Now You See It
Gang Tao
Information Visualization
Term - Visualization
•Exploration
•Sense-makingActivity
•Information Visualization
•Scientific Visualization
Technologies
...
Definition of Data Visualization
• Computer-supported
• Interactive
• Visual Representations
• Abstract Data
• Amplify Cog...
Thinking with Our Eyes
The Power of Visual Perception
Visual Perception
• We do not attend
to everything that
we see.
• Visual perception is
selective, at it must
be for awaren...
Visual Perception
Visual Perception
Making Abstract Data Visible
Making Abstract Data Visible
Visualization Attributes
• Form
• Length, Width, Orientation, Size, Shape, Curvature, Enclosure, Blur
• Color
• Hue, Inten...
Visualization Attributes
Comparison – Visual Context
Comparison – Visual Context
Building Block
Information
Visualization
Visual Patterns,
Trends, and
Exceptions
Understanding Good Decision
Quantitative
...
Analytical Interaction
Effectiveness of Visualization
• Ability to clearly and accurately represent information
• Ability to interact with visual...
Ways of Interacting
• Comparing
• Sorting
• Adding
variables
• Filtering
• Highlighting
• Re-scaling
• Accessing
details o...
Compare
Nominal Ranking Part-to-whole
Comparing
Time SerialsDeviation
3D
Wrong Scale
Comparing
• Provide a selection of graphs that support the full
spectrum of commonly needed comparisons
• Provide graphs t...
Sorting
Sorting
• Provide the means to sort items in a graph based on
various values, especially the values that are featured
in t...
Adding Variables
Adding Variables
Adding Variables
• Provide convenient access to every available variable
that might be needed for analysis
• Provide easy ...
Filtering
• Easy filtering based on any information in the connected data
source not just based o information that is curr...
Highlighting
Brush and Link
Highlighting
• Provide the means to highlight a subset of data by selecting
from lists of categorical items.
• Provide the...
Aggregating
• Provide the means to easily aggregate the quantitative
data to the level of items In a categorical variable
...
Drill
• Define hierarchical relationship among categorical
variables
• Drill down/up through hierarchy with no more than
o...
Re-expressing
Re-expressing
Re-Expressing
• Switch current unit of measure to percentage
• Re-express values in terms of how they compare to a
referen...
Re-visualizing
• Easily and Rapidly switch from one type to another
• List the available graph types that are appropriate ...
Zooming and Panning
Zooming and Pan
• Directly select an area of a graph and then zoom into
it with a single click
• Zoom back
• Pan when some...
Re-scaling
Re-scaling
• Change the quantitative scale from linear to
logarithmic
• Set log scale’s base
• Set starting and ending val...
Accessing Details on Demand
• View details related to
an item in a visualization
when needed, in form of
text
• Make detai...
Annotating
• Add notes to a visualization
so that they are associated
with the visualization as a
whole, a particular regi...
Bookmarking
• Save the state of an analysis for later access without
interrupting the flow of analysis
• Maintain a histor...
Navigation : Directed vs. Exploratory Navigation
Analytical Techniques
Techniques and practices
• Optimal quantitative scales
• Reference lines and regions
• Trellises and crosstabs
• Multiple ...
Optimal Quantitative Scales
• When using a bar graph, begin
the scale at zero and end at
the scale a little above the
high...
Optimal Quantitative Scales
Reference Line and Region
• Add reference line based on a specific value and ad hoc
calculation or statistical calculation...
Reference Line and Region
Trellises and Crosstabs
Trellises and Crosstabs
Trellises and Crosstabs
Multiple Concurrent Views and Brushing
Multiple Concurrent Views
Link and Brush
Link and Brush
Focus and Context
Together
Details on Demand
Details on Demand
• Control in
tooltips
• Information for
multiple
selected data
points
Over-plotting Reduction
• Reduce the size of data
objects
• Remove fill color from data
objects
• Changing the shape of da...
Encoding the density of values
Reducing the number of values
• Aggregation
• Filtering
• Layout with multiple views /trellis
Now you see it
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Now you see it

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This is a reading note I made after reading the book.
