Visual Analytics - An Interaction of Sight and Thought - JMP

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Shneiderman’s Mantra “Overview first, zoom and filter, then details-on-demand.” When new recruits by intelligence organizations are trained in spy craft, they are taught a method of observation that begins by getting an overview of the scene around them while being sensitive to things that appear abnormal, not quite right, which they should then focus in on for close observation and analysis. A visual information-seeking mantra for designers: ‘Overview first, zoom and filter, then details-on-demand.’ (Readings in Information Visualization: Using Vision to Think, Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman, Academic Press, San Diego, California, 1999, page 625) Having an overview is very important. It reduces search, allows the detection of overall patterns, and aids the user in choosing the next move. A general heuristic of visualization design, therefore, is to start with an overview. But it is also necessary for the user to access details rapidly. One solution is overview + detail: to provide multiple views, an overview for orientation, and a detailed view for further work. (Ibid., page 285) Users often try to make a ‘good’ choice by deciding first what they do not want, i.e. they first try to reduce the data set to a smaller, more manageable size. After some iterations, it is easier to make the final selection(s) from the reduced data set. This iterative refinement or progressive querying of data sets is sometimes known as hierarchical decision-making. (Ibid., page 295)
Overview first, zoom and filter, then details-on-demand Shneiderman’s technique begins with an overview of the data — the big picture. Let your eyes search for particular points of interest in the whole.
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Visual Analytics An Interaction of
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Hurray data! Hurray technology! We
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Upon this gifted age, in its dark h
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We must tap into that steady stream
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Data analysts JMP are now sexy. Dat
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To do this we need the right skills
Page 14 and 15: Two of the right skills • Statist
Page 16 and 17: What is visual thinking? Temple Gra
Page 18 and 19: We need greater balance. Modes of r
Page 20 and 21: Leonardo da Vinci For some four hun
Page 22 and 23: Complex problems require visual thi
Page 24 and 25: Tools extend our reach when they’
Page 26 and 27: Can computers replace us? Our brain
Page 28 and 29: Visualization empowers statistics.
Page 30 and 31: “I see!” There is an intimate c
Page 32 and 33: Vision is our dominant sense. 30% 7
Page 34 and 35: Trends, patterns, and exceptions co
Page 36 and 37: This visualization fails to do this
Page 38 and 39: The right balance 38 Information vi
Page 40 and 41: Many visualization tools feature de
Page 42 and 43: Bling your graph. Many software ven
Page 44 and 45: 3-D graphs are rarely useful. One o
Page 46 and 47: Even when there’s a 3 rd variable
Page 48 and 49: We don’t want to confuse people w
Page 50 and 51: Good visualizations and good visual
Page 52 and 53: This arrangement is even better yet
Page 54 and 55: Visual perception works according t
Page 56 and 57: Visual perception is not just camer
Page 58 and 59: Two essential activities • Multi-
Page 60 and 61: Data analysis requires rapid intera
Page 62 and 63: Key interactions of visual analysis
Page 66 and 67: Overview first, zoom and filter, th
Page 72 and 73: Attention is limited. Ever saw a ma
Page 74 and 75: Capacity = 3 or 4 chunks at a time
Page 76 and 77: Good analysis tools augment memory
Page 78 and 79: More dimensions through multiple vi
Page 80 and 81: Coordinated views expand what we ca
Page 82 and 83: Analytical challenges demand statis
Page 84 and 85: Predictions rely on statistical mod
Page 86 and 87: Finding and understanding the stori
Page 88 and 89: Wisdom Knowledge Information Inform
Page 90 and 91: O perpetual revolution of configure
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