Enriching data to improve food distribution in crisis areas all over the world.
Amsterdam, 18 May 2016: Data Mission and the World Food Programme organized a hackathon on 14 May 2016, inviting teams of hackers, designers and programmers to create a solution for enriching data collected through the Mobile Vulnerability Analysis and Mapping (mVAM), a program used by the United Nations’ World Food Programme (WFP). The WFP distributes food in crisis areas all over the world.
The mVAM project collects data by calling people in crisis areas and evaluates their access to food by defining the Food Consumption Score (FCS). Collecting data by calling people is expensive, so they don’t collect data in every region and city.
The hackathon challenge was defined to enrich the existing data and extrapolate to other nearby territories. Marijn van Zelst, software developer at Trifork, had an idea to apply machine learning based on neural networks and try to train a neural network to predict the FCS in unknown areas. The plan was initially to make predictions in three dimensions: latitude, longitude and time. These three dimensions were part of the training set collected by the mVAM project in Yemen. More data could be added later, such as climate data, conflict data, or food prices. This should allow more accurate prediction of the FCS.
After a couple of hours of hacking, we presented our results. Unfortunately, in the time we had to actually implement our idea, we were not able to make accurate predictions right away. “You can’t create a complete and accurate system in a few hours of hacking,” said Marijn van Zelst. “The predictions and algorithms need to be improved before they can actually be used by the WFP.” We showed the jury our approach and a conceptual visualization of the predictions (Figure2). The visualization showed what the output of the neural network could be when trained with enough data. At the end of the presentations, the jury announced our team as winners. The jury commended our approach using novel techniques and saw the potential of our idea.
Our visual dashboard using enriched data was awarded as the best idea to help the WFP gain insight on food distribution to people in high distress, which in turn should help the WFP achieve better and more actionable food distribution.
More on mVAM: https://www.youtube.com/watch?v=L8CbUye_hWc
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