A few weeks ago, I presented the HECToR scientific visualisation training course to a group of researchers at the University of Reading. This course looks at the use of visualisation as a tool for the improved understanding of numerical data (as produced, for example, by calculations or simulations run on HECToR). We started by exploring the different types of data - characterizing each according to its structure, dependencies and dimensionality - before reviewing the different visualisation techniques (such as contouring, particle tracing, volume rendering) that are applicable to each type. Some examples of the techniques, used to display a variety of data types from several application areas, are given in the figures below.
Next, we briefly reviewed a few applications for performing visualisation, of which there are a plethora in both the public domain and commercial arenas. Some of these have comparable functionalities - thus, for example, Figure 1 was produced using IRIS Explorer, ADVISE was used for Figure 2, and Figure 3 was created with ParaView. The plot in Figure 4 (which illustrates the use of a routine from the NAG Toolbox for MATLAB®) was made using MATLAB.
Figure 4 can be used to demonstrate another aspect of the training course - namely, the highlighting of good and bad practice in visualisation. Depending on the resolution of the display medium for the figure, it is usually apparent that the blue solid curve is easier to see than the dashed green one. This may not necessarily be a significant effect, although you might be reminded of other instances you've seen in presentations where - for example - a yellow curve has been rendered more or less invisible by being displayed against a white background, or a blue curve against black. In general, it can be surprising how little attention is apparently paid to issues such as clarity and reproducibility in visualisations by users who've spent a lot of time generating and checking their results, only to see their impact lost or impeded because of a poorly-designed display.
As alluded to above, one of the things that should be taken into account when designing a visualisation is the use of colour. Indeed, Figure 3 also illustrates how care should be taken when selecting colours: many viewers will find its juxtaposition of the red surface against a green background difficult to look at (try reading green text on a red field - or vice versa - for a further illustration of this). The way in which we perceive colour is a complex subject, but one salient point is that, whilst our eyes see absolute colours, our brains perceive differences in colours - i.e., the appearance of a colour depends on its surroundings. This apparently surprising fact underpins the workings of a whole range of optical illusions, of which my current favourite is the so-called same colour illusion:
Looking at Figure 5, it can be difficult to see that the two orange circles are the same colour - although, bearing in mind the discussion above, most people would probably accept it. But what's much harder to believe (although it's also true) is that the squares which surround them are also the same colour as each other. The reason we perceive them as different is that they have different surroundings (and also because of the way in which our brains try and compensate for the effect of shadows).
Demonstrations of illusions such as this (and the examples of good and - mostly - bad practice in visualisation) made the training session quite a lively one. In fact, I suspect that - notwithstanding the undoubted fascination that can be exerted by the study of visualisation techniques and applications - this was the part of the presentation that lingered longest in the minds of many of the attendees. I could be wrong, though.