Data Visualization:
The Good, the Bad & the Ugly



Meg Miller - GIS & Data Visualization Librarian


slides: bit.ly/dvgb_2022

Outline


  1. Define data visualization;
  2. Discuss visualization elements and their uses;
  3. Explore best practices in visualization creation;
  4. Explore usefulness of different display types and tools.

Hanging Rootograms?!


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What are we focussing on?


  • Information visualization.

  • Display data in a way that makes it easier for your audience to explore or understand your project.

We don't want 'chart junk'.


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1. Do you know your data


2. Who is your audience


Audience- Things to consider:


Audience- Purpose



Exploratory: Population Estimate Dashboard

Explanatory: Assisted Suicide Infographic

Font- What about ugly?


  • Font has personality, select combinations appropriate for your audience.

  • Keep things simple: one decorative, one for body text.

Use a tool like FontPair help with font selection.

3. What are best practices in colour selection for classification


Colour- Continuous vs discrete data


Select color schemes appropriate to your data type (shade vs. hue).

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Colour- What about ugly?


  • Keep things neutral with 2-3 accent colours maximum.

Use a tool like Color Picker help with colour selection.

4. What are some unconscious perceptions we have around colour


Colour- Unconscious perceptions


  1. Red is bad, green is good;

  2. Light blue shapes on maps are water;

  3. Light colours represent less, dark more.

5. How can we take accessibility into consideration when we are selecting colour palettes


Colour- Accessibility:


Workshop content Workshop content

6. What is cognitive load, and why does it matter in visualization


Cognitive load- Special Effects & Stacking


  • Humans are awful at interpreting 3D graphics and clutter.

  • 3-5 classes are ideal, 5-7 at most.

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7. How important is consistency


Cognitive load- Consistency


Keep consistency between legends and graphics.

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Cognitive load- Consistency


Keep consistency between axes, don't force correlation.

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8. What can I do to improve the clarity of my graphics


Cognitive load- Clarity


Use explanatory text and colour to create emphasis.

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Cognitive load- Clarity


Use small multiples.

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Cognitive load- Clarity


Keep it simple (and don't mislead the user).

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9. What technology considerations should be taken into account


Technology- Reproducibility


Standard file formats and programs.

Human component

Technology- Considerations


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10. How can I get started


Resources:


Data Visualization LibGuide

Don't forget about training resources!

Upcoming Fall/ Winter Sessions




Key points:


Message - be selective

Audience - you're creating this for them

Data - requirements and structure

Be kind to your future self.

Questions



meg.miller@umanitoba.ca

slides: bit.ly/dvgb_2022