  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.
