A Process-Oriented Spatiotemporal Clustering Method for Complex Trajectories of Dynamic Geographic Phenomena
There exists a kind of trajectories of dynamic geographic phenomena, which have splitting, merging, or merging-splitting branches. Clustering these complex trajectories may help to more deeply explore and analyze the evolution mechanism of geographic phenomena. However, few methods explore the clust...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8880593/ |
| _version_ | 1811209201388617728 |
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| author | Jingyi Liu Cunjin Xue Qing Dong Chengbin Wu Yangfeng Xu |
| author_facet | Jingyi Liu Cunjin Xue Qing Dong Chengbin Wu Yangfeng Xu |
| author_sort | Jingyi Liu |
| collection | DOAJ |
| description | There exists a kind of trajectories of dynamic geographic phenomena, which have splitting, merging, or merging-splitting branches. Clustering these complex trajectories may help to more deeply explore and analyze the evolution mechanism of geographic phenomena. However, few methods explore the clustering patterns of such trajectories. Thus, we propose a Process-oriented Spatiotemporal Clustering Method (PoSCM) for clustering complex trajectories with multiple branches. The PoSCM includes the following three parts: the first represents the trajectories with a “process-sequence-node” structure inspired by a process-oriented semantic model; the second designs a hierarchical similarity measurement method to calculate the similarity of space, time, thematic attributes and evolution structure between any two trajectories; the last uses a density-based clustering algorithm to mine the trajectories’ clustering patterns. Simulation experiments are used to evaluate PoSCM and to demonstrate the advantages by comparing against that of the VF2 algorithm. A case study of sea surface temperature abnormal variation (SSTAV) trajectories in the Pacific Ocean is addressed. The clustering results not only validate well-known knowledge but also provide some new insights about the evolution characteristics of SSTAVs during El Niño Southern Oscillation (ENSO); these insights may provide new references for further study on global climate change. |
| first_indexed | 2024-04-12T04:35:34Z |
| format | Article |
| id | doaj.art-4872fac6756d4d6e8356648715064347 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T04:35:34Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4872fac6756d4d6e83566487150643472022-12-22T03:47:49ZengIEEEIEEE Access2169-35362019-01-01715595115596410.1109/ACCESS.2019.29490498880593A Process-Oriented Spatiotemporal Clustering Method for Complex Trajectories of Dynamic Geographic PhenomenaJingyi Liu0https://orcid.org/0000-0002-5664-9891Cunjin Xue1Qing Dong2Chengbin Wu3Yangfeng Xu4Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaThere exists a kind of trajectories of dynamic geographic phenomena, which have splitting, merging, or merging-splitting branches. Clustering these complex trajectories may help to more deeply explore and analyze the evolution mechanism of geographic phenomena. However, few methods explore the clustering patterns of such trajectories. Thus, we propose a Process-oriented Spatiotemporal Clustering Method (PoSCM) for clustering complex trajectories with multiple branches. The PoSCM includes the following three parts: the first represents the trajectories with a “process-sequence-node” structure inspired by a process-oriented semantic model; the second designs a hierarchical similarity measurement method to calculate the similarity of space, time, thematic attributes and evolution structure between any two trajectories; the last uses a density-based clustering algorithm to mine the trajectories’ clustering patterns. Simulation experiments are used to evaluate PoSCM and to demonstrate the advantages by comparing against that of the VF2 algorithm. A case study of sea surface temperature abnormal variation (SSTAV) trajectories in the Pacific Ocean is addressed. The clustering results not only validate well-known knowledge but also provide some new insights about the evolution characteristics of SSTAVs during El Niño Southern Oscillation (ENSO); these insights may provide new references for further study on global climate change.https://ieeexplore.ieee.org/document/8880593/Spatiotemporal trajectory clusteringdynamic geographic phenomenaevolutionary behaviorsPacific oceansea surface temperature anomalies |
| spellingShingle | Jingyi Liu Cunjin Xue Qing Dong Chengbin Wu Yangfeng Xu A Process-Oriented Spatiotemporal Clustering Method for Complex Trajectories of Dynamic Geographic Phenomena IEEE Access Spatiotemporal trajectory clustering dynamic geographic phenomena evolutionary behaviors Pacific ocean sea surface temperature anomalies |
| title | A Process-Oriented Spatiotemporal Clustering Method for Complex Trajectories of Dynamic Geographic Phenomena |
| title_full | A Process-Oriented Spatiotemporal Clustering Method for Complex Trajectories of Dynamic Geographic Phenomena |
| title_fullStr | A Process-Oriented Spatiotemporal Clustering Method for Complex Trajectories of Dynamic Geographic Phenomena |
| title_full_unstemmed | A Process-Oriented Spatiotemporal Clustering Method for Complex Trajectories of Dynamic Geographic Phenomena |
| title_short | A Process-Oriented Spatiotemporal Clustering Method for Complex Trajectories of Dynamic Geographic Phenomena |
| title_sort | process oriented spatiotemporal clustering method for complex trajectories of dynamic geographic phenomena |
| topic | Spatiotemporal trajectory clustering dynamic geographic phenomena evolutionary behaviors Pacific ocean sea surface temperature anomalies |
| url | https://ieeexplore.ieee.org/document/8880593/ |
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