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By John G. Sanderson (auth.)

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How variable are they? 16 | Chapter 2: Source Trinity: Ingredients of Successful Visualizations • Are the values categorical? Discrete or continuous? Linear or non-linear? How are they bounded? • How many categories are there? If this sounds a little bit like a spec for a database table, it’s with good reason. You must understand what you’re dealing with in order to treat it well. This understanding about the shape of your data will inform your design decisions. Each relevant relationship and property of your data needs to be encoded with an appropriate visual property; the characteristics of each dimension of your data will inform which visual property you choose to use to encode it.

Like the Periodic Table, pie graphs are useful for a very specific purpose, but quickly devolve into unhelpful parody when drafted into extended service. 42 | Chapter 4: Choose Appropriate Visual Encodings Figure 4-13. A radial layout distorts the data and renders this disk usage map totally ineffective for all but the coarsest comparisons. The specialty of a pie graph is comparison—specifically, comparison of a few parts to a larger whole. We’ve already established above in our discussion of concentric circle graphs and Nightingale’s Roses that the human brain is lousy at comparing the lengths and surface areas of curved or irregularly-shaped fields; pie graphs fall directly into this category.

Distinct Values The second main factor to consider when choosing a visual property is how many distinct values it has that your reader will be able to perceive, differentiate, and possibly remember. For example, there are a lot of colors in the world, but we can’t tell them apart if they’re too similar. We can more easily differentiate a large number of shapes, a huge number of positions, and an infinite number of numbers. When choosing a visual property, select one that has a number of useful differentiable values and an ordering similar to that of your data (see Figure 4-3).

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