IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v34y2023i2ne2788.html
   My bibliography  Save this article

Environmental data science: Part 2

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.2788
    Download Restriction: no
    File URL: https://libkey.io/10.1002/env.2788?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. Stephan R. Sain, 2023. "Data science and climate risk analytics," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    3. Andrew Zammit‐Mangion & Nathaniel K. Newlands & Wesley S. Burr, 2023. "Environmental data science: Part 1," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    4. E. Marian Scott, 2023. "Framing data science, analytics and statistics around the digital earth concept," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    5. 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.
    6. 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.
    7. Yunlong Nie & Liangliang Wang & Jiguo Cao, 2023. "Estimating functional single index models with compact support," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    8. 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.
    9. 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.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kehui Yao & Jun Zhu & Daniel J. O'Brien & Daniel Walsh, 2023. "Bayesian spatio‐temporal survival analysis for all types of censoring with application to a wildlife disease study," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), December.
    2. Sara Zapata‐Marin & Alexandra M. Schmidt & Scott Weichenthal & Eric Lavigne, 2023. "Modeling temporally misaligned data across space: The case of total pollen concentration in Toronto," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), December.
    3. Luca Aiello & Matteo Fontana & Alessandra Guglielmi, 2023. "Bayesian functional emulation of CO2 emissions on future climate change scenarios," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), December.
    4. Salim Bouzebda, 2024. "Limit Theorems in the Nonparametric Conditional Single-Index U -Processes for Locally Stationary Functional Random Fields under Stochastic Sampling Design," Mathematics, MDPI, vol. 12(13), pages 1-81, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:envmet:v:34:y:2023:i:2:n:e2788. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1180-4009/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.