I scoured articles, books, and Google for trending bars, urban legends, and legendary mixologists. After defining the all variables of the data set, I reframed the ingredients into three tiers.

First, the recipes were arranged according to their origin. Each continent was then assigned a distinct color. Next, the ingredients were divided into spirits, mixers, and garnishments. For the final abstraction, each ingredient was then categorized by color according to whether it was: a flower, fruit/extract, luxury, meat/dairy, spice/nuts, sweets, tea/juice, or vegetable.

The application created interesting webs of data for each continent. From the visuals, we can start to ask broader questions about what potential combinations and categories of ingredients appeal most to each culture.

Asia and South America, for example, appear to have completely opposites tastes. From this data set, we can see that cocktail enthusiasts in Asia might prefer sweeter drinks (heavy distribution in the upper left quadrant), while folks in South America generally prefer fruity varieties overall (heavy data distribution in the lower left quadrant).

Color and weight brought the data to life. Initial explorations proved that fully-saturated tones were too playful and distracting to differentiate six data sets. Ultimately, I landed on a sophisticated palette of neutral tones. Line width was another crucial design choice. I used varying weights in the final print to create an element of dimension and variability.

The most memorable element of the process was spending an agonizing day of rotating all 128 of those ingredients around a circle by hand. I’m sure there’s a better way to do that in Illustrator now.