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    Rights statement: This is the author’s version of a work that was accepted for publication in Earth-Science Reviews. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Earth-Science Reviews, 197, 2019 DOI: 10.1016/j.earscirev.2019.102897

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Principles and methods of scaling geospatial Earth science data

Research output: Contribution to journalJournal articlepeer-review

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  • Yong Ge
  • Yan Jin
  • Alfred Stein
  • Yuehong Chen
  • Jianghao Wang
  • Qiuming Cheng
  • Hexiang Bai
  • Mengxiao Liu
  • Peter M. Atkinson
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Article number102897
<mark>Journal publication date</mark>1/10/2019
<mark>Journal</mark>Earth-Science Reviews
Volume197
Number of pages17
Publication StatusPublished
Early online date9/07/19
<mark>Original language</mark>English

Abstract

The properties of geographical phenomena vary with changes in the scale of measurement. The information observed at one scale often cannot be directly used as information at another scale. Scaling addresses these changes in properties in relation to the scale of measurement, and plays an important role in Earth sciences by providing information at the scale of interest, which may be required for a range of applications, and may be useful for inferring geographical patterns and processes. This paper presents a review of geospatial scaling methods for Earth science data. Based on spatial properties, we propose a methodological framework for scaling addressing upscaling, downscaling and side-scaling. This framework combines scale-independent and scale-dependent properties of geographical variables. It allows treatment of the varying spatial heterogeneity of geographical phenomena, combines spatial autocorrelation and heterogeneity, addresses scale-independent and scale-dependent factors, explores changes in information, incorporates geospatial Earth surface processes and uncertainties, and identifies the optimal scale(s) of models. This study shows that the classification of scaling methods according to various heterogeneities has great potential utility as an underpinning conceptual basis for advances in many Earth science research domains. © 2019 Elsevier B.V.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Earth-Science Reviews. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Earth-Science Reviews, 197, 2019 DOI: 10.1016/j.earscirev.2019.102897