Data Mining and Knowledge Discovery

, Volume 26, Issue 2, pp 398–433 | Cite as

Enhanced spatiotemporal relational probability trees and forests

  • Amy McGovern
  • Nathaniel Troutman
  • Rodger A. Brown
  • John K. Williams
  • Jennifer Abernethy
Open Access


Many real world domains are inherently spatiotemporal in nature. In this work, we introduce significant enhancements to two spatiotemporal relational learning methods, the spatiotemporal relational probability tree and the spatiotemporal relational random forest, that increase their ability to learn using spatiotemporal data. We enabled the models to formulate questions on both objects and the scalar and vector fields within and around objects, allowing the models to differentiate based on the gradient, divergence, and curl and to recognize the shape of point clouds defined by fields. This enables the model to ask questions about the change of a shape over time or about its orientation. These additions are validated on several real-world hazardous weather datasets. We demonstrate that these additions enable the models to learn robust classifiers that outperform the versions without these new additions. In addition, analysis of the learned models shows that the findings are consistent with current meteorological theories.


Spatiotemporal relational learning Statistical relational learning Hazardous weather 



The authors thank Jason Craig, David J. Gagne II, Nathan Hiers, Gregory Meymaris, Timothy Supinie, and Derek Rosendahl for their work in generating some of the data used in this research. This research was supported by the National Science Foundation under Grant No. NSF/IIS/0746816 and by NASA under Grant No. NNX08AL89G. Much of the computing for this project was performed at the OU Supercomputing Center for Education & Research (OSCER) at the University of Oklahoma (OU).

Open Access

This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.


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Copyright information

© The Author(s) 2012

Authors and Affiliations

  • Amy McGovern
    • 1
  • Nathaniel Troutman
    • 1
  • Rodger A. Brown
    • 2
  • John K. Williams
    • 3
  • Jennifer Abernethy
    • 3
  1. 1.School of Computer ScienceUniversity of OklahomaNormanUSA
  2. 2.NOAA/National Severe Storms LaboratoryNormanUSA
  3. 3.Research Applications LaboratoryNational Center for Atmospheric ResearchBoulderUSA

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