• Journal of Geo-information Science
  • Vol. 22, Issue 6, 1394 (2020)
Qiuliang XIANG1、1、2、2、3、3, Qunyong WU1、1、2、2、3、3、*, and Liangpan ZHANG1、1、2、2、3、3
Author Affiliations
  • 1. 数字中国研究院(福建),福州 350003
  • 1The Academy of Digital China (Fujian), Fuzhou 350003, China
  • 2. 福州大学卫星空间信息技术国家地方联合工程研究中心,福州 350108
  • 2National & Local Joint Engineering Research Center of satellite-spatial Information Technology, Fuzhou University, Fuzhou 350108, China
  • 3. 空间数据挖掘与信息共享教育部实验室,福州 350108
  • 3Key Laboratory of Spatial Data Mining & Information Sharing of MOE, Fuzhou 350108, China
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    DOI: 10.12082/dqxxkx.2020.190276 Cite this Article
    Qiuliang XIANG, Qunyong WU, Liangpan ZHANG. An OD Flow Spatio-temporal Joint Clustering Algorithm based on Step-by-step Merge Strategy[J]. Journal of Geo-information Science, 2020, 22(6): 1394 Copy Citation Text show less
    Comparison diagram of OD flow spatial similarity relationship
    Fig. 1. Comparison diagram of OD flow spatial similarity relationship
    OD flow time similarity relationship judgment
    Fig. 2. OD flow time similarity relationship judgment
    Relationship between ratio and sim
    Fig. 3. Relationship between ratio and sim
    Flow chart of step-by-step merge strategy
    Fig. 4. Flow chart of step-by-step merge strategy
    Comparison diagram of clusters' consolidation condition
    Fig. 5. Comparison diagram of clusters' consolidation condition
    Clusters' merging order and results under different clustering methods
    Fig. 6. Clusters' merging order and results under different clustering methods
    Comparison of spatial ranges of flow clusters under different values of parameter k
    Fig. 7. Comparison of spatial ranges of flow clusters under different values of parameter k
    Comparison of flow clusters under different values of time parameter。。。
    Fig. 8. Comparison of flow clusters under different values of time parameter。。。
    Chengdu's OD flow clusters of a large number at early rush hour and late rush hour
    Fig. 9. Chengdu's OD flow clusters of a large number at early rush hour and late rush hour
    Top five flow clustersof New YorkCitytaxi data discovered by our method
    Fig. 10. Top five flow clustersof New YorkCitytaxi data discovered by our method
    Centers of flow clusters of New York City taxi data with different volumes。。。
    Fig. 11. Centers of flow clusters of New York City taxi data with different volumes。。。
    Top five flow clustersof New York City taxi data discovered by Gao's method
    Fig. 12. Top five flow clustersof New York City taxi data discovered by Gao's method
    flowiflowjmax(simij,simji)flowiflowjmax(simij,simji)
    flow1flow20.85flow2flow6<0
    flow1flow30.55flow3flow40.30
    flow1flow40.10flow3flow50.10
    flow1flow5<0flow3flow60.05
    flow1flow6<0flow4flow50.55
    flow2flow30.60flow4flow60.50
    flow2flow40.15flow5flow60.80
    flow2flow5<0
    Table 1. The similarity value between the OD flows of the synthesized sample data
    Qiuliang XIANG, Qunyong WU, Liangpan ZHANG. An OD Flow Spatio-temporal Joint Clustering Algorithm based on Step-by-step Merge Strategy[J]. Journal of Geo-information Science, 2020, 22(6): 1394
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