Visualizing data tells compelling stories that increase research impact. It is important to know the audience and find the key story or message in the data. The type of visualization should be chosen based on the data, goals, and audience. Effective use of color, choosing the right visualization type, and understanding visual literacy principles are important for communicating with visualizations.

1. Marié Roux (Manager: Research Impact Services)
Photo by ThisisEngineering
PRINCIPLES OF DATA VISUALISATION

2. CONTENT
• Why do we visualise data?
• Know your audience
• Find the story
• Choose visualisation type
• Effective use of colour
• Visualising qualitative data
• Visualising networks
• References
• Useful links

3. Data Visualisation process
Getting data
Know the data
Setting goals
Visualise or not?
What to visualise
Cleaning data
Visualising
Designing
Exporting/
embedding

4. Why
visualise?

5. Why do we visualise data?
• Primary reason to visualise – Communicate a point
or a finding revealed in your analysis
• To add legitimacy or credibility, tell stories with
numbers
• Better understand the data you gathered
• Data more persuasive when shown in graphs or
visualisations
• To inspire others into action
• Effective data visualisation can increase the impact
of your research and your engagement efforts
Only a picture
can carry such
a volume of
data in such a
small space
Edward Rolf Tufte

6. Why do we visualise data? (Cont.)
Two broad goals according to Angela Zoss:
1. Visualisation for analysis:
• Explore and analyse data relationships
• Try many views and combinations to find meaningful stories
2. Visualisation for communication:
• Select a particular view of the data to share
• Construct the visualisation with a goal in mind and taking the
audience into account, for example when a decision needs to be
made

7. 3 Types of Data Visualisation
Data visualisation is an umbrella term, usually covering both information and
scientific visualisation. This is a general way of talking about anything that
converts data sources into a visual representation (like charts, graphs,
maps, sometimes even just tables).
Scientific visualisation: generally, the visualisation of scientific data that have
close ties to real-world objects with spatial properties. The different scientific
fields often have very specific conventions for doing their own types of
visualisations.
Information visualisation: also a broad term, covering most statistical charts
and graphs but also other visual/spatial metaphors that can be used to represent
data sets that don't have inherent spatial components.
Infographic: a specific sort of genre of visualisations. Infographics have become
popular on the web as a way of combining various statistics and visualisations
with a narrative.

8. Visual literacy
Visual literacy skills equip a learner to understand and analyse the contextual,
cultural, ethical, aesthetic, intellectual, and technical components involved in
the production and use of visual materials. A visually literate individual is both a
critical consumer of visual media and a competent contributor to a body of
shared knowledge and culture.
In an interdisciplinary, higher education environment, a visually literate individual
is able to:
• Determine the nature and extent of the visual materials needed
• Find and access needed images and visual media effectively and efficiently
• Interpret and analyse the meanings of images and visual media
• Evaluate images and their sources
• Use images and visual media effectively
• Design and create meaningful images and visual media
• Understand many of the ethical, legal, social, and economic issues
surrounding the creation and use of images and visual media, and access and
use visual materials ethically
ACRL Visual Literacy Competency Standards for Higher Education

10. Know your
audience

11. “When you tell the right story to the right
audience, and are able to identify data
points that the specific audience can
relate to and encourage them to start a
conversation, you increase your story’s
share-ability and give it the chance of
going viral”
Jessica Dubow, Amanda Makulec.

12. Know your audience
Understand your audience before designing your visualisation
The first and most important consideration is your audience. Their preferences will
guide every other decision about your visualisation—the dissemination mode, the
graph type, the formatting, and more. You might be designing charts for
policymakers, funders, the general public, or your own organisation’s leaders, among
many others.
What type of decisions do your viewers make?
What information do they already have available?
What additional information can your charts provide?
Do they have time (and interest) to explore an interactive website, or should you
design a one-page handout that can be understood at a glance?

