Earth Science Informatics

, Volume 12, Issue 1, pp 87–96 | Cite as

Efficient spatiotemporal interpolation with spark machine learning

  • Weitian TongEmail author
  • Lixin Li
  • Xiaolu Zhou
  • Jason Franklin
Research Article


To better assess the relationships between environmental exposures and health outcomes, an appropriate spatiotemporal interpolation is critical. Traditional spatiotemporal interpolation methods either consider the spatial and temporal dimensions separately or incorporate both dimensions simultaneously by simply treating time as another dimension in space. Such interpolation results suffer from relatively low accuracy as the true space-time domain is skewed inappropriately and the distance calculation in such domain is not accurate. We employ the efficient k-d tree structure to store spatiotemporal data and adopt several machine learning methods to learn optimal parameters. To overcome the computational difficulty with large data sets, we implement our method on an efficient cluster computing framework – Apache Spark. Real world PM2.5 data sets are utilized to test our implementation and the experimental results demonstrate the computational power of our method, which significantly outperforms the previous work in terms of both speed and accuracy.


Spatiotemporal interpolation Spark Machine learning Inverse distance weighting (IDW) k-d tree Bootstrap aggregating 



We would like to thank Brandon Kimmons, Director of Computational Research Technical Support at Georgia Southern University, for helping us set up Spark. Franklin, Tong and Zhou were supported in part by funds from the Office of the Vice President for Research & Economic Development at Georgia Southern University. Beseny, Franklin, Li, Tong were supported in part by cooperative engineering and health sciences faculty seed grants from Allen E. Paulson College of Engineering & Information Technology, Georgia Southern University, and the College of Allied Health Sciences, Augusta University.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Weitian Tong
    • 1
    Email author
  • Lixin Li
    • 1
  • Xiaolu Zhou
    • 2
  • Jason Franklin
    • 1
  1. 1.Department of Computer ScienceGeorgia Southern UniversityStatesboroUSA
  2. 2.Department of Geology and GeographyGeorgia Southern UniversityStatesboroUSA

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