Comparing averages in time series data

Michael Correll*, Danielle Albers, Steven Franconeri, Michael Gleicher

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations

Abstract

Visualizations often seek to aid viewers in assessing the big picture in the data, that is, to make judgments about aggregate properties of the data. In this paper, we present an empirical study of a representative aggregate judgment task: finding regions of maximum average in a series. We show how a theory of perceptual averaging suggests a visual design other than the typically-used line graph. We describe an experiment that assesses participants' ability to estimate averages and make judgments based on these averages. The experiment confirms that this color encoding significantly outperforms the standard practice. The experiment also provides evidence for a perceptual averaging theory.

Original languageEnglish (US)
Title of host publicationConference Proceedings - The 30th ACM Conference on Human Factors in Computing Systems, CHI 2012
Pages1095-1104
Number of pages10
DOIs
StatePublished - May 24 2012
Event30th ACM Conference on Human Factors in Computing Systems, CHI 2012 - Austin, TX, United States
Duration: May 5 2012May 10 2012

Other

Other30th ACM Conference on Human Factors in Computing Systems, CHI 2012
CountryUnited States
CityAustin, TX
Period5/5/125/10/12

Keywords

  • Colorfields
  • Information visualization
  • Line graphs
  • Visualization evaluation

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

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