1. I think Robert Simmons lecture on color theory in data visualization was incredibly informative and interesting. He made so many valuable points but one of the things that stood out to me the most was his motivation to address accessibility. He talks about colorblindness specifically but it is such a strong consideration of understanding your audience and he states that as designers versus artists, we rely on research and fact instead of opinion. With these two ideals in mind, I am curious: what if they contradict one another? How do we design to consider accessibility while still maintaining the pieces as a representation of research? Maybe they don't contradict and that's just a misconception?

2. How do we consider meaning? There is no global understanding (in color- red here means anxiety, red in China is pride), so can we create universal understanding? Will it always be distracted or skewed by geographic, cultural or religious meanings?

3. Does color theory have trends? Are some pallets more accepted or rejected in the past?

Human color perception is non-linear and uneven yet many of us have trained ourselves to use color 'linearly' till now. Will using the suggested color in a perceptual color space like CIE/ LCh/ Munsell help improve our color choices?

When you visualize data and incorporate colors, have you always considered the different usage in sequential, divergent, and qualitative data? Or is it natural for us to use a set of colors we are often used to?

Can a black& white data visualization (Varying line weights, different lightness) be as effective as a colored data visualization?

1. This series of posts was very clear, comprehensive, and informative - I suppose a general question of mine is why isn't this taught more widely? The color exercises we went through in foundation year certainly didn't address the important issues of "lumpy" color perception, the difficulty of equalization when you can't have a dark yellow, and other related concepts.

2. Interestingly, it seems that many of the palettes that follow the perceptual and data-representation rules Simmon sets out also happen to be aesthetically pleasing. Could it be that some of the things that translate to "pleasing" in our minds share principles with hat which is legible?

3. How about discrete datasets with only two or three categories? I feel like it's very difficult not to indicate value judgments under these circumstances, and using similar saturations often leads to a clashing map.

Cultural interpretations of colour vary, so how can we design for a more universal perception of information?

How does colour blindness affect the representation of the data and how do we mindfully/inclusively design for that while maintaining aesthetics?

“satellite visualizations used by many scientists are not intelligible to novice users” What are other ways of communicating the same level of information to the general public? What is the balance?

1) If so much of the standard of data visualization is set by professional cartographers in the past decades, can we still innovate?

2) Is there such a thing as a "perfect" color palette?

3) If we have to adjust our color palettes for color blindness, does it limit us to few choices in terms of color selection?

1. How do color meanings change based on the demographic you're presenting to?
2. Are there special considerations/tools when it comes to color blindness?
3. How did palette tools, like the Color Brewer, become so standard? Are new ones being developed?

1. I am curious about which stage of the design process to land on the color range to use? I can see it being helpful to commit early but there are often occasions when it can be difficult to know what you are working with until it has been completed.

2. If such certain rules can be set around color, why don't universal data visualization tools (like excel) have a more approachable/attractive visualization output?

3. Given the cone/rod performance in our eyes, should each visualization start out in black and white and then add color for emphasis from there?

(I just saw that I never published this post, sorry!)