Understanding & using color for storytelling in data visualization
summary
Workshop claims that human perception relies on luminance more than hue so effective color maps use uniform lightness trajectories rather than rainbow ramps, demonstrates why RGB cube is a poor chooser while HSV/HSB and CIE‑Lab reasoning help, and presents heuristics (monochromatic, analogous, complementary, split‑complementary, triadic) plus mapping rules for sequential vs diverging data. Tools and exercises use colors, Viz Palette, color brewer 2.0, color‑blind simulators and show how to build reproducible palettes and plotly templates; material comes from a Penn State course and NSF‑funded resources.
AI-written summary · about
chapters (12)
- 0:00Color in data storytelling
- 1:51Color perception vs wavelength
- 5:01Color models for design
- 12:31Hue saturation brightness
- 22:48Color choices for storytelling
- 36:13Categorical color mapping
- 45:01Quantitative color mapping
- 53:53Accessible color design
- 1:00:29Color palette tools
- 1:07:08Corporate color constraints
- 1:10:19Color for data encoding
- 1:12:49Q&A
related talks
26mBeautiful and Balanced: Using Color Theory in Data Visualization
53mPractical Color Theory for People Who Code
49mInclusive Color: from Photons and Physiology to Psychology and Philosophy
41mExploring Colormaps and UI Styles for Data and Information Visualization
19mWhen colour theory meets technology
25mPractical Color Theory for People Who Code
19mAffective Colour in Visualization
31mFundamentals of Data Visualization