Visual Analytics - An Interaction of Sight and Thought - JMP

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Vision is our dominant sense. 30% 70% Human perception is amazing. I cherish all five of the senses that connect us to the world, that allow to experience beauty and an inexhaustible and diverse wealth of sensation. But of all the senses, one stands out dramatically as our primary and most powerful channel of input from the world around us, that is vision. Approximately 70% of the body’s sense receptors reside in the eye. Perhaps the world’s top expert in visual perception and how its power can be harnessed for the effec display of information is Colin Ware, who has convincingly described the importance of data visualization. He asks: Why should we be interested in visualization? Because the human visual system is a pattern seeke enormous power and subtlety. The eye and the visual cortex of the brain form a massively parallel processor that provides the highest-bandwidth channel into human cognitive centers. At higher lev of processing, perception and cognition are closely interrelated, which is the reason why the words ‘understanding’ and ‘seeing’ are synonymous. However, the visual system has its own rules. We ca easily see patterns presented in certain ways, but if they are presented in other ways, they become invisible…The more general point is that when data is presented in certain ways, the patterns can readily perceived. If we can understand how perception works, our knowledge can be translated in rules for displaying information. Following perception-based rules, we can present our data in such way that the important and informative patterns stand out. If we disobey the rules, our data will be incomprehensible or misleading. (Information Visualization: Perception for Design, Second Edition, Colin Ware, Morgan Kaufmann Publishers, 2004, page xxi) Perhaps the best known expert in data visualization, Edward Tufte, says: “Clear and precise seeing becomes as one with clear and precise thinking.” (Visual Explanations, Edward R. Tufte, Graphics Pr Cheshire, CT.1997 page 53)
Visualization weaves numbers into pictures. Data visualization is the loom that will weave the data that we collect into the fabric of understanding. Pictures of data can make visible the meanings that might forever otherwise remain hidden.
Page 2 and 3: Visual Analytics An Interaction of
Page 4 and 5: Hurray data! Hurray technology! We
Page 6 and 7: Upon this gifted age, in its dark h
Page 8 and 9: We must tap into that steady stream
Page 10 and 11: Data analysts JMP are now sexy. Dat
Page 12 and 13: 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 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 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
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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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