The document discusses modern approaches to data visualization. It begins by noting the problem of information overload in today's data-rich world. Effective visualization is presented as a solution by allowing humans to leverage their highly efficient visual processing abilities. Fundamental principles from gestalt psychology that underlie effective visualization are described, such as grouping and emergence of patterns. Common basic visualizations like bar graphs, line graphs and pie charts are explained. More advanced visualizations including maps, heat maps and parallel coordinate plots are also outlined. The document emphasizes matching the right visualization technique to the data and question being explored.

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!