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Visualization of multivariate data using preattentive processing

University of British Columbia

A new method for designing multivariate data visualization tools is presented. Multivariate data visualization involves representation of data elements with multiple dimensions in a low dimensional enviroment, such as a computer screen or printed media. Our tools are designed to allow users to perform simple tasks like estimation, target detection, and detection of data boundaries rapidly and accurately. These techniques could be used for large datasets where more traditional techniques do not work well or for time-sensitive applications that require rapid understanding and informative data displays. Our design technique is based on principles arising in an area of cognitive psychology called preattentive processing. Preattentive processing studies visual features that are “preattentively” detected by the human visual system. Viewers do not have to focus their attention on particular regions of an image to determine whether elements with certain features are present or absent. Examples of preattentive features include colour, orientation, intensity, size, shape, curvature, and line length. Because this ability is part of the low-level human visual system, detection is performed very rapidly, almost certainly using a large degree of parallelism. In this thesis we investigate the hypothesis that these features can be used to effectively represent multivariate data elements. Visualization tools that use this technique will allow users to perform rapid and accurate visual processing of their data displays. We chose to investigate two known preattentive features, colour and orientation. The particular question investigated is whether rapid and accurate estimation is possible using these preattentive features. Experiments that simulated displays using our preattentive visualization tool were run. The experiments used data similar to that which occurred in a set of salmon migration studies. This choice was made to investigate the likelihood of our techniques being relevant to real-world problems. Analysis of the results of the experiments showed that rapid and accurate estimation is possible with both colour and orientation. A second question, whether interaction occurs between the two features, was answered negatively. Additional information about exposure durations and feature and data interaction were also discovered.

Thesis/Dissertation

application/pdf

2008-12-24

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http://hdl.handle.net/2429/3321

Computer Science

University of British Columbia

UBCV

DSpace