4. Graphical Complexity: The Need for Clarification
In the previous section, we discussed three strategies used to organize the information to be presented. As mentioned earlier, it is important to select information about mappings based on either complexity or ambiguity if the caption is to be both succinct and informative. We have identified the following five types of graphical complexities that can make it difficult for a user to understand complex data to grapheme mappings.
4.1 Encoder Complexity
Understanding the encoders used in designing a picture is necessary for users to be able to read data values shown in the picture. Encoders allow the user to map between graphical values and attribute values. Two examples of encoders are the axes (which allow users to map between positional values in the picture and data values along the axes), and graphical keys (these can illustrate mappings between variables such as size and shape and attribute values). Complexities can arise either (i) when an encoder is complex, or (ii) when an encoder mapping uses a scale that is complex.
Consider for instance, Figure 10. Among the encoders used in this picture are the X and Y axes which map positional information to house prices and house addresses respectively. In the chart shown here, the X axis does not have a zero origin (presumably in order to make the differences between the data items clearer by having more screen real estate to display a smaller range of data values). Because of this translation of the origin, it is no longer possible to conclude in this chart that a bar twice as long as another bar encodes a value twice as large (for instance, bars representing houses WALNUT-6343 and VERMONT-637 in 10). Both axis translation and truncation--to compress empty regions in quantitative data--can lead to false inferences. Similar decoding problems can occur with other encoding techniques as well, as when a quantitative attribute is mapped to the area of a circle or non-linear scales are used along axes.
A more complex example of encoding technique complexity can be seen in Figure 1. Saturation and color are combined in a single encoding technique to express temperature. Dark red indicates 100 degrees and dark blue indicates -40 degrees. As the color gets paler (less saturated) it indicates a less extreme temperature. For example, pale red (pink) indicates 65 degrees, while pale blue indicates -5 degrees. White indicates a transition point. Thus both the frame of reference (the color-saturation key) and the technique are potentially complex here. Figure 1 also illustrates range complexity: the user must determine what the transition point is (whether it is the center of the scale, or some special value, such as 32 degrees F). The graphic is not explicit about whether the two ranges on both sides of this special transition point are balanced.
Comprehension can be hindered by encoding technique complexities (e.g., a truncated X-axis).
4.2 Grapheme Complexity
Although the encoder (e.g., positional encoding on an axis) and the mapping (e.g., the scale used along the axis) may both be simple, a grapheme that uses that encoder and mapping may still be difficult for users to interpret. This may occur for a variety of reasons ranging from too many mappings to problems in identifying the mappings. Complexities of this type can arise from:
Comprehension difficulties can result from complex graphemes with multiple properties being used in the encoding.
4.3 Ambiguous Mapping Complexity
A user's ability to identify the mapping of even simple techniques can be hindered when dissimilar graphemes (or dissimilar properties of a grapheme) are used to map to similar attribute types. Consider for instance, the charts in Figures 12 and 13. The left and right edges of the bar in 12 refer to the selling-price and asking-price of a house in the domain. However, the X axis represents prices in general, and there is no way to distinguish between the two from the figure itself. Similarly, in Figure 13, the two text labels refer to two different prices, but the two attributes cannot be distinguished from one another solely from the figure.
Complexities can arise from ambiguous mappings (a).
4.4 Composition Complexity
When multiple graphemes occur in a space, they can be confusing at first until their relationship to each other are clarified. Compositions can result in clusters of two types:
Complexities can arise from ambiguous mappings
4.5 Alignment Complexity
As illustrated in Figures 6, 7, and 9, alignment of multiple charts and/or tables can be a useful technique for supporting comparisons, rapid lookups for many attributes of the same object, and for maintaining consistent scales. Whenever an alignment occurs, all but one of the charts become separated from the aligning axis labels and the relation between the aligned axis and the rest of the charts may not be clear.
The complexity assessment module in the system is capable of identifying the graphemes in the display that are complex for any of the five reasons described in this section. It annotates the picture representation generated by sage to indicate the graphemes and their types of complexity. The result of the complexity assessment for the Minard graphicFigure 1is shown in Figure 15. As discussed earlier, for instance, the mapping between the attribute temperature and the color of the line is complex for two reasons: (i) encoding complexity, because of the use of color and saturation, and (ii) range complexity, because of the unequal distributions of warm and cold temperatures. Figure 16 gives the complexity assignment for the graphic shown in Figure 6. In this case, the mapping between the attribute asking price and the bar is complex for three reasons: (i) grapheme complexity, since the interval bar is a complex grapheme (ii) ambiguous mapping, since from the graphic, it is not possible to determine whether the attribute is mapped to the left edge or the right edge of the bar, and (iii) composition complexity, since the bar and the mark can overlap and occlude each other (as indicated by the "i" for interfering). The annotated picture representation can then be used as one of the knowledge sources in the NLG system to select and structure information appropriately in generating the captions.
To next section.
Paper Sections:To Title page
To Part 1: Introduction
To Part 2: SAGE: A System for Automatic Graphical Explanations
To Part 3: Discourse Strategies for Generating Captions
To Part 5: Generating Explanatory Captions
To Part 6: System Implementation and Evaluation
To Part 7: Related Work
To Part 8: Conclusions and Future Work
To Appendix A
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