Visual Analytics - An Interaction of Sight and Thought - JMP

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Attention is limited. Ever saw a magic show and wondered just how the magician took your watch without you even noticing? Ever wonder why is it that you can search for a set of misplaced keys for a long time, only to later find them sitting in the exact place where you were looking? Research has shown that we don’t always see everything we’re looking at, and that attention plays a big part in what consciously registers to us. The effect where we’re blind to things we don’t attend to is known as ‘Inattentional Blindness’. (Source: This demonstration of inattentional blindness and the explanation above was prepared under the direction of Ronald A. Rensink of the University of British Columbia. Several other examples of this visual phenomenon can be found at http:// psyclab1.psych.ubc.ca/~viscoglab/.)
Memory is limited. What’s not stored in working memory is lost. In addition to understanding visual perception, visual analysis tools must also be rooted in an understanding of how people think. Only then can they recognize and support the cognitive operations that are necessary to make sense of information. Memory plays an important role in human cognition. Because memory suffers from certain limitations, visual analysis tools must be able to augment memory. The example above illustrates one of the limitations of working memory. We only remember that to which we attend. Any part of this image that never gets our attention will not be missed when we shift to another version of the image that lacks that particular part. If we don’t attend to it, we might notice the change from one version of the image to the next, but only if the transition shift immediately from one to another, without even a split second of blank space between them. In addition to not remembering, we also don’t clearly see that on which we don’t focus. To see something clearly, we must focus on it, for only a small area of receptors on the retinas of our eyes are designed for high-resolution vision. (Source: This demonstration of change blindness was prepared by Ronald A. Rensink of the University of British Columbia. Several other examples of this visual phenomenon can be found at http://www.psych.ubc.ca/%7erensink/flicker/download/index.html.)
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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
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Two of the right skills • Statist
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What is visual thinking? Temple Gra
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We need greater balance. Modes of r
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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 64 and 65: Shneiderman’s Mantra “Overview
Page 66 and 67: Overview first, zoom and filter, th
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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Visual Analytics - An Interaction of Sight and Thought - JMP

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