National Geographical Conditions Statistical Analysis in the Era of Big Data

被引：0
作者：
Liu J. [1 ,2 ]
Dong C. [1 ]
Kang X. [1 ]
Qiu S. [2 ]
Zhao R. [1 ]
Li B. [1 ]
Sun L. [1 ]
机构：
[1] Chinese Academy of Surveying and Mapping, Beijing
[2] Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2019年 / 44卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Big data; National geographical conditions; Service modeling; Statistical computation; Technical framework;
D O I：
10.13203/j.whugis20180420
中图分类号：
学科分类号：
摘要：
Statistical analysis is an important way of extracting information from the national geographical conditions data. It can reflect the internal spatial characteristics of resources, environment, ecology and economy, and their interactions from different dimensions. In view of the high-efficiency management, high-intensity computation and deep-level service for statistical analysis based on the big data, this paper puts forward a technical framework of national geographical conditions statistical analysis, and discusses the core process of statistical analysis from three dimensions: big data storage and integration, key technologies for statistical computation, service modeling and application. This paper will help to improve the application level of national geographical conditions monitoring and statistical analysis service in natural resources supervision, ecological protection and restoration, etc., and can promote the transformation and upgrading of geographical information industry in China. © 2019, Research and Development Office of Wuhan University. All right reserved.
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页码：68 / 76and83
页数：7615
相关论文
共 26 条
  • [1] Li Q., Li D., Big Data GIS, Geomatics and Information Science of Wuhan University, 39, 6, pp. 641-644, (2014)
  • [2] Wu B., Zhang M., Zeng H., Et al., Agricultural Monitoring and Early Warning in the Era of Big Data, Journal of Remote Sensing, 20, 5, pp. 1027-1037, (2016)
  • [3] Li D., Yao Y., Shao Z., Big Data in Smart City, Geomatics and Information Science of Wuhan University, 39, 6, pp. 631-640, (2014)
  • [4] Liu J., Fang Y., Guo C., Et al., Research Progress in Location Big Data Analysis and Processing, Geomatics and Information Science of Wuhan University, 39, 4, pp. 379-385, (2014)
  • [5] Chen J., Study Notes on Geographic National Condition Monitoring, Acta Geodaetica et Cartographica Sinica, 41, 5, pp. 633-635, (2012)
  • [6] Shi W., Qin K., Chen J., Et al., Key Theories and Technologies on Reliable Dynamic Monitoring for National Geographical State, Chinese Science Bulletin, 57, 24, pp. 2239-2248, (2012)
  • [7] Li D., Ma J., Shao Z., Innovation in the Census and Monitoring of Geographical National Conditions, Geomatics and Information Science of Wuhan University, 43, 1, pp. 1-9, (2018)
  • [8] Li D., Sui H., Shan J., Discussion on Key Technologies of Geographic National Conditions Monitoring, Geomatics and Information Science of Wuhan University, 37, 5, pp. 505-512, (2012)
  • [9] Niu Z., Li J., Gao Z., Et al., Progress and Future of China's Annual Report on Remote Sensing Monitoring of Global Ecosystem and Environment, Journal of Remote Sensing, 22, 4, pp. 672-685, (2018)
  • [10] Andersonteixeira K.J., Davies S.J., Bennett A.C., Et al., CTFS-ForestGEO: A Worldwide Network Monitoring Forests in an Era of Global Change, Global Change Biology, 21, 2, pp. 528-549, (2015)
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