A Scalable 2D-Multivariate Data Visualization

It is important for many applications to visualize 2D-multivariate data with multiple attributes in one window. In this paper, we present a scalable zoom-independent 2D-multivariate data visualization method. This method not only visualizes the changes of 2D-multivariate data in one window, but also allows users to interactively explore data at different zooming levels. First, we design and perform visual experiments to determine how to map the changes of visual channels of textons to the changes of data attribute values. Next, the example-based anisometric texture synthesis algorithm is adopted to visualize the patterns of 2D-multivate data based on the above visual mapping. Then we design user interactions including zooming and translation to explore the data at different levels of precision. We propose to accelerate the anisometric texture synthesis with CUDA in order to achieve the interactivity of such user operations. Last, our visualization method is applied to the global climate data to get the visualization result of global climate pattern. To verify the effectiveness of our method, we design a user study to compare our method to the other two visualization methods. The results of our user study show that our method accomplishes the visualization tasks more efficiently than the other two existing methods.