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One way to construct visualisations is the bottom up approach, first establish the most basic building blocks, typically points, and then combine these building blocks in addition with visual variables to construct a data visualisation. For instance, one would create points for every data entry, after this these points are assigned visual variables to encode different dimensions of the data. Carpendale [20] applies this method, he constructs visualisation from marks, and distinguishes them by assigning visual variables to marks. Each of these visual variables has certain characteristics. Carpendale [20] states that substantial power comes from choosing which visual variable would be most appropriate to represent each aspect of the data to create the most accurate visualisations. The ability to make these choices can be greatly enhanced by understanding how a change in particular visual variable is likely to affect the performance of an interpretation task of the visualisation. This method provides some insight, though still heavily relies on the designer, since it depends on the understanding of both the data and visual variables. Card et al. [28] have developed a framework to aid this process. They propose to order and categorize variables in a table, this table shows what data variable translates into what visual variables. Designers should realise that it is essential for users to be able to invert this mapping. An example of such a table can be found in Table 5. The table is structured with data on the left and users on the right.

Variable Data Type Mark Type Visual Variables Position over time Interaction

Table 5 - Simplified version of the framework proposed by Card et al. [28] on how data translate into visual variables in a visualisation.

If these changes have the same effect for every visualisation, a general ranking can be established for visual variables. Thus, further eliminating the dependency of the designers experience on the visualisations’ succes. Cleveland et al. [17] takes such an approach. Data visualisations are described as a set of elementary graphical encodings that people use to extract quantitative information called elementary tasks e.g. position, length, direction. Cleveland et al. [17] argue that the goal of data visualisation is to convey data as accurate as possible. Thus, data which is more important should be encoded more accurately. Therefore, this study forms a

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information, because of the ranking the visualisations are no longer dependent on the designers understanding of the visual variables. The designer solely has to determine the importance of each of the data variables.

However, only having one ranking of visual variables may seem overly simplistic since there are different types of data variables. Therefore Mackinlay [18] expands further upon this idea of ranking. Instead of having one hierarchy, data variables are categorised into three categories: quantitative, ordinal and nominal [34]. A variable is said to be nominal when its a collection of unordered items, such as {Jay, Eagle, Robin}. A variable is said to be ordinal when it is an ordered tuple, such as {Monday, Tuesday, Wednesday}. Lastly, a variable is said to be quantitative when it is a range, such as [0, 255]. Each of these categories has its own ranking of accuracy. Mackinlay [18] also introduces a term for the guideline of encoding more important data more accurately, the principle of ordering; encode more important information more effectively.

Another, more top-down, approach is providing the overview first and let users explore the data. Schneiderman [29] introduces a mantra for designing advanced graphical user interfaces. This is the Visual Information-Seeking Mantra: provide an overview of the data first, allow for zooming and filtering, then provide details of the data on demand. The benefits of this mantra are described as attractive because it presents information rapidly and allows for rapid user-controlled exploration [29]. Even though this mantra takes a whole different approach compared to starting with the most basic unit, it can still be applied together with the discussed methods. Since this mantra does not specify how one constructs the actual data visualisation, solely how one presents the data.

The methods discussed so far are abstract and help contribute towards a framework for a data visualisation. There is also literature that provides valuable insight and guidelines for more specific scenarios of visualisations [17], [30]. One of such guidelines that is suggested is the usage of circles and rounded edges, because these tend to be more memorable and visually pleasing [30]. Another example is the guideline that bar charts should always be used over pie charts, since judgment of position along common scale are ranked higher than angle, in figure 28 the visual representation of bar graphs versus pie charts can be compared. Cleveland et al. [17] conducted tests that showed this. This should also hold up to the guidelines proposed by Mackinlay [18], since their principle is based on the same idea, although this is not explicitly stated in their paper

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