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Aside from maps, I would say my next biggest obsession is coffee. For the last couple of months, my favourite bean, Finca La Bolsa, has been out of season and unavailable from my local roasters. I've therefore been using the opportunity to experiment with beans from different regions, different taste profiles and different processing methods. For some reason, it has only just dawned on me that there might be some coffee maps I can use to help me discover new beans to add to my coffee repertoire. Map of Coffee Origins & Flavors Lightyear Coffee has produced a number of data visualizations based on data collected from coffee roasters. These include a Map of Coffee Origins & Flavors, which shows the dominant flavour notes associated with beans from different countries. Using the map, you can select an individual country to explore the dominant tasting notes found in its coffee beans, or choose a particular flavour note to discover which countries produce coffees associated with that taste. Multiple Origin Multiple Origin is a personal project by Ahmad Merii that maps the information found on coffee bean packaging. The result is a world map of 318 specialty coffees from Merii's own collection, purchased and consumed between 2018 and 2026. The coffees are categorized by country and region of origin, variety, processing method, altitude, roast date, tasting notes and weight. Using the map, you can search for and filter coffees by roaster, processing method, variety and altitude. You can also explore a gallery of scanned coffee bags, with additional details available for each coffee. BeanMap BeanMap is a free, open-source atlas of specialty coffee that starts with an interactive 3D globe. It currently maps 55 coffee origins across 41 countries, with each origin accompanied by information about its altitude, processing method, harvest season and flavour profile. You can filter the map by region or flavour, or approach the data from the other direction using an interactive SCA flavour wheel. Click on a flavour such as jasmine or blackcurrant and the map filters down to coffees associated with that taste.
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As I often mock the AI-slop maps that I see on X (“Twitter”), my account has apparently responded by sending even more garbage my way. One example recently caught my attention as perhaps the worst map that I had ever seen. Then, several days later, I encountered something even worse. The first map, from the […] The post Is This the Worst Map in Current Circulation? No, It Is This One! appeared first on GeoCurrents.
I would like to direct my readers’ attention to a new Substack publication by Pete Morris called Terra Cognita. Pete is a frequent GeoCurrents commentor and an insightful geographer. His introductory article in Terra Cognita is an excellent overview of the recent history of academic geography in the United States. And as can be seen […] The post Recommending Terra Cognita, a New Geographical Substack Publication appeared first on GeoCurrents.
It’s finally time for me to write something about generative AI. While I’ve made occasional comments on social media, I haven’t spoken in any detail. I touched on some colleagues’ genAI work in a recent blog post, but my focus wasn’t really on genAI itself — it was how and where its products were shared. … Continue reading GenAI for the Static Cartographer →
As noted in the previous GeoCurrents post on the “deserts” of Colombia, Colombia is usually considered to be the world’s rainiest country, receiving some 127 inches (3249 mm) of precipitation annually, averaged over the entire country. Learning this fact inspired me to make a map of average annual precipitation by country, which is posted below. […] The post Average Annual Precipitation by Country Mapped appeared first on GeoCurrents.