13. Know your audience (2)
Ask two questions:
- Who is the data visualisation intended for?
- What does the audience know about the topic?
Also consider the audience’s level of
- Literacy: You can use symbols, illustrations, animations and other universally
understood graphics
- Numerical literacy: Even educated audiences are not always comfortable with data
and stats. Do they understand ratios, complex formulas or statistics. Or do they need
simplified data?
- Education/Level of technical expertise: Simplify content and define terms for less
technical audiences but provide more detail for those with expertise
- Job function: What is the purpose of your data visualisation? Academics will want to
know how data fits into existing literature. Funders will want to see results compared to
money spent. An executive will want high-level results to guide decisions. A programme
manager may only be interested in data relevant to their specific topic/area.

14. Know your audience (3)
Remember that the key is to keep your audience engaged
If you are sharing results in client meetings, staff retreats, conferences, or webinars,
try breaking up your charts into several slides so the chart appears to be animated.
This is called a storyboarding technique.
Draw Attention to key charts with handouts
Curated handouts might be useful to draw attention to specific highlights of your
data.
Tweeting your results
If you are planning to tweet a chart or two, be sure to adjust your charts to fit a 2:1
aspect ratio. Otherwise your visualisation will get chopped in half because when you
are scrolling through your Twitter feed, the images automatically display about twice
as wide as they are tall.
Consider these questions:
How do you engage your audience when creating and presenting data? What types
of communication modes are you currently using to share your visualisations?

15. Find the
story

16. Find the story
Data Visualisation as storytelling
- Think of your data visualisation message as a thesis statement which needs a
summary in a few concise sentences
- The ability to create a compelling, visual argument will be greater if you begin with a
clear and focused message
- The type of story you tell affects the platform you will use.
- Infographics might be more useful for persuading the audience of your point of view,
where dashboards leave the interpretation to the audience.
- Animations may be effective for college students, but not amongst older adults.
Examples:
Maps: John Snow’s map of 1854 cholera outbreak, identify point of origin, influence policy
makers to improve water sanitation.
Graphs: Florence Nightingale’s diagram of causes of death in the Crimean War, more
soldiers died from preventable illnesses than battle wounds, allocated more resources and
training to health workers
Infographics: For the general audience without background knowledge, data is simplified
by visuals and is not numerical intimidating, engaging with broad audience. It follows a
well-defined story, key messages highlighted, clear purpose.

20. Choose
visuali-
sation
type

21. https://extremepresentation.com/tools/

22. Data Visualisation Catalogue
https://datavizcatalogue.com/

23. Chart selection guide
https://datavizcatalogue.com/blog/chart-selection-guide/

24. Visualisation types
1D/Linear
Lists of data items, organized by a single feature (e.g.,
alphabetical order, not commonly visualised)
2D/Planar (incl. Geospatial)
• Choropleth - shows statistical data aggregated over predefined regions,
such as countries or states, by coloring or shading these regions.
• Cartogram - A cartogram map is a map that purposely distorts geographic
space based on values of a theme.
• Dot distribution map: A dot distribution map might be used to locate
each occurrence of a phenomenon, as in the map made by Dr. Snow during
the 1854 Broad Street cholera outbreak, where each dot represented one death
due to cholera.
• Proportional symbol map
• Contour/isopleth/isarithmic map
• Dasymetric map - land cover data (forest, water, grassland,
urbanization) may be used to model the distribution of population density
• Self-organizing map
Sources:
https://guides.library.duke.edu/datavis/vis_typ
es
https://uark.libguides.com/dataviz
https://en.wikipedia.org/wiki/Thematic_map

25. Visualisation types (2)
3D/Volumetric
• 3D computer models
• Surface and volume rendering
• Computer simulations
Temporal
• Timeline
• Time series
• Connected scatter plot
• Gantt chart
• Stream graph/ThemeRiver
• Arc diagram
• Polar area/rose/circumplex chart
• Sankey diagram
• Alluvial diagram
Timeline
Time series
Gantt chart
Sankey diagramme
Alluvial diagramme

