Beautiful and Balanced: Using Color Theory in Data Visualization
summary
Claims that color choices can bias perception and demonstrates concrete fixes: use color weight and color temperature, work in OKCH/HSL rather than RGB, desaturate to black‑and‑white to check weights, and use contrast checkers flipped to target ratios near 1. Built examples include a dot‑chart where the banana party actually has 15% more delegates (56 vs 49) and a rebalanced palette that makes that gap visible. Cautions include brand constraints and accessibility.
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chapters (17)
- 0:00Welcome
- 1:51Brain and color
- 5:05Albers and context
- 7:35Color theory in data viz
- 8:18Color weight basics
- 10:04Fixing color weight
- 11:54Color temperature
- 13:01Balancing map colors
- 14:03Choosing palettes
- 16:29HSL adjustments
- 17:54Contrast ratios
- 18:37Grayscale balancing
- 19:19Managing loud colors
- 20:44Accessibility ethics
- 21:49Practicing techniques
- 22:31Learning resources
- 24:57Closing
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