Computational Benchmark Study in Spatio-Temporal Statistics With a Hands-On Guide to Optimise R

Abstract

This study provides a comprehensive evaluation of the computational performance of R, MATLAB, Python, and Julia for spatial and spatio-temporal modelling, focusing on high-dimensional datasets typical in geospatial statistical analysis. We benchmark each language across key tasks, including matrix manipulations and transformations, iterative programming routines, and Input/Output processes, all of which are critical in environmetrics. The results demonstrate that MATLAB excels in matrix-based computations, while Julia consistently delivers competitive performance across a wide range of tasks, establishing itself as a robust, open-source alternative. Python, when combined with libraries like NumPy, shows strength in specific numerical operations, offering versatility for general-purpose programming. R, despite its slower default performance in raw computations, proves to be highly adaptable; by linking to optimized libraries like OpenBLAS or MKL and integrating C++ with packages like Rcpp, R achieves significant performance gains, becoming competitive with the other languages. This study also provides practical guidance for researchers to optimize R for geospatial data processing, offering insights to support the selection of the most suitable language for specific modelling requirements.