The document discusses the importance of effective data visualization in revealing insights, ensuring quality control, and facilitating communication of findings. It emphasizes the need for skilled crafting of visualizations to accurately convey information, using principles from Edward Tufte and the significance of minimizing misleading elements like 'chartjunk.' Essential techniques covered include maximizing data ink ratio, maintaining graphic integrity, and ensuring clarity in design to enhance understanding and appreciation of data.

1. By:
Asst. Prof. Dr. Kian Jazayeri

2. The Importance of
Data Visualization
• Investigative Analysis: Unveiling
the true form of your data
• Quality Control: Did an
oversight lead to error?
• Knowledge Sharing:
Communicating your findings
with others
Many charts and graphs out there
fall short: Crafting effective
visualizations requires more skill
than one might assume.

3. I II III IV
X1 Y1 X2 Y2 X3 Y3 X4 Y4
10 8.04 10 9.14 10 7.46 8 6.58
8 6.95 8 8.14 8 6.77 8 5.76
13 7.58 13 8.74 13 12.74 8 7.71
9 8.81 9 8.77 9 7.11 8 8.84
11 8.33 11 9.26 11 7.81 8 8.47
14 9.96 14 8.1 14 8.84 8 7.04
6 7.24 6 6.13 6 6.08 8 5.25
4 4.26 4 3.1 4 5.39 19 12.5
12 10.84 12 9.13 12 8.15 8 5.56
7 4.82 7 7.26 7 6.42 8 7.91
5 5.68 5 4.74 5 5.73 8 6.89
MEAN 9 7.5 9 7.5 9 7.5 9 7.5
VAR 10 3.75 10 3.75 10 3.75 10 3.75
CORR. 8.16 8.16 8.16 8.16
Anscombe's Quartet Demonstrates
Visualization Necessity
Four data sets that
have nearly identical
simple descriptive
statistics, yet have
very different
distributions and
appear very different
when graphed.
Francis Anscombe
1918-2001

4. Plotting Anscombe's Quartet

5. Appreciating Art: Which One is Better?

6. Salvator Mundi (Latin for 'Savior of the World')
Artist Leonardo da Vinci
Year 1499–1510
Type Oil on walnut panel
Dimensions 45.7 cm × 65.7 cm
Sold for
US$ 450.3 Million

7. Number 17A
Artist Jackson Pollock
Year 1948
Type Oil paint on
fiberboard
Dimensions 112 cm × 86.5 cm
Sold for
US$ 200 Million
Sensible appreciation of art
requires developing a
particular visual aesthetic.

8. Appreciating Data Visualization Art
• Cultivate design sensibility and specialized vocabulary.
• Balance visual appeal with clear data interpretation.
• Merge artistic creativity and analytical precision for
impactful storytelling.

9. Tufte's Visualization
Aesthetic
•Maximize data ink-ratio
•Minimize lie factor
•Minimize chartjunk
•Use proper scales and clear
labeling
• Edward Rolf Tufte
• (Born 1942 -age 82)
• Also known as "ET", is an
American statistician and professor
emeritus of political science,
statistics, and computer science at
Yale University.
• He is noted as a pioneer in the
field of data visualization.

10. Maximize Data-Ink Ratio
• This formula signifies the importance of focusing on the essential
parts of the visualization that convey information, minimizing the
non-essential ink that does not add meaningful value to the
understanding of the data.

11. Less is more

12. The Lie Factor: Dimensionality
• Using single dimensions to represent multi-dimensional data can skew perception.
• Beware the 'lie factor': graphic size vs. actual data effect.
• Misrepresentations can mislead viewers and damage data credibility.
Must be 1

13. The Lie Factor: Dimensionality

14. Graphical Integrity: Scale Distortion
• Always start bar graphs at zero to avoid misrepresenting the data.
• Always properly label your axes to provide clear context for the
data displayed.
• Use continuous scales that are either linear or clearly labeled to
ensure the data's proportions are accurately represented.

15. Graphical Integrity: Scale Distortion

16. Graphical Integrity: Scale Distortion

17. Aspect Ratios and Lie Factors
• The steepness of apparent cliffs in a chart is
influenced by the aspect ratio of the chart.
• It is recommended to target a 45-degree
angle for trend lines or to use the Golden Ratio
(approximately 1.618) for the most accurate
and interpretable visual representation.

18. Aspect Ratios and Lie Factors

19. Reduce Chartjunk
• Unnecessary visual elements can detract from the core message of the data.
• Avoid extra dimensionality that doesn't serve a purpose.
• Steer clear of coloring that doesn't convey useful information.
• Refrain from using excessive grids and decorative features that don't add value.
• In a compelling graphic, it's the data that should capture the audience, not the
superfluous embellishments known as chartjunk.

20. Reduce Chartjunk / Graphical Ducks
The term "ducks" is borrowed
a duck-shaped building that
sold ducks and duck-related
products.

21. Reduce Chartjunk / Graphical Ducks

22. Reduce Chartjunk / Graphical Ducks

23. Reduce Chartjunk / Graphical Ducks

24. Reduce Chartjunk / Graphical Ducks

25. Let’s play a game!
There are two different visualizations of the same data in the
next two slides. Look at each visualization for 10 seconds and
try to conclude what each visualization conveys.

