Topics Covered

Raw Data

Visualization

1. Clear - easily seen; sharply defined
   • Who is the audience
   • What is the message
   • Clarity is more important than aesthetics
2. Clean - thorough; complete; unadulterated
   • Avoid over-labeling axis and data points
   • Avoid too many or too prominent grid lines
   • Use proper number formatting
   • Use the correct chart type for the message
   • Use optimal color choice
3. Concise - brief but comprehensive
   • Not minimalist but not verbose
4. Captivating - attract and hold by beauty or excellence
   • Does it capture attention?
   • Is it interesting?
   • Does it tell a story?

Tree map breaking all the rules.

1. Show Your Data - Once data is gathered, the first step is to show it or determine how to show it based on the type of data, your audience, and your medium.
2. Use Graphics - When you can’t communicate your message effectively through words, use pictures and graphics.
3. Avoid Chartjunk - Chartjunk is a term coined by Tufte that advocates for the removal of unnecessary or confusing visual elements from charts and graphs.
4. Maximize Data-Ink Ratio - Compare the ink on the page associated with the data vs. all of the ink on the page and erase redundant data-ink.
5. Use Labels - Labels are informative guides and are essential in data design. Effective labels focus on clarity, readability, and differentiation. Labels should stand out from the data.
6. Utilize Micro/Macro - The clarity of the macro is determined by the quality and quantity of the micro.
7. Separate Layers - Create layers by adjust the tone, color, and/or weight of visual elements.
8. Multiples - Use consistency in design to highlight differences or inconsistencies.
9. Color - Choose colors wisely. Use color to create emphasis, draw attention. Poor color choice can lead to confusion and disruption.
10. Understand Narrative - A proper narrative focuses on time and space to tell it’s story.

Obtained from The Visual Display of Quantitative Information by Edward Tufte

1. Similarity Principle - Elements that share similar attributes, color, or shape, are perceived as a whole.
2. Proximity Principle - Elements tend to be perceived as aggregated into groups if they are near or connected.
3. Common Fate Principle - Elements that move together are perceived as a whole.
4. Enclosure Principle - Objects that appear to have a boundary or a continuation around them are perceived as a whole.
5. Continuity Principle - Elements, unites, or groups aligned with each other are perceived as a whole.
6. Closure Principle - Open structures can be easily be perceived as closed/complete.

Color can be very subjective

• Use of color in visualization can be essential for delivering a message , making a point, capturing attention, influencing feelings, and branding an image.
• Reactions to individual colors can vary between people and culture. However, color theory is an advanced and evidence-based science that can help us make appropriate decisions about color choice in a visualization.
• Here are some general color choice guidelines for visualizations:
  • Use similar colors for similar variables
  • Use legends to explain colors
  • Consider mixing greyscale with colors to create accents and highlights
  • Use high contrast ratios for legibility
  • Test how colors appear next to each other and adjust accordingly
  • Use intuitive colors and consider culture (reds vs. green, light vs. dark, converging vs. diverging, color-blind palettes.)

Use where an item’s position in an ordered list is more important than its absolute or relative value. Don’t be afraid to highlight the points of interest.

Show values in a dataset and how often they occur. The shape (or ‘skew’) of a distribution can be a memorable way of highlighting the lack of uniformity or equality in the data.

Show how a single entity can be broken down into its component elements. If the viewer is interested in solely the size of the components, consider a magnitude-type chart instead.

Aside from locator maps, only used when precise locations or geographical patterns in data are more important to the reader than anything else.