Large Scale Information

Transcription

1 Large Scale Information Visualization Jing Yang Fall Relevant Information Course webpage: Schedule Grading policy Slides Assignments 2 1

2 Visualization Visualization - the use of computer- supported, interactive visual representations of data to amplify cognition From [Card, Mackinlay Shneiderman 98] The purpose of visualization is insight, not pictures Insight: discovery, decision making, explanation Slide courtesy of John Stasko 3 Information Visualization numerical data ordinal data nominal data structured data such as trees and graphs Unstructured data such as text documents space time InfoVis is about analyzing, communicating, and decision making 4 2

3 Review: Multidimensional Visualization 5 Multi-dimensional (Multivariate) Dataset 6 3

4 Item (Object, Record, Case) 7 Dimension (Variable, Attribute) 8 4

5 Multidimensional Data Example: Iris Data Scientists measured the sepal length, sepal width, petal length, petal width of many kinds of iris... 9 Multidimensional Data Example: Iris Data sepal sepal petal petal length width length width

6 Classification Table-based techniques HeatMap, tablelens Geometric techniques Scatterplot matrices, parallel coordinates, landscapes, Dust&Magnet Icon-based techniques glyphs, shape-coding, *color icons, Hierarchical techniques Dimensional stacking, worlds-within-worlds, Pixel-oriented techniques Recursive pattern, circle segments, spiral, axes techniques, 11 Table-Based Techniques Basic idea: improving the existing spreadsheet table format HeatMap Table Lens [RC 94] 12 6

7 This figure is used by Dr. M. Ward s permission 13 This figure is used by Dr. M. Ward s permission 14 7

8 This figure is used by Dr. M. Ward s permission 15 This figure is used by Dr. M. Ward s permission 16 8

9 This figure is used by Dr. M. Ward s permission 17 This figure is used by Dr. M. Ward s permission 18 9

10 MicroArray Data Visualization Heat-map Increased gene-expression values: red brightness scale Decreased gene-expression values: green brightness scale Nochange: black. 19 Table Lens Rao R., Card S. K.: The Table Lens: Merging Graphical and Symbolic Representation in an Interactive Focus+Context Visualization for Tabular Information, Proc. Human Factors in Computing Systems CHI 94 Conf., Boston, MA, 1994, pp

11 Table Lens 21 Geometric Techniques Basic idea: Visualizing geometric transformations and projections of data Scatterplot-matrices [And 72, Cle 93] Parallel coordinates [Ins 85, ID 90] Parallel Glyphs [Fanea:05] Parallel Sets [Bendix:05] Star coordinates [Kan 2000] Landscapes [Wis 95] Dust & Magnet [Yi 2005] Projection Pursuit Techniques [Hub 85] Prosection Views [FB 94, STDS 95] Hyperslice [WL 93] 22 11

12 Recall 1-Dimensional Visualization (1.6) Parallel Coordinates sepal length sepal width petal length petal width A. Inselberg. The Plane with Parallel Coordinates. Special Issue on Computational Geometry, The Visual Computer,

13 sepal sepal petal petal length width length width Cluster and Outlier Cluster A group of data items that t are similar il in all dimensions. Outlier A data item that is similar to FEW or No other data items

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15 The Clutter Problem A dataset with 1000 data items 29 Scatterplot Matrix 30 15

16 Scatterplot Matrix 31 Landscapes Hot topics of a news collection L. Nowell, E. Hetzler, and T. Tanasse. Change Blindness in Information Visualization: A Case Study. Infovis

17 Landscapes How was the figure generated? Documents (data items) Keywords (dimensions) N-d vector for each documents Projection from N-d space to 2-d space Landscape view Wise, J., Thomas, J., et al. Visualizing the Non-Visual: Spatial Analysis and Interaction with Information from Text Documents, Infovis Hierarchical Techniques Basic ideas: Visualization of the data using a hierarchical partitioning into subspaces Dimensional Stacking [LWW90] Worlds-within-Worlds [FB 90a/b] Treemap [Shn 92, Joh 93] Cone Trees [RMC 91] InfoCube [RG93] 34 17

