Brushing Moments in Interactive Visual Analysis

Johannes Kehrer, Peter Filzmoser, Helwig Hauser
Brushing Moments in Interactive Visual Analysis
Computer Graphics Forum, 29(3):813-822, June 2010. [Draft] [Slides]

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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.

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BibTeX

@article{Kehrer-2010-mom,
  title =      "Brushing Moments in Interactive Visual Analysis",
  author =     "Johannes Kehrer and Peter Filzmoser and Helwig Hauser",
  year =       "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.",
  month =      jun,
  journal =    "Computer Graphics Forum",
  number =     "3",
  volume =     "29",
  pages =      "813--822",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2010/Kehrer-2010-mom/",
}