But nighttime lights don’t tell you which neighborhoods or villages within a large region are merely poor and which are home to people living in abject poverty. So researchers at Stanford came up with a way to get computers and satellites to do the work for them. Then they had their computer program compare the nighttime images to higher-resolution daytime images available via Google Static Maps. The final computer model was “strongly predictive” of two important measures of poverty — average spending by households and average household wealth. When a computer program churned through satellite data from just one of the five countries, the resulting model worked best in that country.

Source: Los Angeles Times August 19, 2016 10:00 UTC