Now You See It: Simple Visualization Techniques for Quantitative Analysis teaches simple, practical means to explore and analyze quantitative data--techniques that rely primarily on using your eyes. This book features graphical techniques that can be applied to a broad range of software tools, including Microsoft Excel, because so many people have nothing else, but also more powerful visual analysis tools that can dramatically extend your analytical reach. You'll learn to make sense of quantitative data by discerning the meaningful patterns, trends, relationships, and exceptions that measure your organization's performance, identify potential problems and opportunities, and reveal what will likely happen in the future. Now You See It is not just for those with "analyst" in their titles, but for everyone who's interested in discovering the stories in their data that reveal their organization's performance and how it can be improved.

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  • Change the level of details to view the data
  • 1983 – Edward R TUFTE – small multiples
  • Now you see it

    1. 1. Now You See It Gang Tao
    2. 2. Information Visualization
    3. 3. Term - Visualization •Exploration •Sense-makingActivity •Information Visualization •Scientific Visualization Technologies •UnderstandingImmediate Goal •Good DecisionsEnd Goal Communication Graphical Presentation
    4. 4. Definition of Data Visualization • Computer-supported • Interactive • Visual Representations • Abstract Data • Amplify Cognition The purpose of information visualization is not to make pictures, but to help us to think
    5. 5. Thinking with Our Eyes
    6. 6. The Power of Visual Perception
    7. 7. Visual Perception • We do not attend to everything that we see. • Visual perception is selective, at it must be for awareness of everything would overwhelm us. • Our attention is often drawn to contrasts to the norm. • Memory plays an important role in human cognition, but working memory is extremely limited. • Our eyes are drawn to familiar patterns. • We see what we know and expect.
    8. 8. Visual Perception
    9. 9. Visual Perception
    10. 10. Making Abstract Data Visible
    11. 11. Making Abstract Data Visible
    12. 12. Visualization Attributes • Form • Length, Width, Orientation, Size, Shape, Curvature, Enclosure, Blur • Color • Hue, Intensity • Spatial Position • 2-D Position, Spatial Grouping • Motion • Direction
    13. 13. Visualization Attributes
    14. 14. Comparison – Visual Context
    15. 15. Comparison – Visual Context
    16. 16. Building Block Information Visualization Visual Patterns, Trends, and Exceptions Understanding Good Decision Quantitative Reasoning Quantitative Relationship Quantitative Comparisons Visual Perception Visual Properties Visual Objects
    17. 17. Analytical Interaction
    18. 18. Effectiveness of Visualization • Ability to clearly and accurately represent information • Ability to interact with visualization to figure out what the information is
    19. 19. Ways of Interacting • Comparing • Sorting • Adding variables • Filtering • Highlighting • Re-scaling • Accessing details on demand • Annotating • Bookmarking • Aggregating • Re-expressing • Re-visualizing • Zooming and Panning
    20. 20. Compare Nominal Ranking Part-to-whole
    21. 21. Comparing Time SerialsDeviation
    22. 22. 3D
    23. 23. Wrong Scale
    24. 24. Comparing • Provide a selection of graphs that support the full spectrum of commonly needed comparisons • Provide graphs that are designed for easy comparison of those values and relevant patterns without distraction • Provide the means to place a great deal of information that we wish to compare on the screen at the same time, thereby avoiding the need to scroll or move from screen to screen
    25. 25. Sorting
    26. 26. Sorting • Provide the means to sort items in a graph based on various values, especially the values that are featured in the graph • Provide extremely quick and easy means to re-sort data in different ways, ideally with a single click of the mouse • Provide the means to link multiple graphs and easily sort the data in each graph in the same way, assuming that the graphs share a common categorical variable.
