Large-Scale Spatial Data Science

Spatial data science involves the analysis of spatial data distributions, patterns, and correlations within a predefined geographic area. This science field assumes that nearby spatial data points exhibit some association. Historically, most spatial datasets were manageable, allowing for exact inference using sequential processing software. However, recent advancements in data collection techniques have led to a surge in data volume, posing significant challenges for large-scale spatial data analysis. High-Performance Computing (HPC) has emerged as a valuable tool for addressing these challenges in various spatial applications, allowing researchers to tackle the vast datasets that have become commonplace. The advent of parallel processing hardware systems, including shared and distributed memory multiprocessors and GPU accelerators, has made it feasible to process big data in spatial statistics. Parallel computing can relieve the computational and memory limitations of large-scale Gaussian random process inference. This course aims to provide an overview of spatial statistics, explore existing approximation methods for Gaussian random processes, delve into state-of-the-art HPC techniques, and demonstrate how these techniques can solve large-scale spatial problems. We aim to encompass the parallel implementation of existing tools and modern approximation methods, such as low-rank approximation at a granular level and multi- and mixed-precision approximation to mitigate the computational load. The course content will cover the basic concepts of large-scale spatial statistics on parallel systems through synthetic and real data examples using both exact and approximation methods. The course will also provide a comprehensive comparison between existing Geostatistics packages (fields and GeoR) with the cutting-edge HPC package (ExaGeoStatR) to show the main contribution and benefits of using HPC techniques on leading-edge parallel hardware architectures such as GPUs and supercomputers. In general, the course will cover both the theoretical part of spatial statistics and HPC systems and the practical part through coding exercises and performance measurements.