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<p>Urban functional zone (UFZ) classification is fundamental to urban planning and precision management of cities. Urban functions emerge from multi-scale spatial interactions, where both local dependencies and long-range structural associations coexist with varying importance. However, existing studies typically focus on local dependencies, while only a few consider long-range structural associations between UFZs, and even these fail to differentiate the two spatial interactions, thereby missing their heterogeneous influence patterns. To address this, we propose a Graph Adaptive Propagation Network (GAPN) utilizing a dual-branch architecture to integrate POIs and street-view imagery. GAPN adaptively aggregates multiscale features on a shared graph, learning weights to balance local and long-range dependencies, before selectively fusing modalities via a cross-modal gating mechanism. Additionally, a graph-diffusion attribution scheme ensures interpretability by quantifying multi-hop contributions. Evaluations in Qingdao’s Shinan District achieved an accuracy of 0.840 (0.790 Kappa), outperforming baselines, while retraining and validation in Beijing’s Chaoyang District (OA = 0.826) confirmed the framework’s applicability to other urban contexts.</p>

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An urban functional zone classification framework based on graph adaptive propagation network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月10日

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  • Xin Yang,
  • Xiaoxiao Zhao,
  • Jinfeng Zhou,
  • Rong Huang

摘要

Urban functional zone (UFZ) classification is fundamental to urban planning and precision management of cities. Urban functions emerge from multi-scale spatial interactions, where both local dependencies and long-range structural associations coexist with varying importance. However, existing studies typically focus on local dependencies, while only a few consider long-range structural associations between UFZs, and even these fail to differentiate the two spatial interactions, thereby missing their heterogeneous influence patterns. To address this, we propose a Graph Adaptive Propagation Network (GAPN) utilizing a dual-branch architecture to integrate POIs and street-view imagery. GAPN adaptively aggregates multiscale features on a shared graph, learning weights to balance local and long-range dependencies, before selectively fusing modalities via a cross-modal gating mechanism. Additionally, a graph-diffusion attribution scheme ensures interpretability by quantifying multi-hop contributions. Evaluations in Qingdao’s Shinan District achieved an accuracy of 0.840 (0.790 Kappa), outperforming baselines, while retraining and validation in Beijing’s Chaoyang District (OA = 0.826) confirmed the framework’s applicability to other urban contexts.