on UV at different time periods at the scale of the community life circle. The four well-performing base models are integrated to reveal the mechanism of differential effects of BE variables on UV under different time periods in the old city of Nanjing through Shapley addition explanation. The findings reveal that (1) the seven most important built environment variables existed in different time periods of the week: floor area ratio, service POI density, remote sensing ecological index, POI mixability, average building height, fractional vegetation cover, and maximum building area; (2) The nonlinear and threshold effects of the built environment factors differed across time periods of the week; (3) There is a dominant interaction between built environment variables at different time periods of the week. This study can provide guidance for the refined management of complex urban systems.

Keywords: urban vitality; built environment; ensemble machine learning; temporal heterogeneity; Shapley additive explanation (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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