AffectCity: An Empirical Investigation of Complexity, Transparency, and Materiality in Shaping Affective Perception of Building Facades

A research team from Cambridge has published the AffectCity study, introducing the Cambridge Facade Affect Dataset (CFAD) containing 86 orthogonally rectified building facade images with continuous arousal and valence ratings from 85 participants. The study establishes a validated pipeline linking machine-vision-derived surface metrics to human affective responses, focusing on three quantifiable attributes: complexity, transparency (window-to-wall ratio), and materiality (proportion of natural versus artificial surface composition).

Their findings reveal that perceived complexity dominates affective prediction, showing significant positive associations for both arousal (β=0.507, p<0.001) and valence (β=0.376, p<0.001) with curvilinear amplification at higher complexity levels. Transparency exhibits an inverted-U relationship with valence, while increasing surface artificiality suppresses arousal and reduces pleasantness, consistent with biophilic response theory. Critically, machine-derived metrics show limited direct predictive power, with mediation analyses revealing human perceptual evaluation functions as a necessary intermediate layer.

The research demonstrates moderate stability of complexity and materiality ratings across image-based and in-situ conditions, while affective responses, particularly valence, exhibit significant context-dependence (ICC=0.332). These findings advance facade research from descriptive morphological analysis toward predictive, perception-grounded modeling, providing an empirically validated basis for affect-informed urban design that considers how buildings shape emotional states.