Aesthetic “Dissensus” in Public Architecture: Computational Analysis of Contextual Strangeness and Place Appropriation

Architecture has the potential to stimulate thought and engagement by challenging conventional forms and functions, which is rooted in Rancière’s concept of “aesthetic dissensus.” This paper investigates how architectural forms that deviate from expected norms within a specific urban or cultural context affect social integration in public spaces. Although significant attention has been directed toward functional performance in public architecture, there is limited understanding of how formal deviations influence appropriation. Using a two-phase approach, we first examine architectural aesthetics using computer vision algorithms after conducting a literature review on Rancièrean dissensus in design. We then merge spatial analysis with crowdsourced social data and behavioral heat maps from computer vision to monitor user interactions and establish an “appropriation index” for the chosen architectural space. These computational methods objectively measure physical engagement, while crowdsourced social data assesses public sentiment and participation. The findings propose a technique for correlating formal deviations with inclusivity and new strategies for architects to create socially engaging environments. This paper demonstrates how aesthetic dissensus can foster place appropriation in public architecture by combining computational analysis with social data.