Laidlaw David H, Kirby Robert M, Jackson Cullen D, Davidson J Scott, Miller Timothy S, da Silva Marco, Warren William H, Tarr Michael J
Computer Science Department, Brown University, Providence, RI 02912, USA.
IEEE Trans Vis Comput Graph. 2005 Jan-Feb;11(1):59-70. doi: 10.1109/TVCG.2005.4.
We present results from a user study that compared six visualization methods for two-dimensional vector data. Users performed three simple but representative tasks using visualizations from each method: 1) locating all critical points in an image, 2) identifying critical point types, and 3) advecting a particle. Visualization methods included two that used different spatial distributions of short arrow icons, two that used different distributions of integral curves, one that used wedges located to suggest flow lines, and line-integral convolution (LIC). Results show different strengths and weaknesses for each method. We found that users performed these tasks 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. Expert user performance was not statistically different from nonexpert user performance. We used several methods to analyze the data including omnibus analysis of variance, pairwise t-tests, and graphical analysis using inferential confidence intervals. We concluded that using the inferential confidence intervals for displaying the overall pattern of results for each task measure and for performing subsequent pairwise comparisons of the condition means was the best method for analyzing the data in this study. 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 the tradeoffs among the methods. In the future, we also envision extending this work to more ambitious comparisons, such as evaluating two-dimensional vectors on two-dimensional surfaces embedded in three-dimensional space and defining analogous tasks for three-dimensional visualization methods.
我们展示了一项用户研究的结果，该研究比较了用于二维向量数据的六种可视化方法。用户使用每种方法的可视化进行了三项简单但具有代表性的任务：1）在图像中定位所有关键点，2）识别关键点类型，以及3）平流一个粒子。可视化方法包括两种使用不同短箭头图标空间分布的方法、两种使用不同积分曲线分布的方法、一种使用定位以暗示流线的楔形的方法以及线积分卷积（LIC）。结果显示了每种方法的不同优缺点。我们发现用户使用以下方法能更好地完成这些任务：1）显示向量场内向量的符号，2）直观地表示积分曲线，以及3）直观地表示关键点的位置。专家用户的表现与非专家用户的表现没有统计学差异。我们使用了几种方法来分析数据，包括方差的综合分析、成对t检验以及使用推断置信区间的图形分析。我们得出结论，使用推断置信区间来显示每个任务度量的结果总体模式以及对条件均值进行后续成对比较是本研究中分析数据的最佳方法。这些结果为一些关于可视化方法的传闻证据提供了定量支持。这些任务和测试框架也为比较其他可视化方法、创建更有效的方法以及定义额外任务以进一步理解这些方法之间的权衡提供了基础。未来，我们还设想将这项工作扩展到更具挑战性的比较，例如在嵌入三维空间的二维曲面上评估二维向量，并为三维可视化方法定义类似的任务。
