Authors
Xinrui Liu, Ruiying Zhang, Xiang Liu, Dufeng Xiong, Zhihao Liang
Published in
Scientific reports. Jul 02, 2026. Epub Jul 02, 2026.
Abstract
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.
PMID:
42393276
Bibliographic data and abstract were imported from PubMed on 03 Jul 2026.
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