Interactive Dynamics for Visual Analysis by Jeffrey Heer and Ben Shneiderman.
From the article:
The increasing scale and availability of digital data provides an extraordinary resource for informing public policy, scientific discovery, business strategy, and even our personal lives. To get the most out of such data, however, users must be able to make sense of it: to pursue questions, uncover patterns of interest, and identify (and potentially correct) errors. In concert with data-management systems and statistical algorithms, analysis requires contextualized human judgments regarding the domain-specific significance of the clusters, trends, and outliers discovered in data.
Visualization provides a powerful means of making sense of data. By mapping data attributes to visual properties such as position, size, shape, and color, visualization designers leverage perceptual skills to help users discern and interpret patterns within data. [cite omitted] A single image, however, typically provides answers to, at best, a handful of questions. Instead, visual analysis typically progresses in an iterative process of view creation, exploration, and refinement. Meaningful analysis consists of repeated explorations as users develop insights about significant relationships, domain-specific contextual influences, and causal patterns. Confusing widgets, complex dialog boxes, hidden operations, incomprehensible displays, or slow response times can limit the range and depth of topics considered and may curtail thorough deliberation and introduce errors. To be most effective, visual analytics tools must support the fluent and flexible use of visualizations at rates resonant with the pace of human thought.
The goal of this article is to assist designers, researchers, professional analysts, procurement officers, educators, and students in evaluating and creating visual analysis tools. We present a taxonomy of interactive dynamics that contribute to successful analytic dialogues. The taxonomy consists of 12 task types grouped into three high-level categories, as shown in table 1: (1) data and view specification (visualize, filter, sort, and derive); (2) view manipulation (select, navigate, coordinate, and organize); and (3) analysis process and provenance (record, annotate, share, and guide). These categories incorporate the critical tasks that enable iterative visual analysis, including visualization creation, interactive querying, multiview coordination, history, and collaboration. Validating and evolving this taxonomy is a community project that proceeds through feedback, critique, and refinement.
This rocks! I missed it earlier this year but you should not miss it now! (BTW, if you see something interesting, post a note to patrick@durusau.net. I miss lots of interesting and important things. Share what you see with others!)
Two lessons I would draw from this article:
- Visual analysis, enabled by the number-crunching and display capabilities of modern computers, is just in its infancy, if that far along. This is a rich area for research and experimentation.
- There is no “correct” visualization for any data set. Only ones that give a particular analyst more or less insight into a given data set. What visualizations work for one task or user may not be appropriate for another.