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The Attention Restoration Theory (ART) presents a theoretical framework withfour essential indicators (being away, extent, fascinating, and compatibility)for comprehending urban and natural restoration quality. However, previousstudies relied on non-sequential data and non-spatial dependent methods, whichoverlooks the impact of spatial structure defined here as the positionalrelationships between scene entities on restoration quality. The past methodsalso make it challenging to measure restoration quality on an urban scale. Inthis work, a spatial-dependent graph neural networks (GNNs) approach isproposed to reveal the relation between spatial structure and restorationquality on an urban scale. Specifically, we constructed two different types ofgraphs at the street and city levels. The street-level graphs, using sequentialstreet view images (SVIs) of road segments to capture position relationshipsbetween entities, were used to represent spatial structure. The city-levelgraph, modeling the topological relationships of roads as non-Euclidean datastructures and embedding urban features (including Perception-features,Spatial-features, and Socioeconomic-features), was used to measure restorationquality. The results demonstrate that: 1) spatial-dependent GNNs modeloutperforms traditional methods (Acc = 0.735, F1 = 0.732); 2) spatial structureportrayed through sequential SVIs data significantly influences restorationquality; 3) spaces with the same restoration quality exhibited distinct spatialstructures patterns. This study clarifies the association between spatialstructure and restoration quality, providing a new perspective to improve urbanwell-being in the future.

How does spatial structure affect psychological restoration? A method based on Graph Neural Networks and Street View Imagery