Visualizing Time: Designing Graphical Representations for by Graham Wills

By Graham Wills

Art, or technology? Which of those is the way to think about the sector of visualization? this isn't a simple query to respond to, even if you happen to have a long time adventure in making graphical depictions of knowledge that allows you to aid humans know it and take motion. during this e-book, Graham Wills bridges the distance among the artwork and the technological know-how of visually representing facts. He doesn't easily provide ideas and suggestion, yet bases those on common rules and supply a transparent course among them

 

This e-book is anxious with the graphical illustration of time facts and is written to hide quite a number diversified clients. A visualization professional designing instruments for showing time will locate it helpful, yet so additionally should still a financier assembling a document in a spreadsheet, or a clinical researcher attempting to show gene sequences utilizing a advertisement statistical package.

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Extra resources for Visualizing Time: Designing Graphical Representations for Statistical Data

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5, we map data consisting of categories to color. In Chap. 4 we will consider the differences between displaying categorical and continuous values more thoroughly, but the difference between aesthetics for categories and aesthetics for continuous values is fundamental to the design of good visualizations and needs to be understood when talking about aesthetics. Some aesthetics are naturally suited to categorical data. Pattern, also known as texture, is an examples of this. In Fig. 19 we have taken the same data as in Fig.

Not only might we have different abilities to perceive color differences (color-blindness in one form or another is an 7 Home runs are big hits that in today are virtually always hit out of the field of play. Stolen bases, in contrast, are when the runner advances to the next base without making a hit at all. In a sense, they are opposite types of play that advance the bases for the batting team. 0 1/1/50 1/1/75 1/1/00 1/1/25 1/1/50 1/1/75 1/1/00 Fig. 19 Baseball players’ average number of stolen bases per game.

A surface for the loess smooth has been plotted the process, but the principle is still applicable. 16 shows an example of a more complex statistic, the self-organizing map (SOM) invented by Teuvo Kohonen. The SOM is described in detail in his book [68], with what follows being a brief introductory description only. A SOM first must be trained on data. This is a process that creates a grid of vectors that represent clusters for the data. The training algorithm for a simple SOM implementation on k-dimensional data table is as follows: 1.

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