Spotted: PrePrint: Automated Box-Cox Transformations for Improved Visual Encoding

With Bill Cleveland, a pioneer in statistical graphics. 

PrePrint: Automated Box-Cox Transformations for Improved Visual Encoding

IEEE CS TVCG

This concept of pre-conditioning 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 semi-automatically adjust visual parameters. We focus on time series scaling, axis transformations and color binning for choropleth maps. We illustrate the usage of this transformation using various examples, and discuss the value and some issues in semi-automatically using these transformations for more effective data visualization.