Predictive modeling of very large datasets, such as environmental measurements, across a wide area can be a highly computationally intensive exercise. These computational demands can be significantly reduced by applying various approximations, but at what cost to accuracy? KAUST researchers have now developed statistical tools that help remove the guesswork from this approximation process.