Categorization - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 109

features in the data. In addition to these basic categories, existing multifield visu-
alizations often rely on the mathematical habit of reducing complex problems to
simpler problems with known solutions. In the context of multifield visualization,
this usually means computing a single scalar or vector field based on the input data,
then visualizing that single field.
This therefore leaves four broad categories of approaches to multifield visualiza-
tion, in approximate order of difficulty:
1. Visual Channel Mapping
2. Derived Fields
3. Interactive Exploration
4. Feature Detection and Analysis
Each of these will be covered in a separate section, but we start with a high-level
overview of these methods first.
11.2.1 Visual Channel Mapping
For single fields or for multifields with small numbers of variables, the first set of
approaches, including much of the work published to date, involves mapping data
properties to visual properties. So, for example, one dependent variable may be
mapped to the red channel, a second to the green channel, and a third to the blue
channel. Alternately, one channel could map to hue, a second to saturation, and a
third to brightness. 1 Visual channels that can be exploited this way are not, however,
restricted to colour alone—as we will see in Chap. 12 , texture and geometric shape
are also used to represent data properties.
A core problemwith visual channel mapping is that the human visual system has a
limit on how many different visual channels can be perceived at once. Moreover, the
amount of precision in the visual system limits the qualitative conclusions that can
be drawn. However, due to the simplicity and straightforwardness of visual channel
mapping, it often forms the basis for the methods to be developed in subsequent
chapters.
11.2.2 Derived Fields
Once the visual channel limitations are realized, the next set of methods relies on
reducing the number of visual channels by combining elements of multiple data
variables in a single channel. This is usually done by computing some summative
property that encapsulates a relationship between the variables, thus reducing the
1 We note that both of these mappings are poor choices visually, but they are the easiest illustrations
of the principle.