Visual Analysis of the Air Pollution Problem in Hong Kong
IEEE Transactions on Visualization and Computer Graphics
Toward visual analysis of ensemble data sets
Proceedings of the 2009 Workshop on Ultrascale Visualization
Visualization of uncertain scalar data fields using color scales and perceptually adapted noise
Proceedings of the ACM SIGGRAPH Symposium on Applied Perception in Graphics and Visualization
Feature identification and extraction in function fields
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
Interactive visualization of function fields by range-space segmentation
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
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In this paper, we define distributions as a new data type and address the challenges of visualizing spatial distribution data sets. Numerous visualization techniques exist today for dealing with scalar data. That is, there is a scalar value at each spatial location, which may also be changing over time. Likewise, techniques exist for dealing with vector, tensor and multivariate data sets. However, there is currently no systematic way of dealing with distribution data where there is a collection of values for the same variable at every location and time. Distribution data is increasingly becoming more common as computers and sensor technologies continue to improve. They have also been used in a number of fields ranging from agriculture, engineering design and manufacturing to weather forecasting. Rather than developing specialized visualization techniques for dealing with distribution data, the approach presented in this paper is to find a systematic way of extending existing visualization methods to handle this new data type. For example, we would like to be able to generate isosurfaces of 3D scalar distribution data sets, or generate streamlines of vector distribution data sets. In order to accomplish this goal, we propose the use of a set of mathematically and procedurally defined operators that allow us to work directly on distributions. Color images can also be found in www.cse.ucsc.edu/research/avis/operator.html.