Question:

How can I further encode quantitative values in graphs?

Seven Principles For Effective Visual Presentations In Performance Reporting - #3: Use Lengths Or Other Dimensional Technique To Further Encode Quantitative Values In Graphs.

Stephen Few, a respected IT innovator, consultant, and educator has studied the art and science of visual presentation for many years. In a White Paper prepared for Cognos Corporation, he presents seven principles for the effective display of quantitative information. The third of these is presented below:
“There is an important set of visual properties that are called “preattentive attributes” of visual perception. They are preattentive in that the process of perceiving them does not involve conscious thought; it is automatic and immediate. This includes such properties as an object's length (for example, the length of bar in a bar graph), its 2-D location (for example, the position of a data point in a scatterplot), its size, its shape, its orientation, its hue, and so on. If objects in a graph vary from one another along one of these properties to a great enough degree to appear different, we see those differences immediately, without conscious effort. For example, if a single data point is orange in a scatterplot that contains 100 data points, 99 of which are black, the orange dot will stand out as different. We can use this knowledge to intentionally make particular items in a graph stand out as different or important.”

“Of the full set of preattentive attributes, a few are perceived quantitatively. By this I mean that we perceive differences between varying expressions of a visual property (for example, length, exhibited as long bars, short bars, medium-length bars, etc.) as greater than or less than one another. Apart from these preattentive attributes, those that are not perceived quantitatively are simply seen as different, such as the different hues of black, green, blue, orange, purple, and so on. Two of the preattentive attributes that are perceived quantitatively are also perceived with a fair amount of quantitative precision: length and 2-D position.”

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