26. Visualisation types (3)
Multidimensional
Bubble chart
https://images.app.goo.gl/fSiB9ZPdoQke4ymb8
• Pie chart
• Histogram
• Wordle, tag cloud
• Tree map
• Scatter plot
• Bubble chart
• Line chart
• Step chart
• Unordered bubble
chart/bubble cloud
• Bar chart
• Radial bar chart
• Area chart/stacked
graph
• Heat map
• Parallel coordinates
Radar/spider chart
• Box and whisker plot
• Mosaic display
• Waterfall chart

27. Visualisation types (4)
Tree/Hierarchical
• General tree visualization
• Dendrogram
• Radial tree
• Hyperbolic tree
• Tree map
• Wedge stack graph (radial hierarchy)/sunburst
• Icicle/partition chart
Network
• Matrix
• Node-link diagram
• Dependency graph/circular hierarchy
• Hive plot
• Alluvial diagram
See detailed lists of different types of visualisations:
https://datavizproject.com/
https://datavizcatalogue.com/index.html
Source: https://datavizproject.com/data-type/hyperbolic-tree/

28. Effective
use of
colour

29. Use of colour
Use of colour is a subjective topic, but there are some basic principles to think
about when choosing the colours of your visualisations.
One colour theory concept: the HSL model.
HSL breaks colour down into three separate channels: hue, saturation and
luminance.
Hue – is what most people think of as colour – red, blue, yellow, green, purple, etc.
Each colour is plotted on a scale from 0° to 359° to form a colour wheel.
Saturation – is another word for a colour’s intensity. The scale measures how
different the colour looks from neutral gray, which has 0% saturation. Colours with
high saturation look brighter and more vivid.
Luminance – describes the spectrum of a hue from dark, based on the amount of
black added.
Source: https://cambridge-intelligence.com/choosing-colors-for-your-data-
visualization/

30. Use of colour (2)
See how choosing different colours can make a difference.
Source: https://cambridge-intelligence.com/choosing-colors-for-your-data-
visualization/

31. Use of colour (3)
Play this game: Color
Your goal is to match colours.

32. Use of colour (4)
How to choose colours
Step 1: Decide what the colours
will represent
Decide which aspects of your data you
want to represent with colour.
Step 2: Understand your data scale
The ColorBrewer tool defines three types
of scale:
• Sequential – when data values go from
low to high
• Divergent – when data has data points
at both ends of the scale, with an
important pivot in the middle.
• Qualitative – when the data does not
have an order of magnitude.

33. Use of colour (5)
How to choose colours
Step 3: Decide how many hues you need
Based on the scale you chose in step 2, you can decide how many hues you need in the palette:
• Sequential data usually requires one hue, using luminance or saturation to define scale.
• Divergent data requires two hues, decreasing in saturation or luminance towards a neutral (usually
white, black or gray).
• Qualitative data requires as many hues as values, but remember the limitations of the human brain.
Try to not use more than seven or eight colours, otherwise the brain cannot recall what each one
represents.
Step 4: Look for obvious options
Before getting too creative, take a look at your data to see if there’s an obvious set of colours.
Your application or corporate style guide might be a good starting point. See example on next slide.

34. Use of colour (6)
How to choose colours
Example: Weather temperatures. Here blue and red are understood without
explanation

35. Use of colour (7)
How to choose colours
Step 5: Create your palette
Use one of the many web resources. ColorBrewer is one of the best for picking schemes for sequential,
diverging and qualitative data. Or if you have a starting point in mind, Adobe Color creates palettes
from a single colour.
There are several groups of colours that work well together. You can identify them by their relative
positions on the colour wheel:
• Monochromatic – shades of a single hue (sequential data).
• Analogous colours – colours that sit beside each other on the colour wheel (varied alternative for
sequential data).
• Complementary colours – from opposite sides of the colour wheel (diverging data).
• Triadic colours – 3 colours equally spaced around the wheel (good starting point for a qualitative
palette).

