程诗奋, 彭澎, 张恒才, 陆锋. 异质稀疏分布时空数据插值、重构与预测方法探讨[J]. 武汉大学学报 ( 信息科学版), 2020, 45(12): 1919-1929. DOI: 10.13203/j.whugis20200488
引用本文: 程诗奋, 彭澎, 张恒才, 陆锋. 异质稀疏分布时空数据插值、重构与预测方法探讨[J]. 武汉大学学报 ( 信息科学版), 2020, 45(12): 1919-1929. DOI: 10.13203/j.whugis20200488
CHENG Shifen, PENG Peng, ZHANG Hengcai, LU Feng. Review of Interpolation, Reconstruction and Prediction Methods for Heterogeneous and Sparsely Distributed Geospatial Data[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1919-1929. DOI: 10.13203/j.whugis20200488
Citation: CHENG Shifen, PENG Peng, ZHANG Hengcai, LU Feng. Review of Interpolation, Reconstruction and Prediction Methods for Heterogeneous and Sparsely Distributed Geospatial Data[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1919-1929. DOI: 10.13203/j.whugis20200488

异质稀疏分布时空数据插值、重构与预测方法探讨

Review of Interpolation, Reconstruction and Prediction Methods for Heterogeneous and Sparsely Distributed Geospatial Data

  • 摘要: 时空数据挖掘是地理信息科学的核心研究命题。大数据时代,地理时空数据的爆炸性增长对时空知识发现提出了迫切的需求,促进了时空数据挖掘技术不断发展。然而,时空大数据普遍存在的异质性与稀疏分布特征制约了时空数据挖掘算法的实现,显著影响了自然和社会复杂系统刻画与分析能力。鉴于此,围绕异质稀疏分布时空数据表达与应用过程中面临的系列瓶颈问题开展研究,探讨了缺失时空数据插值、稀疏时空数据重构、时空状态预测等时空数据挖掘重点任务的研究现状和存在问题,凝练了关键的科学问题,并提出了相应的解决方案,以期丰富时空数据挖掘领域的方法体系,提升时空数据建模的质量与应用价值。

     

    Abstract: Spatiotemporal data mining is the core research topic of geographic information science. In the era of big data, the explosive growth of geographic spatiotemporal data puts forward an urgent demand for spatiotemporal knowledge discovery, which promotes the continuous development of spatiotemporal data mining technology. However, the universal heterogeneity and sparse distribution characteristics of spatiotemporal big data restrict the realization of spatiotemporal data mining algorithms, and significantly affect the description and analysis capabilities of natural and social complex systems. Thus, this paper focuses on the series of bottlenecks faced in the expression and application of heterogeneous and sparsely distributed spatiotemporal data. We systematically summarized the research status and existing problems of several key spatiotemporal mining tasks including missing spatiotemporal data interpolation, sparse spatiotemporal data reconstruction, and spatiotemporal state prediction, condensed four key scientific problems, and gave four corresponding solutions. The proposed methods are expected to enrich the method system in the field of spatiotemporal data mining and improve the quality and application value of spatiotemporal data modeling.

     

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