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More frequent and severe shocks combined with more plentiful data and increasingly powerful predictive algorithms heighten the promise of data science in support of humanitarian and development programming. We advocate for embrace of, and investment in, machine learning methods for poverty and malnutrition targeting, mapping, monitoring, and early warning while also cautioning that distinct tasks require different data inputs and methods. In particular, we highlight the differences between poverty and malnutrition targeting and mapping, identification of those in a state of structural versus stochastic deprivation, and the modeling and data challenges of developing early warning systems. Overall, we urge careful consideration of the purpose and possible use cases of big data and machine learning informed models.


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