Competition sheds light on approximation methods for large spatial datasets

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Organizing a global competition between approximation methods used for analyzing and modeling large spatial datasets enabled KAUST researchers to compare the performance of these different methods.

Spatial datasets can contain many different types of data, from topographical, geometric or geographic information, such as environmental or financial data, comprising measurements taken across many locations. The development of advanced observation techniques has led to increasingly larger datasets with high dimensionality, making statistical inference in spatial statistics computationally challenging and very costly.

Various approximation methods can be used to model and analyze these large real-world spatial datasets, where exact computation is no longer feasible and inference is typically validated empirically or via prediction accuracy with the fitted model. However, there have been few studies that compare the statistical efficiency of these approximation methods, and these have been limited to small- and medium-sized datasets for only a few methods.

This motivated Marc Genton, Huang Huang and colleagues from KAUST to organize a competition between different approximation methods to assess their model inference performance.

The competition “was designed to achieve a comprehensive comparison between as many different methods as possible and also involved more recently developed methodologies,” said Huang. “It was also designed to overcome weaknesses in previous studies by incorporating several key features.”

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