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Linked Data Views

  • Graham Wills
Chapter
Part of the Springer Handbooks Comp.Statistics book series (SHCS)

Abstract

The linked views paradigm is a method of taking multiple simple views of data and allowing interactions with one to modify the display of data in all the linked views. A simple example is that selecting a data case in one view shows that data case highlighted in all other views. In this section we define the underlying methodology and show how it has been applied historically and how it can be extended to provide enhanced power. In particular we focus on displays of aggregated data and linking domain-specific views such as graph layouts and maps to statistical views.

Keywords

Salary Data Dynamic Graphic Data View Summary Function National League 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Ahlberg, C. and Shneiderman, B. (1994). Visual information seeking: tight coupling of dynamic query filters with starfield displays, Human Factors in Computing Systems. Conference Proceedings CHI’94, pp. 313–317. citeseer.ist.psu.edu/ahlberg94visual.htmlGoogle Scholar
  2. Apple Computer (1992). Macintosh Human Interface Guidelines, Addison-Wesley Longman, Boston.Google Scholar
  3. Becker, R., Cleveland, W. and Wilks, A. (1987). Dynamic graphics for data analysis (with discussion), Stat Sci 2:355–395. Reprinted in: Cleveland WS, McGill ME (eds) Dynamic Graphics for Data Analysis, Wadsworth and Brooks/Cole, Pacific Grove, CA.Google Scholar
  4. Haslett, J., Wills, G. and Unwin, A. (1990). Spider – an interactive statistical tool for the analysis of spatially distributed data, Int J Geograph Inf Syst 4(3):285–296.CrossRefGoogle Scholar
  5. Inselberg, A. (1985). The plane with parallel coordinates, Visual Comput 1:69–91.zbMATHCrossRefGoogle Scholar
  6. Maxis (1985). SimCity User Manual, Maxis, Walnut Creek, CA.Google Scholar
  7. McDonald, J., Stuetzle, W. and Buja, A. (1990). Painting multiple views of complex objects, Proceedings of ECOOP/OOPSLA’90 European conference on object oriented programming, Vol. 1, ACM Press, pp. 245–257.Google Scholar
  8. Microsoft (1999). The Microsoft Windows User Experience, Microsoft Press, Redmond, WA.Google Scholar
  9. Shneiderman, B. (1992). Tree visualization with tree-maps: A 2-d space-filling approach, ACM Trans Graph 11(1):92–99. citeseer.ist.psu.edu/shneiderman91tree.htmlGoogle Scholar
  10. Swayne, D., Cook, D. and Buja, A. (1991). Xgobi: interactive dynamic graphics in the X Window system with a link to s, Proceedings of the ASA Section on Statistical Graphics, American Statistical Association, Alexandria, VA, pp. 1–8. citeseer.ist.psu.edu/swayne91xgobi.htmlGoogle Scholar
  11. Swayne, D., Lang, D., Buja, A. and Cook, D. (2003). Ggobi: evolving from xgobi into an extensible framework for interactive data visualization, Comput Stat Data Anal 43(4):423–444.CrossRefMathSciNetGoogle Scholar
  12. Tierney, L. (1990). LISP-STAT: an object oriented environment for statistical computing and dynamic graphics, Wiley-Interscience, New York.zbMATHGoogle Scholar
  13. Velleman, P. (1988). The DataDesk Handbook, Odesta, Ithaca, NY.Google Scholar
  14. Wilkinson, L. (1999). The Grammar of Graphics, Springer, New York.zbMATHGoogle Scholar
  15. Wills, G. (1999). Nicheworks – interactive visualization of very large graphs, J Comput Graph Stat 8(2):190–212. citeseer.ist.psu.edu/wills97nicheworksinteractive.htmlGoogle Scholar
  16. Wills, G. (2000). Natural selection: interactive subset creation, J Comput Graph Stat 9(3):544–557. http://www.amstat.org/publications/jcgs/abstracts00/Wills.htmGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Graham Wills
    • 1
  1. 1.SPSS Inc. ChicagoChicagoUSA

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