Information Technology Reference
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Beyond trees and graphs, there are also spherical visualizations of
images, video, computer screens, self-organizing maps, pie charts, Euler
diagrams, and even 3D touch displays e.g. [Gal03], [Roc09], [Wu08],
[Rod12], [Nie11], [Ben08].
Fig. 2.6.
Data visualization sphere of a self-organizing map.
[Wu08], Euler diagram [Rod12], pie chart [Nie11], and 3D touch display [Ben08].
Sphere visualization experiments:
SphereCorr and SphereTree
To experiment with spherical visualization concepts and gain feedback
from users, two data visualizations were constructed: A correlation sphere
visualization (SphereCorr) and a tree sphere visualization (SphereTree).
SphereCorr
SphereCorr is a representation of a densely connected graph drawn upon a
sphere, using a force-directed physical model to lay out all the nodes based
on the weights of the links. The intended use is to understand correlations
between related objects, for example, time series correlations of stocks.
A correlation is a statistical measurement of the relationship between
two variables that ranges between +1 and -1. For time series data, a value
approaching +1 indicates that both time series move together similarly
(Fig. 2.7 left), while a value of 0 indicates no correlation and -1 indicates
an inverse correlation. For a larger dataset, a correlation can be computed
between each pair of items, and the result shown in a grid (Fig. 2.7 right).
Correlations are interesting because they have real-world use cases. In
financial services, correlations are used to inform tasks such as hedging,
diversification and offset trading. In hedging, strong correlations
(correlations approaching a value of 1) are useful for finding stocks that
tend to move together, which can be used to identify alternative (cheaper)
stocks. For diversification, non-correlated stocks (correlation values
approaching 0) are of interest for finding stocks whose price movements
are independent of each other. In offset trades, it is important to find a
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