New way of tracking has potential to replace expensive door-to-door household surveys to predict poverty

A team of researchers from Stanford University has developed a new algorithm model, which is considered to be better at predicting poverty than all existing methods. The model is more effective than both satellite imagery and household data independently. To eliminate poverty, it is vital to find out the regions that are most affected with it. But the current situation is such that on-the-ground economic measures are sparse. These measures might not be reliable in poorer nations, as they lack resources to collect accurate data. In this situation, satellite data has been considered to be the best solution for the problem.