• Journal of Geo-information Science
  • Vol. 22, Issue 1, 41 (2020)
Min DENG1、1, Jiannan CAI1、1、*, Wentao YANG2、2, Jianbo TANG1、1, Xuexi YANG1、1, Qiliang LIU1、1, and Yan SHI1、1
Author Affiliations
  • 1Department of Geo-information, Central South University, Changsha 410083, China
  • 1中南大学地理信息系,长沙 410083
  • 2National-Local Joint Engineering Laboratory of Geospatial Information Technology, Hunan University of Science and Technology, Xiangtan 411100, China
  • 2湖南科技大学地理空间信息技术国家地方联合工程实验室,湘潭 411100
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    DOI: 10.12082/dqxxkx.2020.190491 Cite this Article
    Min DENG, Jiannan CAI, Wentao YANG, Jianbo TANG, Xuexi YANG, Qiliang LIU, Yan SHI. Spatio-temporal Analysis Methods for Multi-modal Geographic Big Data[J]. Journal of Geo-information Science, 2020, 22(1): 41 Copy Citation Text show less
    Dependence of spatio-temporal clustering on scale
    Fig. 1. Dependence of spatio-temporal clustering on scale
    Spatial distributions of two different types of events
    Fig. 2. Spatial distributions of two different types of events
    Spurious spatial co-location patterns caused by the random interactions
    Fig. 3. Spurious spatial co-location patterns caused by the random interactions
    Illustration of human visual system
    Fig. 4. Illustration of human visual system
    Relationship between spatio-temporal clusters and data scale
    Fig. 5. Relationship between spatio-temporal clusters and data scale
    Meteorological division identified by the statistical method for spatio-temporal clustering
    Fig. 6. Meteorological division identified by the statistical method for spatio-temporal clustering
    Illustration of spatio-temporal cross outliers
    Fig. 7. Illustration of spatio-temporal cross outliers
    Significance tests on the cross outliers
    Fig. 8. Significance tests on the cross outliers
    Induced spatial auto-correlations between different features
    Fig. 9. Induced spatial auto-correlations between different features
    Pattern reconstruction based on multi-modal summary characteristics
    Fig. 10. Pattern reconstruction based on multi-modal summary characteristics
    Spatio-temporal co-location patterns determined by the statistical method
    Fig. 11. Spatio-temporal co-location patterns determined by the statistical method
    Space-time support vector regression model considering both auto-correlation and heterogeneity
    Fig. 12. Space-time support vector regression model considering both auto-correlation and heterogeneity
    Min DENG, Jiannan CAI, Wentao YANG, Jianbo TANG, Xuexi YANG, Qiliang LIU, Yan SHI. Spatio-temporal Analysis Methods for Multi-modal Geographic Big Data[J]. Journal of Geo-information Science, 2020, 22(1): 41
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