KAUST Competition on Spatial Statistics

2021 KAUST Competition on Spatial Statistics for Large Datasets

Introduction With the development of observing techniques and computing devices, it has become easier and more common to obtain large datasets. Statistical inference in spatial statistics becomes computationally challenging. For decades, various approximation methods have been proposed to model and analyze large-scale spatial data when the exact computation is infeasible. However, in the literature, the performance of the statistical inference using those proposed approximation methods was usually assessed with small and medium datasets only, for which the exact solution can be obtained. Then

2023 KAUST Competition on Spatial Statistics for Large Datasets

Introduction The rapid increase in the volume of geospatial data over recent years has added more challenges to processing these data using traditional methods. Thus, geospatial applications have brought High-Performance Computing (HPC) into the mainstream and further increased its use in the spatial statistics field. ExaGeoStat is one example of an HPC software that enables large-scale parallel generation, modeling, and prediction of large geospatial data via covariance matrices. Unlike other existing tools, which typically rely on approximations to deal with the vast data volume on day-use

2022 KAUST Competition on Spatial Statistics for Large Datasets

Introduction Spatial statistics is an example of a scientific field that requires novel methods to model and analysis large-scale spatial data. In the literature, research studies have proposed different approximation methods to handle large data sizes on traditional hardware. However, with the availability of modern High-Performance Computing (HPC) systems, large-scale exact computation becomes possible and allows processing larger data sizes more easily than before. For decades, the lack of large-scale exact computation has led to an inefficient assessment of spatial modeling approximation