Principles of Data Visualisation 2025.pptx

Part of a series of workshops on data visualisation

1. Marié Roux, Manager: Research Impact Services Library and Information Service Principles of Data Visualisation

2. Why do we visualise data? Know your audience Find the story Choose visualisation type Do’s and don’ts for design Effective use of colour Visualising qualitative data Visualising networks References Outline

3. It forms part of the Research Process: https://libguides.sun.ac.za/researchprocess Why visualise your data?

4. Data Visualisation Process Why visualise your data?

5. Why visualise your data? Only a picture can carry such a volume of data in such a small space Edward Rolf Tufte

6. Books by Edward Tufte

7. 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 https://guides.library.duke.edu/datavis/ Why visualise your data?

8. “The human brain processes visuals incredibly fast. In fact, research shows that it can understand things like shape, color , and orientation in as little as 13 milliseconds. This speed makes visual data much more immediate and impactful than text-based information . Even a simple chart supplemented with percentage changes and numbers does a much better job of explaining how your cloud cost optimization efforts are paying off for the organization compared to a simple, bland statement of fact. Additionally, the Dual Processing Theory explains that we have two types of thinking: fast, instinctual (System 1) and slow, analytical (System 2). Visualisations tap into System 1, letting us grasp complex information quickly without needing to engage in deeper, slower analysis . The way we use color , shape, and placement can affect how well people remember and make decisions based on the visualisation . Understanding how visual elements influence perception and memory helps in creating more effective visuals?” Article: The Psychology of Data Visualization: How to Present Data that Persuades The psychology of data visualisation

9. 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. Types of data visualisation

10. “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 .

11. 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.

12. Know your audience

13. 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. Find the story in your data

14. Maps: John Snow’s map of 1854 cholera outbreak, identify point of origin, influence policy makers to improve water sanitation. (dot distribution map) Graphs : Florence Nightingale’s diagramme 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. Find the story in your data - Examples

15. Dot distribution map

16. Diagramme

17. Infographics https://www.crazyegg.com/blog/effective-infographic-designs/ https://www.spinxdigital.com/blog/how-infographics-can-help-you/

18. https://www.spinxdigital.com/blog/wp-content/uploads/2019/05/Infographic-Example-2-1.jpg

19. https://www.techprevue.com/wp-content/uploads/2016/12/abela-chart-chooser.jpg Choose visualisation type

20. https://datavizproject.com/ Data visualisation tools

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

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

23. More advice on choosing visualisation types Flourish – Choose the right visualisation Flourish – Choose the right map type for your data Flourish – Visualising survey data https://kanerika.com/blogs/data-visualization/ Other resources: Book: Fundamentals of data visualization Choose visualisation type

24. 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-organising map Sources: https://guides.library.duke.edu/datavis/ / https://uark.libguides.com/dataviz / https://en.wikipedia.org/wiki/Thematic_map Visualisation types

25. 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 Sankey diagramme Alluvial diagramme Time series Gantt chart Timeline Visualisation types

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

27. 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 diagramme See detailed lists of different types of visualisations : https://datavizproject.com/ https://datavizcatalogue.com/index.html Source: https://datavizproject.com/data-type/hyperbolic-tree/ Visualisation types

28. Do Use the full axis Simplify less important information Be creative with your labels and legends Pass the squint test Ask other’s opinion Don’ts Do not use 3D or blow apart effects Do not use too many different colours Avoid changing styles midstream Do not let users/readers do their own maths Do not overload the chart/ visualisation Do’s and don’t for design Source: https://guides.library.duke.edu/datavis/topten

29. Do’s and don’t for design Flourish’s advice on common mistakes in data visualisation Mistake #1: Focusing on form over function Mistake #2: Over-exaggerating your data with your axes Mistake #3: Not giving enough context to your readers Mistake #4: Confusing correlation with causation

30. 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/ Effective use of colour

31. See how choosing different colours can make a difference. A B C 1 2 3 Source: https://cambridge-intelligence.com/choosing-colors-for-your-data-visualization/ Effective use of colour

32. 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 scales: 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. http://colorbrewer2.org/ Effective use of colour

33. 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. Effective use of colour

34. Example: Weather temperatures. Here blue and red are understood without explanation Effective use of colour

35. 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). Effective use of colour

36. Visualise qualitative data

37. 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. Visualise qualitative data

38. Visualise 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

39. VosViewer for network analysis Visualise networks https://www.vosviewer.com/ Use VosViewer for citation, bibliographic coupling, co-citation, or co-authorship relationships. VOSviewer also offers text mining functionality that can be used to construct and visualise co-occurrence networks of important terms extracted from a body of scientific literature.

40. Find inspiration https://ourworldindata.org/ https://informationisbeautiful.net/

41. Bibliography 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 . University of North Carolina. Guide to Data Visualization: Qualitati ve. Library guide. 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 ).

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