One Dimensional scatter plot: **E ^{P}_{1 }**

The data are points on a single axis, e.g., the distance of taverns from your house.

Two Dimensional scatter plot: **E ^{P}_{2} **

The pairs of values are points in a plane, e.g., the weight and engine size of automobiles. Note that each point represents, and must be labeled by, not only the data but also the type of automobile.

Three Dimensional scatter plot: **E ^{P}_{3 }**

Special methods must be used to facilitate the understanding of 3D data, since it is mapped to a 2D output device. Attribute mapping, such as color is one method. Another method is brightness (closer points are brighter). But the best method appears to be animation, allowing the user to rotate the 3D point cloud about the axes.

Higher Dimensional scatter plot: **E ^{P}_{n }**

Special glyphs are a method used for this. Chernoff faces, or the Cox, etal glyphs are examples.

Here is an example of a glyph that can display 12 D data.

Visualization
Techniques: Table of Contents

HyperVis Table of
Contents