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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

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Principles and methods of scaling geospatial Earth science data. / Ge, Yong; Jin, Yan; Stein, Alfred; Chen, Yuehong; Wang, Jianghao; Cheng, Qiuming; Bai, Hexiang; Liu, Mengxiao; Atkinson, Peter M.

In: Earth-Science Reviews, Vol. 197, 102897, 01.10.2019.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Ge, Y, Jin, Y, Stein, A, Chen, Y, Wang, J, Cheng, Q, Bai, H, Liu, M & Atkinson, PM 2019, 'Principles and methods of scaling geospatial Earth science data', Earth-Science Reviews, vol. 197, 102897. https://doi.org/10.1016/j.earscirev.2019.102897

APA

Ge, Y., Jin, Y., Stein, A., Chen, Y., Wang, J., Cheng, Q., Bai, H., Liu, M., & Atkinson, P. M. (2019). Principles and methods of scaling geospatial Earth science data. Earth-Science Reviews, 197, [102897]. https://doi.org/10.1016/j.earscirev.2019.102897

Vancouver

Ge Y, Jin Y, Stein A, Chen Y, Wang J, Cheng Q et al. Principles and methods of scaling geospatial Earth science data. Earth-Science Reviews. 2019 Oct 1;197. 102897. https://doi.org/10.1016/j.earscirev.2019.102897

Author

Ge, Yong ; Jin, Yan ; Stein, Alfred ; Chen, Yuehong ; Wang, Jianghao ; Cheng, Qiuming ; Bai, Hexiang ; Liu, Mengxiao ; Atkinson, Peter M. / Principles and methods of scaling geospatial Earth science data. In: Earth-Science Reviews. 2019 ; Vol. 197.

Bibtex

@article{b0be6ca520bd464380b884e7ce3e9117,
title = "Principles and methods of scaling geospatial Earth science data",
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. {\textcopyright} 2019 Elsevier B.V.",
keywords = "Autocorrelation, Change-of-support, Heterogeneity, Scaling",
author = "Yong Ge and Yan Jin and Alfred Stein and Yuehong Chen and Jianghao Wang and Qiuming Cheng and Hexiang Bai and Mengxiao Liu and Atkinson, {Peter M.}",
note = "This is the author{\textquoteright}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",
year = "2019",
month = oct,
day = "1",
doi = "10.1016/j.earscirev.2019.102897",
language = "English",
volume = "197",
journal = "Earth-Science Reviews",
issn = "0012-8252",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Principles and methods of scaling geospatial Earth science data

AU - Ge, Yong

AU - Jin, Yan

AU - Stein, Alfred

AU - Chen, Yuehong

AU - Wang, Jianghao

AU - Cheng, Qiuming

AU - Bai, Hexiang

AU - Liu, Mengxiao

AU - Atkinson, Peter M.

N1 - 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

PY - 2019/10/1

Y1 - 2019/10/1

N2 - 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.

AB - 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.

KW - Autocorrelation

KW - Change-of-support

KW - Heterogeneity

KW - Scaling

U2 - 10.1016/j.earscirev.2019.102897

DO - 10.1016/j.earscirev.2019.102897

M3 - Journal article

VL - 197

JO - Earth-Science Reviews

JF - Earth-Science Reviews

SN - 0012-8252

M1 - 102897

ER -