One of the challenges to providing interesting content for this site lies in the scarcity of interesting data sources on the beer industry, especially at the craft beer level. There is certainly data in the beer universe (think Untappd, Beer Advocate, BreweryDB, etc.) but not a lot that is accessible via API or traditional databases. My other sites have it good – Visual-Baseball currently uses three great data sources (Baseball-Databank, Retrosheet, and Neil Paine’s 538 github site), JazzGraphs has the colossal MusicBrainz database available for exploration, and Visual-Detroit gets to choose from sources as varied as SEDA and the Detroit Open Data Portal for starters.
In short, I’m constantly on the lookout for publicly available beer data to create interesting visual stories. Therefore, I was encouraged when I stumbled across the Beer Maverick site today; not only is it a great resource for homebrewers, craft beer drinkers, and other beer industry followers, but it is also visually driven. While there is no publicly available API at this stage, I’m hopeful that will change, and in the meantime I should be able to pull together some interesting content based on their data.
Here’s an example from a page on their site, featuring the great Strata hop, one of my personal favorites:
Note the smart use of a radar plot (normally not a favorite of mine) to display the aroma and flavor footprint of this hop variety.
Here’s another example showing their use of simple yet clean and informative graphics to deliver insights on this variety of hop:
These are obviously technical terms that perhaps only resonate with brewers; what I love are the concise displays and context for how Strata compares to other hops. This is a wonderful evocation of Edward Tufte’s data:ink ratio; a lot of information is delivered using a minimal amount of ink (or pixels, in this case). There are additional attributes displayed, as well as sections on which other hops can be substituted or are compatible with Strata.
To summarize, I’m hoping to leverage data from the site to create some interesting visualizations – think of a hop network which displays each hop as a node and positions it relative to other hops. There are more than 300 hops on the Beer Maverick site, as well as > 500 strains of yeast; I think either would make for a very interesting network graph. And maybe I can talk to the Beer Maverick folks about getting access to an API for this purpose :). In the meantime, I’ll play with some small datasets to create some interesting content around the craft beer world.
That’s it for now, and thanks for reading!