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

Yuchen Xie, Jiaxin Zhang, Yunqin Li, Zehong Zhu, Junye Deng and Zhixiu Li ()
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Yuchen Xie: College of Architecture and Design, Nanchang University, Nanchang 330000, China
Jiaxin Zhang: College of Architecture and Design, Nanchang University, Nanchang 330000, China
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

Land, 2024, vol. 13, issue 12, 1-24

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.

Keywords: urban street vitality; multi-source big data; nonlinear associations; extreme gradient boosting; 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: 2024
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