气候变化领域集成服务门户(CCPortal): Principles and methods of scaling geospatial Earth science data

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DOI	10.1016/j.earscirev.2019.102897
	Principles and methods of scaling geospatial Earth science data
	Ge Y.; Jin Y.; Stein A.; Chen Y.; Wang J.; Wang J.; Cheng Q.; Bai H.; Liu M.; Atkinson P.M.
发表日期	2019
ISSN	00128252
卷号	197
英文摘要	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.
关键词	Autocorrelation Change-of-support Heterogeneity Scaling
英文关键词	autocorrelation; downscaling; Earth science; heterogeneity; spatial analysis; upscaling
语种	英语
来源期刊	Earth Science Reviews
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/203434
作者单位	State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China; School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China; Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing, 210023, China; Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, AE 7500, Netherlands; School of Earth Sciences and Engineering, Hohai University, Nanjing, 210098, China; State Key Lab of Geological Processes and Mineral Resources, China University of Geosciences, Beijing, 100083, China; School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China; Lancaster Environment Center, Faculty of Science and Technology, Lancaster University, Lancaster, LA1 4YR, U...
推荐引用方式 GB/T 7714	Ge Y.,Jin Y.,Stein A.,et al. Principles and methods of scaling geospatial Earth science data[J],2019,197.
APA	Ge Y..,Jin Y..,Stein A..,Chen Y..,Wang J..,...&Atkinson P.M..(2019).Principles and methods of scaling geospatial Earth science data.Earth Science Reviews,197.
MLA	Ge Y.,et al."Principles and methods of scaling geospatial Earth science data".Earth Science Reviews 197(2019).