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Environmental data science: Part 2

Author

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

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 research themes that appear in the contributions to Part 2, which focuses on applications. These include spatio‐temporal modeling; the problem of aggregation and sparse sampling; the importance of community‐building and training for the next generation of specialists in environmental data science; and the need to look forward at the challenges that lie ahead for the discipline. This editorial complements that of Part 1, which largely focuses on statistical methodology; see Zammit‐Mangion, Newlands, and Burr (2023).

Suggested Citation

  • Wesley S. Burr & Nathaniel K. Newlands & Andrew Zammit‐Mangion, 2023. "Environmental data science: Part 2," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    • Handle: RePEc:wly:envmet:v:34:y:2023:i:2:n:e2788
    DOI: 10.1002/env.2788
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    References listed on IDEAS

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    1. Dixin Zhang & Yulin Wang & Hua Liang, 2023. "A Novel Estimation Method in Generalized Single Index Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(2), pages 399-413, April.
    2. Andrew Zammit‐Mangion & Nathaniel K. Newlands & Wesley S. Burr, 2023. "Environmental data science: Part 1," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    3. Kevin Granville & Douglas G. Woolford & C. B. Dean & Dennis Boychuk & Colin B. McFayden, 2023. "On the selection of an interpolation method with an application to the Fire Weather Index in Ontario, Canada," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    4. Samantha M. Roth & Ben Seiyon Lee & Sanjib Sharma & Iman Hosseini‐Shakib & Klaus Keller & Murali Haran, 2023. "Flood hazard model calibration using multiresolution model output," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    5. Gordon S. Blair & Peter A. Henrys, 2023. "The role of data science in environmental digital twins: In praise of the arrows," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    6. Stephan R. Sain, 2023. "Data science and climate risk analytics," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    7. E. Marian Scott, 2023. "Framing data science, analytics and statistics around the digital earth concept," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    8. Yunlong Nie & Liangliang Wang & Jiguo Cao, 2023. "Estimating functional single index models with compact support," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    9. Clément Laroche & Madalina Olteanu & Fabrice Rossi, 2023. "Pesticide concentration monitoring: Investigating spatio‐temporal patterns in left censored data," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    10. Ujjal Kumar Mukherjee & Benjamin E. Bagozzi & Snigdhansu Chatterjee, 2023. "A Bayesian framework for studying climate anomalies and social conflicts," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
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    Cited by:

    1. Andrew Zammit‐Mangion & Nathaniel K. Newlands & Wesley S. Burr, 2023. "Environmental data science: Part 1," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.

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