16.1.7 Multiple Scalar Fields

Rather than visualize a single data set, you may want to go a step farther and explore the interrelationships betweens two more more scalar fields. This can be done, at the expense of visual complexity, by tagging data values with multiple independent attributes. Some possibilities are:

1.

point field: color for one data value, transparency for the other

2.

point field: color for one data value, point size for the other

3.

point field: color for one data value, texture pattern for the other

4.

point field: two complementary color scales that can be mixed to result in unique color values

5.

height field: height for one data value, color for the other

Of course, sharing attributes on sample points only works if both data sets were sampled at the same locations. If not, there will be two interacting sets of independent sample points, which will be hard to decipher visually, especially if the data set is dense.