Satellite nighttime lights reveal how poorer districts in India catch up. Accounting for connections between neighboring districts raises the estimated pace of catch-up and shortens the time needed to close half the gap from about 23 to 13 years.
When a major bridge connected millions of people in northwest Bangladesh to the capital in 1998, did the region's economy take off? This beginner-friendly Python tutorial uses satellite images of nighttime lights to compare the connected region with a similar one that stayed isolated, before and after the bridge opened.
Learn how to turn satellite images of nighttime lights into estimates of regional income, then measure how unequal regions are within each country. Step by step in Python, we test whether regional inequality first rises and then falls as countries develop.
Satellite images of lights at night can track how China's provinces grow, but the link between light and income shifts over time and weakens during downturns. Newer satellite data measure economic activity more accurately than older data, especially for industry and services.
Do regional gaps within a country first widen and then narrow as the country gets richer? We find that this inverted-U pattern holds, and that natural resource wealth, farmland, and ethnic inequality are also among the most reliable predictors of regional inequality.
This paper introduces a user-friendly geocomputational notebook that illustrates how to process and analyze satellite NTL images.
This study explores the potential of higher-quality nighttime light (NTL) data to predict economic activity across various sectors within regions.