Skip to Main Content
Presents results from a user study that compared six visualization methods for 2D vector data. Two methods used different distributions of short arrows, two used different distributions of integral curves, one used wedges located to suggest flow lines, and the final one was line-integral convolution (LIC). We defined three simple but representative tasks for users to perform using visualizations from each method: (1) locating all critical points in an image, (2) identifying critical point types, and (3) advecting a particle. The results show different strengths and weaknesses for each method. We found that users performed better with methods that: (1) showed the sign of vectors within the vector field, (2) visually represented integral curves, and (3) visually represented the locations of critical points. These results provide quantitative support for some of the anecdotal evidence concerning visualization methods. The tasks and testing framework also provide a basis for comparing other visualization methods, for creating more effective methods and for defining additional tasks to further understand tradeoffs among methods. They may also be useful for evaluating 2D vectors on 2D surfaces embedded in 3D and for defining analogous tasks for 3D visualization methods.