In this paper we describe a technique for choosing multiple colours for use during data visualization. Our goal is a systematic method for maximizing the total number of colours available for use, while still allowing an observer to rapidly and accurately search a display for any one of the given colours. Previous research suggests that we need to consider three separate effects during colour selection: colour distance, linear separation, and colour category. We describe a simple method for measuring and controlling all of these effects. Our method was tested by performing a set of target identification studies; we analysed the ability of thirty-eight observers to find a colour target in displays that contained differently coloured background elements. Results showed our method can be used to select a group of colours that will provide good differentiation between data elements during data visualization.

This paper presents a new method for using texture to visualize multidimensional data elements arranged on an underlying threedimensional height field. We hope to use simple texture patterns in combination with other visual features like hue and intensity to increase the number of attribute values we can display simultaneously. Our technique builds perceptual texture elements (or pexels) to represent each data element. Attribute values encoded in the data element are used to vary the appearance of a corresponding pexel. Texture patterns that form when the pexels are displayed can be used to rapidly and accurately explore the dataset. Our pexels are built by controlling three separate texture dimensions: height, density, and regularity. Results from computer graphics, computer vision, and cognitive psychology have identified these dimensions as important for the formation of perceptual texture patterns. We conducted a set of controlled experiments to measure the effectiveness of these dimensions, and to identify any visual interference that may occur when all three are displayed simultaneously at the same spatial location. Results from our experiments show that these dimensions can be used in specific combinations to form perceptual textures for visualizing multidimensional datasets. We demonstrate the effectiveness of our technique by applying it to two real-world visualization environments: tracking typhoon activity in southeast Asia, and analyzing ocean conditions in the northern Pacific.

With the advent of computers and more sophisticated electronics, scientists can now collect massive amounts of information. The increasing size and dimensionality of these datasets makes them challenging to visualize in an effective manner. Visualizations must show the global structure of spatial relationships within the dataset while simultaneously representing the local detail of each data element being visualized. Techniques in information visualization provide views of the dataset at multiple scales allowing the user to visualize large numbers of data elements. Unfortunately, these techniques often do not address the problems of visualizing multidimensional datasets. Multidimensional visualizations use color, texture, and other visual features to represent the values of multiple attributes at a single spatial location. However, these techniques do not address how to visualize large numbers of data elements.Visualizations of datasets with large sizes and high dimensionalities are often forced to omit data elements from the current view. This thesis proposes to combine ideas from information and multidimensional visualization with a navigation assistant to help users identify and explore areas of interest within their data. The assistant identifies data elements that are potentially "interesting" to the user, clusters them into spatially coherent regions, and constructs underlying graph structures to connect the regions and the elements they contain. Using graph traversal algorithms, optimal viewpoint construction, and camera planning techniques, the navigation assistant builds informative animated tours of these regions. In this way, the assistant provides an effective tool for exploring a dataset.

The Sitka eddy is a mesoscale eddy, 300 km in diameter, that develops off SE Alaska in about one year in two. The eddy has surface currents exceeding 50 km day−1 and it has been suggested that the eddy could deflect migrating salmon to the south, thereby reducing the proportion of British Columbia (BC) sockeye salmon accessible to Alaskan fishers. We modelled its effects on the migration of sockeye salmon (Oncorhynchus nerka) returning to northern BC, using an individual‐based model to simulate migration paths, migration timing and metabolic costs of salmon with different migration behaviours. Except when their migration behaviour included positive rheotaxis, salmon that encountered the eddy had faster migration times and lower metabolic costs than those that did not. The least complex migration behaviour, compass orientation with no rheotaxis, was only slightly less efficient in metabolic terms than the optimal migration paths determined by dynamic programming. Our simulations show that the Sitka eddy itself does not deflect migrating salmon to the south or south‐east regardless of migration behaviour, but that by interrupting the normal northward flow of the Alaskan Current, the eddy could influence latitude of landfall of migrating salmon.

No abstract