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

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  • Lorenzo Tedesco
  • Jacopo Rodeschini
  • Philipp Otto

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.

Suggested Citation

  • Lorenzo Tedesco & Jacopo Rodeschini & Philipp Otto, 2025. "Computational Benchmark Study in Spatio‐Temporal Statistics With a Hands‐On Guide to Optimise R," Environmetrics, John Wiley & Sons, Ltd., vol. 36(5), July.
    • Handle: RePEc:wly:envmet:v:36:y:2025:i:5:n:e70017
    DOI: 10.1002/env.70017
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