  Depression and anxiety disorders are prevalent mental health challenges
affecting a substantial segment of the global population. In this study, we
explored the environmental correlates of these disorders by analyzing
street-view images (SVI) of neighborhoods in the Netherlands. Our dataset
comprises 9,879 Dutch SVIs sourced from Google Street View, paired with
statistical depression and anxiety risk metrics from the Dutch Health Monitor.
To tackle this challenge, we refined two existing neural network architectures,
DeiT Base and ResNet50. Our goal was to predict neighborhood risk levels,
categorized into four tiers from low to high risk, using the raw images. The
results showed that DeiT Base and ResNet50 achieved accuracies of 43.43% and
43.63%, respectively. Notably, a significant portion of the errors were between
adjacent risk categories, resulting in adjusted accuracies of 83.55% and
80.38%. We also implemented the SHapley Additive exPlanations (SHAP) method on
both models and employed gradient rollout on DeiT. Interestingly, while SHAP
underscored specific landscape attributes, the correlation between these
features and distinct depression risk categories remained unclear. The gradient
rollout findings were similarly non-definitive. However, through manual
analysis, we identified certain landscape types that were consistently linked
with specific risk categories. These findings suggest the potential of these
techniques in monitoring the correlation between various landscapes and
environmental risk factors for mental health issues. As a future direction, we
recommend employing these methods to observe how risk scores from the Dutch
Health Monitor shift across neighborhoods over time.
