These are converted into uniform 1-9 scores (e.g. MacEachren and Mazza provide ordered rankings such as poor-marginal-good or no-limited-suitable. Jock provides a rank diagram, which is converted into a rank 1-n. Under each author is the original score/rank. Visual Attributes, ranked by suitability per data type Maz09 – Riccardo Mazza, Introduction to Information Visualization Mac06 – Jock MacKinlay, Designing Great Visualizations Mac86 – Jock MacKinlay, Automating the Design of Graphical Presentations The table immediately below brings together the rankings of several researchers:īer67 – Jacques Bertin, Semiology of Graphics At the same time, it would be very interesting to bring all the ranking models together to compare them and create some kind of meta rank. But they don’t all list the same visual attributes. Several researchers have created rankings indicating which visual attributes are best for which types of data. In fact, errors in the choice of encoding is the most common error in creating a visualization. The challenge is to find the right encoding for the data to be represented. Encoding data into visual attributes is one of the fundamental processes of data visualization.
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