An improved HASM method for dealing with large spatial data sets |
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Authors: | Na Zhao Tianxiang Yue Chuanfa Chen Miaomiao Zhao Zhengping Du |
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Institution: | 1.State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing,China;2.Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application,Nanjing,China;3.College of Resources and Environment,University of Chinese Academy of Sciences,Beijing,China;4.Geomatics College,Shandong University of Science and Technology,Qingdao,China |
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Abstract: | 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. |
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