Editorial: Large-Scale Spatial Data Science

Abstract

This special issue features eight articles on “Large-Scale Spatial Data Science.” Data science for complex and large-scale spatial and spatio-temporal data has become essential in many research fields, such as climate science and environmental applications. Due to the ever-increasing amounts of data collected, traditional statistical approaches tend to break down and computa-tionally efficient methods and scalable algorithms that are suitable for large-scale spatial data have become crucial to cope with many challenges associated with big data. This special issue aims at highlighting some of the latest developments in the area of large-scale spatial data science. The research papers presented showcase advanced statistical methods and machine learn-ing approaches for solving complex and large-scale problems arising from modern data science applications. Abdulah et al. (2022) reported the results of the second competition on spatial statistics for large datasets organized by the King Abdullah University of Science and Technology (KAUST). Very large datasets (up to 1 million in size) were generated with the ExaGeoStat software to design the competition on large-scale predictions in challenging settings, including univariate nonstationary spatial processes, univariate stationary space-time processes, and bivariate stationary spatial processes. The authors described the data generation process in detail in each setting and made these valuable datasets publicly available. They reviewed the methods used by fourteen competing teams worldwide, analyzed the results of the competition, and assessed the performance of each team.