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Brushing Moments in Interactive Visual Analysis

Johannes Kehrer, Peter Filzmoser, Helwig Hauser

ARTICLE, Computer Graphics Forum, june, 2010

Abstract

We present a systematic study of opportunities for the interactive visual analysis of multi-dimensional scientific data. This is based on the integration of statistical aggregations along selected data dimensions in a framework of coordinated multiple views (with linking and brushing). Traditional and robust estimates of the four statistical moments (mean, variance, skewness, and kurtosis) as well as measures of outlyingness are integrated in an iterative visual analysis process. Brushing particular statistics, the analyst can investigate data characteristics such as trends and outliers. We present a categorization of beneficial combinations of attributes in 2D scatterplots: (a) k-th vs. (k+1)-th statistical moment of a traditional or robust estimate, (b) traditional vs. robust version of the same moment, (c) two different robust estimates of the same moment. We propose selected view transformations to iteratively construct this multitude of informative views as well as to enhance the depiction of the statistical properties in the scatterplots. In the framework, we interrelate the original distributional data and the aggregated statistics, which allows the analyst to work with both data representations simultaneously. We demonstrate our approach in the context of two visual analysis scenarios of multi-run climate simulations.

Published

Computer Graphics Forum

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BibTeX

@article{kehrer10moments,
  author = {Johannes Kehrer and Peter Filzmoser and Helwig Hauser},
  title = {Brushing Moments in  Interactive Visual Analysis},
  year = {2010},
  month = {june},
  abstract = {We present a systematic study of opportunities for the 
interactive visual analysis of multi-dimensional scientific data. 
This is based on the integration of statistical aggregations along 
selected data dimensions in a framework of coordinated multiple views 
(with linking and brushing). Traditional and robust estimates of the 
four statistical moments (mean, variance, skewness, and kurtosis) as 
well as measures of outlyingness are integrated in an iterative visual 
analysis process. Brushing particular statistics, the analyst can 
investigate data characteristics such as trends and outliers. We 

2D scatterplots: (a) k-th vs. (k+1)-th statistical moment of a 
traditional or robust estimate, (b) traditional vs. robust version 
of the same moment, (c) two different robust estimates of the same 
moment. We propose selected view transformations to iteratively 
construct this multitude of informative views as well as to enhance 
the depiction of the statistical properties in the scatterplots. In 
the framework, we interrelate the original distributional data and 
the aggregated statistics, which allows the analyst to work with both 
data representations simultaneously. We demonstrate our approach in 
the context of two visual analysis scenarios of multi-run climate 
simulations.},
  journal = {Computer Graphics Forum},
  event = "EuroVis 2010",
  volume = {29},
  number = {3},
  pages = {813--822},
  location = "Bordeaux, France",




  URL = {http://dx.doi.org/10.1111/j.1467-8659.2009.01697.x}
}






 Last Modified: Jean-Paul Balabanian, 2014-04-09