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Environmental data science: Part 1

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  • Andrew Zammit‐Mangion
  • Nathaniel K. Newlands
  • Wesley S. Burr

Abstract

Environmental data science is a multi‐disciplinary and mature field of research at the interface of statistics, machine learning, information technology, climate and environmental science. The two‐part special issue ‘Environmental Data Science’ comprises a set of research articles and opinion pieces led by statisticians who are at the forefront of the field. This editorial identifies and discusses common strands of research that appear in the contributions to Part 1, which largely focus on statistical methodology. These include temporal, spatial and spatio‐temporal modeling; statistical computing; machine learning and artificial intelligence; and the critical question of decision‐making in the presence of uncertainty. This editorial complements that of Part 2, which largely focuses on applications; see Burr, Newlands, and Zammit‐Mangion (2023).

Suggested Citation

  • Andrew Zammit‐Mangion & Nathaniel K. Newlands & Wesley S. Burr, 2023. "Environmental data science: Part 1," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    • Handle: RePEc:wly:envmet:v:34:y:2023:i:1:n:e2787
    DOI: 10.1002/env.2787
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    References listed on IDEAS

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    1. Christopher K. Wikle & Abhirup Datta & Bhava Vyasa Hari & Edward L. Boone & Indranil Sahoo & Indulekha Kavila & Stefano Castruccio & Susan J. Simmons & Wesley S. Burr & Won Chang, 2023. "An illustration of model agnostic explainability methods applied to environmental data," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    2. William Kleiber & Stephan Sain & Luke Madaus & Patrick Harr, 2023. "Stochastic tropical cyclone precipitation field generation," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    3. Sally Cripps & Hugh Durrant‐Whyte, 2023. "Uncertainty: Nothing is more certain," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    4. Ranadeep Daw & Christopher K. Wikle, 2023. "REDS: Random ensemble deep spatial prediction," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    5. Wesley S. Burr & Nathaniel K. Newlands & Andrew Zammit‐Mangion, 2023. "Environmental data science: Part 2," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    6. Shinichiro Shirota & Andrew O. Finley & Bruce D. Cook & Sudipto Banerjee, 2023. "Conjugate sparse plus low rank models for efficient Bayesian interpolation of large spatial data," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    7. Xu Ning & Francis K. C. Hui & Alan H. Welsh, 2023. "A double fixed rank kriging approach to spatial regression models with covariate measurement error," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    8. Paulo Canas Rodrigues & Elisabetta Carfagna, 2023. "Data science applied to environmental sciences," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    9. Hassani, Hossein & Beneki, Christina & Silva, Emmanuel Sirimal & Vandeput, Nicolas & Madsen, Dag Øivind, 2021. "The science of statistics versus data science: What is the future?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    10. Peter J. Diggle, 2015. "Statistics: a data science for the 21st century," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 793-813, October.
    11. Noel Cressie, 2023. "Decisions, decisions, decisions in an uncertain environment," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    12. Oliver Baerenbold & Melanie Meis & Israel Martínez‐Hernández & Carolina Euán & Wesley S. Burr & Anja Tremper & Gary Fuller & Monica Pirani & Marta Blangiardo, 2023. "A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
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    1. Wesley S. Burr & Nathaniel K. Newlands & Andrew Zammit‐Mangion, 2023. "Environmental data science: Part 2," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.

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