The Attention Restoration Theory (ART) proposed four essential indicators (being away, extent, fascinating, and compatibility) for understanding urban and natural restoration quality. However, previous studies have overlooked the impact of spatial structure (the visual relationships between scene entities) and neighboring environments on restoration quality as they mostly relied on isolated questionnaires or images. This study introduces a spatial-dependent graph neural networks (GNNs) approach to address this gap and explore the relationship between spatial structure and restoration quality at a city scale. Two types of graphs were constructed: street-level graphs using sequential street view images (SVIs) to capture visual relationships between entities and represent spatial structure, and city-level graphs modeling the topological relationships of roads to capture the spatial features of neighboring entities, integrating perceptual, spatial, and socioeconomic features to measure restoration quality. The results demonstrated that spatial-dependent GNNs outperform traditional models, achieving an accuracy (Acc) of 0.742 and an F1 score of 0.740, indicating their exceptional ability to capture features of adjacent spaces. Ablation experiments further revealed the substantial positive impact of spatial structure features on the predictive performance for restoration quality. Moreover, the study highlighted the greater significance of naturally relevant entities (e.g., trees) compared to artificial entities (e.g., buildings) in relation to high restoration quality. This study clarifies the association between spatial structure and restoration quality, providing a new perspective to improve urban well-being in the future.

中文翻译：

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空间结构如何影响心理修复？基于图神经网络和街景图像的方法


注意力恢复理论（ART）提出了理解城市和自然恢复质量的四个基本指标（距离、范围、吸引力和兼容性）。然而，之前的研究大多依赖于孤立的问卷或图像，忽视了空间结构（场景实体之间的视觉关系）和邻近环境对恢复质量的影响。本研究引入了一种空间相关的图神经网络（GNN）方法来解决这一差距，并探索城市尺度的空间结构和恢复质量之间的关系。构建了两种类型的图：使用顺序街景图像（SVI）来捕获实体之间的视觉关系并表示空间结构的街道级图，以及对道路拓扑关系进行建模以捕获相邻实体的空间特征的城市级图，整合感知、空间和社会经济特征来衡量恢复质量。结果表明，空间相关的 GNN 优于传统模型，准确率 (Acc) 为 0.742，F1 分数为 0.740，表明其捕获相邻空间特征的卓越能力。消融实验进一步揭示了空间结构特征对修复质量预测性能的显着积极影响。此外，该研究强调了与人工实体（例如建筑物）相比，自然相关实体（例如树木）在高恢复质量方面具有更大的重要性。这项研究阐明了空间结构与恢复质量之间的关联，为未来改善城市福祉提供了新的视角。