In the sustainable aviation literature, it is common to rely on multiple data sets, including traffic, noise, and meteorological data. Spatial interpolation of these data is a frequent practice. This article presents a comprehensive comparison of interpolation techniques for aviation data, focusing on stochastic methods. Two primary case studies are investigated: the interpolation of noise in the vicinity of airports (two-dimensional interpolation) and the interpolation of weather values across multiple pressure layers (threedimensional interpolation). In the first case, standard geostatistical methods are particularly well-suited. The interpolation of meteorological values is more complex. The underlying physics gives rise to a trend and complex anisotropies that are challenging to estimate with the basic geostatistical framework. In the absence of outliers and if meteorological data are sampled on a sufficiently dense regular grid, trilinear interpolation performs comparably to advanced statistical methods. Otherwise, statistical methods appear to be more flexible.