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

TL;DR

This study employs graph neural networks and street view imagery to analyze how spatial structure influences urban psychological restoration, revealing significant impacts and distinct spatial patterns associated with restoration quality.

Contribution

It introduces a novel GNN-based method utilizing street view images and urban features to quantify the effect of spatial structure on restoration quality at an urban scale.

Findings

Spatial-dependent GNNs outperform traditional models in predicting restoration quality.

Sequential street view images significantly influence restoration assessments.

Distinct spatial structures can correspond to similar restoration quality levels.

Abstract

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.