An improved HASM method for dealing with large spatial data sets

doi:10.1007/s11430-017-9205-1

	An improved HASM method for dealing with large spatial data sets
	Zhao, Na 1,2,3; Yue, Tianxiang 1,2,3; Chen, Chuanfa 4; Zhao, Miaomiao 1,3; Du, Zhengping 1
刊名	SCIENCE CHINA-EARTH SCIENCES
	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
DOI	10.1007/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.
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关键词	SURFACE MODELING METHOD ; PRECONDITIONED CONJUGATE-GRADIENT ; INTERPOLATION METHODS ; DEM CONSTRUCTION ; PRECIPITATION ; CHINA ; CONVERGENCE ; ALGORITHMS ; ELEVATION ; RAINFALL
WOS研究方向	Geology
语种	英语
出版者	SCIENCE PRESS
WOS记录号	WOS:000440139000007
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
内容类型	期刊论文
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.