Visual Data Analysis

Time-to-Adoption: Two to Three Years
Placed on the far-term horizon in the 2010 Horizon Report, visual data analysis continues to evolve and move closer to widespread adoption as exciting new examples of its use come to light. Visual data analysis is characterized by its focus on making use of the pattern matching skills that seem to be hard-wired into the human brain and also by the way in which it facilitates the work of teams collaborating to tease out meaning from complex sets of information. While the most sophisticated tools are still mostly found in research settings, a variety of tools are emerging that make it possible for almost anyone with an analytical bent to easily interpret all sorts of data. One of the most compelling aspects of visual data analysis is in the way it augments the natural abilities humans have to seek and find patterns in what they see. By manipulating variables, or simply seeing them change over time (as Gapminder has done so famously), it is easy to discover whether or not patterns exist. Such tools have applicability in nearly every field.

The full promise for teaching and learning remains further afield, but because of the intuitive ways in which it can expose complex relationships to even the uninitiated, there is tremendous opportunity to integrate visual data analysis into undergraduate research, even in survey courses. Models of complex processes in quantum physics, organic chemistry, medicine, or economics are just a few of the ways in which the outcomes of visual data analysis can be applied to learning situations.

Relevance for Teaching, Learning & Creative Inquiry

  • Visual data analysis provides a way for engineering students to come to grips with massive amounts of data generated through research, system monitoring, or other standard processes.
  • The Visual Understanding Environment (VUE) created at Tufts University enables students and faculty to work with large amounts of electronic content. The visualizations can be annotated, and users can create and save paths through them to make guided walk-throughs.
  • Ready-to-use visualization tools and public data provide an avenue for exploring a wide variety of relationships, as the Google Public Data Explorer shows.

Visual Data Analysis in Practice

  • Truthy analyzes Twitter posts to identify memes, aiding in the study of social epidemics and helping users to distinguish between actual organic memes and those planted by marketing campaigns: http://truthy.indiana.edu
  • The Visual Complexity project explores and collects visualization tools, best practices, and examples: http://www.visualcomplexity.com/vc
  • Crimespotting is an interactive map of crimes in San Francisco and Oakland that visualizes crime by location, type, date, and time, allowing users to quickly grasp patterns and trends: http://sanfrancisco.crimespotting.org

For Further Reading

Report from the DOE/ASCR Workshop on Visual Analysis and Data Exploration at Extreme Scale
http://www.sci.utah.edu/vaw2007/DOE-Visualization-Report-2007.pdf
(Chris Johnson, Rob Ross, et al., October 2007.) This report describes fundamental research in visualization and analysis involving computational science applications at extreme scale.

Visualizing
http://www.visualizing.org
(Accessed 10 October 2010.) Visualizing is a community of practice for sharing work, best practices, and academic resources, and engaging in dialog about the field.