Satellite images of Earth help predict poverty better than ever

(TheVerge) - The newest way to accurately predict poverty comes from satellite images and machine learning. This imaging technique could make it easier for aid organizations to know where and how to spend their money; it may also help governments develop better policy.

We already know that the more lit up an area is at night, the richer and more developed it is. Researchers use this method to estimate poverty in places where we don’t have exact data. But “night light” estimates are rough and don’t tell us much about the wealth differences of the very poor. Scientists at Stanford University fed a computer three data sources — night light images, daytime images, and actual survey data — to build an algorithm that predicts how rich or poor any given area is. This method, described in a study published today in the journal Science, estimates poverty in more detail than we’ve had before.

It’s hard to measure poverty in the developing world. The best way is by looking at economic data — like household wealth and assets — collected through household surveys. Problem is, we don’t have these surveys for much of the world because they’re expensive, according to study co-author Neal Jean, a doctoral candidate studying machine learning at Stanford. “The idea is that if we train our models right, they help us predict poverty in areas where we don’t have the surveys,” he says, “which will help out aid orgs that are working on this issue.”

Using night lights to predict poverty provides important information about the economic growth of different countries, says Simon Franklin, an economics researcher at the London School of Economics who was not involved with the study. But they don’t show detailed levels of poverty within a country.

They don’t tell us whether a place is rural and densely populated, or wealthy and sparsely populated. A village near a lake and a village near a forest could both show up as having zero lights at night. But the two have access to different natural resources, and this affects how wealthy they are. “In Africa, a lot of these places that are the most poor are actually just uniformly dark at night,” says Jean. “So if you use nighttime lights only to try to find these people, since there’s no variation in nighttime lights you can’t predict any variation in poverty.” Daytime imagery creates a fuller picture.