18 Dimensional Stacking Imagine each data item (4 attributes) as a small block. We place all blocks on a table. 35 Dimensional Stacking Add grids on the table. Place the blocks in the grids according to their values of attribute1. According to values of attribute

19 Dimensional Stacking Add grids in grids. Place the blocks in the grids according to their values of attribute2. According to values of attribute 2 37 Dimensional Stacking Add grids in grids. Place the blocks in the grids according to their values of attribute3. According to values of attribute

20 Dimensional Stacking Add grids in grids. Place the blocks in the grids according to their values of attribute4. According to values of attribute 4 39 Dimensional Stacking Fix one block! Expand the block 40 20

21 Dimensional Stacking Fix another block 41 Dimensional Stacking Dimensional stacking! 42 21

22 Dimensional Stacking visualization of oil mining data with longitude and latitude mapped to the outer x-, y- axes and ore grade and depth mapped to the inner x-, y- axes M. Ward, Worcester Polytechnic Institute 43 Icon-Based Techniques Basic idea: visualizing data values as features of icons Chernoff-Faces [Che 73, Tuf 83] Stick Figures [Pic 70, PG88] Shape Coding [Bed 90] Color Icons [Lev 91, KK94] TileBars [Hea 95] 44 22

23 Glyphs Profile Glyphs Each bar encodes a variable s value Max Min v1 v2 v3 v4 1 data item with 4 attributes 3 data items in the iris dataset 45 Chernoff faces (1973, Herman Chernoff) 46 23

24 Dr. Eugene Turner, Star Glyphs Space out variables at equal angles around a circle Each arm encodes a variable s value v2 v3 v1 v4 1 data item with 4 attributes 4 data items in the iris dataset 48 24

25 Glyphs 49 Stick Figures The mapping Two attributes - display axes Others - angle and/or length of limbs Texture patterns in the visualization show certain data characteristics Pickett R. M., Grinstein G. G.: Iconographic Displays for Visualizing Multidimensional Data, Proc. IEEE Conf. on Systems, Man and Cybernetics, 1988, pp Stick figure icon 50 25

26 Stick Figures 5-dim. image data from the great lake region G. Grinstein, University of Massachusetts at Lowell 51 Stick Figures Stick figure icon family Try all different mappings 12X5! = 1440 pictures Movie 52 26

27 Stick Figures The same dataset, different mapping G. Grinstein, University of Massachusetts at Lowell 53 Stick Figures Black background White background G. Grinstein, University of Massachusetts at Lowell 54 27

28 Pixel-Oriented Techniques Basic idea Each value - one colored pixel (value ranges -> fixed colormap) Values for each attribute are presented in separate subwindows Values of the same data item are at the same positions of all subwindows 55 Pixel-Oriented Techniques sepal length sepal width sepal length sepal width petal length petal width petal length petal width 56 28

29 Major Challenge Textures of the subwindows reflect patterns. How to order and lay the pixels out to get informative textures? 57 Pixel-Oriented Techniques The figure is taken from Dr. D. Keim s tutorial notes in Infovis

30 Query-Independent Techniques Space-Filling Curve Arrangements The figure is taken from Dr. D. Keim s tutorial notes in Infovis Query-Independent Techniques Space-Filling Curve Arrangements The figure is taken from Dr. D. Keim s tutorial notes in Infovis

31 Query-Independent Techniques Recursive pattern arrangements The figure is taken from Dr. D. Keim s tutorial notes in Infovis Query-Independent Techniques Recursive pattern arrangements The figure is taken from Dr. D. Keim s tutorial notes in Infovis

