Article Agrawala, M, Li, W, & Berthouzoz, F. (2011). Design principles for visual communication. Communications of the ACM. 10 http://m.cacm.acm.org/magazines/2011/4/106586-design-principles-for-visual-communication/fulltext "Skilled visual designers manipulate the perception, cognition, and communicative intent of visualizations by carefully applying principles of good design." "Stage 1. Identify design principles. We identify domain-specific design principles by analyzing the best hand-designed visualizations within a particular information domain. We connect this analysis with research on perception and cognition of visualizations; Stage 2. Instantiate design principles. We encode the design principles into algorithms and interfaces for creating visualizations; and Stage 3. Evaluate design principles. We measure improvements in information processing, communication, and decision making that result from our visualizations. These evaluations also serve to validate the design principles" The authors here used the three stage approach to build automated visualization design systems (cartographic visualization and technical illustration). They are showing the generalizability of these stages/techniques to help build a better understanding of strategies people use to make inferences from visualziation Stage 1 - Identify Design Principles Strategies for extracting and formulating domain-specific design principles: analyze the best hand-designed visualization in the domain Style independence. In order to identify a general set of principles we could apply to a variety of complex 3D objects, we looked for visual techniques common across different artistic styles and types of objects; Generative rules. To ensure that we could apply the principles in a generative manner to create cutaways or exploded views, we formed explicit, well-defined rules describing when and how each principle should be applied. We designed the rules to be as general as possible while remaining consistent with the evidence from the example illustrations; and Perceptual/cognitive rationale. We motivated each principle by hypothesizing a perceptual or cognitive rationale explaining how the convention helps viewers better understand the structure of the 3D object depicted. examine prior research on the perception and cognition of visualizations conduct new user studies that investigate how visual techniques affect perception and cognition Production. Participants create visualizations for a given domain. In the context of assembly instructions, they assembled a TV stand without instructions using only a photograph of the assembled stand as a guide. They then drew a set of instructions showing how to assemble it; Preference. Participants rate the effectiveness of the visualizations. In the assembly-instructions project, a new set of participants assembled the TV stand, without instructions. They then rated the quality of the instructions created by the first set of participants, redrawn to control for clarity, legibility, and aesthetics; and Comprehension. Participants use the ranked visualizations, and we test for improvements in learning, comprehension, and decision making. In the assembly-instructions project, yet another set of participants assembled the TV stand, this time using the instructions rated in the preference phase. Tests showed the highly rated instructions were easier to use and follow; participants spent less time assembling the TV stand and made fewer errors. Stage 2 - Instantiate Design Principles "These principles explain how visual techniques can be used to either emphasize important information or de-emphasize irrelevant details in the display" Stage 3 - Evaluate Design Principles User feedback. We find it is critical to involve users early on and conduct qualitative interviews and surveys to check their overall impressions of the visualizations produced by our systems. User studies. To quantitatively assess the effectiveness of a visualization, we conduct user studies comparing visualizations created with our design algorithms to the best hand-designed visualizations in the domain. Conclusion "Though we presented three strategies for identifying design principles, other strategies may be possible as well. The strategies we presented all require significant human effort to identify commonalities in hand-designed visualizations, synthesize the relevant prior studies in perception and cognition, and conduct such studies. Moreover, the Internet makes a great deal of visual content publicly available, often with thousands of example visualizations within an individual information domain. Thus, a viable alternative strategy for identifying design principles may be to learn them from a large collection of examples using statistical machine-learning techniques."