Visual Analytics - An Interaction of Sight and Thought - JMP

More documents

Recommendations

Info

Key interactions of visual analysis • Sorting • Filtering • Adding/removing variables • Highlighting • Aggregating/Disaggregating • Grouping • Zooming/Panning • Re-visualizing • Re-expressing • Re-scaling The process of visual data analysis involves several common interactions with data to uncover what’s meaningful. Here are some of the primary interactions: • Sorting. The act of sorting data, especially by the magnitude of the values from high to low or low to high, features the ranking relationship between those values and makes it easier to compare the magnitude of value to the next. • Adding/removing variables. You might need to view different variable at different times during the analysis process, so it is common to add or remove field of data from view as necessary • Filtering. When you want to focus on a subset of data, nothing makes it easier to do so than filtering—the removal from view of everything your not interested in at the moment. • Highlighting. Sometimes you want to focus on a subset of information, but do so in a way that allows you to maintain a sense of how that subset relates to the whole. Rather than filtering out the data that falls outside your range of focus, you can simply reduce its visual salience or increase the visual salience of the data you wish to focus on. This allows you to focus on the subset with less distraction from the whole in a way that allow you to remain aware of the whole. This is one way of achieving what’s called a focus+context view. • Aggregating/Disaggregating. Analysis often requires that you examine data a different levels of detail. Aggregation involves viewing data at a higher level of summarization. Disaggregation involves viewing data at a lower level of detail. • Grouping. Sometimes it is useful to combine members of a variable together, treating them as a single member of the variable. This may take the form of combining some members and leaving others as they are, or of creating an entirely new variable that combines all members of an existing variable into a groups to form members of a higher level variable. • Zooming/Panning. When a data visualization contains so much that it is difficult to clearly see all the data at once, it is useful to zoom in on that portion that you want to see more clearly. Panning involves moving around (for example, up, down, right, or left) in a zoomed view to focus on a different part of the larger visualization. • Re-visualizing. No one visual representation of data can show you everything there is to see, so visual analysis involves shifting from one type of visualization to another to explore data from various perspectives. • Re-expressing. Sometimes it is useful to express a quantitative variable as a different unit of measure, such as expressing dollars as percentages. • Re-scaling. No single quantitative scale on a graph can serve every analytical need. Rescaling involves changing the range of the quantitative scale to make it easier to see particular patterns and sometimes even changing the nature of the scale, such as from a normal scale to a logarithmic scale.
Dynamic data filters support analytical flow. Direct dynamic interaction with the data allows you to manipulate the data easily and immediately (such as by filtering it), without interrupting your stream of thought.
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 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 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
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
show all

Visual Analytics - An Interaction of Sight and Thought - JMP

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?