Liquid Conductor: Animated Transitions Across Dimensions

Abstract

This thesis explores the power of visualization in conveying complex data through intuitive visual representations. It investigates the effectiveness of animated transitions in understanding correlations between different visualizations of the same data. While volume is generally considered a less effective encoding of magnitude in visualization, 3D becomes crucial when shape recognition is required. However, when comparing the volume of 3D objects, it is necessary to incorporate the objects themselves in the visualization. The Liquid Conductor program is introduced to address this challenge, which seamlessly transforms 3D objects into bar charts using animated transitions. Leveraging fluid simulation, the program effectively communicates volume differences by filling each object with liquid and pouring it into cylinders, acting as bars within the bar chart visualization.

This thesis explores the power of visualization in conveying complex data through intuitive visual representations. It investigates the effectiveness of animated transitions in understanding correlations between different visualizations of the same data. While volume is generally considered a less effective encoding of magnitude in visualization, 3D becomes crucial when shape recognition is required. However, when comparing the volume of 3D objects, it is necessary to incorporate the objects themselves in the visualization. The Liquid Conductor program is introduced to address this challenge, which seamlessly transforms 3D objects into bar charts using animated transitions. Leveraging fluid simulation, the program effectively communicates volume differences by filling each object with liquid and pouring it into cylinders, acting as bars within the bar chart visualization.