Tracing the Sociospatial Affordances of Physical Environment: An AI-Based Unified Framework for Modeling Social Behavior in Campus Open Spaces
Year: 2026
Type: article
Study design: Observational Study
Abstract: In educational settings, it is crucial to comprehend and manage individuals’ social interaction behaviors through the physical environment. However, analyzing social interaction patterns manually is a time-consuming and energy-intensive process. This study aims to reveal the socio-behavioral implications of spatial features, based on the Affordance Theory, using artificial intelligence (AI). To this end, the study proposes a unified quantitative methodology that leverages diverse AI approaches. Behavioral data are gathered via systematic observation and analyzed using (1) Deep Learning (DL)-based Human Detection and classified by (2) Machine Learning (ML)-based Interaction Score Prediction approach. The behavioral findings were analyzed in relation to spatial data via (3) Spatial Feature Selection. As the study area, the ATU Faculty of Engineering building complex was selected, and behavioral data from 746 participants were collected in the complex’s open spaces. The results indicated that AI-based approaches provide a high degree of precision in analyzing the relationships between social interaction and spatial features within the addressed context. Also, (1) the existence and (2) the rotation of seating units and (3) shading strategies are identified as the spatial features that contribute to higher interaction scores in the educational settings. The study proposes an integrated and transferable methodology based on diverse AI approaches for determining social interaction and its spatial aspects, leading to a comprehensive and reproducible approach.
Source: Architecture
Authors: Ecem Kara, Barış Dinç
Institution: Adana Science and Technology University
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,207
OpenAlex ID: W7124175315
