National Geographic's Future of Food Hackathon took place last weekend in Washington, D.C., as a part of their “Future of Food” project. ProPublica’s Eric Sagara, Mike Tigas and Sisi Wei were part of a team with WNYC’s Noah Veltman and Tim Wong from The Washington Post. We also received expert help and advice from National Geographic's Dennis Dimick and Maggie Zackowitz.
Our team’s final project, FareTrade, was awarded Best in Show. FareTrade is a mobile web app that allows users to see how far, on average, items on their grocery list traveled to reach them. We express this in terms of “food miles.” For all of the 103 types of food in the app, we also show the percentage of each crop produced domestically and the percentage imported from the top foreign producers.
You can see all the projects at http://futurefood.hackdash.org/.
How We Calculated Food Miles
Countries in the FAO dataset report how much they produce of a given crop each year, as well as how much of that crop they import and export from every other country in the database. In calculating food miles, we determined the average expected mileage that a particular crop traveled to reach the U.S.
As an example, let’s imagine that in 2011:
- Ecuador exported 40 metric tons of bananas to the U.S.
- Honduras exported 60 metric tons of bananas to the U.S.
- The U.S. didn’t grow any bananas.
From this data we can calculate the fraction of bananas in the U.S. that came from each source and weigh the distances accordingly.
foodMiles = (0.4 * distance between US and Ecuador) + (0.6 * distance between US and Honduras)
Hackathons are very short sprints and as such they limit the scope of the projects they produce. FareTrade is a bit unfinished and more a proof of concept than a fully formed work of data journalism. There are more than a few caveats you should know about when you use it, including:
The data is aggregated by country, so we're only computing distance from centroid to centroid. This can mean very inaccurate distances for a big country like the U.S. We assume a domestically grown artichoke traveled 0 miles when it might have traveled 2,500 miles from California to New York, and we assume that a piece of ginger exported from China traveled from the middle of China to Kansas, when it might have only traveled from coast to coast. To make this more meaningful we’d have to know more about the proportion of food produced in specific areas of an exporting country, and consumed in specific areas of the importing country.
The data is aggregated by year, so it doesn’t account for the seasonality, which is obviously crucial. In the U.S., berries may be domestically sourced in the summer and then shipped from the Southern Hemisphere when they’re out of season. In cases like this, the time of year that you choose to eat the food makes a huge difference in the expected food mileage, but we average that out into one number for the year. We can determine what foods are in season in particular months and make recommendations accordingly, but incorporating that into our mileage calculations would require data on consumption or imports by month instead of by year, data which is not currently available (to our knowledge).
We limited our app to a subset of all the things the U.N. tracks, because the different data sources classify commodities differently. Some of them are consistent, but others are not, so you might find two similar-but-not-identical categories like "bacon and ham" and "pork products" in two different datasets. These mismatches tend to occur more with livestock products than with crops.
The math doesn’t always add up. How many avocados did the US import from Mexico last year? It depends whom you ask. The FAO's trade matrix data gives us these two different records:
- The U.S. imported 318,938 metric tons of avocados from Mexico in 2011.
Mexico exported 269,600 metric tons of avocados to the U.S. in 2011.
There are lots of cases like this, where the numbers are fairly close but not the same; this is presumably because the U.N. gets data reported by national agencies, and the two sides of the exchange report it differently. For our purposes, when conflicting reports existed, we took the mean of the quantities
- We are assuming that the percentage of a crop that gets eaten is the same for each food item in our app, when it may vary considerably between categories. Some crops are more likely than others to end up as animal feed, biofuel, or something else entirely.
We ended up calling our project “FareTrade” but we came up with other good options that are too good not to share. Here are some of the potential project names that didn’t make the cut:
- The Sisterhood of the Traveling Plants
- Seasoned Travelers
- Final Destination
- Banantastic Voyage
- Around the World in Eaty Days
- Secretary of Plate
- Fruit Commute