Variables in Visual Encoding


2 October 2011 | Pikto | Data Collection & Research

Jock Mackinlay was born in NurembergGermany and received his BA in Mathematics and Computer Science from UC Berkeley in 1975 and his PhD in computer science fromStanford University in 1986, where he pioneered the automatic design of graphical presentations of relational information.

Wait, what has he got to do with visual encoding and how we see information?

Mackinlay invented several methods of visualization and information graphics (otherwise known as infographics) and laid the foundation for Design Criteria, which he breaks into two:

(1) Expressiveness
A set of facts is expressible in a visual language if the sentences
(i.e. the visualizations) in the language express all the facts in
the set of data, and only the facts in the data.
(2) Effectiveness
A visualization is more effective than another visualization if the
information conveyed by one visualization is more readily
perceived than the information in the other visualization.

There are several things that allow visualizations within the sentences to express only the data. For example, clarifying the title, labels, legend, captions and not leaving it up to interpretation, would be a good method of committing the data set to expressiveness.

Separately, it has been advocated to avoid things which will not help with the visualizations, e.g.:

  • Unexpressive marks (lines, bars, gradients)
  • Do not distract with faint gridlines, pastel highlights or other fills which do not explain the data
  • Describe the most important part of the data and keep everything to a minimal.

Taken from the Stanford University presentation slides, they have displayed over 20 ways of visualizing the same data, effectiveness of multiple strains of antibiotics.

 




Is there any method that is clearer and appears more salient to you?


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