Increasingly plentiful data and powerful predictive algorithms have heightened the promise of data science for humanitarian and development programming.  As agencies increasingly embrace and invest in machine learning methods for poverty and malnutrition targeting, mapping, monitoring, and early warning, it is essential to recognize that different objectives require distinct data and methods. In this webinar, we highlight the differences between poverty and malnutrition targeting and mapping, the differences between structural and stochastic deprivation, and the modeling and data challenges of early warning systems development based on machine learning methods. We also present two studies that apply machine learning methods to predict poverty and malnutrition.

This webinar is the second of a two-part webinar to present new data and findings from ongoing research under the United States Agency for International Development (USAID)-funded project “Harnessing Big Data and Machine Learning to Feed the Future”, based at Cornell University. Researchers and analysts from operational agencies are invited to join these events for a presentation and discussion of key principles, data sources, methods, and applications.