Scale Mechanism Design for Data Analysis in Immersive Environments

This study proposes a scale-based interaction framework for multidimensional data exploration in three-dimensional immersive analytic environments. Grounded in egocentric and allocentric reference frames, the framework integrates four variables—distance, sensitivity, visual density, and information display—that adapt to data scale. Two VR experiments examined interaction and visualization effects across four scale levels (Small Allo, Large Allo, Small Ego, Large Ego). Results showed that performance and exploration strategies varied by reference frame: allocentric conditions supported higher accuracy, while egocentric conditions enabled faster task completion. Cognitive load was lowest under fixed distance and density, and immersion was strongest in structurally stable settings. Spatial ability and prior VR experience also influenced performance and user experience. These findings provide empirical support for scale-aware interaction design and offer a foundation for developing adaptive principles in immersive data interfaces.