Comparing Averages in Time Series Data
Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems, page 1095--1104 — May 2012
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.
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BibTex references
@InProceedings{CAFG12, author = "Correll, Michael and Albers, Danielle and Franconeri, Steve and Gleicher, Michael", title = "Comparing Averages in Time Series Data", booktitle = "Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems", pages = "1095--1104", month = "May", year = "2012", publisher = "ACM", doi = "10.1145/2207676.2208556", projecturl = "http://dl.acm.org/citation.cfm?id=2207676.2208556", url = "http://graphics.cs.wisc.edu/Papers/2012/CAFG12" }