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.

2. Now You See It
Gang Tao

3. Information Visualization

4. Term - Visualization
•Exploration
•Sense-makingActivity
•Information Visualization
•Scientific Visualization
Technologies
•UnderstandingImmediate Goal
•Good DecisionsEnd Goal
Communication
Graphical
Presentation

5. 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

6. Thinking with Our Eyes

7. The Power of Visual Perception

8. 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.

9. Visual Perception

10. Visual Perception

11. Making Abstract Data Visible

12. Making Abstract Data Visible

13. Visualization Attributes
• Form
• Length, Width, Orientation, Size, Shape, Curvature, Enclosure, Blur
• Color
• Hue, Intensity
• Spatial Position
• 2-D Position, Spatial Grouping
• Motion
• Direction

14. Visualization Attributes

15. Comparison – Visual Context

16. Comparison – Visual Context

17. Building Block
Information
Visualization
Visual Patterns,
Trends, and
Exceptions
Understanding Good Decision
Quantitative
Reasoning
Quantitative
Relationship
Quantitative
Comparisons
Visual
Perception
Visual
Properties
Visual Objects

18. Analytical Interaction

19. Effectiveness of Visualization
• Ability to clearly and accurately represent information
• Ability to interact with visualization to figure out what
the information is

20. 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

21. Compare
Nominal Ranking Part-to-whole

22. Comparing
Time SerialsDeviation

23. 3D

24. Wrong Scale

25. 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

26. Sorting

27. 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.

28. Adding Variables

29. Adding Variables

30. 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

31. 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

32. Highlighting

33. Brush and Link

34. 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)

35. 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

36. 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

37. Re-expressing

38. Re-expressing

39. 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

40. 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

41. Zooming and Panning

42. 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

43. Re-scaling

44. 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

45. 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

46. 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

47. 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

48. Navigation : Directed vs. Exploratory Navigation

49. Analytical Techniques

50. 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

51. 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.

52. Optimal Quantitative Scales

53. 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)

54. Reference Line and Region

55. Trellises and Crosstabs

56. Trellises and Crosstabs

57. Trellises and Crosstabs

58. Multiple Concurrent Views and Brushing

59. Multiple Concurrent Views

60. Link and Brush

61. Link and Brush

62. Focus and Context
Together

63. Details on Demand

64. Details on Demand
• Control in
tooltips
• Information for
multiple
selected data
points

65. 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

66. Encoding the density of values

67. Reducing the number of values
• Aggregation
• Filtering
• Layout with multiple views /trellis