14.1 Vis_04 Data Visualization Lecture 14 Information Visualization : Part 2

14.2 Vis_04 Glyph Techniques – Star Plots n Star plots – Each observation represented as a ‘star’ – Each spike represents a variable – Length of spike indicates the value Crime in Detroit

14.3 Vis_04 Chernoff Faces n 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

14.4 Vis_04 Chernoff Faces n Here are some of the facial features you can use

14.5 Vis_04 Chernoff Faces n Demonstration applet at: – ects/Faces/

14.6 Vis_04 Chernoff’s Face n.. And here is Chernoff’s face

14.7 Vis_04 Daisy Charts 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 }

14.8 Vis_04 Daisy Charts - Underground Problems

14.9 Vis_04 Networks of Information n In many applications of InfoVis, the observations are linked in a graph structure – Directory trees – Web sites n We can still represent as a data table – The link(s) appear as column(s) in the data table 1 23 Graph may be directed or undirected

14.10 Vis_04 Examples of Networks of Information My Windows2000 filestore Automobile web site - visualizing links

14.11 Vis_04 Graph Drawing Algorithms n There are various general graph layout software packages n Example is dotty from AT&T suite called GraphViz n Nodes of graph laid out automatically – here an undirected graph – applications?

14.12 Vis_04 Dotty Directed graph for software engineering application

14.13 Vis_04 Hierarchical Information n Important special case is where information is hierarchical – Graph structure can be laid out as a tree

14.14 Vis_04 Tree Maps n Screen filling method which uses a hierarchical partitioning of the screen into regions depending on attribute values n Alternate partitioning parallel to X and Y axes n Suitable for hierarchical type data – size of files in a user directory

14.15 Vis_04 Tree Map of Filestore Suppose user has three subdirectories: A, B and C First partition in X according to total size of each sub- directory ABC

14.16 Vis_04 Tree Map of Filestore ABC Then within each subdirectory, we can partition in Y by the size of individual files, or further subdirectories

14.17 Vis_04 Treemap Example Usenet news groups For history of treemaps see: hcil/treemap-history Developed over many years by Ben Schneiderman and colleagues

14.18 Vis_04 Hyperbolic Trees n This is popular method of displaying hierarchical structures such as Web sites n Place home page in centre – with linked pages connected by hyperbolic arcs – further arcs link to further links – see: papers/jl_bdy.htm

14.19 Vis_04 Hyperbolic Trees Automobiles web site Home page in centre Click on link you want...

14.20 Vis_04 Hyperbolic Trees Auto History moves to centre of screen Click on next link...

14.21 Vis_04 Hyperbolic Trees Henry Ford is now at the centre and so on...

14.22 Vis_04 Hyperbolic Trees Also works for family trees...

14.23 Vis_04 Document Visualization n Large collections of electronic text – the Web is prime example! n Powerful search and retrieval engines – return documents based on some sort of keyword search n How do we visualize the results of a query? n

14.24 Vis_04 Document Retrieval n 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

14.25 Vis_04 Document Spiral Arrange docs in spiral, most relevant at centre

14.26 Vis_04 Document Three-Keyword Axes Display One keyword per axis Plot docs in a scatter plot using keyword strengths to position along axes Same keyword on all axes lines docs up on X=Y=Z line

14.27 Vis_04 Nearest Neighbour Sequence Choose one doc and place on circle Find the closest in ‘keyword strength’ space and place adjacent to it.... and so on