Skip to main content
SpringerLink
Log in

Multivariate Data Glyphs: Principles and Practice

  • Chapter
Handbook of Data Visualization

Part of the book series: Springer Handbooks Comp.Statistics ((SHCS))

Abstract

In the context of data visualization, a glyph is a visual representation of a piece of data where the attributes of a graphical entity are dictated by one or more attributes of a data record. For example, the width and height of a box could be determined by a student’s score on the midterm and final exam for a course, while the box’s color might indicate the genderof the student.Thedefinitionabove is ratherbroad, as it can cover such visual elements as the markers in a scatterplot, the bars of a histogram, or even an entire line plot. However, a narrower definition would not be sufficient to capture the wide range of data visualization techniques that have been developed over the centuries that are termed glyphs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 429.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 549.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Anderson, E. (1957). A semigraphical method for the analysis of complex problems. Proc Natl Acad Sci USA, 13:923–927.

    Article  Google Scholar 

  • Andrews, D.F. (1972). Plots of high dimensional data. Biometrics, 28:125–136.

    Article  Google Scholar 

  • Ankerst, M., Berchtold, S. and Keim, D. (1998). Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In: Proceedings of 1998 IEEE symposium on information visualization, pp. 52–60. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Beddow, J. (1990). Shape coding of multidimensional data on a microcomputer display. In: Proceedings of 1990 IEEE conference on visualization, pp. 238–246. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Bertin, J. (1981). Semiology of graphics. University of Wisconsin Press, Madison, WI.

    Google Scholar 

  • Borg, I. and Staufenbiel, T. (1992). Performance of snow flakes, suns, and factorial suns in the graphical representation of multivariate data. Multivariate Behav Res, 27:43–45.

    Article  Google Scholar 

  • Chernoff, H. (1973). The use of faces to represent points in k-dimensional space graphically. J Am Stat Assoc, 68:361–368.

    Article  Google Scholar 

  • Chernoff, H. and Rizvi, M.H. (1975). Effect on classification error of random permutations of features in representing multivariate data by faces. J Am Stat Assoc, 70:548–554.

    Article  Google Scholar 

  • Chuah, M., Eick, S. (1998). Information rich glyphs for software management data. IEEE Comput Graph Appl, 18:24–29.

    Article  Google Scholar 

  • Cleveland, W. and McGill, R. (1984). Graphical perception: theory, experimentation and application to the development of graphical methods. J Am Stat Assoc, 79:531–554.

    Article  MathSciNet  Google Scholar 

  • Cleveland, W. (1993). Visualizing Data. Hobart, Summit, NJ.

    Google Scholar 

  • Cluff, E., Burton, R.P. and Barrett, W.A. (1991). A survey and characterization of multidimensional presentation techniques. J Imag Technol, 17:142–153.

    Google Scholar 

  • Di Battista, G., Eades, P., Tamassia, R. and Tollis, I. (1999). Graph Drawing: Algorithms for the Visualization of Graphs. Prentice-Hall, Upper Saddle River, NJ.

    MATH  Google Scholar 

  • du Toit, S., Steyn, A. and Stumpf, R. (1986). Graphical Exploratory Data Analysis. Springer, Berlin Heidelberg New York.

    MATH  Google Scholar 

  • Ebert, D., Rohrer, R., Shaw, C., Panda, P., Kukla, J. and Roberts, D. (1999). Procedural shape generation for multi-dimensional data visualization. In: Proceedings of Data Visualization ’99, pp. 3–12. Springer, Berlin Heidelberg New York.

    Google Scholar 

  • Friedman, J., Farrell, E., Goldwyn, R., Miller, M. and Sigel, J. (1972). A graphic way of describing changing multivariate patterns. In: Proceeding of the 6th Interface Symposium on Computer Science and Statistics, pp. 56-59.

    Google Scholar 

  • Friendly, M. and Kwan, E. (2003). Effect ordering for data displays. Comput Stat Data Anal, 43:509–539.

    Article  MathSciNet  Google Scholar 

  • Fuhrmann, A. and Groller, E. (1998). Real-time techniques for 3D flow visualization. In: Proceedings of 1998 IEEE conference on visualization, pp. 305–312. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Globus, A., Levit, C. and Lasinski, T. (1991). A tool for visualizing the topology of three-dimensional vector fields. In: Proceedings of 1991 IEEE conference on visualization, pp. 33–40. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Gnanadesikan, R. (1977). Methods of Statistical Data Analysis of Multivariate Observations. Wiley, New York.

    MATH  Google Scholar 

  • Hartigan, J. (1975). Printer graphics for clustering. J Stat Comput Simulat, 4:187–213.

    Article  MATH  Google Scholar 

  • Helman, J. and Hesselink, L. (1991). Visualizing vector field topology in fluid flows. Comput Graph Appl, 11:36–46.

    Article  Google Scholar 

  • Keim, D. and Hermann, A. (1998). The Gridfit algorithm: an efficient and effective approach to visualizing large amounts of spatial data. In: Proceedings of 1998 IEEE conference on visualization, pp. 181–186. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Kerlick, G. (1990). Moving iconic objects in scientific visualization. In: Proceedings of 1990 IEEE conference on visualization, pp. 124–130. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Klassen, R. and Harrington, S. (1991). Shadowed hedgehogs: a technique for visualizing 2D slices of 3D vector fields. In: Proceedings of 1991 IEEE conference on visualization, pp. 148–153. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Kleiner, B. and Hartigan, J. (1981). Representing points in many dimension by trees and castles. J Am Stat Assoc, 76:260–269.

