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An improved HASM method for dealing with large spatial data sets
Zhao, Na1,2,3; Yue, Tianxiang1,2,3; Chen, Chuanfa4; Zhao, Miaomiao1,3; Du, Zhengping1
2018-08-01
Source PublicationSCIENCE CHINA-EARTH SCIENCES
ISSN1674-7313
Volume61Issue:8Pages:1078-1087
Corresponding AuthorZhao, Na(zhaon@lreis.ac.cn)
AbstractSurface 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.
KeywordSurface modeling HASM Large spatial data
DOI10.1007/s11430-017-9205-1
WOS KeywordSURFACE MODELING METHOD ; PRECONDITIONED CONJUGATE-GRADIENT ; INTERPOLATION METHODS ; DEM CONSTRUCTION ; PRECIPITATION ; CHINA ; CONVERGENCE ; ALGORITHMS ; ELEVATION ; RAINFALL
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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
WOS Research AreaGeology
WOS SubjectGeosciences, Multidisciplinary
WOS IDWOS:000440139000007
PublisherSCIENCE PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54571
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhao, Na
Affiliation1.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
Recommended Citation
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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