27. 0%
10%
20%
30%
40%
50%
60%
70%
Free & Rest
Time
Sports
and/or a
Hobby
Time with
Friends
Time with
Family
Community
&
Volunteer
Work
Further
Education
and
Keeping Up
with
Current
Events
Nothing at
all
WHAT ENTREPRENEURS SCARIFICED
TO START THEIR OWN BUSINESS

28. The 10-Second Rule
A good data visualization should allow different
people to come to the same conclusion about the
data in 10 seconds or less!

29. Chart
Suggestions
Dr. Andrew Abela
1965 (age 58 years)
• Chairman of the
Department of
Business &
Economics at the
Catholic University of
America in
Washington, DC
• Associate professor
of marketing

52. Data Maps and Cartograms
Cartograms distort regions to reflect an underlying variable

53. Scatter Plots Remember: Reduce overplotting
by Small Points

54. Heatmaps Reveal Finer Structures
Distribution of Americans by Height and Weight

55. Which one is a better visualization?

56. Colors have meanings!
Understanding Color Scales and Color Maps
Hue
Rainbow
0% Saturation %100
0% Brightness %100

57. Colors matter!
GDP Per Capita Covid-19 Deaths per Million People
World Population

58. Do not forget that some people are color-blind
GDP Per Capita
Colors
as seen
with
normal
vision
Same
colors as
seen with
red-green
color
deficiency

59. Exceptions!

60. Exceptions!

61. Do you remember this painting?

62. Do you see anything meaningful here?

63. Marey’s Train Schedule
Étienne-Jules Marey
(1830-1904)
French scientist,
physiologist and
chronophotographer

64. Marey’s Train Schedule
Never Imprison
Your Data!
Can be further enhanced with a lighter data grid

65. Charles Joseph Minard
(1781-1870)
• French civil engineer
• Recognized for his significant
contribution in the field of
information graphics in civil
engineering and statistics
Napoleon's advance
and retreat

66. Napoleon's advance and retreat Two dimensions
and six types of
Data:
• The number of
Napoleon's troops;
• Distance
• Temperature
• The latitude and
longitude
• Direction of travel
• Location relative to
specific dates

67. Tabular Data
•Precision in Numerical Data
•Clarity in Multivariate Analysis
•Heterogeneous Data Representation
•Ideal for Concise Data Sets

68. Can this table be further improved?
Country Area Density Birthrate Population Mortality GDP
Russia 17075200 8.37 99.6 142893540 15.39 8900
Mexico 1972550 54.47 92.2 107449525 20.91 9000
United Kingdom 244820 247.57 99 127463611 3.26 28200
Japan 377835 337.35 99 127463611 3.26 282200
New Zeland 268680 15.17 99 4076140 5.85 21600
Afghanistan 647500 47.96 36 31056997 163.07 700
Israel 20770 305.83 95.4 6352117 7.03 19800
United States 9631420 30.99 97 298444215 6.5 37800
China 9596960 136.92 90.9 1313973713 24.18 5000
Tajikistan 143100 51.16 99.4 7320815 110.76 1000
Burma 678500 69.83 85.3 47382633 67.24 1800
Tanzania 945087 39.62 78.2 37445392 98.54 600
Tonga 748 153.33 98.5 114689 12.62 2200
Germany 357021 230.86 99 82422299 4.16 27600
Australia 7686850 2.64 100 20264082 4.69 29000

69. How to Improve Tabular Data
•Facilitate Comparisons with Row Ordering
•Prioritize Data with Column Sequencing
•Align Numbers for Precision
•Highlight Key Data with Styling (bold, italic, color)
•Avoid excessive-length column descriptions

70. Improved Tabular Presentation
Country Population Area Density Mortality GDP Birthrate
Afghanistan 31,056,997 647,500 48.0 163.1 700 36.0
Australia 20,264,082 7,686,850 2.6 4.7 29,000 100.0
Burma 47,382,633 678,500 69.8 67.2 1,800 85.3
China 1,313,973,713 9,596,960 136.9 24.2 5,000 90.9
Germany 82,422,299 357,021 230.9 4.2 27,600 99.0
Israel 6,352,117 20,770 305.8 7.0 19,800 95.4
Japan 127,463,611 377,835 337.4 3.3 28,200 99.0
Mexico 107,449,525 1,972,550 54.5 20.9 9,000 92.2
New Zeland 4,076,140 268,680 15.2 5.9 21,600 99.0
Russia 142,893,540 17,075,200 8.4 15.4 8,900 99.6
Tajikistan 7,320,815 143,100 51.2 110.8 1,000 99.4
Tanzania 37,445,392 945,087 39.6 98.5 600 78.2
Tonga 114,689 748 153.3 12.6 2,200 98.5
United Kingdom 127,463,611 244,820 247.6 3.3 28,200 99.0
United States 298,444,215 9,631,420 31.0 6.5 37,800 97.0

71. Can you simplify this plot?

72. Can you simplify this plot?

73. Can you simplify this plot?

74. Can you simplify this plot?

75. Can you simplify this plot?