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Big data approaches to measure productivity, prices, and assets

Information on cropland productivity, market prices of staples, and household assets is of primary importance for food security assessments and policies.  Traditionally, this type of information has been collected through surveys at a national and local level. Though such surveys are valuable, they also are costly and labor and time intensive.  In this era dominated by unprecedented amounts of information, new technologies and data analysis approaches provide powerful tools to overcome such limitations. The goal of this project is to develop and test novel big data approaches to measure productivity, assets, and prices at different scales and in near-real time. We will make use of the vast satellite data pool and the computational power of Google Earth Engine to monitor crops with a new methodology (SCYM algorithm), classify land cover use, and explore possible proxies of assets and prices. We will gather other insights on key factors in food security by exploiting social media data.

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