The phenomenon of big data has thrown up all kinds of weird and wonderful ideas as to how this mass of information might be used to improve our lives. From obvious uses such as saving money, and identifying renewable resources, to more far-out visions of one day running our cities, it’s become pretty clear that big data has the potential to shape every aspect of our lives.

When leveraging big data, the trend is to focus on how it can be used to better fulfill our immediate desires, which is why it’s quite surprising that up until recently, no one has really tried to apply big data to that most primal of human needs – food.

Times change however, and it seems that those in the food industry are quickly gaining an appetite to explore big data’s potential. One of the most obvious ways in which big data can be applied is in the distribution of food across our cities and countries, as demonstrated in this video by America Revealed, which follows the nightly travails of a Domino’s Pizza delivery guy in Manhattan.

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The video shows that our pizzas have already travelled quite some distance by the time they reach our doors, with the ingredients having arrived at the restaurant from a regional distribution center, and before that, having been shipped in from all over the country. Clearly, food distribution is already a well-oiled machine, but with the rapid advances in tracking technology, it’s likely that such demonstrations will open the eyes of many more distributers who are keen to leverage this and improve the efficiency of their operations.

Of course, there’s nothing really too strange about that one, but what about using big data to track global food sentiment? Thanks to Twitter, it’s already been done. Affect Lab, Ai Applied, and Jana + Koos recently got together to create FoodMood, an interactive data visualization project that allows users to track food sentiments around the world, according to types of food, different countries and various other factors.

FoodMood utilizes language processing techniques, applying these to geo-located tweets and then overlaying what people are saying about various kinds of foods with obesity and GDP data from sources such as the World Health Organization and the CIA World Factbook. The result is an impressive visualization that allows users to look at various eating trends in different countries, with the ability to compare different nations, and filter data according to number of tweets, time and popularity.

The block sizes in the visualization correspond to the number of times each particular type of food has been mentioned (the bigger the block, the more popular it is), while a simple color scheme is used to represent overall sentiment for each food type.

You’re probably asking yourself, what’s the point of all this? And that would be a pretty valid question, given the jargon-filled response under FoodMood’s stated goals:

“As a sentiment analysis tool, FoodMood develops a more informed global picture about food and emotion. As a data visualization project, FoodMood shows the connections, patterns and relationships that exist between the variables — insights that are otherwise practically infeasible. Ultimately, FoodMood helps reveal a hidden layer of digital and social data that pushes the boundaries of awareness and understanding of our surroundings one step further.”

Perhaps this food sentiment data can be used in some way to help us all stick to healthier diets? Or more likely, will it be exploited by the food industry to make even more money from us all?

Whatever the likely use, it’s certainly given us food for thought.