“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.”
This comes from a recent paper by Jeffrey Heer of Stanford's Visualization Group and Ben Schneiderman titled Interactive Dynamics for Visual Analysis in ACM Queue, following up on another Queue article from 2010 by Jeffrey Heer, Michael Bostock and Vadim Ogievetsky, A Tour Through the Visualization Zoo. The Interactive Dynamics article categorizes aspects of visual analysis that deserve careful consideration when designing visual analysis tools.
Taxonomy of interactive dynamics for visual analysis
- Data and View Specification
- Derive (values or models)
- View Manipulation
- Navigate (zoom, drill-down)
- Coordinate (linked views)
- Organize (multiple windows)
- Analysis Process & Provenance
Heer and collaborators created a series of software libraries for interactive visualization: prefuse, flare, Protovis and D3. These frameworks are designed to "represent data to facilitate reasoning", "flexibly construct representations" and "enable representational shifts" or transformations.
Protovis, which I've fooled around with a bit, is a functional domain specific language for data visualization. It's successor, D3 (Data Driven Documents), is an adaptation that increases performance and expressivity by making more direct use of the model (the DOM) inherent in the browser.
Jeffrey Heer spoke recently at the University of Washington (video available) about his research, citing as an early influence a 1962 paper by John W. Tukey titled The Future of Data Analysis.
In some ways, Heer nicely echoes themes from Hadley Wickam's talk on Engineering Data Analysis.
Data analysis pipeline:
Heer outlines an iterative process with these steps: Acquisition > Cleaning > Integration > Visualization > Modeling > Presentation > Dissemination.
Also from the Interactive Dynamics paper:
“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.”
I've been a Jeffrey Heer fan-boy for some time, starting with his 2006 paper, Software Design Patterns for Information Visualization. Those interested in learning data science skills could do a lot worse than study Jeffrey Heer's work.
- GraphPrism: Compact Visualization of Network Structure
- Orion, a tool for network modeling and analysis
- Wrangler, a declarative data transformation language
- Stanford Vis group papers
- Interactive High-Dimensional Data Visualization (Buja 1996)