09-11-2012, 01:10 PM
Data Visualization Principles and Techniques
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Data Visualization Principles
(a) Focus on content.
(b) Comparison rather than mere description.
© Integrity
(d) High resolution
(e) Utilization of classic designs and concepts proven by time.
Content Focus
"Above all else show the data." The focus should be on the content of the data, not the visualization technique. This leads to design transparency. Avoid "fooling around with data" and use a clear, simple, straight-forward design with a richness of data. The success of visualization is based on deep knowledge and care about the substance, and the quality, relevance and integrity of the content.
Assume that the viewer is just as smart as you and cares just as much. Never `dumb-down' visualization.
Comparison vs. Description
"At the heart of quantitative reasoning is a single question: Compared to what?" Most visualizations today are descriptive rather than comparative. This may be part of the reason why scientific graphics, even those about multivariate phenomenon, are dominated by the XY-plot. The XY-plot invites natural reasoning about causality. We should strive for relational, rather than merely descriptive, visualizations.
To focus a visualization on "Compared to what?" enforce visual comparisons, particularly within the eye span. Avoid relying on the viewer's memory to make visual comparisons; a weak facility in most of us.
Integrity
Misleading visualizations are common. Although the following suggestions are tuned to statistical graphics, following a few rules that may help limit unintentional visualization lies:
• "The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the numerical quantities represented.
• Clear, detailed, and thorough labeling should be used to defeat graphical distortion and ambiguity.
• Write out explanations of the data on the graphic itself. Label important events in the data.
• Show data variation, not design variation.
• The number of information-carrying (variable) dimensions depicted should not exceed the number of dimensions in the data.