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An improved HASM method for dealing with large spatial data sets

文献类型：期刊论文

作者Zhao, Na1,2,3; Yue, Tianxiang1,2,3; Chen, Chuanfa4; Zhao, Miaomiao1,3; Du, Zhengping1
刊名SCIENCE CHINA-EARTH SCIENCES
出版日期2018-08-01
卷号61期号:8页码:1078-1087
关键词Surface modeling HASM Large spatial data
ISSN号1674-7313
DOI10.1007/s11430-017-9205-1
通讯作者Zhao, Na(zhaon@lreis.ac.cn)
英文摘要Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method (HASM) is proposed, and HASM_Big is developed to handle very large data sets. A large data set is defined here as a large spatial domain with high resolution leading to a linear equation with matrix dimensions of hundreds of thousands. An augmented system approach is employed to solve the equality-constrained least squares problem (LSE) produced in HASM_Big, and a block row action method is applied to solve the corresponding very large matrix equations. A matrix partitioning method is used to avoid information redundancy among each block and thereby accelerate the model. Experiments including numerical tests and real-world applications are used to compare the performances of HASM_Big with its previous version, HASM. Results show that the memory storage and computing speed of HASM_Big are better than those of HASM. It is found that the computational cost of HASM_Big is linearly scalable, even with massive data sets. In conclusion, HASM_Big provides a powerful tool for surface modeling, especially when there are millions or more computing grid cells.
WOS关键词SURFACE MODELING METHOD ; PRECONDITIONED CONJUGATE-GRADIENT ; INTERPOLATION METHODS ; DEM CONSTRUCTION ; PRECIPITATION ; CHINA ; CONVERGENCE ; ALGORITHMS ; ELEVATION ; RAINFALL
资助项目National Natural Science Foundation of China[41541010] ; National Natural Science Foundation of China[41701456] ; National Natural Science Foundation of China[41421001] ; National Natural Science Foundation of China[41590840] ; National Natural Science Foundation of China[91425304] ; Key Programs of the Chinese Academy of Sciences[QYZDY-SSW-DQC007] ; Cultivate Project of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences[TSYJS03]
WOS研究方向Geology
语种英语
WOS记录号WOS:000440139000007
出版者SCIENCE PRESS
资助机构National Natural Science Foundation of China ; Key Programs of the Chinese Academy of Sciences ; Cultivate Project of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/54570]  
专题中国科学院地理科学与资源研究所
通讯作者Zhao, Na
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100101, Peoples R China
4.Shandong Univ Sci & Technol, Geomat Coll, Qingdao 266510, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Na,Yue, Tianxiang,Chen, Chuanfa,et al. An improved HASM method for dealing with large spatial data sets[J]. SCIENCE CHINA-EARTH SCIENCES,2018,61(8):1078-1087.
APA Zhao, Na,Yue, Tianxiang,Chen, Chuanfa,Zhao, Miaomiao,&Du, Zhengping.(2018).An improved HASM method for dealing with large spatial data sets.SCIENCE CHINA-EARTH SCIENCES,61(8),1078-1087.
MLA Zhao, Na,et al."An improved HASM method for dealing with large spatial data sets".SCIENCE CHINA-EARTH SCIENCES 61.8(2018):1078-1087.

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来源：地理科学与资源研究所

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