Automated box-cox transformations for improved visual encoding

Ross Maciejewski; Avin Pattath; Sungahn Ko; Ryan Hafen; William S. Cleveland; David S. Ebert

doi:10.1109/TVCG.2012.64

Automated box-cox transformations for improved visual encoding

Ross Maciejewski, Avin Pattath, Sungahn Ko, Ryan Hafen, William S. Cleveland, David S. Ebert

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

Abstract

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.

Original language	English (US)
Article number	6155715
Pages (from-to)	130-140
Number of pages	11
Journal	IEEE Transactions on Visualization and Computer Graphics
Volume	19
Issue number	1
DOIs	https://doi.org/10.1109/TVCG.2012.64
State	Published - Jan 1 2013

Keywords

Box-Cox
Data transformation
color mapping
normal distribution
statistical analysis

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Computer Graphics and Computer-Aided Design

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10.1109/TVCG.2012.64

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