We propose a radial method for visualizing time-series data on mobile devices. We display a single "snapshot" of the data at a given moment in time, and allow the user to easily navigate forwards and backwards in time to view other snapshots. Transitions from one snapshot to another are smoothly animated to preserve context. As a proof of concept of this idea, we have implemented U.S. Supreme Court, a mobile app that lets users visualize the membership of the United States Supreme Court at any date in history.

Radial visualization continues to be a popular design choice in information visualization systems, due perhaps in part to its aesthetic appeal. However, it is an open question whether radial visualizations are truly more effective than their Cartesian counterparts. In this paper, we describe an initial user trial from an ongoing empirical study of the SQiRL (Simple Query interface with a Radial Layout) visualization system, which supports both radial and Cartesian projections of stacked bar charts. Participants were shown 20 diagrams employing a mixture of radial and Cartesian layouts and were asked to perform basic analysis on each. The participants' speed and accuracy for both visualization types were recorded. Our initial findings suggest that, in spite of the widely perceived advantages of Cartesian visualization over radial visualization, both forms of layout are, in fact, equally usable. Moreover, radial visualization may have a slight advantage over Cartesian for certain tasks. In a follow-on study, we plan to test users' ability to create, as well as read and interpret, radial and Cartesian diagrams in SQiRL.

Many courses on the history of computing are designed for a general student audience, and as such, include fewer technical details than one might find in a typical CS course. While this approach is appropriate in some contexts, it risks losing the interest of the students who could perhaps benefit most from the subject-namely, future computer scientists. This paper describes a technically-oriented History of Computing course which we taught at the University of Utah in 2008. Like other History of Computing courses, ours included a significant amount of writing and discussion. However, inasmuch as our course was created specifically for CS students, we also incorporated several "hands-on" programming exercises and demonstrations, giving students actual experience with the computing environments of the past. Students and faculty alike have responded enthusiastically to this dual-faceted approach.

Radial visualization, or the practice of displaying data in a circular or elliptical pattern, is an increasingly common technique in information visualization research. In spite of its prevalence, little work has been done to study this visualization paradigm as a methodology in its own right. We provide a historical review of radial visualization, tracing it to its roots in centuries-old statistical graphics. We then identify the types of problem domains to which modern radial visualization techniques have been applied. A taxonomy for radial visualization is proposed in the form of seven design patterns encompassing nearly all recent works in this area. From an analysis of these patterns, we distill a series of design considerations that system builders can use to create new visualizations that address aspects of the design space that have not yet been explored. It is hoped that our taxonomy will provide a framework for facilitating discourse among researchers and stimulate the development of additional theories and systems involving radial visualization as a distinct design metaphor.

Surveys and opinion polls are extremely popular in the media, especially in the months preceding a general election. However, the available tools for analyzing poll results often require specialized training. Hence, data analysis remains out of reach for many casual computer users. Moreover, the visualizations used to communicate the results of surveys are typically limited to traditional statistical graphics like bar graphs and pie charts, both of which are fundamentally noninteractive. We present a simple interactive visualization that allows users to construct queries on large tabular data sets, and view the results in real time. The results of two separate user studies suggest that our interface lowers the learning curve for naive users, while still providing enough analytical power to discover interesting correlations in the data.