# ENV 20064.1 Envisioning Information Lecture 4 – Multivariate Data Exploration Glyphs and other methods Hierarchical approaches Ken Brodlie.

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ENV 20064.1 Envisioning Information Lecture 4 – Multivariate Data Exploration Glyphs and other methods Hierarchical approaches Ken Brodlie

ENV 20064.2 Glyph Techniques

ENV 20064.3 Glyph Techniques Map data values to geometric and colour attributes of a glyph – or marker symbol Very many types of glyph have been suggested: –Star glyphs –Faces –Arrows –Sticks –Shape coding

ENV 20064.4 Glyph Layouts How do we place the glyphs on a chart? Sometimes there will be a natural location – for example? If not… two of the variates can be allocated to spatial position, and the remainder to the attrributes of the glyph

ENV 20064.5 Glyph Techniques – Star Plots Each observation represented as a star Each spike represents a variable Length of spike indicates the value

ENV 20064.6 Glyph Techniques – Star Plots Each observation represented as a star Each spike represents a variable Length of spike indicates the value Crime in Detroit

ENV 20064.7 Star Glyphs – Iris Data Set

ENV 20064.8 Chernoff suggested use of faces to encode a variety of variables - can map to size, shape, colour of facial features - human brain rapidly recognises faces Chernoff Faces

ENV 20064.9 Chernoff Faces Here are some of the facial features you can use http://www.bradandkathy.com/software/faces.html

ENV 20064.10 Chernoff Faces Demonstration applet at: –http://www.hesketh.com/schampeo/projects/Faces/

ENV 20064.11 Chernoffs Face.. And here is Chernoffs face http://www.fas.harvard.edu/~stats/People/http://www.fas.harvard.edu/~stats/People/Faculty/Herman_Chernoff/Herman_Chernoff_Index.html

ENV 20064.12 Stick Figures Glyph is a matchstick figure, with variables mapped to angle and length of limbs As with Chernoff faces, two variables are mapped to display axes Stick figures useful for very large data sets Texture patterns emerge Idea due to RM Pickett & G Grinstein - different angles that may be varied are shown

ENV 20064.13 5D image data from Great Lakes region Stick Figures

ENV 20064.14 Suitable where a variable has a Boolean value, ie on/off A data item is represented as an array of elements, each element corresponding to a variable 1 2 3 4 5 6 shade in box if value of corresponding variable is on Arrays laid out in a line, or plane, as with other icon-based methods Shape Coding

ENV 20064.15 Time series of NASA earth observation data Shape Coding

ENV 20064.16 Dry Wet Showery Saturday Sunday Leeds Sahara Amazon * variables and their values placed around circle * lines connect the values for one observation This item is { wet, Saturday, Amazon } http://www.daisy.co.uk Daisy Charts

ENV 20064.17 Daisy Charts - Underground Problems

ENV 20064.18 Daisy Charts – News Analysis Four variates: day, source, search terms, keywords

ENV 20064.19 Reducing Complexity in Multivariate Data Exploration

ENV 20064.20 Clustering as a Solution Success has been achieved through clustering of observations Hierarchical parallel co- ordinates –Cluster by similarity –Display using translucency and proximity-based colour http://davis.wpi.edu/~xmdv/docs/vis99_HPC.pdf

ENV 20064.21 Comparison One of 3 clusters

ENV 20064.22 Hierarchical Parallel Co-ordinates

ENV 20064.23 Reduction of Dimensionality of Variable Space Reduce number of variables, preserve information Principal Component Analysis –Transform to new co-ordinate system –Hard to interpret Hierarchical reduction of variable space –Cluster variables where distance between observations is typically small –Choose representative for each cluster Subgroup has then been identified – showing what? http://davis.wpi.edu/%7Exmdv/docs/vhdr_vissym.pdf 42 dimensions, 200 observations

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