Statistical methods for very large spatial datasets

What is it about?

Very large spatial data sets present problems for statistical modeling, such as regression, and for prediction, such as making maps. The problem occurs because we need to invert large matrices, and that is very time consuming, even for very large and fast computers. In this article, we show a fast and simple way to work with these large datasets, and still have valid regression models. We can also make maps where the uncertainty about the predictions is valid. These methods are available as open source software in an R package called spmodel.

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Photo by Markus Spiske on Unsplash

Photo by Markus Spiske on Unsplash

Why is it important?

From satellite images to automated instruments, datasets keep getting larger and larger. Simple methods can be used to summarize these data. However, there is uncertainty in understanding relationships among variables, and in making predictions. This requires a statistical model that quantifies uncertainty in estimates and predictions so that we can make decisions in the face of risk. Thus, it is important to advance statistical methods for big spatial datasets as found in this paper.

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