Environmental data science: Part 2. Issue 2 (16th February 2023)

• Record Type: Journal Article
• Authors: Burr, Wesley S.; Newlands, Nathaniel K.; Zammit‐Mangion, Andrew
• Other Names: Burr Wesley S. guestEditor.; Newlands Nathaniel K. guestEditor.; Zammit‐Mangion Andrew guestEditor.
• Is Part Of: Environmetrics. Volume 34:Issue 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-16
• Subjects: applications -- community -- spatial -- spatio‐temporal -- training -- uncertainty; Environmental sciences -- Statistical methods -- Periodicals; 550.72
• DOI: 10.1002/env.2788
• Languages: English
• ISSNs: 1180-4009

Abstract

Summary: 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).