Geographic spatiotemporal big data correlation analysis via the Hilbert-Huang transformation

Abstract As a typical representative of big data, geographic spatiotemporal big data present new features especially the non-stationary feature, bringing new challenges to mine correlation information. However, representation of instantaneous information is the main bottleneck for non-stationary data, but the traditional non-stationary analysis methods are limited by Heisenberg's uncertainty principle. Therefore, we firstly represent instantaneous frequency of geographic spatiotemporal big data based on Hilbert–Huang transform to overcome traditional methods' weakness. Secondly, we propose absolute entropy correlation analysis method based on KL divergence. Finally, we select five geographic factors to certify that the absolute entropy correlation analysis method is effective and distinguishable.

• Publication year

  2017

• Venue

  Journal of computer and system sciences (Print)

• Publication date

  2017-11-01

• Fields of study

  Geography, Computer Science, Mathematics

• Identifiers
  DOI 10.1016/j.jcss.2017.05.010
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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