What question did this study set out to answer?

The aim is to understand how spatial features influence social interaction behaviors in campus environments using AI.

January 16, 2026Open Access

Tracing the Sociospatial Affordances of Physical Environment: An AI-Based Unified Framework for Modeling Social Behavior in Campus Open Spaces

EKEcem KaraAdana Science and Technology University BDBarış DinçAdana Science and Technology University

Key Points

The aim is to understand how spatial features influence social interaction behaviors in campus environments using AI.
Analyzed social interaction patterns using an AI-based framework.
Gathered behavioral data through systematic observation of 746 individuals.
Employed deep learning for human detection and machine learning for interaction score prediction.
Conducted spatial feature selection to relate behavioral findings to spatial data.
AI methods demonstrated high precision in analyzing social interactions and spatial features.
Identified seating unit existence, rotation, and shading strategies as key contributors to interaction scores.

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.

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