Comparison with Bertini ’ s clutter reduction strategies

Table 3-10 shows a comparison of Bertini’s classification of clutter reduction techniques with those used in the Clutter-reduction Taxonomy.

We can see from Table 3-10 that Bertini’s visual density reduction and spatial organisation strategies match quite closely with the choice appearance group of techniques (green boxes) and spatial distortion group (blue boxes). The only exceptions being topological distortion and change opacity. It is also clear from Table 3-10 that many of Bertini’s methods are less specific. These differences will now be discussed. Under the visual density reduction strategy Bertini classifies sampling and filtering examples as suppression where, as discussed in Section 3.4.1, there is an important

strategy method examples clutter reduction distortion possibilities visual density reduction suppression sampling, filtering reduce number of data items subsetting

zooming in reduce number of

data items by changing x-y scale

allocate more screen space to detriment of adjacent areas (e.g. Fisheye)

level of detail

reduce size, aggregation, clustering

reduce screen area covered by data items, reduce number of data items size of object dependent on screen space (Woodruff’s constant density) spatial organisation layout graphs, Treemap minimise arc crossings (graphs), low average aspect ratio (Treemap)

random displacement (jittering), displacement calculated by

optimisation algorithm (e.g. Keim GridFit)

ordering

pixel plotting, parallel coordinates axis reordering

pixel plots made more effective, reduce line crossings in parallel coordinates retinal properties brightness Information Mural [Jerding and Stasko 98]

conveys data density magic lenses provide local distortion, non- linear mapping of colours to graph edges [Herman et al. 00]

colour mapping

transparency detect hidden objects

shading Vis. Million Items [Fekete and Plaisant 02] disambiguate overlapping shapes

difference in that sampling does not require the user to make choices on what data to exclude. Although subsetting is included within the same strategy, it can be argued that subsetting involves a spatial distortion of the background rather than an operation on the data items and hence it should be classified as topological distortion. Furthermore, unlike Bertini who regards both reduce size and clustering examples as level of detail methods, these have been classified separately as change point size and clustering (see Section 3.4.1). Aggregation has not been included as this more as pre- processing, as mentioned in Section 3.2.

Under spatial organisation strategy, layout is similar to Ward’s hierarchical and network classes of structure-driven strategies. Treemaps are down as being a space- filling technique. Bertini’s ordering method is similar to Ward’s ordered class. It is difficult to see how the ordering of points in the pixel-plotting example is a clutter reduction method, when in fact pixel-plotting itself is a valid technique. Similarly, the reordering of parallel coordinate axes as dimensional reordering is more specific than Bertini’s ordering method.

Of the four methods under retinal properties strategy, only transparency has been taken selected and is referred to as change opacity. Brightness and colour mapping are useful to highlight and differentiate data items but these have been excluded as mentioned in Section 3.4.1.

It appears that the point/line displacement technique is not included in Bertini’s classification, however in the accompanying text the author suggests that displacement is a form of spatial organisation distortion. In addition, he considers focus+context techniques such as Fisheyes and hyperbolic mapping as subsetting distortion, whereas it can be argued that these are topological distortion techniques, which better model the process by which the data items are given more space.

Animation is mentioned by Ward in the context of smooth transition between original and distorted views but neither authors class animation as a clutter reduction technique. Finally, like Ward, Bertini suggests that some degree of automatic clutter reduction is desirable, provided that changes to the display do not disorientate the user. Automatic clutter reduction is an integral part of this work and is demonstrated later in Chapter 5 through auto-sampling.

We will now consider the usefulness of Bertini’s clutter reduction strategies. To aid this, an expanded version of the clutter reduction strategies is given in Table 3-11 that includes examples, together with an explanation (as summarised in the clutter reduction column) taken from the accompanying text of his thesis. In addition, a column has been added summarising the distortion possibilities, discussed by Bertini.

