¶ 1 Leave a comment on paragraph 1 0 If choosing the data to go into a visualization is the first step, picking a general form the second, and selecting appropriate visual encoding the third, the final step for putting together an effective information visualization is in following proper aesthetic design principles. This step will help your visualization be both effective and memorable. We draw inspiration for this section from Edward Tufte’s many books on the subject, and Angela Zoss’s excellent online guide to Information Visualization.
¶ 2 Leave a comment on paragraph 2 0 One seemingly obvious principle that is often not followed is to make sure your visualization is high resolution. The smallest details and words on the visualization should be crisp, clear, and completely legible. In practice, this means saving your graphics in large resolutions or creating your visualizations as scalable vector graphics. Keep in mind that most projectors in classrooms still do not have as high a resolution as a piece of printed paper, so creating a printout for students or attendees of a lecture may be more effective than projecting your visualization on a screen.
¶ 3 Leave a comment on paragraph 3 0 Another important element of visualizations often left out are legends that describe each graphic variable in detail, and explains how those graphic variables relate to the underlying data. Most visualization software do not automatically create legends, and so they become a neglected afterthought. A good legend means the difference between a pretty but undecipherable picture, and an informative scholarly visualization. Adobe Photoshop and Illustrator, as well as the free Inkscape and Gimp, are all good tools for creating legends.
¶ 4 Leave a comment on paragraph 4 0 A good rule of thumb when designing visualizations is to reduce your data:ink ratio as much as possible. Maximize data, minimize ink. Extraneous lines, bounding boxes, and other design elements can distract from the data being presented. Figure 5.32 shows a comparison between two identical charts, except for the amount of extraneous ink.
¶ 10 Leave a comment on paragraph 10 0 A related rule is to avoid chartjunk at all costs. Chartjunk are those artistic flourishes that newspapers and magazines stick in their data visualizations to make them more eye-catching: a man blowing over in a heavy storm next to a visualization of today’s windy weather, or house crumbling down to represent the collapsing housing market. Chartjunk may catch the eye, but it is ultimately distracting from the data being presented, and readers will take more time to digest the information being presented to them.
¶ 11 Leave a comment on paragraph 11 0 Stylized graphical effects can be just as distracting as chartjunk. “Blown out” pie charts where the pie slices are far apart from one another, 3D bar charts, and other stylistic quirks that Excel provide are poor window decoration and can actually decrease your audience’s ability to read your visualization. In a 3D tilted pie chart, for example, it can be quite difficult to visually estimate the area of each pie slice. The tilt makes the pie slices in the back seem smaller than those in the front, and the 3D aspect confuses readers about whether they should be estimating area or volume.
¶ 12 Leave a comment on paragraph 12 0 While not relevant for every visualization, it is important to remember to label your axes and to make sure each axis is scaled appropriately. Particularly, the vertical axis of bar charts should begin at zero. Figure 5.33 is a perfect example of how to lie with data visualization by starting the axis far too high, making it seem as though a small difference in the data is actually a large difference.