Abstracts

In this talk, I will chart the course of visual depictions of data over the last 200 years, from the classic data graphics of Playfair and Minard to modern interactive visualization systems. Along the way, we will consider the factors that contribute to the effectiveness of information graphics and the role of computational tools in the visualization process. I will conclude with a review of current research directions in interactive visualization and suggest opportunities for future investigation.

Effective visualizations can help analysts rapidly find patterns lurking within large data sets and they can help audiences quickly understand complex ideas. Yet, even with the aid of computers, hand-designing effective visualizations is time-consuming and requires considerable human effort. The challenge is to develop new algorithms and user interfaces that facilitate visual communication by making it fast and easy to generate compelling visual content.

Skilled human designers use a variety of design principles to improve the perception, cognition and communicative intent of an image. In this talk I'll describe techniques for identifying the appropriate design principles within specific domains including cartography, and structural illustrations of complex architectural, mechanical and anatomical objects. For each of these domains I'll show how to algorithmically instantiate design principles within an automated design system or interactive design tool. I'll conclude by showing how this line of research opens new directions for future work on creating effective visual content.

One reason diagrams work is that they use the concrete to convey the abstract as well as the concrete: elements and spatial relations on the page stand for elements and relations in abstract domains. This allows turning abstract judgments, which are often difficult, into spatial judgments, which are usually easier. Another reason diagrams work is that the elements and spatial relations can carry abstract meanings that can be readily understood in context. This controversial contention will be supported by research on comprehension and production.

Problems concerning scientists’ uses of representations have received quite a bit of attention recently. The focus has been on how such representations get their contents and on just what those contents are. Less attention has been paid to what makes certain kinds of scientific representations different from one another and thus well suited to this or that epistemic end. This article considers the latter question with particular focus on the distinction between images and graphs on the one hand and descriptions and related representations on the other. The article claims that images are special in that they present information immediately across many levels of abstraction. Immediacy is explicated in terms of extractability of information, syntactic salience, and semantic salience. Thinking of representations in this way sheds light on the way in which images are and should be used in science.

Since the later eighteenth century, diagrams have been enlisted increasingly to represent complex natural processes, objects uncovered by scientific investigations, or intricate new mechanical inventions. "The Culture of Diagram" argues that the emergent value of diagrams as working objects depends upon the open-ended nature of their representations. Today, process as knowledge is the premise real-time data systems, but the principal was explored well before computers in the early history of the culture of diagram.

I discuss Euclid’s diagrammatic inferences in the light of Eu, a formalization of the arguments in the text. Eu was developed to demonstrate that, contrary to the received modern view, Euclid’s use of diagrams is mathematically legitimate and rigorous. After describing how Eu works as a formal system, I focus on the inferences that it isolates as distinctly diagrammatic. If the Eu picture of Euclid’s geometric method is accurate, Euclid presupposes of those reading his proofs the ability to ascertain certain kinds of positional invariants from diagrams. A single diagram in an Eu proof serves to represent a range of configurations---i.e. the range of configurations the proof is about. In making a diagrammatic inference, one determines that a positional relation manifest in the single diagram holds independently of all possible variations within this range of configurations.