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Intro to data visualization
This document provides an introduction to data visualization. It discusses what data visualization is, why it is used, and the stages involved in creating visualizations from data. Key points include: - Data visualization involves using visual representations of data to help people analyze and communicate information more effectively. - Visualizations are used for tasks like recording information, analyzing data to support reasoning, and communicating information. - The process of creating visualizations involves understanding the properties of the data, properties of images and perception, and rules for mapping data to visual encodings. - Important considerations include which visual variables to use to encode different data properties, principles of visual perception, and enabling interaction with the data. Validation of the effectiveness of
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Intro to data visualization
- 1. Data Visualization -An introduction Prof Jan Aerts Biodata Visualization and Analysis ESAT/SCD University of Leuven Belgium twitter: @jandot Google+: +Jan Aerts [email protected] http://biovizanlab.wordpress.com http://saaientist.blogspot.com
- 2. 1. What isdata visualization?
- 3. “A good sketchis better than a long speech” (Napoleon)
- 4. “A good sketchis better than a long speech” (Napoleon) shows: size of the army, geographical coordinates, direction that the army was traveling, location of the army with respect to certain dates, temperature along the path of the retreat
- 5. John Snow -cholera map
- 6. Shape of Songs:“Like a Prayer” (Madonna) Martin Wattenberg
- 7.
- 9. What I useas a definition: “computer-based visualization systems providing visual representations of datasets intended to help people carry out some task more effectively.” (T Munzner)
- 11. cognition <=> perception cognitivetask => perceptive task “eyes beat memory”
- 12. Why do wevisualize data? • record information • blueprints, photographs, seismographs, ... • analyze data to support reasoning • develop & assess hypotheses • discover errors in data • expand memory • find patterns (see Snow’s cholera map) • communicate information • share & persuade • collaborate & revise
- 13. exploration explanation pictorial superiority effect “information” 72hr “informa” “i” 65% 1%
- 14. 2. Exploration <->explanation
- 15. exploration explanation
- 16. exploration explanation visual infographics analytics
- 17. exploration explanation visual infographics analytics
- 18. exploration explanation visual infographics analytics hypothesis generation
- 19. exploration explanation “visual analytics” => identify unexpected patterns
- 20. exploration explanation J van Wijk
- 21. Anscombe’s quartet • uX= 9.0 • uY = 7.5 • sigma X = 3.317 • sigma Y = 2.03 • Y = 3 + 0.5X • R2 = 0.67
- 24. A concrete example:hive plots
- 25. same network Martin Krzewinsky
- 26. different networks! Martin Krzewinsky
- 27.
- 28. 3D, anyone? occlusion interaction complexity perspective distortion text legibility
- 29. Functions in linuxoperation system: “function A calls function B” Gene interaction data: “gene A regulates gene B”
- 30. regulator workhorse manager
- 31. 3. Why specificallylearn about dataviz?
- 32. Isn’t it alljust about using common sense?
- 33. • huge spaceof design alternatives => many tradeoffs • many possibilities known to be ineffective • avoid random walk through parameter space • avoid some of our past mistakes • extensive experimentation has already been done • guidelines continue to evolve • we reflect on lessons learned in design studies • iterative refinement usually wise
- 34. 4. Stages ofdata visualization
- 35. How do weget from data to visualization? We need to understand: • properties of the data • properties of the image • the rules mapping data to image
- 36. 4.1. Properties ofthe data
- 37. S Stevens “Onthe theory of scales and measurements” (1946)
- 38. 4.2. Properties ofthe image - perception
- 39. Semiology of graphics •Jacques Bertin, Gauthier-Villars 1967, EHESS 1998 • semiology = study of signs and sign processes, likeness, analogy, metaphor, symbolism, signification, and communication (Wikipedia) • visual encoding: • what - points, lines, areas (, patterns, trees/networks, grids) • where - positional: XY (1D, 2D, 3D) • how - retinal: Z (size, lightness, texture, colour, orientation, shape) • when - temporal: animation
- 40. “marks” - geometricprimitives H V S “channels” - control appearance of marks
- 41. Gestalt laws -interplay between parts and the whole (Kurt Koffka) series of principles Election results Florida: • black = Bush • white = Gore
- 43. Gestalt - Principleof Simplicity Every pattern we see is seen such that we see a structure that is as simple as possible.
