Getting aid to impoverished Africans is hard enough, what with blockades of bureaucracy and red tape. But in many African countries, bad data, or a lack of it, makes distributing funds even more troublesome.
“Fighting poverty has always been this shining goal of the modern world,” Neal Jean, a doctoral student in computer science at Stanford University’s School of Engineering, told me. “It’s the number one priority for the United Nation’s 2030 Agenda for Sustainable Development, but the major challenge is that there’s not enough reliable data. It’s really hard to help impoverished people when you don’t know where they are.”
This fundamental problem was what Jean and five computer scientists hoped to solve, using satellite imagery and a machine learning model. Their new study, which was published today in Science, provides a proof-of-concept for an algorithm capable of predicting information about poverty in five African countries: Nigeria, Tanzania, Uganda, Malawi, and Rwanda.
Look at Angola, for instance. Forty years have passed since the country gained independence from Portugal, but its first postcolonial census was conducted just two years ago. The African nation is unfathomably rich in crude oil, but after 27 continuous years of civil war, half of its people live in poverty. Unfortunately, with scarce data on their economic well-being, it’s nearly impossible to create programs that could help Angola’s poorest communities, because no one knows exactly what is needed.
Countries can be loath to report their own inequality, due to corruption and conflict. According to the World Bank, 39 out of 59 African countries completed less than two population surveys related to poverty between 2000 and 2010. Of these nations, 14 reported no data at all, and most of the information amassed will never reach the public domain.
For decades, researchers have struggled to measure poverty using alternative data sets, such as social media, web search queries, and mobile network usage. In Rwanda, for example, where nearly 72 percent of people had mobile access in 2014, researchers were able to map their location based on the country’s telecommunications data. While nontraditional methods like this were informative, the study mentions, they also raised issues of privacy and scalability, due their reliance on proprietary information.
Meanwhile, traditional collection efforts like household surveys were too expensive, costing hundreds of billions of dollars, and were sometimes hampered by civil unrest. Often, donors would offer African countries loans for census taking, instead of grants, which many could not afford to accept.