Combined and Superimposed TT-Plots

In this short section I will provide two representational extensions to the TT-plots. Both of them are combinations of different spatial aspects into one comprehensive plot.

In the TT-plots introduced by now the information was always mirrored from the lower left triangle to the upper right triangle of the square plot area. This was done for two reasons. The first one is often applied in pattern detection. It is the replication of information, so that patterns at the border of a plot can also be seen, because often only half of the pattern is visible due to border effects. The second reason is to keep the TT-plots consistent with the plots presented later (section 5.10), which make use of the whole area in the plot to depict all the information. Now I will break this restriction in spite of the general rules mentioned in chapter II.

Another way of using the space normally used by mirroring the data at the base diagonal line in a single TT-plot is to combine two different TT-plots into a single graph. This is illustrated in figure 5.15.

Figure 5.15: Example for a superimposed TT-plot from artificial data. It is a combination of a TT-$\delta$ plot (lower right triangle, short distances are indicated in blue, medium ones in green and large distances have a yellow to red color) with a TT-$\pi$ plot (upper left triangle, blue indicates parallel, green intermediate, red anti-parallel).

The lower right triangle is representing a TT-$\delta$ plot describing distance characteristics whereas the upper left triangle of the graph is used for a TT-$\pi$ plot visualizing the parallelity aspects. Here the main purpose is the graphical representation of these aspects and no further analytical computations can be performed upon this data in contrast to the previous TT-plots (cf. section 5.8). This combination of two TT-plots into one graph has the advantage that they can be directly compared with time as reference system.

A second way of combining two TT-plots into one plot is to use different representational techniques for each of them. In figure 5.16 a TT-$\delta$ plot is plotted in the standard manner with the color scheme. But in addition the graph contains an overlayed TT-$\pi$ plot represented by the small black arrows indicating the parallelity. An arrow with a northern direction represents a parallel direction, south represents anti-parallel. Compared to figure 5.15 it is easier to look at both aspects in areas with a large time difference, whereas in the previous graph it is easier to interpret the data lying relatively close to the base diagonal line.

Figure 5.16: Example for a superimposed TT-plot from artificial data. The TT-$\delta$ plot is overlayed with the TT-$\pi$ plot (arrows). Short distances are indicated in blue, medium ones in green and large distances have a yellow to red color. Arrows are indicating the parallelity: north = parallel, south = antiparallel.

It is not my intention to go into much detail about visualization techniques. Instead I would only like to encourage the readers, software developers and users to have a look at the specialized literature in visualization (e.g., Shepherd, 1995; Kraak, 1999; MacEachren and Kraak, 1997; Lippert-Stephan, 1996).