Journal: Local Foods

M

Brainstorming

Project Goals

Objective:
We want to eat more locally produced food.

Question:
Where does our food come from?

Hypothesis:
the majority of food in the vending machine is not locally produced.

Tips

Explain one or more mistakes you've done during that phase?
What would you change if will do it again?

Our expectations were too high: we assumed that a lot of the data regarding food production would be available to the public.
Maybe we could re-orient our objective from location to nutrition.

From hypothesis to data

Tools selection

Post multiple images about the tool. What tool did you use? Would you choose a different tool now?

Web scraping: Manually and Automated through python

Finding websites that have databases about food production, import and export

Oec

ITC Trade Map

Tool usage documentation

How can others replicate your data capturing process again?

They can find the base code of our web scraping tool on the FabLab hackmd (Here.)

All database sources are written below.

Data capturing strategy

How do you combine the tool provided with your creativity to prove your hypothesis? How long did you capture data?

We decided which categories to research, basing ourselves on the ingredients within IAAC's specific vending machine. We started small, then built up until we reached a global scale of interconnected supply chains.

Materials needed

List all the materials needed, including those given to you, those you source or even things you built yourself.

Techniques used:

• Manual “web scraping”
• Automated web scraping
• Scanning products through Open Food Facts app
• Researching through food brand website

Resources used:

• delikia.es
• www.girofibra.com/
• es.openfoodfacts.org
• oec.world
• trademap.org
• Oscar ❤️

Detail setup instructions

Explain the setup process. You can simply publish multiple images about your setup.

Map of our process:

Data collected

Describe the raw data you collected by posting a sample i.e. a picture, a screen capture, etc.

Excel sheets generated from open food facts:

Map from open food facts

Excel sheet from ITC trade map

via GIPHY

Interactive map from OEC

Interactive map from CIAT

Thanks to all of these sources, we managed to cross reference the information which we obtained. We noticed many differences from one resource to the other.

Data capture

Data summary

Data insights

Post at least two images of a chart, a screen-shoot of your data, that you used to prove if your hypothesis is false.

We were surprised to see that the Natwins cookies claimed their product was "local". However, they do not define what exactly local means, and later state that their ingredients come from the "Mediterranean".
The mediterranean area includes 21 countries, which means that the food origins are almost untraceable (Albania, Algeria, Bosnia and Herzegovina, Croatia, Cyprus, Egypt, France, Greece, Israel, Italy, Lebanon, Libya, Malta, Monaco, Montenegro, Morocco, Slovenia, Spain, Syria, Tunisia, and Turkey)

We decided to buy a sandwich from the vending machine and trace the possible origins of the main ingredients, using OEC’s data concerning Spain’s imported products.
The unit of measurement was the value of product in USD$ and not in tonnes.

The primary ingredients of the sandwich were:

• wheat
• pig meat
• cheese
• nuts
• eggs
• yeast
• olive oil

And these were the primary imports in Spain:

via GIPHY

Of course, this only displays the probability of where each component originated if they were imported.

Web scraping v/s Open APIs

Sometimes it might be beneficial to see if there is an open API to access a database instead of going for web scraping the frontend data right away. In the case of Openfoodfacts.com, they offered an open and very well-documented API, offering various export formats. This allowed us to easily download and analyze the complete dataset for the product category of 'sandwiches'. This was made possible thanks to all the data being covered by the Open Data Commons License.

Conclusions

It is very difficult to retrieve information about where food comes and goes
There is a lack of transparency regarding the movement of goods
There is no detailed information available to the public about food sources
Recognising that Web Scraping is an option, but not always the best or more efficient one.

Tips

Explain one or more mistakes you've done during that phase? What would you change if will do it again? What if you will have more time? (max 560 char)

Defining a more specific target in our hypothesis, would have allowed us to access more relevant information.

Possibly using a different context (restaurant, grocery store) would have yielded more interesting results.

Find the full group presentation here

Activity conducted by Angel Cho, Chris Ernst, Julia Steketee, Tattiana Butts, Paula Del Rio and Vikrant Mishra.