Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Dynamic Graphics

  • Dianne Cook
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_1372

Synonyms

Animation; Motion graphics; Multiple linked plots; Multivariate data visualization; Rotation; Tour

Definition

Dynamic graphics for data, means simulating motion or movement using the computer. It may also be thought of as multiple plots linked by time. Two main examples of dynamic graphics are animations, and tours. An animation, very generally defined, may be produced for time-indexed data by showing the plots in time order, for example as generated by an optimization algorithm.

A tour is designed to study the joint distribution of multivariate data, in search of relationships that may involve several variables. It is created by generating a sequence of low-dimensional projections of high-dimensional data – typically 1D or 2D – so that many different aspects of high-dimensional data can be observed. Tours are thus used to find interesting lower-dimensional projections of the data, ideally for data which contains real-valued variables. The data Xn×p is projected into Ap×dto...

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

Recommended Reading

  1. 1.
    Andrews DF. Plots of high-dimensional data. Biometrics. 1972;28(1):125–36.CrossRefGoogle Scholar
  2. 2.
    Asimov D. The grand tour: a tool for viewing multidimensional data. SIAM J Sci Stat Comput. 1985;6(1):128–43.CrossRefMathSciNetzbMATHGoogle Scholar
  3. 3.
    Buja A, Asimov D. Grand tour methods: an outline. Comput Sci Stat. 1986;17:63–7.Google Scholar
  4. 4.
    Buja A, Cook D, Asimov D, Hurley C. Computational methods for high-dimensional rotations in data visualization. In: Rao CR, Wegman EJ, Solka JL, editors. Handbook of statistics: data mining and visualization. North-Holland: Elsevier; 2005. p. 391–414.CrossRefGoogle Scholar
  5. 5.
    Buja A, Hurley C, McDonald JA. A data viewer for multivariate data. Comput Sci Stat. 1986;17(1):171–4.Google Scholar
  6. 6.
    Carr DB, Wegman EJ, Luo Q, Explor N. Design considerations past and present, technical report 129, center for computational statistics. Fairfax: George Mason University; 1996.Google Scholar
  7. 7.
    Cook D, Buja A. Manual controls for high-dimensional data projections. J Comput Graph Stat. 1997;6(4):464–80.Google Scholar
  8. 8.
    Cook D, Buja A, Cabrera J, Hurley C. Grand tour and projection pursuit. J Comput Graph Stat. 1995;4(3):155–72.Google Scholar
  9. 9.
    Cook D, Lee E-K, Buja A, Wickham H. Grand tours, projection pursuit guided tours and manual controls. In: Chen C-H, Härdle W, Unwin A, editors. Handbook of data visualization. Berlin: Springer; 2006.Google Scholar
  10. 10.
    Cook D, Swayne DF. Interactive and dynamic graphics for data analysis: with R and GGobi. New York: Springer; 2007.CrossRefzbMATHGoogle Scholar
  11. 11.
    Huh MY, Kim K. Visualization of multidimensional data using modifications of the grand tour. J Appl Stat. 2002;29(5):721–8.CrossRefMathSciNetzbMATHGoogle Scholar
  12. 12.
    Scott D. Incorporating density estimation into other exploratory tools. In: Proceedings of the Section on Statistical Graphics; 1995. p. 28–35.Google Scholar
  13. 13.
    Tierney L. LispStat: an object-oriented environment for statistical computing and dynamic graphics. New York: Wiley; 1991.Google Scholar
  14. 14.
    Wegman EJ. The grand tour in k-dimensions, Technical Report 68, Center for Computational Statistics, George Mason University. 1991.Google Scholar
  15. 15.
    Wegman EJ, Poston WL, Solka JL. Image grand tour, in automatic target recognition VIII – Proc. SPIE, 3371. Bellingham: SPIE; 1998. p. 286–94.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Iowa State UniversityAmesUSA

Section editors and affiliations

  • Hans Hinterberger
    • 1
  1. 1.Inst. of Scientific ComputingETH ZürichZurichSwitzerland