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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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    References listed on IDEAS

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    1. Abhirup Datta & Sudipto Banerjee & Andrew O. Finley & Alan E. Gelfand, 2016. "Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 800-812, April.
    2. Joseph Guinness, 2019. "Spectral density estimation for random fields via periodic embeddings," Biometrika, Biometrika Trust, vol. 106(2), pages 267-286.
    3. Matthew J. Heaton & Abhirup Datta & Andrew O. Finley & Reinhard Furrer & Joseph Guinness & Rajarshi Guhaniyogi & Florian Gerber & Robert B. Gramacy & Dorit Hammerling & Matthias Katzfuss & Finn Lindgr, 2019. "A Case Study Competition Among Methods for Analyzing Large Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 398-425, September.
    4. Michael Dumelle & Matt Higham & Jay M Ver Hoef, 2023. "spmodel: Spatial statistical modeling and prediction in R," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-32, March.
    5. Lee, Duncan, 2013. "CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i13).
    6. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    7. Bates, Douglas & Eddelbuettel, Dirk, 2013. "Fast and Elegant Numerical Linear Algebra Using the RcppEigen Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i05).
    8. Finley, Andrew O. & Banerjee, Sudipto & Gelfand, Alan E., 2015. "spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i13).
    9. Guinness, Joseph, 2022. "Nonparametric spectral methods for multivariate spatial and spatial–temporal data," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    10. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
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