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 | |
| State | Published - 2013 |
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Keywords
- Box-Cox
- color mapping
- Data transformation
- normal distribution
- statistical analysis
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Software
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
Automated box-cox transformations for improved visual encoding. / Maciejewski, Ross; Pattath, Avin; Ko, Sungahn; Hafen, Ryan; Cleveland, William S.; Ebert, David S.
In: IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 1, 6155715, 2013, p. 130-140.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Automated box-cox transformations for improved visual encoding
AU - Maciejewski, Ross
AU - Pattath, Avin
AU - Ko, Sungahn
AU - Hafen, Ryan
AU - Cleveland, William S.
AU - Ebert, David S.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Box-Cox
KW - color mapping
KW - Data transformation
KW - normal distribution
KW - statistical analysis
UR - http://www.scopus.com/inward/record.url?scp=84870532940&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84870532940&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2012.64
DO - 10.1109/TVCG.2012.64
M3 - Article
VL - 19
SP - 130
EP - 140
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
SN - 1077-2626
IS - 1
M1 - 6155715
ER -