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Issue No.06 - November/December (2010 vol.16)
pp: 1036-1043
Justin Talbot , Stanford University
Sharon Lin , Stanford University
Pat Hanrahan , Stanford University
ABSTRACT
The non-data components of a visualization, such as axes and legends, can often be just as important as the data itself. They provide contextual information essential to interpreting the data. In this paper, we describe an automated system for choosing positions and labels for axis tick marks. Our system extends Wilkinson’s optimization-based labeling approach to create a more robust, full-featured axis labeler. We define an expanded space of axis labelings by automatically generating additional nice numbers as needed and by permitting the extreme labels to occur inside the data range. These changes provide flexibility in problematic cases, without degrading quality elsewhere. We also propose an additional optimization criterion, legibility, which allows us to simultaneously optimize over label formatting, font size, and orientation. To solve this revised optimization problem, we describe the optimization function and an efficient search algorithm. Finally, we compare our method to previous work using both quantitative and qualitative metrics. This paper is a good example of how ideas from automated graphic design can be applied to information visualization.
INDEX TERMS
axis labeling, nice numbers
CITATION
Justin Talbot, Sharon Lin, Pat Hanrahan, "An Extension of Wilkinson’s Algorithm for Positioning Tick Labels on Axes", IEEE Transactions on Visualization & Computer Graphics, vol.16, no. 6, pp. 1036-1043, November/December 2010, doi:10.1109/TVCG.2010.130
REFERENCES
[1] W. S. Cleveland, The Elements of Graphing Data. Wadsworth Publ. Co., Belmont, CA, USA, 1985.
[2] W. S. Cleveland and R. McGill, The many faces of a scatterplot. Journal of the American Statistical Association, 79 (388): 807–822, 1984.
[3] P. Heckbert, Nice numbers for graph labels. In A. Glassner editor, Graphics Gems, pages 61–63 657–659. Academic Press, Boston, 1990.
[4] J. A. Nelder, Algorithm AS 96: A simple algorithm for scaling graphs. Applied Statistics, 25 (1): 94–96, 1976.
[5] R Development Core Team. , R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2009. ISBN 3–900051–07–0.
[6] R. E. Reys, J. F. Rybolt, B. J. Bestgen, and J. W. Wyatt, Processes used by good computational estimators. Journal for Research in Mathematics Education, 13 (3): 183–201, 1982.
[7] D. N. Sparks, Algorithm AS 44: Scatter diagram plotting. Applied Statistics, 20 (3): 327–331, 1971.
[8] W. D. Stirling, Algorithm AS 168: Scale selection and formatting. Applied Statistics, 30 (3): 339–344, 1981.
[9] R. P. Thayer and R. F. Storer, Algorithm AS 21: Scale selection for computer plots. Applied Statistics, 18 (2): 206–208, 1969.
[10] E. R. Tufte, The Visual Display of Quantitative Information. Graphics Press, Cheshire, CT, USA, 1986.
[11] L. Wilkinson, The Grammar of Graphics (Statistics and Computing). Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2005.
[12] L. Wilkinson, Personal communication, March 2010.
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