    Article  Google Scholar 

  • Lee, M.D., Reilly, R.E. and Butavicius, M.A. (2003). An empirical evaluation of Chernoff faces, star glyphs, and spatial visualizations for binary data. In: Proceedings of 2003 Australasian symposium on information visualisation. Australian Computer Society, Australia.

    Google Scholar 

  • Leung, Y. and Apperley, M. (1994). A review and taxonomy of distortion-oriented presentation techniques. ACM Trans Comput-Hum Interact, 1:126–160.

    Article  Google Scholar 

  • Levkowitz, H. (1991). Color icons: merging color and texture perception for integrated visualization of multiple parameters. In: Proceedings of 1991 IEEE conference on visualization, pp. 164–170. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Lipchak, B. and Ward, M. (1997). Visualization of cyclic multivariate data. In: Proceedings of 1997 IEEE conference on visualization, Late Breaking Hot Topics, pp. 61–64. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Mezzich, J.D. (1978). A comparison of graphical representations of multidimensional psychiatric diagnostic data. In: Wang, P. (ed) Graphical Representation of Multivariate Data. Academic, New York.

    Google Scholar 

  • Morris, C.J., Ebert, D.S. and Rheingans, P. (1999). An experimental analysis of the effectiveness of features in Chernoff faces. In: Proceedings of SPIE AIPR Workshop: 3D visualization for data exploration and decision making, 3905:12–17.

    Google Scholar 

  • Peng, W., Ward, M. and Rundensteiner, E. (2004). Clutter reduction in multi-dimensional data visualization using dimension reordering. In: Proceedings of 2004 IEEE symposium on information visualization, pp. 89–96. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Pickett, R. and Grinstein, G. (1988). Iconographic displays for visualizing multidimensional data. In: Proceedings 1988 IEEE conference on systems, man, and cybernetics, pp. 164–170.

    Google Scholar 

  • Post, F., Walsum, T., Post, F. and Silver, D. (1995). Iconic techniques for feature visualization. In: Proceedings of 1995 IEEE conference on visualization, pp. 288–295. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Ribarsky, W., Ayers, E., Eble, J. and Mukherjea, S. (1994). Glyphmaker: creating customized visualizations of complex data. Computer, 27:57–64.

    Article  Google Scholar 

  • Rohrer, R., Ebert, D. and Sibert, J. (1998). The shape of Shakespeare: visualizing text using implicit surfaces. In: Proceedings of 1998 IEEE conference on visualization, pp. 121–129. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Rosario, G.E., Rundensteiner, E.A., Brown, D.C., Ward, M.O. and Huang, S. (2004). Mapping nominal values to numbers for effective visualization. Inf Visualizat, 3:80–95.

    Article  Google Scholar 

  • Schroeder, W., Volpe, C. and Lorensen, W. (1991). The Stream Polygon: a technique for 3D vector field visualization. In: Proceedings of 1991 IEEE conference on visualization, pp. 126–132. IEEE Computer Society Press, Los Alamitos, CA.

    Google Scholar 

  • Siegel, J., Farrell, E., Goldwyn, R. and Friedman, H. (1972). The surgical implication of physiologic patterns in myocardial infarction shock. Surgery, 72:126–141.

    Google Scholar 

  • Ward, M. and Keim, D. (1997). Screen layout methods for multidimensional visualization. In: Proceedings of 1997 CODATA Euro-American workshop on visualization of information and data

    Google Scholar 

  • Ward, M. and Lipchak, B. (2000). A visualization tool for exploratory analysis of cyclic multivariate data. Metrika, 51:27–37.

    Article  MATH  Google Scholar 

  • Ward, M. (2002). A taxonomy of glyph placement strategies for multidimensional data visualization. Inf Visualizat, 1:194–210.

    Article  Google Scholar 

  • Wilkinson, L. (1982). An experimental evaluation of multivariate graphical point representations. In: Proceedings of the conference on human factors in computing systems, pp 202–209. Association for Computing Machinery, New York.

    Google Scholar 

  • Wittenbrink, C., Pang, A. and Lodha, S. (1996). Glyphs for visualizing uncertainty in vector fields. IEEE Trans Visualizat Comput Graph, 2:266–279.

    Article  Google Scholar 

  • Woodruff, A., Landay, J. and Stonebraker, M. (1998). Constant density visualization of non-uniform distributions of data. In: Proceedings of 1998 ACM symposium on user interface software and technology, pp 19–28.

    Google Scholar 

  • Yang, J., Ward, M.O. and Rundensteiner, E.A. (2002). Interactive hierarchical displays: a general framework for visualization and exploration of large multivariate data sets. Comput Graph, 27:265-283.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Worcester Polytechnic Institute, Worcester, USA

    Matthew O. Ward

Authors
  1. Matthew O. Ward
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Ward, M. (2008). Multivariate Data Glyphs: Principles and Practice. In: Handbook of Data Visualization. Springer Handbooks Comp.Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-33037-0_8

Download citation

Publish with us

Policies and ethics

Search

Navigation