How does spatial structure affect psychological restoration? A method based on Graph Neural Networks and Street View Imagery
This work addresses the challenge of measuring psychological restoration on an urban scale for urban planners and psychologists, offering a new perspective to improve urban well-being, though it is incremental as it builds on existing Attention Restoration Theory with a novel method.
The study tackled the problem of understanding how spatial structure affects psychological restoration in urban environments by proposing a spatial-dependent graph neural networks approach, which outperformed traditional methods with an accuracy of 0.735 and F1 score of 0.732, revealing that spatial structure significantly influences restoration quality and that spaces with the same quality exhibit distinct patterns.
The Attention Restoration Theory (ART) presents a theoretical framework with four essential indicators (being away, extent, fascinating, and compatibility) for comprehending urban and natural restoration quality. However, previous studies relied on non-sequential data and non-spatial dependent methods, which overlooks the impact of spatial structure defined here as the positional relationships between scene entities on restoration quality. The past methods also make it challenging to measure restoration quality on an urban scale. In this work, a spatial-dependent graph neural networks (GNNs) approach is proposed to reveal the relation between spatial structure and restoration quality on an urban scale. Specifically, we constructed two different types of graphs at the street and city levels. The street-level graphs, using sequential street view images (SVIs) of road segments to capture position relationships between entities, were used to represent spatial structure. The city-level graph, modeling the topological relationships of roads as non-Euclidean data structures and embedding urban features (including Perception-features, Spatial-features, and Socioeconomic-features), was used to measure restoration quality. The results demonstrate that: 1) spatial-dependent GNNs model outperforms traditional methods (Acc = 0.735, F1 = 0.732); 2) spatial structure portrayed through sequential SVIs data significantly influences restoration quality; 3) spaces with the same restoration quality exhibited distinct spatial structures patterns. This study clarifies the association between spatial structure and restoration quality, providing a new perspective to improve urban well-being in the future.