<title>Strategies for scientific visualization: analysis and comparison of current techniques</title>

Abstract

STRATEGIES FOR SCIENTIFIC VISUALIZATION:ANALYSIS AND COMPARISON OF CURRENT TECHNIQUESJohn Andrew Berton, Jr.The Ohio Supercomputer Graphics Project1224 Kinnear Road, Columbus, Ohio 43212-1154ABSTRACTThis presentation consists of an overview of scientific images and animation produced recently at van-ous research centers. Strategies of visualization are discussed with respect to these images and the datathey represent. This discussion focuses not only on software issues, such as interactivity and data handling,but also on visual and cognitive issues associated with visualization.Presented images include computational fluid dynamics simulations, meteorological and atmosphericsimulations and recordings, astrophysical simulations, and field recording of natural data. Specific visual-ization techniques under discussion include color contour mapping in two and three dimensions, surfaceand isosurface mapping, volume rendering, and glyph and particle representation.In addition to explanations of techniques and interpretations of data provided by these techniques, dif-ferent strategies within each technique are explored. Comparisons are also made between different strate-gies relative to identical or similar databases.In the process of these explanations and comparisons, some general ideas about visualization are re-vealed. These points are emphasized, as they relate to a specific database, ranges of similar databases, andscientific datain general. Thes.e points are ofinterest to scientists working in visualization, as they indicateefficient and effective routes to the better understanding of large databases.1. INTRODUCTIONThere are as many ways of visualizing scientific data as there are different data sets to visualize. Scien-tists must select from the range of options a visualization strategy which fits the nature of his data, and thenature of his exploration of that data. A strategy for visualization is not just the selection of a method, it isthe orchestration of the hardware and software tools upon which the method relies. Additionally, the suc-cessful execution of the strategy is dependant on an understanding of the strengths and weaknesses of thestrategy vis -a- vis the data under analysis, combined with an informed application of the elements whichdefine the method. In the following pages, some of the more common visualization strategies are dis-cussed, with special attention to how different strategies interpret the same data base, and how the controlof individual elements can enhance or detract from the success of the visualization strategy.2. CATEGORIZING STRATEGIES FOR VISUALIZATIONThe nature of how a data base is visualized is dependant on many factors, including the size of the data,and the ability of the computer system in use to handle data. Visualization strategies can be divided into afew concise groups which define how the scientist interacts with his data. Marshall et. al define these threegroups as post-processing, tracking, and The first of these, post processing, is the most common.In this method, data is calculated before the visualization process begins. Tracking is slightly more inter-active, in that the scientist does some visualization as the calculations proceed, allowing the monitor toterminate the process when the visualization goals have been reached. Steering is fully interactive graph-ical response and control of both the concurrent calculation and the visualization of that data.These three categories show a progression from non-interactive to highly interactive with respect to the