Journal of Geo-information Science, Volume. 22, Issue 1, 41(2020)

Spatio-temporal Analysis Methods for Multi-modal Geographic Big Data

Min DENG1...1, Jiannan CAI1,1,*, Wentao YANG2,2, Jianbo TANG1,1, Xuexi YANG1,1, Qiliang LIU1,1, and Yan SHI11 |Show fewer author(s)
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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    Figures & Tables(12)
    Dependence of spatio-temporal clustering on scale
    Spatial distributions of two different types of events
    Spurious spatial co-location patterns caused by the random interactions
    Illustration of human visual system
    Relationship between spatio-temporal clusters and data scale
    Meteorological division identified by the statistical method for spatio-temporal clustering
    Illustration of spatio-temporal cross outliers
    Significance tests on the cross outliers
    Induced spatial auto-correlations between different features
    Pattern reconstruction based on multi-modal summary characteristics
    Spatio-temporal co-location patterns determined by the statistical method
    Space-time support vector regression model considering both auto-correlation and heterogeneity
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    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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    Paper Information

    Received: Sep. 4, 2019

    Accepted: --

    Published Online: Sep. 16, 2020

    The Author Email: CAI Jiannan (jiannan.cai@csu.edu.cn)

    DOI:10.12082/dqxxkx.2020.190491

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