stage 2. instantiate Design Principles

Designing a visualization usually requires choosing visual properties or attributes for each element in the display; for example, to create a route map, the designer must choose attributes, including position, size, and orientation for each road, landmark, and label that appears in the map. Similarly, to create a cutaway illustration, the designer must choose how and where to cut each structure that occludes the target part. Because there are many possible choices for each attribute, the design space of possible visualizations is usually quite large. To build automated visualization design systems, we treat the relevant design principles as guidelines for making these design decisions. The principles help us navigate through the design space and obtain an effective design.

Most design principles are stated as qualitative guidelines, rather than as procedures we can directly instantiate in an automated design algorithm. The challenge is to transform such high-level principles into imple-mentable algorithms.

Design principles generally fall into two categories: design rules and evaluation criteria. Design rules separate the design space into regions containing effective designs and those containing inviable designs. They are essentially hard constraints in the design space. In creating route maps, for example, designers commonly adjust the turn angle to emphasize the orientation of the turn, to the left or to the right. However, adjusting the turn angle so much that a left turn appears to be a right turn or vice versa is unacceptable. This design rule puts a hard constraint on how much designers are able to adjust the turn angle.

Evaluation criteria quantify the effectiveness of some aspect of the visualization. We can assess the overall effectiveness of a visualization by considering a set of evaluation criteria covering all major aspects of the visual design. In creating an exploded view, for instance, designers must balance two such criteria: part separation and compactness. A good exploded view separates the parts so all of them are visible, yet the visualization must also remain compact and maintain a

these principles
explain how
visual techniques
can be used to
either emphasize
important
information or
de-emphasize
irrelevant details
in the display.

roughly square aspect ratio to make the best use of available screen space. To quantify the overall effectiveness of an exploded view we measure the visibility of each part, as well as the compactness of the overall visualization. Similarly, in designing route maps, designers must ensure that all roads are visible. To quantify this criterion, we compute the length of each road in the map and check the length is greater than some minimum visibility threshold. The number of roads longer than the threshold length is a quantitative measure of the effectiveness of the map with respect to this criterion.

Given a set of design rules and quantitative evaluation criteria, we can use procedural techniques to build an automated visualization design system; for example, our system for designing cutaways and exploded views is driven exclusively by procedural techniques. In this case, we encode the design rules as a decision tree describing how to cut or explode away occluding parts based on their geometry. Another approach is to consider visualization design as an energy-minimizing optimization problem. In this case, we treat the design rules as hard constraints that define the boundaries of the design space and the evaluation criteria as soft constraints that guide the system to the optimal visualization. While this op-timization-based approach is general, we have found it essential to develop a set of design rules and evaluation criteria that sufficiently limit the design space so it is feasible to complete the optimization. Both LineDrive and our assembly-instruction design system use such an energy-minimizing optimization.

 

stage 3. evaluate Design Principles The final stage of our approach is to measure the usefulness of the visualizations produced by our automated design systems. We consider several such measures, including feedback from users in the form of qualitative interviews and quantitative usage statistics. In some cases, we have also conducted more-formal user studies to check how well the visualizations improve information processing, communication, and decision making.