Spatial Behavior Data in Virtual Conference: Proximity and Network Analysis

Recent advances in virtual environments have extended real-life activities to virtual spaces, thereby generating enormous amounts of data on spatial behaviors. Spatial behaviors in physical spaces have been extensively studied; however, in virtual spaces, it is limited to measuring proxemic distances or visualizing spatial diffusion. Therefore, to understand users’ spatial behaviors in virtual spaces, a novel network approach that connects users and spaces is proposed in this study. Using a proxemics dataset from a virtual conference, positional connectivity between users and spatial units was measured using Euclidean and temporal measurements. A bipartite network and projections were used to extract Close-Proximity Interactions. The proposed method quantified user activities using topological features. In addition, log analysis revealed observations of spatial interactions in virtual spaces without a dedicated tool, such as an eye tracker. The results of this study can improve the understanding of remote-learning activities in virtual spaces. Its application can help education managers, designers, and marketers to assess user interactions based on specific contexts in virtual spaces.