Introduc)on to Informa)on Visualiza)on

Transcription

1 Introduc)on to Informa)on Visualiza)on

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5 Seeing the Science with Visualiza)on Raw Data Visualiza(on Applica(on

6 Visualiza)on on Stampede

7 Data Visualiza)on

8 Why Data Visualiza)on Ma?ers Anscombe's Quartet

9 mean variance correlation regression Y=3+0.5x Y=3+0.5x Y=3+0.5x Y=3+0.5x Four datasets are sta)s)cally iden)cal

10 Why Data Visualiza)on Ma?ers Posi)ve linear Linear? Is something wrong here? Linear with outliers

11 Simple Data Visualiza)ons % 80% 60% 40% 20% R 2 = % Line Graph x- axis requires quan)ta)ve variable Variables have con)guous values Familiar/conven)onal ordering among ordinals Sca?er Plot Convey overall impression of rela)onship between two variables Bar Graph Comparison of rela)ve point values Pie Chart Emphasizing differences in propor)on among a few numbers Histogram vs. Pie

12 More Complex Data Visualiza)on Map n- D space onto 2- D screen Visual representa)ons: Mul)ple views E.g. plot matrices, brushing histograms, More axes E.g. Parallel coords, star coords, Complex glyphs E.g. star glyphs, faces

13 Using Mul)ple Views Basic idea: Showing mul)ple views of same data set at the same )me. Each individual visualiza)ons might be of same or different types. Brushing and linking With interac)ve visualiza)ons, All views might be linked so that ac)on, such as selec)on, on one view might be reflected in all other views. Example: Sca?er plot matrix Create a 2d views for all a?ributes pairs

14 Example Data

15 Sca?er Plot Matrix Example

16 Using Addi)onal Axes Easy example: 2D sca?er plot à 3D sca?er plot Space > 3D?

17 Parallel Coordinates Instead of orthogonal axes, use parallel axes x y z w (0,1,- 1,2)= Inselberg, Mul)dimensional detec)ve (parallel coordinates)

18 Parallel Coordinates Inverse variables to clarify rela)onships

19 1D view of 3D func)on: Hierarchical Axes f(x 1, x 2, x 3 ) x3 x2 x1

20 Dimensional Stacking 2D view of 4D func)on (using heat maps) y = f(x 1, x 2, x 3, x 4 ) Discrete: x i = [0,1,2,3,4] y = f(x 1,x 2,0,0) as color x2 x1 x3 x4

21 Dimensional Stacking Break each dimension range into bins Break the screen into a grid using the number of bins for two dimensions Repeat the process for two more dimensions within the sub images formed by first grid, recursively iterate through all dimensions Look for repeated pa?erns, outliers, trends, gaps

22 Pixel- Oriented Visualiza)on Techniques Represent each a?ribute value as a single pixel Map the range of possible a?ribute values to a fixed color map Maximizes the amount of informa)on represented at one )me without any overlap

23 Pixel- Oriented Techniques Each dimension creates an image Each value controls color of a pixel Many organiza)ons of pixels possible (raster, spiral, circle segment, space- filling curves) Reordering data can reveal interes)ng features, rela)ons between dimensions

24 Pixel- Oriented Techniques Bar Visualiza)on For each a?ribute A?ribute values are sorted into a?ribute lists Classes are defined by colors Within a bar, sorted a?ribute values are mapped to pixels, line by line Each a?ribute is placed in a different bar

25 Circle Segments Technique Data is a circle divided into segments Each segment represents an a?ribute A?ribute values are mapped by a single colored pixel and arrangement starts in the center and proceeds outward

26 Light = high stock price Dark = low stock price Represents 50 stocks. 1 circle represents the prices of different stocks at the same )me

27 Tree Visualiza)on Good for directed search tasks subtree filtering (+/- ) Not good for learning structure No a?ributes Apx 50 items visible Lose path to root for deep nodes Scroll bar!

28 From flare demo: h?p://flare.prefuse.org/demo

29 From flare demo: h?p://flare.prefuse.org/demo

30 Challenge What if the tree is too big? Interac)on and Distor)on Hyperbolic Tree Fisheye View Show overview or adap)ve hierarchy

31 Treemap Showing en)re tree as a Warehouse Nodes will be contained within their parents. Calculate node sizes to corresponding to a selected proper)es. Recursively to children node size = sum children sizes Draw Treemap (node, space, direc+on) Draw node rectangle in space Alternate direc+on (slice or dice) For each child: Calculate child space as % of node space using size and direc+on Draw Treemap (child, child space, direc+on)

32 Treemap Showing 971k files in file structure from h?p://

33 Text and Document Visualiza)ons A collec)on of words Showing usages of the words Word cloud Showing structure/ words flow. Phrase Net Document Arc Comparing two documents Document contrast diagram

34 Word cloud Tag cloud h?p:// Font Size (and/or color) corresponding to the number of item associated with each tag. Word Cloud Based on the frequency of word occurrences. Bigger font size for more frequently occurred words.

35 Tag Cloud Example

36 Tag Cloud Example with 2 words

37 Word Cloud Example h?p://

38 Phrase Net Shows rela)onships between two words

39 Word Tree All examples are from: h?p://manyeyes.alphaworks.ibm.com

40 Word Spectrum Showing two words and their common associa)ons in spectrum h?p://

41 Document Arc Showing similar parts within a document h?p://

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43 Document Contrast Diagrams Showing shared and unique words between two documents h?p://

44 Examples: Visualizing.org

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47 Infovis Libraries Prefuse Java library Flare h?p://prefuse.org/ General purpose Based on Prefuse project h?p://flare.prefuse.org/ Ac)on scripts library for Flash. IVTK: Infovis tool kits Jung Java library h?p://ivtk.sourceforge.net/ Parallel coordinates, adjacency matrix Java library h?p://jung.sourceforge.net/ Specialized for graph/network visualiza)ons

48 Applica)ons & Toolkits Tableau Closed- source, licensable professional visual analy)cs sowware Windows- only install or cloud SaaS model h?p:// VTK Titan toolkits for InfoVis h?p:// A more detailed presenta)ons at h?p:// Informa)on_Visualiza)on_in_VTK.pdf OverView Infovis plugin for ParaView Support server- client mode to use compu)ng cluster h?ps://

49 Applica)ons & Toolkits Processing An open source programming enviroment. h?p://processing.org/ Based on Java, can export jar file as applet. HCE Hierarchical Clustering Explorer h?p:// cluster Tulip Support > 1m elements Build on opengl h?p:// XMDV Tool Specialized for mul)variate data h?p://davis.wpi.edu/xmdv/

50 Thank