Table 3-12

examples clutter source remarks

placement strategy partitioning Treemap, PixelMap graphic element (line/white space) required to divide the space non-overlapping but not necessarily space- filling overlapping marks Scatterplot, parallel coordinates partial or total overlap degree of freedom fixed maps, scatterplots overlapping points/lines no degree of freedom constrained Treemap, parallel coordinates, TableLens

lines may overlap (parallel

coordinates)

Treemap columns and parallel coordinates axes constrained by the order of the original data but may be reordered free ball-and- spring graphs, multidimens ional scaling

graph edges may overlap

no spatial ordering but often relative

positioning implies the strength of the relationship visual marks pixel Keim’s pixel plotting total overlap or visual interference of neighbouring pixels no partial occlusion as elementary graphic element line parallel coordinates intersection noise, saturation, ambiguous patterns

includes curved and poly lines area scatterplots, Treemaps partial or total overlap includes points (>1 pixel)

text labels distraction, overlap

other lines/areas

must avoid overlap and be attached to object

Expanded version of Bertini’s design space characterisation [Bertini 07]. The examples, clutter source and remarks columns do not appear in the original publication – their content has been extracted from the published text.

As with Ward’s taxonomy, there is no attempt to assess or compare the chosen clutter reduction methods and hence, apart from making the designer aware of some of the available methods, it is difficult to imagine how they could “revise and improve the tools they have built”, one of Bertini’s stated aims. Another aim is to “make them [designers] aware of undesired degradation effects some new designs may have” and whilst there is little in the section on clutter reduction strategies towards meeting this aim, he does provide a useful analysis of the visualisation design space – this is given in the first three columns of Table 3-12. The other columns are summaries of relevant information within the published text. We will now compare this design space characterisation with the Clutter-reduction Taxonomy and Ward’s taxonomy.

The visual marks classification provides a useful discussion of the different types of graphic elements, indicating possible sources of clutter. In contrast, the Clutter- reduction Taxonomy is based on a classification and assessment of clutter reduction techniques rather than a list of graphic elements, however the techniques do of course operate on these graphics. For example, displacement of points and lines, opacity, sampling and change point size.

Text has not been considered explicitly as a visual mark, but there is no reason why the aforementioned techniques cannot be applied to the text labels on a chart. For example, text can be displaced and the size of text is changed by cartographers to fit the available space, which was behind Woodruff’s constant density visualisation [Woodruff et al. 98b]. In semantic zooming maps (e.g. Google maps) the detail which is displayed, including place and street names, is filtered based on the map scale. Thinking of single line display screens, often found on trains or busses or in a doctors waiting room, text is animated by way of a virtual scrolling display if there is insufficient space to hold the complete message. Also, topological distortion has been applied to lists of text in Fisheye Menus [Bederson 00].

We have seen that Bertini’s classification is focused on clutter reduction as illustrated by his top-level strategies of visual density reduction, spatial organisation and retinal properties. As demonstrated by the extended version of his classification (Table 3-12), visualisation examples, an explanation of clutter reduction mechanisms and distortion possibilities are included for most of the chosen methods within the accompanying text, but it is difficult to make a comparison between methods. In Bertini’s design space characterisation, he discusses how different placement strategies and types of visual marks can produce clutter. This is useful in making a visualisation designer aware of sources of clutter but, as before, a summary table similar to Table 3-12 would add to its usability.

A comparison of natural building techniques for walls. Adapted from information on wall systems by M.G.Smith [The Natural Building Network]

Table 3-13

1 2 3 4 5

straw bale rammed

earth stone

wattle and

daub papercrete

A good insulation ✓ ✓ ✘ ✘ ✓

B high thermal mass ✘ ✘ ✓ ✘ possibly

C easy to build ✓+ _{possibly ✘}+ _{✓ ✓}+

D decorative ✘ ✘ ✓ ✓ possibly

E durable ✘+ _{✘ ✓ ✘}+ _possibly

F good resistance to earth

quakes ✓ ✓ ✘ ✘ ✘+