The challenges of a Big Data Earth. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Boulton, Geoffrey
• Is Part Of: Big earth data. Volume 2:Issue 1(2018)
• Page Start: 1
• Page End: 7
• Publication Date: 2018-01-02
• Subjects: Big data -- digital Earth -- digital revolution -- Anthropocene -- networked Earth -- Earth system; Earth sciences -- Periodicals; Earth sciences -- Research -- Periodicals; Geographic information systems Periodicals; 550
• DOI: 10.1080/20964471.2017.1397411
• Languages: English
• ISSNs: 2096-4471

Abstract

Abstract: The potential of big data fused with the vision of a digital Earth offers powerful opportunities to deepen understanding of the whole Earth system and the management of a sustainable planet. It is important to stand back from often confusing detail to clarify what those opportunities are and how they might be seized. The essential scientific potential of data, big or small, is to reveal patterns, which have often been the fundamental first step in stimulating inquiry, leading to new questions, new perspectives and potentially to new answers. The digital revolution has created a "digital microscope" that permits us to see patterns that have not been seen before, and when coupled with machine learning technologies to analyse them in creating statistical predictions of the behaviour of both human and non-human systems. These potentials converge with the imperative to represent an Earth system with interacting non-human and human components, as a vital contribution to the understanding and actions required in working towards planetary sustainability. But a digital Earth is also capable of being represented mathematically as a digitally networked phenomenon, analogous to an analogue computer, and should be an important target for a Big Earth Data Journal. We should also return to Al Gore's vision of an accessible digital Earth with wide usability. Pre-determining the separate functions of parallel digital Earths risks losing one of the great potentials of big data and learning algorithms, the identification and analysis of unanticipated relationships and processes.