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This report documents the program and the outcomes of Dagstuhl Seminar 18041 "Foundations of Data Visualization". It includes a discussion of the motivation and overall organization, an abstract from each of the participants, and a report about each of the working groups.

This report documents the program and the outcomes of Dagstuhl Seminar 14231 "Scientific Visualization". It includes a discussion of the motivation and overall organization, an abstract from each of the participants, and a report from each of the working groups.

We describe an application of geospatial visualization and AI search techniques to the problem of school redistricting, in which students are assigned to home schools within a county or school district. This is a multicriteria optimization problem in which competing objectives must be considered, such as school capacity, busing costs, and socioeconomic distribution. Additionally, school assignments need to be made for three different levels (elementary, middle, and high school) in a way which allows children to move from one school to the next with a cohort of sufficient size. Because of the complexity of the decision-making problem, tools are needed to help end users generate, evaluate, and compare alternative school assignment plans. A key goal of our research is to aid users in finding multiple qualitatively different redistricting plans that represent different tradeoffs in the decision space. We present visualization techniques which can be used to visualize the quality of spatial partititioning plans, compare the alternatives presented by different plans, and understand the interrelationships of plans at different educational levels. We demonstrate these techniques on partitions created through both manual construction and intelligient search processes for the population data of the school district of Howard County, Maryland.

Accurately and automatically conveying the structure of a volume model is a problem not fully solved by existing volume rendering approaches. Physics-based volume rendering approaches create images that may match the appearance of translucent materials in nature, but may not embody important structural details. Transfer function approaches allow flexible design of the volume appearance, but generally require substantial hand tuning for each new data set in order to be effective. We have introduced the volume illustration approach, combining the familiarity of a physics-based illumination model with the ability to enhance important features using non-photorealistic rendering techniques. Since features to be enhanced are defined on the basis of higherorder model characteristics rather than volume sample value, the application of volume illustration techniques requires less manual tuning than the design of a good transfer function. Volume illustration provides a flexible unified framework for enhancing structural perception of volume models through the amplification of features, the addition of illumination effects, and the application of procedural textures. Volume illustration works on both presampled and procedurally defined volume models, enabling a range of image styles from practical technical illustrations to more abstract painterly effects.