This document provides an overview of data visualization, including why it is used and how to effectively visualize data. It discusses how visualization helps turn data into useful information. It then covers the main types of visualization techniques (categorical, hierarchical, relational, temporal, spatial) and examples. The document concludes with tips on dos and don'ts, including principles from Edward Tufte on graphical integrity and data-ink ratio. The goal is to help people understand how to choose the right visualization and design effective visuals.

1. CBRE
Warsaw School of Economics
Analyst Team: Michał Mokwiński, Magdalena Zielińska
DATA VISUALIZATION
WHY AND HOW?
May 2018

2. CBRE
Agenda:
The WHYs
The HOWs
DOs and DON’Ts
-Tufte’s Principles
Get INSPIRED

3. CBRE
WHY?

4. CBRE
WHY DO WE VISUALIZE?
SALARY VS EXPERIENCE – CATEGORY COMPARISON
CAT I CAT II CAT III CAT IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
X – time in profession
Y – salary coefficient

5. CBRE
WHY DO WE VISUALIZE?
NEED SOME HELP?
CAT I CAT II CAT III CAT IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
CAT 1
X_mean = 9.0
Y_mean = 7.5
Corr = 0.82
LM Y = 3 + 0.5x
CAT 2
X_mean = 9.0
Y_mean = 7.5
Corr = 0.82
LM Y = 3 + 0.5x
CAT 3
X_mean = 9.0
Y_mean = 7.5
Corr = 0.82
LM Y = 3 + 0.5x
CAT 4
X_mean = 9.0
Y_mean = 7.5
Corr = 0.82
LM Y = 3 + 0.5x

6. CBRE
WHY DO WE VISUALIZE?
NEED SOME HELP?
CAT 1 CAT 2
CAT 3 CAT 4

7. CBRE
ALBERTO CAIRO;
WHY DO WE VISUALIZE?
FROM DATA TO INFORMATION
The greatest value of a picture is when it forces us to notice
what we never expected to see.
John Tukey

8. CBRE
WHY DO WE VISUALIZE?
FROM DATA TO INFORMATION
ELECTRICITY BILL EXAMPLE
https://flowingdata.com/2012/07/02/electricity-bill-redesigned/

9. CBRE
HOW?

10. CBRE
HOW TO VISUALIZE?
TYPES OF DATA
DIMENSIONS
QUALITATIVE VALUES
• Categorize
• Segment
• Detail
TYPES
• Continuous
• Discrete
MEASURES
NUMERIC, QUANTITATIVE
• Use for calculations
• Aggregate
TYPES
• Continuous
• Discrete
Continuous data can take any value
Discrete data can only take certain values.

11. CBRE
HOW TO VISUALIZE?
ENCODE USING MARKS AND ATTRIBUTES
MARKS
REPRESENT DATA ITEMS
• Point
• Line
• Shape
• Form
ATTRIBUTES
REPRESENT DATA VALUES
• Position
• Color
• Size
• Symbol
• Angle
• Connection
• Quantity
• Enclosure
• Pattern

12. CBRE
• Categorical
Comparing categories and distributions of qualitative values
• Hierarchical
Charting part-to-whole relationships and hierarchies
• Relational
Graphing relationships to explore correlations and connections
• Temporal
Showing trends and activities over time
• Spatial
Mapping spatial patters through overlays and distortions
FIVE FAMILIES OF CHARTS

13. CBRE
CATEGORICAL
COMPARING QUANTITIES BY CATEGORYBAR CHART
https://www.telegraph.co.uk/football/2018/02/23/mousa-dembele-became-premier-leagues-complete-midfielder/

14. CBRE
CATEGORICAL
COMPARING QUANTITIES BY CATEGORYBULLET CHART
http://www.storytellingwithdata.com/blog/2017/5/25/the-bullet-graph

15. CBRE
CATEGORICAL
COMPARING QUANTITIES BY CATEGORY
LOLLIPOP
CHART
https://informationisbeautiful.net/visualizations/gender-pay-gap-us/