    27. 27. Adding Variables
    28. 28. Adding Variables
    29. 29. Adding Variables • Provide convenient access to every available variable that might be needed for analysis • Provide easy means to add a variable to or remove one from the display, such as by directly grabbing the variable and placing it or removing
    30. 30. Filtering • Easy filtering based on any information in the connected data source not just based o information that is currently being displayed • Allow date to be filtered rapidly using simple controls, the lag time between issuing the filter command and seeing the result should be almost unnoticeable. • Provide means to directly select items in a graph and then remove them from display by single/two click • Visible feedback on filter • Complex filter with multiple conditions • Filter multiple graph that linked together
    31. 31. Highlighting
    32. 32. Brush and Link
    33. 33. Highlighting • Provide the means to highlight a subset of data by selecting from lists of categorical items. • Provide the means to highlight a subset of data by directly selecting it in a graph (mouse click, brush) • Highlight selected information so that it can be seen independently from the rest while still allowing viewers to se the entire set of data • Provide the means to highlights a set of items I none graph and have those same items automatically highlighted in other graphs that share the same dataset (link)
    34. 34. Aggregating • Provide the means to easily aggregate the quantitative data to the level of items In a categorical variable • Provide the means to easily aggregate data in a number of useful way, especially summing, averaging and counting • Provide the means to easily aggregate data based on equal intervals of a quantitative variable. • Process the transition from one level of aggregation to another without noticeable delay (Drill down/up) • Ad Hoc Grouping
    35. 35. Drill • Define hierarchical relationship among categorical variables • Drill down/up through hierarchy with no more than one/two click • Can skip levels • Support nature hierarchies such as time
    36. 36. Re-expressing
    37. 37. Re-expressing
    38. 38. Re-Expressing • Switch current unit of measure to percentage • Re-express values in terms of how they compare to a reference value or as a rolling value
    39. 39. Re-visualizing • Easily and Rapidly switch from one type to another • List the available graph types that are appropriate for current data • Prevent or make more difficult the selection of the graph that would display the data inappropriately
    40. 40. Zooming and Panning
    41. 41. Zooming and Pan • Directly select an area of a graph and then zoom into it with a single click • Zoom back • Pan when some portion of the graph is out of the view
    42. 42. Re-scaling
    43. 43. Re-scaling • Change the quantitative scale from linear to logarithmic • Set log scale’s base • Set starting and ending value for the scale • Prevent or make inconvenient the use of log scale for bar and box plot
    44. 44. Accessing Details on Demand • View details related to an item in a visualization when needed, in form of text • Make details disappear when it is no longer required
    45. 45. Annotating • Add notes to a visualization so that they are associated with the visualization as a whole, a particular region, or one or more particular value • The note should reposition to the associated data value
    46. 46. Bookmarking • Save the state of an analysis for later access without interrupting the flow of analysis • Maintain a history of the steps and states during the analytical process
    47. 47. Navigation : Directed vs. Exploratory Navigation
    48. 48. Analytical Techniques
    49. 49. Techniques and practices • Optimal quantitative scales • Reference lines and regions • Trellises and crosstabs • Multiple concurrent views and brushing • Focus and context together • Details on demand • Over-plotting reduction
    50. 50. Optimal Quantitative Scales • When using a bar graph, begin the scale at zero and end at the scale a little above the highest value • With every type of graph other than a bar graph, begin the scale a little below the lowest value and end it a little above the highest value • Begin the end the scale at round numbers, and make the intervals round number as well.
    51. 51. Optimal Quantitative Scales
    52. 52. Reference Line and Region • Add reference line based on a specific value and ad hoc calculation or statistical calculation • Automated calculations for : mean, median, standard deviation, specific percentiles, minimum and maximum • Reference line based on the values that appear in the graph only or on a larger set of value • Label the reference lines to clearly indicate what the lines represent • Format the reference line as needed (hue, color intensity, line weight, line styles etc)
    53. 53. Reference Line and Region
    54. 54. Trellises and Crosstabs
    55. 55. Trellises and Crosstabs
    56. 56. Trellises and Crosstabs
    57. 57. Multiple Concurrent Views and Brushing
    58. 58. Multiple Concurrent Views
    59. 59. Link and Brush
    60. 60. Link and Brush
    61. 61. Focus and Context Together
    62. 62. Details on Demand
    63. 63. Details on Demand • Control in tooltips • Information for multiple selected data points
    64. 64. Over-plotting Reduction • Reduce the size of data objects • Remove fill color from data objects • Changing the shape of data objects • Jittering data objects • Making data objects transparent • Encoding the density of values • Reducing the number of values
    65. 65. Encoding the density of values
    66. 66. Reducing the number of values • Aggregation • Filtering • Layout with multiple views /trellis

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