The researchers developed a new method called GSARNN to predict unsampled spatial data in three-dimensional space. By combining neural networks with a new unit, they were able to better capture complex spatial patterns. The GSARNN model outperformed traditional methods like inverse distance weighted and ordinary Kriging in two case studies, showing it is more adaptable and accurate for spatial interpolation.