14.1Vis_04
Data VisualizationData Visualization
Lecture 14Information Visualization :
Part 2
14.2Vis_04
Glyph Techniques – Star Plots
Glyph Techniques – Star Plots
Star plots– Each observation
represented as a ‘star’
– Each spike represents a variable
– Length of spike indicates the value
Crime inDetroit
14.3Vis_04
Chernoff FacesChernoff Faces
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
14.4Vis_04
Chernoff FacesChernoff Faces
Here are some of the facial features you can use
http://www.bradandkathy.com/software/faces.html
14.5Vis_04
Chernoff FacesChernoff Faces
Demonstration applet at:– http://www.hesketh.com/
schampeo/projects/Faces/
14.6Vis_04
Chernoff’s FaceChernoff’s Face
.. And here is Chernoff’s face
http://www.fas.harvard.edu/~stats/People/Faculty/Herman_Chernoff/Herman_Chernoff_Index.html
14.7Vis_04
Daisy ChartsDaisy Charts
Dry
Wet
Showery
Saturday
Sunday
Leeds
Sahara
Amazon
variables andtheir valuesplaced aroundcircle
lines connectthe values forone observation
This item is { wet, Saturday, Amazon }
http://www.daisy.co.uk
14.8Vis_04
Daisy Charts - Underground Problems
Daisy Charts - Underground Problems
14.9Vis_04
Networks of InformationNetworks of Information
In many applications of InfoVis, the observations are linked in a graph structure
– Directory trees– Web sites
We can still represent as a data table
– The link(s) appear as column(s) in the data table
1
2 3
Graph may bedirected orundirected
14.10Vis_04
Examples of Networks of Information
Examples of Networks of Information
My Windows2000filestore
Automobile web site- visualizing links
14.11Vis_04
Graph Drawing AlgorithmsGraph Drawing Algorithms
There are various general graph layout software packages
Example is dotty from AT&T suite called GraphViz
Nodes of graph laid out automatically
– here an undirected graph
– applications?
http://www.graphviz.org
14.12Vis_04
DottyDotty
Directed graph for softwareengineering application
14.13Vis_04
Hierarchical InformationHierarchical Information
Important special case is where information is hierarchical
– Graph structure can be laid out as a tree
http://www.cwi.nl/InfoVisu/Examples
14.14Vis_04
Tree MapsTree Maps
Screen filling method which uses a hierarchical partitioning of the screen into regions depending on attribute values
Alternate partitioning parallel to X and Y axes
Suitable for hierarchical type data– size of files in a user directory
14.15Vis_04
Tree Map of FilestoreTree Map of Filestore
Suppose user hasthree subdirectories:A, B and C
First partition in Xaccording to totalsize of each sub-directory
A B C
14.16Vis_04
Tree Map of FilestoreTree Map of Filestore
A B C
Then within eachsubdirectory, wecan partition in Yby the size ofindividual files,or furthersubdirectories
14.17Vis_04
Treemap ExampleTreemap Example
Usenet newsgroups
For history oftreemaps see:www.cs.umd.edu/hcil/treemap-history
Developed over many years by Ben Schneiderman and colleagues
14.18Vis_04
Hyperbolic TreesHyperbolic Trees
This is popular method of displaying hierarchical structures such as Web sites
Place home page in centre– with linked pages connected by
hyperbolic arcs– further arcs link to further links– see:
www.acm.org/sigchi/chi95/proceedings/papers/jl_bdy.htm
14.19Vis_04
Hyperbolic TreesHyperbolic Trees
Automobilesweb site
Home pagein centre
Click on linkyou want ...
14.20Vis_04
Hyperbolic TreesHyperbolic Trees
Auto Historymoves to centre of screen
Click on nextlink...
14.21Vis_04
Hyperbolic TreesHyperbolic Trees
Henry Fordis now at the centreand so on...
14.22Vis_04
Hyperbolic TreesHyperbolic Trees
www.inxight.com
Also worksfor familytrees...
14.23Vis_04
Document VisualizationDocument Visualization
Large collections of electronic text– the Web is prime example!
Powerful search and retrieval engines– return documents based on some
sort of keyword search How do we visualize the results of
a query? http://zing.ncsl.nist.gov/~cugini/uicd/
viz.html
14.24Vis_04
Document RetrievalDocument Retrieval
Suppose search returns a keyword strength– ie user enters a number of
keywords– engine returns list of documents– each document has a score for each
keyword specified (eg number of occurrences)
– most relevant document has largest total score
14.25Vis_04
Document SpiralDocument Spiral
Arrange docsin spiral, mostrelevant at centre
14.26Vis_04
Document Three-Keyword Axes Display
Document Three-Keyword Axes Display
One keywordper axis
Plot docs ina scatter plotusing keywordstrengths toposition alongaxes
Same keywordon all axes linesdocs up on X=Y=Z line
14.27Vis_04
Nearest Neighbour Sequence
Nearest Neighbour Sequence
Choose one docand place on circle
Find the closest in‘keyword strength’space and placeadjacent to it.... and so on
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