Integrating Multi-Source Urban Data with Interpretable Machine Learning for Uncovering the Multidimensional Drivers of Urban Vitality Article Swipe
Yuchen Xie
,
Jiaxin Zhang
,
Yunqin Li
,
Zehong Zhu
,
Jiaqi Deng
,
Zhixiu Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/land13122028
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/land13122028
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/land13122028
- OA Status
- gold
- Cited By
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- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4404756828Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/land13122028Digital Object Identifier
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Integrating Multi-Source Urban Data with Interpretable Machine Learning for Uncovering the Multidimensional Drivers of Urban VitalityWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-27Full publication date if available
- Authors
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Yuchen Xie, Jiaxin Zhang, Yunqin Li, Zehong Zhu, Jiaqi Deng, Zhixiu LiList of authors in order
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https://doi.org/10.3390/land13122028Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/land13122028Direct OA link when available
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Vitality, Computer science, Data source, Geography, Data science, Machine learning, Data mining, Philosophy, TheologyTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 8Per-year citation counts (last 5 years)
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53Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W6697769826, https://openalex.org/W1978627039, https://openalex.org/W2044835134, https://openalex.org/W4206365600, https://openalex.org/W2757375999, https://openalex.org/W2956114003, https://openalex.org/W4401833876, https://openalex.org/W2987354670, https://openalex.org/W4385628448, https://openalex.org/W4281741249, https://openalex.org/W4402629038, https://openalex.org/W4368348169, https://openalex.org/W2728446802, https://openalex.org/W6857179748, https://openalex.org/W4200618808, https://openalex.org/W2016802582, https://openalex.org/W4382138521, https://openalex.org/W2769920124, https://openalex.org/W4388602038, https://openalex.org/W3168525722, https://openalex.org/W3116358083, https://openalex.org/W4400454830, https://openalex.org/W6758943909, https://openalex.org/W2979370769, https://openalex.org/W3119550985, https://openalex.org/W3130400444, https://openalex.org/W1994079345, https://openalex.org/W2886905452, https://openalex.org/W4319071959, https://openalex.org/W6797242764, https://openalex.org/W2047120335, https://openalex.org/W4387264758, https://openalex.org/W2653311480, https://openalex.org/W2517491419, https://openalex.org/W2088794999, https://openalex.org/W4323668024, https://openalex.org/W2295598076, https://openalex.org/W4205252595, https://openalex.org/W2590038026, https://openalex.org/W2999615587, https://openalex.org/W2618851150, https://openalex.org/W4402946254, https://openalex.org/W4400673269, https://openalex.org/W4292640240, https://openalex.org/W4256222392, https://openalex.org/W4389412320, https://openalex.org/W4390381325, https://openalex.org/W3007696395, https://openalex.org/W3153948070, https://openalex.org/W4376155524, https://openalex.org/W3170834442, https://openalex.org/W4386424911, https://openalex.org/W2914613071 |
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