Knowledge Exchange Platform (KEP) Workshop 1 - Kate Chalmers.pdf

OECD Knowledge Exchange Platform on Well-being Metrics and Policy Practice (KEP): Virtual Workshop 1, 13 June 2024

Summarising the complexity of well-being data and evidence: Reporting and communicating on well-being dashboards

1. Data Visualization
A quick guide to improving your well-being viz
Kate Chalmers | [email protected]
Data and Knowledge Scientist @ OECD Wellbeing Centre

2. Why is data viz important in well-being?
Good visualizations draw people in. Images gets more likes and shares
than text- based content, amplifying your message
Well-being data is people-centric – good visualizations helps the public
understand the data, engage with your work and gain insights from it.

3. Infographics
Simple and clear. All information should be
self-contained so it can be shared as a
standalone product.
Plots
Can range in complexity as message is
usually supported by surrounding text.
Plot or infographic?

4. Before getting started
What is the
story you want
to tell?
Ranking?
Movement of an
indicator over time?
Benchmarking?
How will you create your viz?
Excel, Programming (R, Python, Stata), Interactive, Click and point (flourish, etc.)
Who is your audience?
Adjust the complexity of your viz accordingly
Where will you be using your viz?
In a report or social media? Should it be a plot or an infographic?

5. Choosing your plot
Correlation Ranking Part of a wholeEvolution Spatial Flow
Scatterplot
Bubble plot
Heatmap
Bar chart
Lollipop
Table
Pie chart
Stacked bar chart
Treemap
Line chart
Line chart with shading
Line chart with points
Map
Choropleth
Bubble map
Flowchart

6. Choosing your plot
Choose the best plot to convey your message, novelty
can introduce unnecessary complexity and detract from
your message !
FT Data Viz Guide
Data to Viz Guide

7. Polishing your plots
Order your plots
Before After

8. Polishing your plots
Avoid rotating text
Option for few comparisons, short labels
Before After

9. Polishing your plots
Avoid rotating text
Option for numerous comparisons, long labels
Before After

10. Polishing your plots
Include direct labels
Allows direct access to the data
Before After

11. Polishing your plots
Use colours and annotations to highlight key messages
Before
After

12. Polishing your plots
Use colours and annotations to highlight key messages
Option 1
Option 2

13. Polishing your plots
Change the font, add some icons and branding and you’ve got a polished final
product
Before
After

14. Polishing your plots
Try a gradient or diverging palette instead of many colours
Before After

15. Polishing your plots
Otherwise, use a premade palette
or build your own!
Many options:
https://palettemaker.com/app
https://www.canva.com/colors/color-palette-generator/
Keep accessibility in mind
https://www.color-blindness.com/ coblis-color-
blindness-simulator/

16. Polishing your plots
Keep accessibility in mind
Before Colortest

17. Polishing your plots
Keep accessibility in mind
After Colortest

18. Polishing your plots
Final point –don’t transform your data unless it’s necessary, the
simpler the better !

19. Creating content for social media:
infographics
Time is a necessary resource
for good mental health
People with low levels of positive mental health are
less satisfied with how they spend their time
compared to the overall population
Note: Positive mental health is calculated using the World Health Organization- 5 (WHO-5) tool. OECD 24 includes: Austria,
the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania,
Luxembourg, Norway, Poland, Portugal, the Slovak Republic, Slovenia, Sweden and the United Kingdom.
Source: OECD calculations based on the 2016 European Quality of Life Surveys(EQLS),
https://www.eurofound.europa.eu/surveys/european- quality-of-life-surveys.
0%
10%
20%
30%
40%
50%
Too tired after
work for
household chores
No time to do
things I enjoy
Long hours in
unpaid work
Difficult to
concentrate on
work because of
family
responsibilities
Share of people with deprivations in time use, by mental health outcome, OECD 24
Plots created with R + Python
Infographics
created with
PowerPoint

20. Creating content for social media: gifs
https://ezgif.com/maker
https://www.canva.com/
create/gif-maker/

21. Some resources
John Burn-Murdoch
https://x.com/jburnmurdoch?lang=en
Cédric Scherer
https://www.cedricscherer.com/top/da
taviz/
Tanya Shapiro
https://www.tanyashapiro.com/
For inspiration, here are some of my favorite Data Visualization experts
Georgios Karamanis
https://karaman.is/

22. Some resources
How Charts Work by Alan Smith (Head of Visual and Data Journalism at Financial Times), https://www.amazon.com/How- Charts-Work-
Alan-Smith/dp/129234279X
Data Visualizationby Kieran Healy, https://socviz.co/
Hands-On Data Visualizationby Jack Dougherty & Ilya Ilyankou, https://handsondataviz.org/
Fundamentals of Data Visualizationby Claus Wilke, https://serialmentor.com/dataviz/

23. Thank you!