- 44. Gestalt - Principleof Proximity Things that are close to each other are seen as belonging together (=> clusters)
- 45. Gestalt - Principleof Similarity Things that are similar in some way are perceived as belonging together.
- 46. Gestalt - Principleof Closure You will try to complete a pattern.
- 47. Gestalt - Principleof Connectedness Things that are connected are perceived as belonging together. This encoding is stronger than similarity, shape, colour, and size.
- 48. Gestalt - Principleof Good Continuation Objects that are arranged in a straight or smooth line tend to be seen as a unit.
- 49. Gestalt - Principleof Common Fate Objects that move in the same direction tend to be seen as a unit.
- 50. Gestalt - Principleof Familiarity
- 54. Gestalt - Principleof Symmetry Symmetrical areas tend to be seen as figures against asymmetrical backgrounds.
- 55.
- 56. Pre-attentive vision = abilityof low-level human visual system to rapidly identify certain basic visual properties • some features “pop out” • used for: • target detection • boundary detection • counting/estimation • ... • visual system takes over => all cognitive power available for interpreting the figure, rather than needing part of it for processing the figure
- 57. Really fast; seehttp://www.csc.ncsu.edu/faculty/healey/PP/
- 58. Limitations of preattentivevision 1. Combining pre-attentive features does not always work => would need to resort to “serial search” (most channel pairs; all channel triplets) e.g. is there a red square in this picture 2. Speed depends on which channel (use one that is good for categorical; see further (“accuracy”))
- 59. 4.3. Mapping datato image: visual encoding
- 60. Language of graphics •graphics = sign system: • each mark (point, line, area) represents a data element • choose visual variables to encode relationships between data elements • difference, similarity, order, proportion • only position supports all relationships (see later) • huge range of alternatives for data with many attributes • find images that express & effectively convey the information
- 61. Which encoding shouldI use? • From huge list of possibilities, you have to choose the best one. • Principle of Consistency • properties of the representation should match properties of the data (e.g. pie chart: area vs radius) • Principle of Importance Ordering • encode the most important piece of information in the most “effective” way (i.e. spatial position)
- 63. Steven’s psychophysical law = proposed relationship between the magnitude of a physical stimulus and its perceived intensity or strength
- 64. Accuracy of quantitativeperceptual tasks how much (quantitative) what/where (qualitative) McKinlay
- 65. Accuracy of quantitativeperceptual tasks how much (quantitative) what/where (qualitative) McKinlay
- 66. Accuracy of quantitativeperceptual tasks how much (quantitative) what/where (qualitative) McKinlay “power of the plane”
- 67. Accuracy of quantitativeperceptual tasks how much (quantitative) what/where (qualitative) grouping: see Gestalt laws McKinlay
- 68.
- 69. COLOUR ... istricky, and often used wrong
- 70. Colour space • =mathematical model to talk about colour • RGB (red-green-blue) • most common, but less useful • HSV (hue-saturation-value) • more useful
- 71. colorbrewer2.org in R: pleaseuse RColorBrewer!
- 72. Context affects colourperception
- 73. Context affects colourperception
- 74. Dangers of Depth(3D) • We do NOT see in 3D; we see in 2.05D. • occlusion • interaction complexity • perspective distortion
- 75.
- 76. Lie factor size of effect shown in graphic “lie factor” = size of effect in data
- 77. 3D scatter plotsare better as series of 2D projections
- 78. Dynamic data • animationis good sometimes, but often not: • we can only follow 3-4 visual cues simultaneously • change in “mental map” • change blindness (e.g. http://nivea.psycho.univ-paris5.fr/CBMovies/ BarnTrackFlickerMovie.gif)
- 79.
- 81.
- 82. Overview, zoom andfilter, details on demand (Schneiderman’s Information Seeking Mantra)
- 83. Operations on thedata • sorting • filtering • browsing/exploring • comparison • characterizing trends & distributions • finding anomalies & outliers • ...
- 84. Techniques to supportthese operations • re-orderable matrices • brushing • linked views • overview & detail • focus & context • ...
- 85.
- 86. Evaluate the rightthing Munzner, 2009
- 87. Slide/picture acknowledgments • JeffreyHeer • Tamara Munzner • Jessie Kennedy • Nils Gehlenborg • Miriah Meyer
- 88. “I think thispresentation went quite well...”