16. CBRE
CATEGORICAL
COMPARING QUANTITIES BY CATEGORYBUBBLE
CHART
https://fivethirtyeight.com/features/hurricane-harveys-impact-and-how-it-compares-to-other-storms/

17. CBRE
CATEGORICAL
COMPARING QUANTITIES BY CATEGORYRADAR CHART
Global Competitiveness Report

18. CBRE
CATEGORICAL
COMPARING DISTRIBUTION
OF QUANTITIES BY
CATEGORY
DOT PLOT
Michał Mokwiński

19. CBRE
CATEGORICAL
COMPARING DISTRIBUTION
OF QUANTITIES BY
CATEGORY
HISTOGRAM
CKE

20. CBRE
CATEGORICAL
COMPARING DISTRIBUTION
OF QUANTITIES BY
CATEGORY
BOX PLOT
Washington Post

21. CBRE
HIERARCHICAL
PART-TO-WHOLE
RELATIONSHIPS
PIE CHART
Andy Kirk

22. CBRE
HIERARCHICAL
PART-TO-WHOLE
RELATIONSHIPS
DONUT CHART
Michał Mokwiński

23. CBRE
HIERARCHICAL
PART-TO-WHOLE
RELATIONSHIPS
WAFFLE
CHART
Andy Kirk

24. CBRE
HIERARCHICAL
PART-TO-WHOLE
RELATIONSHIPS
WATERFALL
CHART
Andy Kirk

25. CBRE
HIERARCHICAL
HIERARCHICAL
RELATIONSHIPS
TREE MAP
Michał Mokwiński

26. CBRE
HIERARCHICAL
HIERARCHICAL
RELATIONSHIPS
DENDROGRAM
Jeffrey A. Shaffer

27. CBRE
RELATIONAL
EXPLORING CORRELATIONSSCATTER PLOT
Washington Post

28. CBRE
RELATIONAL
EXPLORING CORRELATIONSHEAT MAP
Hayley Pfeifer

29. CBRE
RELATIONAL
GRAPHING CONNECTIONSSANKEY DIAGRAM
Michał Mokwiński

30. CBRE
TEMPORAL
TRENDS OVER TIMELINE CHART
Michał Mokwiński

31. CBRE
TEMPORAL
TRENDS OVER TIMESLOPE GRAPH
Michał Mokwiński

32. CBRE
TEMPORAL
ACTIVITIES OVER TIMEGANTT CHART
Data Remixed

33. CBRE
SPATIAL
GEOGRAPHIC OVERLAYCHOROPLETH MAP
Michał Mokwiński

34. CBRE
SPATIAL
GEOGRAPHIC OVERLAYDOT MAP
Learn GIS

35. CBRE
SPATIAL
GEOGRAPHIC OVERLAYFLOW MAP
Michał Mokwiński

36. CBRE
Where to
begin

37. CBRE
Who’s your
audience?

38. CBRE
•Data literacy
•Age
•Profession
•Color blindness
•Dyslexia
WHO’S YOUR AUDIENCE?
Data literacy is the ability to read,
understand, create and communicate data
as information. Much like literacy as a
general concept, data literacy focuses on
the competencies involved in working
data.
- Wikipedia

39. CBRE
What’s your carrier?

40. CBRE
• Type
print
digital
• Color
colorful
black & white
grays
• Size
• Interactivity
WHAT’S YOUR CARRIER?

41. CBRE
The purpose of visualization is insight, not pictures.
Ben Shneiderman

42. CBRE
DOs and
DON’Ts

43. CBRE
Tufte’s
Principles

44. CBRE
•tell the truth
•don’t over or under represent the data
•defeat graphical distortion
•labelling is important
•show data variation, not design variation
GRAPHICAL INTEGRITY DO

45. CBRE
Low graphical integrity example
High lie factor
GRAPHICAL INTEGRITY DON’T
53% increase in fuel
economy, but the line
drawn has a 783%
increase.

