Aesthetics versus Functionality

Information Visualization (2002) 1, 93 – 94. doi:10.1057/palgrave.ivs.9500018 This is the second issue of our young journal. This issue continues what we started in the first issue – to provide an interdisciplinary forum for the field of information visualization. To shape the interdisciplinary forum, we continue to seek original and stimulating articles on a variety of topics that in one way or another contribute to the advancement of information visualization and have the potential to make a significant impact on our research agenda in the field. Contributions to this issue include one commentary and five original research articles towards the fulfillment of this goal. Aaron Marcus in his commentary article addresses a number of challenges which stemmed from the design of advanced vehicle information displays. He outlines these challenges in the broader context of information visualization and emphasizes their implications for information visualization beyond vehicle information displays. His article is entitled ‘Information Visualization for Advanced Vehicle Displays.’ The first research article in this issue is authored by Colin Ware, Helen Purchase, Linda Colpoys and Matthew McGill on cognitive measurements of graph aesthetics. Graph drawing is concerned with ways to depict a network algorithmically. Graph drawing algorithms typically rely on a set of aesthetic heuristics – rules that algorithms aim to optimize quantitatively. However, there have been few empirical studies of these aesthetic heuristics in the relevant literature. The authors of this article introduce a methodology for evaluating the cognitive cost of graph aesthetics and apply it to the task of finding the shortest paths in spring layout graphs, one of the widely known graph drawing algorithms in information visualization. They provide cognitive cost estimates for three important parameters: the length of the path, continuity, and edge crossings. The number of branches emanating from nodes on the path is also found to be important. The second research article is by Archana Sangole and George K. Knopf on representing high-dimensional data in a spherical self-organizing feature map (SOM). SOMs have a unique position in the history of information visualization. Traditionally, SOMs are represented in the form of a flat two-dimensional map. Sangole and Knopf present a three-dimensional color-coded surface model. They illustrate the use of their model with a small synthetic test data set and a large environmental database. Robert Spence addresses a wide-ranging question in the third research article: How should we design information visualization interfaces that would enable users to navigate effectively in information space? He introduces the concept of sensitivity encoding as part of his design guideline for navigation in information spaces. Sensitivity encoding is a feature of a data display. The goal of sensitivity encoding is to provide ‘road signs’ to guide users through information space. The fourth article is strongly application-oriented. Mohamed Sammouda, Rachid Sammouda, Noboru Niki and Kiyoshi Mukai describe a liver cancer detection system. The system can be used to analyze digitized color images of tissue samples obtained using needle biopsy. The key to the system is a combination of an unsupervised segmentation algorithm and an analysis algorithm based on image quantization. The segmentation Information Visualization (2002) 1, 93 – 94 a 2002 Palgrave Macmillan Ltd. All rights reserved 1473 – 8716 $15.00