FanLens: A Visual Toolkit for Dynamically Exploring the Distribution of Hierarchical Attributes

Abstract

Radial, space-filling visualization is very useful for representing the distribution of attributes in hierarchical data; however it also suffers from its drawbacks in terms of view transition, context preservation, thin slices, flexibility and large sized data support. To address these problems, we propose FanLens, an enhancement upon existing approaches with new features like incremental layout and fisheye distortion based selecting. This visual toolkit also features dynamic hierarchy specification, dynamic visual property mapping, smooth animation, etc. We illustrate the effectiveness of our technique with two examples of case study and results from informal user experiments.