graphics/visualisation


Grace, Origin Pro and SigmaPlot, each of which takes a different approach valued by different users, but there are numerous other examples in what may be the most densely populated niches in the data analytic ecology. Tis proliferation reflects both the


importance of such visual plotting and the different priorities brought to its practice by scientists. SDAR, for example, is a new addition in the last couple of years to Griffith University’s platform independent Program Collection for Structural Biology and Biophysical Chemistry (PCSB) of Java applications. Tat institutions such as Griffiths feel it worth investing in new tools rather than automatically taking the easier and cheaper option of buying in well-proven existing ones says much about how vital this area is to mainstream work. Whether plotting one time series variable or


juggling seven in concert, graphic visualisation of data builds on the remarkable adaptability of our own human system soſtware. Originally evolved to aid survival in an arboreal predator/ prey habitat, this has so far proved itself up to the task of embracing every environment our species has encountered – including abstract data spaces with no objective reality. Every time I cross a busy road, usually on autopilot whilst


thinking about something else, I intuitively and subconsciously process an extraordinary quantity and variety of rapidly changing data relations to reach viable solutions of complex equation systems in fractions of a second. To do this consciously would take much longer, and render the crossing of roads impossible; and so it is with less concrete analyses. Intuitive grasp doesn’t, by itself, solve scientific problems; it is, however, the key to isolating potentially fruitful pathways through unmanageably dense data traffic for more rigorous investigation. And, because human beings have evolved with vision as their primary sense, visualisation offers a greater data input density to that intuitive processor than any single conduit. As with simpler one or two variable plotting,


there are many soſtware tools whose purpose in life is to harness this innate processing potential as efficiently as possible. Most of the big statistics packages, as I have already mentioned, offer very good facilities in this area alongside more formal analysis of the promising prospects which they reveal. Tat kind of multidimensional data space navigation is very conveniently illustrated, though, by the product line of Golden Soſtware which gathers in one place a single range of tools


covering a spectrum of visualisation activities. At one end is Didger, which handles tasks from digitising paper maps to harmonising datasets with different coordinate systems. At the other lies Voxler, a 3D solid visualisation package managing the translation of data to volumetric display pixels (voxels). Between the two limits come Grapher (an occupant of the 2D and 3D graphing market), MapViewer (a thematic map generation and spatial analysis tool), Strater (boreholes, wells, geostrata) and Surfer (3D surface mapping and contouring). Golden Soſtware has its origins in mining,


and the most usually described applications for its products are geophysical with a subterranean bias (see, for example, box ‘Te show so far: rubbish’ for combined use of both Surfer and Voxler), but their fetch is much greater. One use for Voxler which reaches upwards instead of downwards is atmospheric modelling: plotting air temperature data over a particular region and then interpolating to visually explore thermal variation over a range of altitudes or geopotentials, for example. Te potential goes way beyond such physical spaces, though all those inbuilt human instincts work as well with metaphorical constructs as with literal ones. My first experience of Surfer on real research was


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