We present a design space exploration of interaction techniques for supporting multiple collaborators exploring data on a shared large display. Our proposed solution is based on users controlling individual lenses using both explicit gestures as well as proxemics: the spatial relations between people and physical artifacts such as their distance, orientation, and movement. We discuss different design considerations for implicit and explicit interactions through the lens, and evaluate the user experience to find a balance between the implicit and explicit interaction styles. Our findings indicate that users favor implicit interaction through proxemics for navigation and collaboration, but prefer using explicit mid-air gestures to perform actions that are perceived to be direct, such as terminating a lens composition. Based on these results, we propose a hybrid technique utilizing both proxemics and mid-air gestures, along with examples applying this technique to other datasets. Finally, we performed a usability evaluation of the hybrid technique and observed user performance improvements in the presence of both implicit and explicit interaction styles.

Finding patterns and trends in spatial and temporal datasets has been a long studied problem in statistics and different domains of science. This paper presents a visual analytics approach for the interactive exploration and analysis of spatiotemporal correlations among multivariate datasets. Our approach enables users to discover correlations and explore potentially causal or predictive links at different spatiotemporal aggregation levels among the datasets, and allows them to understand the underlying statistical foundations that precede the analysis. Our technique utilizes the Pearson's product-moment correlation coefficient and factors in the lead or lag between different datasets to detect trends and periodic patterns amongst them.