Calibrated and Overlapping Columns: Visualizing A single large-range or Multivariate Non-Spatial quantities in Spatial domains
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DepartmentComputer Science and Electrical Engineering
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Recent growth in the field of data collection and storage has far outpaced our ability to effectively analyze the data using traditional techniques. As a result of this ability to effectively visualize data in the form of images and graphics to easily understand and effectively analyze the data has become ever more important. However, representing data in a way that is perceptually efficient brings challenges of its own. One such challenge is the ability to effectively visualize large range and multivariate quantitative data in spatial domains. In this dissertation, we explore the various aspects of visualizing large range and multidimensional quantitative spatial data and propose visualization techniques designed to effectively encode them in a perceptually discernible manner. Using the proposed techniques, we developed a new visualization tool that enables the user to perform new quantitative discrimination tasks on large range data in both 2D and 3D visual environment. The tool also provides multivariate visualization of high-dimensional data that enables easy and faster discovery of details about the differ- ent attributes, all in one visualization. We evaluated its application using data-sets from two visualization domains (Quantum physics visualization and brain tomographic visualization). We conclude with case studies from visualization experts from individual application domain who used the tool to augment their previous efforts in performing various pattern detection and discrimination tasks on the data, providing evidence about the efficacy of the system.