36. Visualise
Qualitative
data

37. Visualise Qualitative Data
Ways to visualise qualitative data
Qualitative data consist of non-numeric information that doesn't involve
measurements or quantities.
Coding Qualitative Data
Qualitative data analysis often use the method of marking a
certain section of data with a particular code when it falls into a specific
theme or category. Tools like Atlas Ti and Dedoose can assist with coding
and visualisation.
Text analysis
Analysis of the structure and linguistic features of a collection of text in
order to detect measurable patterns. Like coding, text analysis allows us
to visualise qualitative information in a quantitative way.
Simple text analysis can be performed with a user-friendly tool like
Voyant, but more complex analysis may require a programming language
such as Python.
Source: https://guides.lib.unc.edu/DataViz/qualitative

38. Ways to visualise qualitative data
Illustrative Diagrammes
If you want to produce a data visualisation
but you don't have any kind of dataset to
work from, you most likely need to create
an illustrative diagramme. Diagrammes that
involve basic shapes such as flowcharts and
mind maps can easily be made with
Microsoft Powerpoint.
Detailed or complex illustrations are usually
created with professional design software
like Adobe Illustrator.
Visualisation methods
Word Clouds and Heat Maps can be used to
visualise coded text and text analysis.

39. Visuali-
sing
networks

40. Visualising networks
Network analysis examines relationships between different entities, such
as collaborations between researchers, interactions between genes, or
communications between people in a company. It can be used for a wide
range of purposes from simply studying the structure of a community to
solving complex math and engineering problems.
Network visualisation is the visual component to network analysis.
There are a wide variety of network visualisation types to choose from
depending on what type of data you have or what types of relationships
you want to show.
Source: https://guides.lib.unc.edu/DataViz/networks

41. https://vimeo.com/18477762
Hans Rosling says there’s nothing boring about stats,
and then goes on to prove it. A one-hour long
documentary produced by Wingspan Productions and
broadcasted by BBC, 2010.

42. References
• A Reader on Data Visualization. MSIS 2629 Spring 2019. Santa Clara University.
https://mschermann.github.io/data_viz_reader/
• Dubow, Jessica & Amanda Makulec. 2014. Identifying your audience and finding your data
story. JSI Center for Health Information.
• Evergreen, Stephanie. 2017. Effective data visualization: the right chart for the right data.
Los Angeles: Sage Publications.
• Kelleher, C & Wagener, T. 2011. Ten guidelines for effective data visualization in scientific
publications. Environmental Modelling & Software. Volume 26, Issue 6, June, Pages 822-
827.
• Olshannikova, E., Ometov, A., Koucheryavy, Y., & Olsson, T. 2016. Visualizing Big Data. In
Big Data Technologies and Applications (pp. 101-131). Springer International Publishing.
• Quinn, Laura. 2017. Creating great data visualizations with low cost tools. July. Idealware.
• WILS. Principles of data visualization. https://goo.gl/eMRHOU
• WinWire Technologies. 2017. Principles for creating effective data visualization. 22 Aug
2017.
• Zoss, Angela. Data Visualization: About Data Visualization. Duke University. Library guide.
• Zoss, Angela. 2012. Introduction to data visualization. (Slideshare).
• University of North Carolina. Guide to Data Visualization: Qualitative. Library guide.

43. Useful links
• https://www.techchange.org/2015/05/21/audience-matters-in-data-visualization/
• https://playfairdata.com/vital-question-1-who-is-the-audience/
• https://infogram.com/blog/how-data-visualization-impacts-engagement/
• https://flowingdata.com/category/tutorials/
• https://d3js.org/
• https://datavizproject.com/
• https://flourish.studio/2018/09/28/choosing-the-right-visualisation/
• https://extremepresentation.com/wp-content/uploads/choosing-a-good-chart-09-1.pdf
• http://labs.juiceanalytics.com/chartchooser/index.html
• http://www.ala.org/acrl/standards/visualliteracy
• https://library.duke.edu/data/data-visualization
• https://blog.datawrapper.de/colorguide/
• https://cambridge-intelligence.com/choosing-colors-for-your-data-visualization/
• https://www.sciencedirect.com/topics/social-sciences/network-analysis

44. Questions?
E-mail: mr@sun.ac.za