In essence, there’s a qualitative change in the data (often a change in sign) as it crosses a threshold. Examples include profits and losses in the stock market, differences from the norm (daily temperature compared to the monthly average), change over time, or magnetic polarity. Sequential data lies along a smooth continuum, and is suited to a palette with a linear change in lightness, augmented by simultaneous shifts in hue and saturation.ĭata that varies from a central value (or other breakpoint) is known as divergent or bipolar data. Sequential data (discussed in depth in my previous post, The “Perfect” Palette) is best represented by color palettes that vary evenly from light to dark, or dark to light, often with a simultaneous shift in hue and/or saturation. Qualitative data is broken up into discrete classes or categories, as in land cover or political affiliation. For example, departure from average temperature, population change, or electric charge. Divergent data has a “break point” in the center, often signifying a difference. In addition to sequential, Cynthia Brewer defines two additional types of data: divergent and qualitative. Continuously varying data, called sequential data, is the most familiar. There are several types of data, each suited to different types of display.

Night time palette blue and yellow