Causal Structure Discovery for Spatio-temporal Data

  • Victor W. Chu
  • Raymond K. Wong
  • Wei Liu
  • Fang Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8421)


Numerous causal structure discovery methods have been proposed recently but none of them has taken possible time-varying structure into consideration. In this paper, we introduce a notion of causal time-varying dynamic Bayesian network (CTV-DBN) and define a causal boundary to govern cross time information sharing. Although spatio-temporal data have been investigated by multiple disciplines; by reducing structure discovery into a set of optimization problems, CTV-DBN is a scalable solution targeting large datasets. CTV-DBN is constructed using asymmetric kernels to address sample scarcity and to adhere to causal principles; while maintaining good variance and bias trade-off. We explore trajectory data collected from mobile devices which are known to exhibit heterogeneous patterns, data sparseness and distribution skewness. Contrary to a naïve method to divide space by grids, we capture the moving objects’ view of space by using density-based clustering to overcome the problems. In our experiments, CTV-DBN is used to reveal the evolution of time-varying region macro structure in a ring road system based on trajectories, and to obtain a local time-varying road junction dependency structure based on static traffic flow sensor data.


Voronoi Diagram Dynamic Bayesian Network Causal Principle Semantic Region Road Junction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Victor W. Chu
    • 1
  • Raymond K. Wong
    • 1
  • Wei Liu
    • 2
  • Fang Chen
    • 2
  1. 1.School of Computer Science and EngineeringUniversity of New South WalesAustralia
  2. 2.National ICTAustralia

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