Information Visualization

facilitated by John Emerson

Introduction

What is information visualization? information visualization tells a specific story with graphics, whereas data visualization is more exploratory, and about finding patterns in the data.

"Picture superiority effect" - images with words have a greeted impact than words alone.

We see difference in particular ways. For example, we notice a line that's twice as long more than we notice a color that's twice as dark.

Examples of information visualization

• Annual reports: heavy use of graphics to engage and make a point.
  • Lets funders see that their money is working.
• Comic books (e.g. "I got arrested. Now what?")
• Brochures with schematics a la IKEA, that bypass language barriers (e.g. Vendor Power!).
• Maps with walking tours to tell history.
• Food wrappers with info about the food and the stories it tells about culture (e.g. Conflict Kitchen).
• Maps.
  • Mapping donors - Mapped zip codes tell a story that's not visible in numbers
  • Mapping layers - e.g. overlay lead paint with demographic mapping.
  • CrashStat 2.0 (mapping ped/cyclist accidents in NYC)
  • Green Maps in NYC
• Logging cronyism in Cambodia with royal family (networking viz)
• Valdis Krebs
• Collusion extension for Chrome
• Little Sis
• Journal of Aesthetics and Protest
• Information is Beautiful blog

Print vs online

• What are the demographics of the people you're trying to reach, and how can you reach them? Is your target audience online?
• Can you hand someone a website? (hint: no).
• Printed material has a certain permanence.
• Budget is always a factor.

Tools

• Online D3 for building vector graphics (keep design simple, focus on key focus point)

Things to Think About

Do usability testing!

• Do usability testing! Do usability testing! Make sure the information is as accessible and obvious as you think it is.
• Edward Tufte's rule of thumb: Eliminate "chart junk" (drop shadows, phony 3d-ness) as much as possible. By the way, Tufte teaches classes that are pretty reasonable.

Lies/misleading documents

• Representing values with area (inaccurate areas make differences seem greater).
• Phony 3d-ness distorts area (e.g. pie charts that appear to be tilted)
• Pyramid charts
• Zooming in that distorts relation between data sources (e.g. starting the Y-axis of a bar chart at 7 makes the difference between 8 and 9 seem greater than it is).

Subjective intensities of sensations tend to fall on log scales.

Other issues

• How to design research questions to get neutral/non-leading questions (e.g. asking about gov't's intentions vs asking about what's actually happening).
• Data is the artifact of its collection (not a natural fact).
• It is easy to be unethical, and to tell slightly dishonest stories by including or not including information.
• Question about making a specific point vs. just giving info andletting the user decide.
• Data to frame the problem vs to propose a solution
• Paradox of the more data leads to less scrutiny