"Principles of Data Visualization" by Asst. Prof. Dr. Kian Jazayeri offers a deep dive into effective data representation techniques. The presentation begins by underlining the importance of data visualization in revealing true data insights, avoiding errors, and facilitating knowledge sharing. It challenges the viewer to think beyond basic charts, highlighting that effective visualization requires sophisticated skills to accurately convey complex information.
The deck uses Anscombe's Quartet to illustrate the misleading nature of statistics without proper visual representation, showcasing how different data distributions can look when graphed, despite having identical statistical summaries. This example sets the stage for discussing the necessity of visual analysis to uncover the real story behind the data.
Art appreciation parallels are drawn to emphasize the importance of visual aesthetics in data visualization. By comparing renowned artworks, the slides suggest that, like art, data visualization requires a developed sense of design and aesthetics to communicate effectively and make an impact.
Edward Tufte's visualization principles are explored in depth, advocating for a high data-ink ratio, and warning against the lie factor—where the representation of data misleads more than it informs. The presentation also addresses chartjunk, encouraging the removal of unnecessary visual elements that do not add value to the data's understanding.
Dr. Jazayeri emphasizes graphical integrity, advising against scale distortion and advocating for accurate, clear labeling to maintain the data's true proportion and context. The concept of aspect ratios is discussed, advising a balance to avoid visual misrepresentation of trends.
Interactive elements within the slides engage viewers, prompting them to analyze different visualizations and understand how quickly and accurately data can be interpreted. This engagement highlights the "10-Second Rule," the idea that effective visualizations should allow quick and unambiguous data interpretation.
Color usage in data visualization is another focal point, with explanations on how different colors and their intensities can significantly affect data interpretation. Special attention is given to designing for color blindness, ensuring inclusivity in data communication.
Advanced topics include data maps, cartograms, scatter plots, and heatmaps, each discussed with their specific applications and potential for overplotting or misinterpretation. The presentation also critiques tabular data, suggesting improvements for clarity, comparison, and highlighting critical information.
Renowned works, like Minard's depiction of Napoleon's Russian campaign and Marey’s train schedule, are dissected to demonstrate how effective visual storytelling can enhance the comprehension of complex data narratives.
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
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.
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
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.
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.
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.
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.
26.
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
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52. Data Maps and Cartograms
Cartograms distort regions to reflect an underlying variable
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
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
