1. Visual Analytics and the Geometry of Thought— Spatial Intelligence through Sapient Interfaces Alexander Klippel & Frank Hardisty Department of Geography, GeoVISTA Center & e-Dutton Institute for Education Penn State

2. Star Plots How Shape Characteristics Influence Classification Tasks Alexander Klippel & Frank Hardisty Department of Geography, GeoVISTA Center & e-Dutton Institute for Education Penn State

3. Overview  Multivariate data displays  Experiment on the influence of shape (of star plots) on the classification of data  Design of a tool to administer grouping experiments  Design of a tool to analyze individual similarity ratings  Does shape matter?  Conclusion and future work

4. Displaying Multivariate Data  We encounter limitations in displaying multivariate data in two dimensions  As a response to these constraints several graphic designs have been advised, for example  Andrews curves  Parallel plots  Chernoff faces  Star plots  Etc etc.  The big question is  Which visualization technique does “work” for which data sets and which does not

5. Parallel Coordinate Plot

6. Chernoff Faces Source: http://mapmaker.rutgers.edu/355/links.html

7. www.ncgia.ucsb.edu

8. www.ghastlyfop.com

9. Star Plots

11. GeoViz Toolkit: http://www.geovista.psu.edu/grants/cdcesda/software/

12. Question  In their work on Chernoff faces Chernoff and Rizvi (1975) found that varying the assignment of variables to facial characteristics has an influence on classification tasks  Question  For star plots the assumption is made that the assignment of variables to rays does not matter, but is that really the case?

13. Experiment: Car Data 1-3-5-7 2-3-6-7 20 participants in each condition Penn State undergraduates

14. 30- 15 65- 50 100- 85 100- 85

15. The Grouping Tool 81 icons (4 variables, 3 levels (high, medium, low)) 1-3-5-7

16. The Grouping Tool 81 icons (4 variables, 3 levels (high, medium, low)) 2-3-6-7

17. Example: All Low Values = 1-3-5-7 2-3-6-7

18. Data  Number of groups  Time to complete  Similarity matrix  Linguistic labels

19. Some Results  There is no statistically significant difference in the number of groups created in 1-3-5-7 and 2-3-6-7 (t =.241, df = 38, p =.811)  There is no statistical significant difference in the time participants needed to complete the task (t = -1.533, df = 38, p =.134)  The similarity values in both similarity matrices are correlated and the correlation is statistically significant (r =.581, N = 3240, p <.0005)

20. Cluster Analysis Ward’s method 1-3-5-7 2-3-6-7

21. MDS Plots 1-3-5-7

22. MDS Plots 2-3-6-7

23. Grouping Analysis Improvise by Chris Weaver (http://www.personal.psu.edu/cew15/improvise/index.html) 2-3-6-7 1-3-5-7

24. 2-3-6-7

25. 1-3-5-7 2-3-6-7

26. 1-3-5-7 2-3-6-7

27. 1-3-5-7 2-3-6-7

28. 1-3-5-7 2-3-6-7

29. 1-3-5-7 2-3-6-7

30. 1-3-5-7 2-3-6-7

31. 1-3-5-7 2-3-6-7

32. Conclusion  Shape does matter  The assignment of variable to rays in a star plot influences classification tasks (compare Chernoff faces)  Characteristic shape features have an influence on rating the similarity of the represented data  The more characteristic the shape, the greater the influence  It may therefore be that star plots are less suitable for lay person exploratory analysis but more effective in communication (if carefully chosen).

33. Outlook  Quantifying data analysis  Cluster validation methods  E.g., Rand statistic, Jaccard coefficient  Individual analysis of “shape families”  Relation to linguistic labels  Continue work on how should variables be assigned to rays  For example, is there a time advantage for salient shapes?  Influence of contextual parameters  Of a star plot as such (e.g. number of variables/rays)  As a symbol in a map (e.g. spatial patterns, and first law or geography).  Star plots in comparison to other visualization techniques

34. Thank you