46. CBRE
Data-ink is the non-erasable core of the graphic
Edward Tufte
DATA-INK RATIO DO

47. CBRE
DATA-INK RATIO DO

48. CBRE
DATA-INK RATIO DO

49. CBRE
DATA-INK RATIO DO

50. CBRE
DATA-INK RATIO DO

51. CBRE
Less is more.
Extent of graphical elements creates, in most
cases, only distraction.
DATA-INK RATIO DO

52. CBRE
•small
•repeated
•indexed
–category or label
–over time
–quantitative variable not used in the chart
SMALL MULTIPLES DO

53. CBRE
SMALL MULTIPLES DO
Slices of
data
Michał Mokwiński

54. CBRE
SPARKLINES/SPARKBARS DO
•small
•simplified
•most times no axes
•usually time series
•condensed
•context specific
Michał Mokwiński

55. CBRE
Data Visualization is a form of design, thus
design principles should be applied to it.

56. CBRE
•try make your data self explanatory, legends only if
necessary
•consistent color use
•simplify the unimportant
•highlight the important
•use labels and annotations
MORE TIPS DO

57. CBRE
•ask for opinion
•smart use of color
•use greys. seriously.
•sort
•interactive is for interactions
•label data
MORE TIPS DO

58. CBRE
•provide context when needed
•be clear
•leave no doubt
•less is more
•be flexible with chart choices
MORE TIPS DO

59. CBRE
SKIPPING ZERO AXIS DON’T

60. CBRE
DISTORTING PERCEPTION DON’T

61. CBRE
UNSYNCED AXIS DON’T

62. CBRE
NOT USING LABELS DON’T

63. CBRE
TOO MUCH OF
EVERYTHING
DON’T
Stephen Few

64. CBRE
OVERPLOTTING DON’T
Solution:
•small multiple
•smaller marks
•remove fill
•change shape
•transparency
•cluster
•jittering

65. CBRE
viz.wtf
MORE

66. CBRE
Get
Creative

67. CBRE
David McCandless

68. CBRE
Michał Mokwiński

69. CBRE
Above all else show the data.
Edward Tufte

70. CBRE
Who to follow
Where to learn
Where to inspire

71. CBRE
• Andy Kirk
• Edward Tufte
• Stephen Few
• Mike Cisneros
• Nadiah Bremer
• David McCandless
• Alberto Cairo
• FiveThirtyEight
• Tableu Zen Masters
WHO TO FOLLOW

72. CBRE
• Tableau Public
• The Pudding
• NYT Graphics
• Kantar IIB Awards
• Malofiej Awards
• South China Morning Post
WHERE TO GET INSPIRED

73. CBRE
TOOLS
PROGRAMMING
• Python
– Bokeh
– Seaborn
• R
– ggplot2
– Shiny
• JavaScript
– Highcharts
– D3.js
– Charts.js
DATA DRIVEN
• Tableau
• PowerBI
• YellowFin
• Spotfire
• Qlik
• Google Datastudio
GENERATORS
• Data Illustrator
• RAWgraphs
• Raw
• Datawrapper
• Infogram
• Plotly
• Flourish

74. CBRE
Presentations
• Andy Kirk - 50 charts in 50 minutes
Books
• Andy Kirk - Data Visualisation: A Handbook for Data Driven Design
• Andy Cotgreave, Jeffrey Shaffer, and Steve Wexler – Big Book of Dashboards
• Edward Tufte - The Visual Display of Quantitative Data
• Stephen Few
Articles
• http://hypsypops.com/axes-evil-lie-graphs/
• Stephen Few - Save the Pies for Dessert
• Stephen Few - Solutions to the Problem of Over-Plotting in Graphs
SOURCES

75. CBRE
KEEP IN TOUCH WITH US
MICHAŁ
• michal.mokwinski@cbre.com
• linkedin.com/in/michalmokwinski
• public.tableau.com/profile/mokwinski
• @michalmokwinski
• datavizard.blogspot.com
MAGDA
• magdalena.zielinska@cbre.com
• linkedin.com/in/magdazielinska
FEEL FREE TO SEND US YOUR CV – WE ARE ALWAYS LOOKING FOR
INTERNS AND JUNIOR ANALYSTS

76. CBRE
THANK YOU