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What is the impact of the graphical visualisation of data within scientific


computing? Felix Grant investigates


I


n the last issue, talking about the statistical work conducted by non-statisticians, I mentioned the importance in that context of graphical visualisation of data. It goes well


beyond that, however. On the one hand, fuelled by the ever-


accelerating growth curve in computing power per unit of investment, visualisation has progressively moved to the core of exploratory and analytic strategies. Te effects on traditional methods are profound, as separate work phases collapse into continuous cybernetic feedback loops and statisticians develop increasingly immersive relationships with their raw material. On the other, data visualisation has penetrated mainstream discourse to become an integral part of vernacular literacy – ‘one of the genuinely new cultural forms enabled by computing’ as Lev Manovich[1,2]


describes it. Tose two aspects, the technical and the


vernacular, are not separate; they are two sides of the same coin. Tey are beginning to interpenetrate with other developments such as direct onscreen haptic manipulation of program interfaces and may in the long run turn out to be the most far reaching and profound effect of the scientific computing revolution. At the heart of this lies the capacity of


Four frames from a 50 year, six variable, animated Gapminder Foundation infographic, illustrating changes over time (background) in relation between per capita GDP (logarithmic x-axis), per capita energy use (linear y-axis), annual CO2 emissions by country (marker size), and geographical region (marker colour). Additional information is being accessed by the user through mouse-over popup boxes and superimposed labels on countries of specific interest


inexpensive desktop, laptop or even handheld devices to manipulate graphics in real-time response to user curiosity. When I started writing for Scientific Computing World, back in the 1990s, it was possible to represent three data variables as a scatter plot cloud, or as a fitted surface, on x, y and z axes, but changing the viewpoint or scale usually involved typing new parameters into a settings box and watching the screen progressively redraw. It seemed pretty


The show so far – rubbish!


Rainer Albert is one of the experts contributing to the modelling of the Kölliken hazardous waste landfill site in Switzerland, combining 3D visualisations from both Surfer and Voxler. The site contains almost half a million tonnes


of organic and inorganic wastes from industry, commerce and the public sectors, deposited over a seven year period. Restoration started in 1985 and the site is currently being excavated and remediated under a large building (the Swalba


12 SCIENTIFIC COMPUTING WORLD


and House Matter Hall) and is scheduled for completion by 2016. Albert uses Surfer to display the topography of the site, along with 3D rendered buildings. Waste type and waste concentration from data collected between the surface and bottom layers (up to 17 metres apart) are displayed in Voxler. The transparent surface layer is overlaid with contour lines to display the landfill terrain, while displaying the chemical types and concentrations below.


cool, then. I remember my excitement when the major statistics packages, one by one, added the ability to grab the plot with a mouse click and intuitively apply zoom, pitch, roll and yaw by dragging. Nowadays, I can do the same on a pocket tablet or even a mobile phone by simply sliding my fingers around the image itself. On a desktop, laptop or heavier tablet I have access to considerably more than three dimensions, not to mention different display types such as vector flows in the same visualisation as positional points, planes or volumes. Not that such impressive psycho-perceptual


pyrotechnics are always necessary or even desirable in every context. Detailed 2D presentation of very traditional plots of the kind that would have been familiar to my primary school self in the late 1950s are, in many circumstances, still the best visualisations of real world situations. Te miracle of current soſtware is that those two extremes, and everything between, are available off the shelf to suit the needs of the moment. Well known soſtware implementations of traditional plotting include (in alphabetic order)


@scwmagazine l www.scientific-computing.com


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