Information visualization shows tremendous potential for helping both expert and casual users alike make sense of temporal data, but current time series visualization tools provide poor support for comparing several foci in a temporal dataset while retaining context and distance awareness. We introduce a method for supporting this kind of multi-focus interaction that we call stack zooming. The approach is based on the user interactively building hierarchies of 1D strips stacked on top of each other, where each subsequent stack represents a higher zoom level, and sibling strips represent branches in the visual exploration. Correlation graphics show the relation between stacks and strips of different levels, providing context and distance awareness among the focus points. The zoom hierarchies can also be used as graphical histories and for communicating insights to stakeholders. We also discuss how visual spaces that support stack zooming can be extended with annotation and local statistics computations that fit the hierarchical stacking metaphor.

Analysts are increasingly encountering datasets that are larger and more complex than ever before. Effectively exploring such datasets requires collaboration between multiple analysts, who more often than not are distributed in time or in space. Mixed-presence groupware provide a shared workspace medium that supports this combination of co-located and distributed collaboration. However, collaborative visualization systems for such distributed settings have their own cost and are still uncommon in the visualization community. We present Hugin, a novel layer-based graphical framework for this kind of mixed-presence synchronous collaborative visualization over digital tabletop displays. The design of the framework focuses on issues like awareness and access control, while using information visualization for the collaborative data exploration on network-connected tabletops. To validate the usefulness of the framework, we also present examples of how Hugin can be used to implement new visualizations supporting these collaborative mechanisms.

Social networks collected by historians or sociologists typically have a large number of actors and edge attributes. Applying social network analysis (SNA) algorithms to these networks produces additional attributes such as degree, centrality, and clustering coefficients. Understanding the effects of this plethora of attributes is one of the main challenges of multivariate SNA. We present the design of GraphDice, a multivariate network visualization system for exploring the attribute space of edges and actors. GraphDice builds upon the ScatterDice system for its main multidimensional navigation paradigm, and extends it with novel mechanisms to support network exploration in general and SNA tasks in particular. Novel mechanisms include visualization of attributes of interval type and projection of numerical edge attributes to node attributes. We show how these extensions to the original ScatterDice system allow to support complex visual analysis tasks on networks with hundreds of actors and up to 30 attributes, while providing a simple and consistent interface for interacting with network data.

Navigating in large geometric spaces---such as maps, social networks, or long documents---typically require a sequence of pan and zoom actions. However, this strategy is often ineffective and cumbersome, especially when trying to study and compare several distant objects. We propose a new distortion technique that folds the intervening space to guarantee visibility of multiple focus regions. The folds themselves show contextual information and support unfolding and paging interactions. We conducted a study comparing the space-folding technique to existing approaches, and found that participants performed significantly better with the new technique. We also describe how to implement this distortion technique, and give an in-depth case study on how to apply it to the visualization of large-scale 1D time-series data.

We present a model for building, visualizing, and interacting with multiscale representations of information visualization techniques using hierarchical aggregation. The motivation for this work is to make visual representations more visually scalable and less cluttered. The model allows for augmenting existing techniques with multiscale functionality, as well as for designing new visualization and interaction techniques that conform to this new class of visual representations. We give some examples of how to use the model for standard information visualization techniques such as scatterplots, parallel coordinates, and node-link diagrams, and discuss existing techniques that are based on hierarchical aggregation. This yields a set of design guidelines for aggregated visualizations. We also present a basic vocabulary of interaction techniques suitable for navigating these multiscale visualizations.

Line graphs have been the visualization of choice for temporal data ever since the days of William Playfair (1759–1823), but realistic temporal analysis tasks often include multiple simultaneous time series. In this work, we explore user performance for comparison, slope, and discrimination tasks for different line graph techniques involving multiple time series. Our results show that techniques that create separate charts for each time series—such as small multiples and horizon graphs---are generally more efficient for comparisons across time series with a large visual span. On the other hand, shared-space techniques---like standard line graphs---are typically more efficient for comparisons over smaller visual spans where the impact of overlap and clutter is reduced.

Visualization plays a crucial role in understanding dynamic social networks at many different