地球科学进展 ›› 2022, Vol. 37 ›› Issue (1): 65 -79. doi: 10.11867/j.issn.1001-8166.2021.108

“青促会成立10周年之地球科学领域”专刊 上一篇    下一篇

过程——一种地理时空动态分析的新视角
薛存金 1 , 2( ), 苏奋振 3( ), 何亚文 4   
  1. 1.中国科学院空天信息创新研究院 数字地球重点实验室,北京 100094
    2.可持续发展大数据国际 研究中心,北京 100094
    3.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点 实验室,北京 100101
    4.中国石油大学(华东)海洋与空间信息学院,山东 青岛 266580
  • 收稿日期:2021-08-06 修回日期:2021-10-31 出版日期:2022-01-10
  • 通讯作者: 苏奋振 E-mail:xuecj@aircas.ac.cn;sufz@lries.ac.cn
  • 基金资助:
    中国科学院A类先导专项子课题“全球海洋异常变化过程遥感监测系统”(XDA19060103);国家自然科学基金项目“面向过程的海洋异常变化时空聚类挖掘模型”(41671401)

Process:A New View of Geographical Spatiotemporal Dynamic Analysis

Cunjin XUE 1 , 2( ), Fenzhen SU 3( ), Yawen HE 4   

  1. 1.Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
    2.International Research Center of Big Data for Sustainable Development Goals,Beijing 100094,China
    3.State Key Laboratory of Resource and Environmental Information System,Institute of Geographical Science and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
    4.College of Oceanography and Space Informatics,China University of Petroleum,Qingdao 266580,China
  • Received:2021-08-06 Revised:2021-10-31 Online:2022-01-10 Published:2022-01-29
  • Contact: Fenzhen SU E-mail:xuecj@aircas.ac.cn;sufz@lries.ac.cn
  • About author:XUE Cunjin (1979-), male, Chengwu County, Shandong Province, Professor. Research areas include marine spatiotemporal data mining. E-mail: xuecj@aircas.ac.cn
  • Supported by:
    the Strategic Priority Research Program of the Chinese Academy of Sciences "Process-oriented global marine remote sensing monitoring system"(XDA19060103);The National Natural Science Foundation of China "Process-oriented spatiotemporal clustering model for mining marine abnormal variations"(41671401)

地理世界中存在一类具有产生、发展和消亡的地理现象/对象,综合对地观测技术和多源信息获取技术的发展提升了获取这种动态地理现象的能力。现行的地理时空分析方法以点、线、面、体为基本单元,以数据获取尺度为分析尺度,割裂了地理现象的时间连续性,限制了地理时空动态的分析能力。把产生、发展和消亡的动态演变抽象为地理过程,从演变过程的尺度,提出一种新的地理时空分析方法。首先,提出“地理过程—演变序列—时刻状态”的分解抽象和逐级包含的地理时空过程语义,并基于“节点—边”的图思想建立地理时空过程图表达方法和存储模型,实现地理对象(节点)和对象演变行为(边)一体化表达和存储;其次,以地理过程为基本单元,设计“地理状态对象提取—演变序列追踪—过程对象重构”的过程对象提取方法,并基于节点的出度(该节点引起其他节点变化的边的个数)和入度(其他节点引起本节点变化的边的个数)实现过程对象演变行为的识别;再次,以地理过程为分析尺度,拓展时空邻域为过程邻域、时空相似性为过程相似性,设计面向过程的地理时空挖掘方法,开展地理对象及其演变行为的时空模式挖掘;最后,以1950—2019年月尺度的太平洋海洋表面温度异常变化过程对象为例,挖掘了海洋表面温度异常变化结构及其行为,并分析了与ENSO类型之间的关联模式,验证了所提方法的可行性和适用性。

There exists a sort of dynamic geographic phenomena with a property from production through development to dissipation in a real world, and the integrated Earth observation technology and the crowd sources technology promote the capabilities of obtaining these dynamics. The traditional spatiotemporal methods take a point, a line, a polygon or a voxel as an analyzing unit, and a scale of data acquisition as the analyzing scale, which splits a continuity of temporal evolutions, and limits their dynamic analysis. This paper abstracts the property from production through development to dissipation into a geographical process, takes it as a scale of analysis, and proposes a novel approach of geographical dynamic analysis. Firstly, a process semantics with a hierarchical abstraction of "geographical process-evolution sequence- instantaneous state" is proposed. And a graph-based model with a "node-edge" is used to represent and store geographical objects and their evolution behaviors. Secondly, a geographical process is used as a unit to design a process-oriented object extracting method with an "extracting of instantaneous state-tracking of evolution sequence- reconstructing of geographical process", and on the basis of in-degree and out-degree of a graph, four evolution behaviors are identified. Then, series of new concepts about spatiotemporal mining are redefined on the basis of a scale of process, and some process-oriented mining methods are designed, e.g. spatiotemporal clustering, association rule mining. Finally, a real dataset of monthly Sea Surface Temperature Anomaly (SSTA) during the period of January. 1950 to December. 2019 is to explore their evolving structures in Pacific Ocean, and the association patterns between the evolving structures of SSTA and the types of ENSO are addressed. Results demonstrate the effectiveness and the advantages of process-oriented dynamic analyzing method.

中图分类号: 

图1 地理时空过程语义
Fig. 1 Geographical semantics of spatiotemporal process
图2 面向过程的地理时空动态分析框架
Fig. 2 A process-oriented spatiotemporal analyzing framework of geographical objects
图3 地理时空过程图表达模型
Fig. 3 A graph-based representing model of geographical spatiotemporal process
图4 地理过程图存储模型
Fig. 4 A graph-based storing model of geographical spatiotemporal process
图5 面向过程的地理时空对象提取
Fig. 5 A process-oriented spatiotemporal extracting algorithm of geographical objects
图6 面向过程的地理时空挖掘
Fig. 6 A process-oriented spatiotemporal mining framework of geographical objects
图7 太平洋SST过程对象发生空间的强度分布图
Fig. 7 Spatial distribution of frequencies of SST process objects occurred in Pacific ocean
图8 太平洋区域SST过程对象演变结构
Fig. 8 Evolution structures of SST process objects occurred in Pacific Ocean
图9 Zone IIISST过程对象的演变行为的空间分布
Fig. 9 Spatial distribution of evolution behaviors of SST process objects occurred in Zone III
图10 EP El Ni?o事件期间SST异常变化空间演变特征
发展关系省略,箭头代表SST异常对象的空间演变方向Here, the development relationship is omitted, and an arrow direction represents a spatial evolution direction of SST process object
Fig. 10 Spatial evolution characteristics of SST process object during the period of El Ni?o
图11 CP El Ni?o事件期间SST异常变化空间演变特征
(a)SST异常变化过程对象的空间分布;(b)SST异常变化空间演变特征
Fig. 11 Spatial evolution characteristics of SST process object during the period of CP El Ni?o
(a) Spatial distribution of SST process objects; (b) Spatialevolution characteristics of a given SST process object
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