The three different types of datasets discussed are sequential, divergent, and qualitative. The series of posts does a great job with analyzing the correct color palettes to use in each situation. When used correctly, color has the ability to enhance quality, aid storytelling, and draw a viewer in. How does color contribute to multi-level storytelling? In what ways can color create hierarchy? I thought it was most interesting to see how color placement can evoke a sense of depth. If used incorrectly, this sense of depth can skew data and create misleading information. Therefore, color truly does play an important part in making data easily accessible while maintaining the data’s integrity.
Sequential datasets require simultaneous shifts in hue, saturation, and lightness because they vary continuously from high to low.
Divergent datasets make use of continuous linear color palettes that rely on variations of hue, lightness, and saturation. Two contrasting hues with a neutral middle color is the best for this dataset.
Qualitative data are best to be represented by various colors since the increased amount of data requires a higher level of differentiation.
After reading the series of articles I am left with questions pertaining to how we can make color accessible for blind people? Especially since color is an integral component to understanding the information presented. Also, I wonder what determines if use of a color is qualitative or quantitative. Qualitative data uses color to separate areas into categories. Therefore, it is is best to have very different colors in the set to avoid confusion. However, I am curious how colors that are too similar can possibly affect the integrity of the data. It seems that color choice is important in providing a viewer with clarity.