This should be a circle

Transcription

1 This should be a circle

2 Information Visualization Jack van Wijk Eindhoven University of Technology Electronics & Automation June 2/3/4, 2015

3 Information Visualization What is it? Presentation Perception Interaction Data

4 Information Visualization The use of computer-supported, interactive, visual representations of abstract data to amplify cognition (Card et al., 1999) Data Visualization User

5 Why is my hard disk full??

6 SequoiaView Van Wijk and Van de Wetering, 1999

7 Generalized treemaps Idea: combine treemaps and business graphics Many options Vliegen, Van Wijk, and Van der Linden, 2006

8 Visualization high school data Cum Laude by MagnaView

9 Information Visualization The use of computer-supported, interactive, visual representations of abstract data to amplify cognition (Card et al., 1999) Data Visualization User

10 SequoiaView Van Wijk and Van de Wetering, 1999

11 Botanically inspired treevis What happens if we map abstract trees to botanical trees? Kleiberg et al., 2001

12 Botanically TreeView inspired treevis Kleiberg, Van de Wetering, van Wijk, 2001

13 Botanically inspired treevis Kleiberg, Van de Wetering, van Wijk, 2001

14 Visualization of vessel traffic Willems et al., 2009

15 Visualization of vessel traffic Willems et al., 2009

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17 Information Visualization The use of computer-supported, interactive, visual representations of abstract data to amplify cognition (Card et al., 1999) Data Visualization User

18 The human visual system

19 The human visual system

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21 Translating data into pictures Position, width, height, colors encode six variables

22 Perception of symbols How many red objects?

23 Perception of symbols How many red objects?

24 Perception of symbols How many circles?

25 Perception of symbols How many circles?

26 Perception of symbols How many blue circles?

27 Perception of symbols How many blue circles?

28 Limits to perception of symbols Combinations of attributes cannot be perceived pre-attentively

29 Color for encoding information Translate data into colors The human as light meter?

30 Adelson checkerboard illusion

31 Adelson checkerboard illusion

32 Use ColorBrewer for palettes Cynthia Brewer:

33 Size matters Maureen Stone: In Color Perception, Size Matters, CG&A 2012

34 Size matters Maureen Stone: In Color Perception, Size Matters, CG&A 2012

35 Information Visualization The use of computer-supported, interactive, visual representations of abstract data to amplify cognition (Card et al., 1999) Data Visualization User

36 Data types multivariate data networks images time series data hierarchical data text video simple hard Vary in complexity One data set, many interpretations Think about your data: What does it mean? What do I want to see? Example

37 Items with attributes name age length sex

38 Multivariate data: tables name Simone Jack Merel Ivo age length sex F M F M

39 Distribution per attribute n length

40 Events

41 Multivariate data: Parallel Coordinates Plot F 10 age 1.50 length M sex

42 Multivariate data: scatterplot age length

43 Sets senior F M young

44 Hierarchy senior s y young

45 Network same sex similar age

46 One data set, many interpretations

47 Abstract data: main types Multivariate visualization: scatterplot Tree visualization: tree diagram Graph visualization: node link diagram

48 Abstract data: often a mix Multivariate visualization: scatterplot Tree visualization: tree diagram Graph visualization: node link diagram

49 Trees+networks+multivariate data Everywhere!

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52 Hierarchy + network Holten, 2006

53 Spin-off: SynerScope Transaction analysis, fraud detection

54 Abstract data: main types Multivariate visualization: scatterplot Tree visualization: tree diagram Graph visualization: node link diagram Challenge: What if we have thousands of dataitems?

55 Data size business graphics infovis visual analytics small (1-10) medium (1000) huge (> 10 6 ) Try to move to the left: Use interaction to select relevant data

56 Information Visualization The use of computer-supported, interactive, visual representations of abstract data to amplify cognition. (Card et al., 1999) Data Visualization User

57 Infographics vs InfoVis Infographics: - Static - Explanation - Made by data journalist - Viewed by lay audience Kentico.com

58 Infographics vs InfoVis Infographics: - Static vs interactive - Explanation vs explorative - Made by data journalist vs domain expert - Viewed by lay audience vs domain expert Kentico.com

59 Data size business graphics infovis visual analytics small (1-10) medium (1000) huge (> 10 6 ) Try to move to the left: Use interaction to select relevant data Use statistics / machine learning (without loosing essential information )

60 Anscombe s quartet Francis Anscombe, 1973

61 Analysis of time-series data Given: 10 minute measurements for one year 52,560 measurements How to visualize these?

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63 Analysis of time-series data Given: 10 minute measurements for one year 52,560 measurements How to visualize these? Cluster similar days Use standard visualizations

64 Analysis of time-series data Cluster & Calendar View, 1999

65 Big Data: D4D challenge Data For Development: UN, MIT, Orange 5 month telecom data Ivory Coast 1000 towers Per hour, #calls between towers What can we learn from these data?

66 Big Data: D4D challenge Telecom data visualization, Stef van den Elzen, 2013

67 Information Visualization The use of computer-supported, interactive, visual representations of abstract data to amplify cognition (Card et al., 1999)

68 Thank you!

69 Questions?

70 BaobabView Decision tree visualization, Stef van den Elzen, 2011

71 Information Visualization The use of computer-supported, interactive, visual representations of abstract data to amplify cognition (Card et al., 1999) Data Visualization User

72 SequoiaView Van Wijk et al., 1999, Bruls et al. 2000

73 MatrixView Van Ham 2003, Van Wijk and Nuy, 2003