A User Study on 2D Wind Visualization Methods

by Cao, Chen

Abstract (Summary)
Many visualization methods exist in the field of scientific visualization, and new methods are being created each year. However, there has been very little research on how effective existing methods are in revealing important features of the underlying data they represent. In fact, lack of empirical user studies has been identified as one of the major problems within the field of scientific visualization. This thesis contributes to this area by comparing the effectiveness of several commonly used visualization methods for 2 dimensional wind data through user testing. The term ¡°effective¡± was re-defined in the context of visualization, and two experiments were carefully designed to target at wind speed and direction respectively. Data gathered through these experiments was used towards the evaluation of the visualization technique involved. In the first experiment dealing with wind speed, the following six visualization methods were tested: (1) Arrows, (2) Cones, (3) Streamlines, (4) Arrows w/ Color Map, (5) Cones w/ Color Map, and (6) Streamline w/ Color Map. Users were asked to locate the strongest wind within a visualization. Data collected was analyzed to determine the difference between the user selected wind speed and the actual maximum wind speed in the given picture. The second experiment focused on wind direction, and five visualization methods were evaluated, namely (1) Arrows, (2) Cones, (3) Streamlines, (4) Normalized Arrows, and (5) Normalized Cones. Users were asked to identify the wind direction pattern type in each picture, and the number of correct answers was recorded and analyzed. Statistical analyses of the data using Analysis of Variance (ANOVA) and pairwise t-test showed that the Cones, Arrows w/ Color map, and Cones w/ Color Map methods outperformed the rest of the methods in experiment one, and that the Streamlines method outperformed all other methods in experiment two. Users¡¯ subjective opinions regarding the ease of use of each visualization methods agreed with the statistical results.
Bibliographical Information:

Advisor:Donald J. Morton; Rudy A. Gideon; Yolanda Jacobs Reimer

School:The University of Montana

School Location:USA - Montana

Source Type:Master's Thesis

Keywords:computer science


Date of Publication:09/19/2007

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