In this reading, Robert Simmon details the subtleties of color, and notes three types of datasets, those being sequential, divergent, and qualitative.
Forgetting the specific definition of these datasets for a second, we first explore the fascinating ideas behind color and the power it has on perception and audiences. Different colors can enhance storytelling, graphics and work to make the viewer more interested and more engaged with the content in front of them. Simmon works to explain and analyze how color palettes should be shifted situationally, playing with ideas of hierarchy, depth and data visualization.
Regarding the three types of datasets, the first is sequential datasets, which are represented with shifts in hue, saturation, and lightness simultaneously. This type of representation is most apt in order to depict data that has variations continuously from high to low.
Divergent datasets are useful in order to focus on differences. The color palettes for this type of dataset rely on variations of hue, lightness and saturation, and work the best when two contrasting hues and a neutral middle color are used.
Qualitative datasets are used to categorize items or data. Similar to how the data is separated into different categories, the use of various colors for qualitative datasets mirror the differentiation of categories and data.
I must note that while I understand how color can affect mood, tone etc, and have aptly used it in other design and artistic endeavors in order to convey the feelings I want, I had never consciously considered how utilizing varieties of color schemes and palettes would be more apt depending on the data. Rather, I believe I was more concerned with color in order to simply provide clarity and ease of understanding, and thinking about color palettes subconsciously.