32 Query-Dependent Techniques Basic idea: data items (a 1, a 2,..., a m ) & query (q 1, q 2,..., q m ) distances (d 1, d 2,... d m ) extend distances by overall distance (d m+1 ) determine data items with lowest overall distances map distances to color (for each attribute) visualize each distance value d i by one colored pixel The slide is taken from Dr. D. Keim s tutorial notes in Infovis Query-Dependent Techniques Spiral technique Arrangement The m+1 dimension (overall distance) The figure is taken from Dr. D. Keim s tutorial notes in Infovis

33 Query-Dependent Techniques Spiral technique The figure is taken from Dr. D. Keim s tutorial notes in Infovis Circular Arrangement Circle segments The figure is taken from Dr. D. Keim s tutorial notes in Infovis

34 Pixel-Oriented Technique Circle segments The figure is taken from Dr. D. Keim s tutorial notes in Infovis Subwindow Positioning Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data [Ankerst 98] 68 34

35 Subwindow Positioning Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data [Ankerst 98] 69 Major References Colin Ware. Information Visualization, 2004 Daniel Keim. Tutorial note in InfoVis 2000 John Stasko. Course slides, Fall 2005 Edward Tufte: The Visual Display of Quantitative Information Envisioning Information Visual Explanation Download XMDV from Binary Release, Microsoft Windows, Xmdv 7.0 and play with it

36 Review: Time Series Data Visualization 71 Datasets Each data case is likely an event of some kind One of the variables can be the date and time of the event Examples: sunspot activity, baseball games, medicines taken, cities visited, stock prices, newswires, network resource measures Partially From John Stasko s class slides 72 36

37 Time Series Visualization Approaches Time-Series Plot Small Multiples l Static State Replacement (Animation) Nested Visualization (embed time-series plot into other display) Brushing and linking 73 Time Series Plot Emc Stock, from Google Finance 74 37

38 Time Series Plot 75 Time Series Plot 76 38

39 ThemeRiver: Visualizing Theme Changes Over Time [Havre et al. Infovis 00] Goal: To depict thematic changes within a temporal document collection over time 77 Baby Name Visualization Baby Names, Visualization, and Social Data Analysis [Wattenberg Infovis 2005] NameVoyager a web-based visualization applet Let users interactively explore name data, historical name popularity figures ard o ag er/lnv0105.html 78 39

40 Visualizing Time-Series on Spirals [weber et al. Infovis 01] 79 Pixel-Oriented Techniques Recursive pattern arrangements The figure is taken from Dr. D. Keim s tutorial notes in Infovis

41 Pixel Oriented Techniques Recursive pattern arrangements The figure is taken from Dr. D. Keim s tutorial notes in Infovis THREAD ARCS: An Thread Visualization [Kerr Infovis 2003] 82 41

42 Small Multiples Small multiples are sets of thumbnail sized graphics on a single page that represent aspects of a single phenomenon. Three air pollutants in six counties in southern California Los Angeles Times, Static State Replacement 84 42

43 Change Blindness in Information Visualization: A Case Study [Nowell et al. Infovis01] Moving from one time slice to another with a wireframe and variable translucency. 85 Nested Visualization Embed time series plot into other displays Example: Time series plot embedded d into a graph Visualization of Graphs with Associated Timeseries Data [Saraiya:05] 86 43

44 Brushing and Linking Link time series display with other displays Visualization of Graphs with Associated Timeseries Data [Saraiya:05] 87 Space and Time 88 Napoleon s army in Russia, author: Charles Minard ( ) 44

45 Space and Time Life circle of Japanese Beetles L. Newman, Man and Insects, GeoTime Information Visualization [Kapler and Wright Infovis 04] 90 45

46 GeoTime Information Visualization [Kapler and Wright Infovis 04] Afghanistan in 2002 Events in three weeks Shootings Bombings Fires Mines Kidnaps Thefts assaults 91 References E. Tufte. The Visual Display of Quantitative Information, 1983 Papers referred 92 46