Data Visualization:
The Good, the Bad & the Ugly



Meg Miller - GIS & Data Visualization Librarian


slides: bit.ly/edub3528

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?!


samples

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'.


samples

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).

samples

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.

Note

Be engaged.






Thanks to Vanessa Lillie, Cary Miller & Lyle Ford
for their insight in the following section.

Be conscientious (1)


  1. Data visualization theory is a colonial construct;

  2. Colour has connotation outside of your own world view;

Be conscientious (2)


  1. Who are you focussing on? Who is being erased?

  2. "Maps have killed more people than guns ever have" Dayrit (2020)

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.

samplessamples

7. How important is consistency


Cognitive load- Consistency


Keep consistency between legends and graphics.

samples

Cognitive load- Consistency


Keep consistency between axes, don't force correlation.

samples

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


Cognitive load- Clarity


Use explanatory text and colour to create emphasis.

samples

Cognitive load- Clarity


Use small multiples.

samples
samples
samples

Cognitive load- Clarity


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

samples

9. What technology considerations should be taken into account


Technology- Reproducibility


Standard file formats and programs.

Human component

Technology- Considerations


samples
simple sample

10. How can I get started


Resources:


Data Visualization LibGuide

Don't forget about training resources!

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/edub3528