Environmental data science: Part 1. Issue 1 (29th January 2023)

• Record Type: Journal Article
• Authors: Zammit‐Mangion, Andrew; Newlands, Nathaniel K.; Burr, Wesley S.
• Other Names: Zammit‐Mangion Andrew guestEditor.; Newlands Nathaniel K. guestEditor.; Burr Wesley S. guestEditor.
• Is Part Of: Environmetrics. Volume 34:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-29
• Subjects: decisions -- emulation -- spatial -- spatio‐temporal -- statistical computing -- uncertainty; Environmental sciences -- Statistical methods -- Periodicals; 550.72
• DOI: 10.1002/env.2787
• 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 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).