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<p>To address the evaluation bias in conventional urban spatial design and governance that prioritizes functional provision over lived experience, this study proposes an implementable multi-source data–driven framework for multi-dimensional urban spatial perception, supporting urban diagnosis and grid-based governance. The framework integrates three categories of information within a unified spatial unit: (1) extracting interpretable environmental attributes from street-view images and predicting six dimensions of affective perception, including beauty, liveliness, and perceived safety; (2) characterizing supply–agglomeration patterns and supply intensity using spatiotemporal trajectories and mobile phone signaling data; and (3) identifying functional rhythms—daily, periodic, and occasional—by leveraging POI semantics and temporal periodicity. Building on these components, we construct a three-dimensional matrix of “affective perception–agglomeration intensity–regional function,” classify Wuhan’s urban space into 18 types of multi-dimensional perception units, and identify key areas requiring targeted, category-specific governance. Furthermore, the typology is linked to governance-oriented references for different spatial types, including feasible street- and community-level actions, corresponding leverage variables, and potential indicators for future monitoring or before–after assessment. Rather than constituting a complete closed-loop health-check system, the framework provides a diagnostic basis for urban problem identification, intervention prioritization, and refined governance.</p>

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A multi-dimensional urban spatial perception framework for urban diagnosis in Wuhan driven by multi-source data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月2日

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  • Xinrui Liu,
  • Ruiying Zhang,
  • Xiang Liu,
  • Dufeng Xiong,
  • Zhihao Liang

摘要

To address the evaluation bias in conventional urban spatial design and governance that prioritizes functional provision over lived experience, this study proposes an implementable multi-source data–driven framework for multi-dimensional urban spatial perception, supporting urban diagnosis and grid-based governance. The framework integrates three categories of information within a unified spatial unit: (1) extracting interpretable environmental attributes from street-view images and predicting six dimensions of affective perception, including beauty, liveliness, and perceived safety; (2) characterizing supply–agglomeration patterns and supply intensity using spatiotemporal trajectories and mobile phone signaling data; and (3) identifying functional rhythms—daily, periodic, and occasional—by leveraging POI semantics and temporal periodicity. Building on these components, we construct a three-dimensional matrix of “affective perception–agglomeration intensity–regional function,” classify Wuhan’s urban space into 18 types of multi-dimensional perception units, and identify key areas requiring targeted, category-specific governance. Furthermore, the typology is linked to governance-oriented references for different spatial types, including feasible street- and community-level actions, corresponding leverage variables, and potential indicators for future monitoring or before–after assessment. Rather than constituting a complete closed-loop health-check system, the framework provides a diagnostic basis for urban problem identification, intervention prioritization, and refined governance.