A presentation I delivered at the How.Camp conference in Veliko Tarnovo, Bulgaria. http://how.camp/

1. Making sense of data visually:
A modern look at data
visualization
VLADIMIR MILEV
NEW VENTURE SOFTWARE

2. Author Bio
Vladimir Milev
MCPD Enterprise
Speaker (Devreach, NTK Slovenia and others)
DV Evangelist
Founder at New Venture Software
@vmilev
www.linkedin.com/in/vladimirmilev/

3. http://www.newventuresoftware.com/

4. Agenda
1. Big data and information overload
2. What problems DataViz solves
3. DataViz fundamental theory
4. Basic visualizations
5. Advanced visualizations

5. Information Overload
Twitter: 500 million tweets per day
Facebook: 55 million status updates per day
Facebook: 900 million interactions per day (comments, likes etc.)
Reddit:

6. Proliferation of smart devices
 We are already living in a world dominated by
smart devices
 What is the meaning of this?
 More connected, data is more accessible
 Less space for tables and text
 Must use visual communication

7. Making Sense of Data
Increasing amount of data available
Increasing number of data consumer devices
Obtaining data no longer a problem
We have an Information Overload issue
Quick data analysis is the new problem
But how quick?

8. A Picture is worth a 1000 words
With about 1,000,000
ganglion cells, the human
retina would transmit data
at roughly the rate of an
Ethernet connection, or 10
million bits per second.”
-Vijay Balasubramanian,
PhD, Professor of Physics at
U Penn

9. OK – That’s a lot of
bandwidth
BUT ARE WE USING IT EFFICIENTLY?

10. Efficiency
Best readers usually read up to about 300 words per minute.
Average word length is 5.1 letters
300 * 5.1 = 1530 characters per minute
Or 1530 / 60 = 25.5 characters per second
1 character is usually stored as 8 bits
26 * 8 = 208 bits per second
Reading bandwidth is ~0.025 KiB/s
Or 0.00208% Efficiency

11. So reading clearly isn’t
the way to go…
BUT WHAT IS THE SOLUTION?

12. Using statistics
For the most part of the 20th century
Using arithmetic mean, average, standard deviation
Variance, correlations, regressions
Turns out this is not good enough

13. Anscombe’s Quartet
I II III IV
x y x y x y x y
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
• Statistical properties are identical:
• Mean of X (9.0) and Y (7.5) values are constant
• Nearly same variances, correlations and regressions
• As far as statistics is concerned these sets are almost the same

14. Anscombe’s Quartet

15. So DataViz is very powerful
But why does it work so well?

16. Gestalt Psychology
Seeing with the brain
The mind understands external stimuli as whole rather than the
sum of their parts
We tend to order our experience in a manner that is regular,
orderly, symmetric, and simple
Key principles of gestalt: reification, multistability, invariance
Gestalt laws of grouping: proximity, similarity, closure, symmetry

17. Gestalt Principles - Reification
Our minds tend to
construct/generate
information

18. Gestalt Principles -Multistability
The tendency of our
mind to jump back and
forth between
ambiguous alternative
interpretations
Spinning Girl Rubin Vase

19. Gestalt Principles - Invariance
The tendency to perceive simple geometric
objects independent of rotation, translation,
and scale
Also elastic deformations, different lighting,
and different component features

20. Gestalt Laws of Grouping - Similarity
We group objects based on visual similarity

21. Gestalt Laws of Grouping - Proximity
We group items based on spatial proximity

22. Gestalt Laws of Grouping - Closure
We perceive objects such as shapes, letters, pictures, etc., as
being whole when they are not complete

23. Application in Data Visualization
 Introducing the visual variables
 Fundamental properties of objects which can encode information into a
picture
 Fundamental visual variables:
◦ Position
◦ Size
◦ Color
◦ Shape
◦ Orientation
Basis for all Data Visualization!

24. Basic/Common Visualizations
Bar graphs
Line graphs
Area charts
Pie charts

25. Bar Graphs
• Using color correctly to encode
gender
• Using position (ordering) to
create an orderly scale
• Using size to encode the values
• Using orientation to differentiate
gender again

26. Bar Graphs continued
• Labels are used
• Color is neutral and does not encode
information
• Again, we have top-down ordering
(position)
• And again size encodes the relative
numeric value

27. Bars and Normal Distribution
Minimum passing grade
• Distribution of test scores for
Polish “Matura” exam
• Normal Distribution is
expected
• Red line shows normal
distribution
• 30 is the minimum expected
grade
• Detecting behavioral changes
• What happened?

28. Line Graphs
Confirming what we already know –
paper media is declining rapidly.
• Shape encodes the value
• Color is not significant
• Design goal is to show a
trend/change

29. Area Graphs
Effect of school year on
Team Fortress 2 players
School starts
• Similar to line graph
• Design goal for area
charts is emphasize
on the
value/quantity, not
so much on the trend
• You can see both
• Color has no
meaning

30. Area Graphs continued
• This time color carries a
meaning (legend)
• The graph is also good for
displaying ratio between series
of data over time

31. Pie Charts

32. Pie Charts
Golden Rules for Pie Charts
• Ratio of one piece to the whole
• Order the values
• Less than 6 pieces
• Avoid legends
• Sum up to 100%

33. Abusing Pie Charts
Don’t break the rules!

34. Maps
Plot millions of journal entries from 18th and 19th century ship logs, and
you reveal a picture of ocean trade you've never seen before
• Visualization of
routes
• Color saturation
indicates heavily
used routes

35. Maps are good with animations too
• Concentration of NO2 from
2005 to 2011
• Using both color and position
to encode concentration
• Using continuous color scale
• Adding another dimension -
time

36. Choropleth Maps
Displaying the most popular
name for a newborn in each
state
• Using discrete
palette to encode
information

37. Heat Maps
• Excellent for plotting
recurring values
• Color
saturation/brightness
encodes the values
• Position also encodes
information
• Easy to spot
concentrations and
find patterns

38. Heat Maps medicine/genetics

39. Tree Maps
• Excellent for representing
hierarchical data
• Color carries a meaning
• Size carries a meaning as well
• Position is irrelevant
• Suitable for annotations

40. Parallel Coordinates Plot
• Interactive visualization
• Good at displaying
relationships between
different dimensions of
data
• Position encodes
dimension
• Color encodes scale

41. Parallel Coordinates Plot – in action
Selecting a subset
of a dimension to
display the
relationships with
the other
dimensions

42. Chord Diagram
• Similar to Parallel Coordinates
plot
• Color and Position used to
encode data
• Design is different
• Filtering of dimensions is not a
design goal
• Focuses on selecting a whole
dimension

43. Some resources
http://www.reddit.com/r/dataisbeautiful/
http://blog.visual.ly/
http://flowingdata.com/
http://eagereyes.org/
http://www.perceptualedge.com/blog/

44. Thank You!