Automated Box-Cox Transformations for Improved Visual Encoding

• Published in: IEEE transactions on visualization and computer graphics Vol. 19; no. 1; pp. 130 - 140
• Main Authors: Maciejewski, R., Pattath, A., Sungahn Ko, Hafen, R., Cleveland, W. S., Ebert, D. S.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: automated box-cox transformations; axis transformations; Box-Cox; choropleth maps; Color; color binning; color mapping; Communities; data preconditioning; Data transformation; data visualisation; Data visualization; Gaussian distribution; Histograms; Hospitals; Image color analysis; improved visual encoding; inference mechanisms; normal distribution; Preconditioning; statistical analysis; statistical community; statistical inference procedures; statistical modeling; time series; time-series scaling; Transformations; Transforms; Visual; Visualization
• ISSN: 1077-2626; 1941-0506
• DOI: 10.1109/TVCG.2012.64

The concept of preconditioning data (utilizing a power transformation as an initial step) for analysis and visualization is well established within the statistical community and is employed as part of statistical modeling and analysis. Such transformations condition the data to various inherent assumptions of statistical inference procedures, as well as making the data more symmetric and easier to visualize and interpret. In this paper, we explore the use of the Box-Cox family of power transformations to semiautomatically adjust visual parameters. We focus on time-series scaling, axis transformations, and color binning for choropleth maps. We illustrate the usage of this transformation through various examples, and discuss the value and some issues in semiautomatically using these transformations for more effective data visualization.