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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
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Star Plots How Shape Characteristics Influence Classification Tasks Alexander Klippel & Frank Hardisty Department of Geography, GeoVISTA Center & e-Dutton Institute for Education Penn State
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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
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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
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Parallel Coordinate Plot
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Chernoff Faces Source: http://mapmaker.rutgers.edu/355/links.html
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www.ncgia.ucsb.edu
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www.ghastlyfop.com
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Star Plots
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GeoViz Toolkit: http://www.geovista.psu.edu/grants/cdcesda/software/
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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?
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Experiment: Car Data 1-3-5-7 2-3-6-7 20 participants in each condition Penn State undergraduates
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30- 15 65- 50 100- 85 100- 85
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The Grouping Tool 81 icons (4 variables, 3 levels (high, medium, low)) 1-3-5-7
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The Grouping Tool 81 icons (4 variables, 3 levels (high, medium, low)) 2-3-6-7
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Example: All Low Values = 1-3-5-7 2-3-6-7
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Data Number of groups Time to complete Similarity matrix Linguistic labels
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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)
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Cluster Analysis Ward’s method 1-3-5-7 2-3-6-7
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MDS Plots 1-3-5-7
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MDS Plots 2-3-6-7
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Grouping Analysis Improvise by Chris Weaver (http://www.personal.psu.edu/cew15/improvise/index.html) 2-3-6-7 1-3-5-7
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2-3-6-7
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1-3-5-7 2-3-6-7
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1-3-5-7 2-3-6-7
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1-3-5-7 2-3-6-7
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1-3-5-7 2-3-6-7
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1-3-5-7 2-3-6-7
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1-3-5-7 2-3-6-7
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1-3-5-7 2-3-6-7
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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).
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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
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Thank you
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