Stanford scientists successfully map poverty from space
Using satellite imagery, researchers from Stanford were able to assess poverty rates from Rwanda, Uganda, Tanzania, Nigeria, and Malawi, revealing variations of the economy and state of income of the average citizens. The process is based on machine learning and only requires the use of publicly available information. The research team has highlighted that there is not enough census data to assess global levels of poverty. The primary factor in determining the level poverty of each region was nighttime lighting. The researchers were inspired by the fact that nighttime maps of the earth show that developing countries and those under detrimental economic situations are dimly illuminated. Researchers analyzed nighttime lights in Africa to measure economic activity. Image credit: Marshall Burke. Poverty puts countries in the dark The study was published…
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Stanford scientists successfully map poverty from space
