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Seasonal Hydrological Loading from GPS Observed Data Across Contiguous United States Using Integrated Apache Hadoop Framework

  • Pece V. Gorsevski
  • Yuning Fu
  • Kurt S. Panter
  • Jeffrey Snyder
  • Asanga M. Ramanayake
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

The study examined the relationship between seasonal vertical loading deformation and seasonal hydrological loading from precipitation specified as rain and snow. The vertical loading deformation is characterized by time-series estimated from continuous Global Positioning System (GPS) network across the contiguous United States for a timeframe of 48 months (January 1st, 2013 to December 31st, 2016). The data processing used custom-built R scripts and spatial libraries that were integrated with Hive framework which is a data warehouse extension of Apache Hadoop that is used as a database query interface. The relationships of vertical displacement were explored by visualization techniques such as spatial maps and wavelet coherence plots.

Keywords

Vertical loading deformation GPS Hadoop Wavelet Crustal deformation 

References

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pece V. Gorsevski
    • 1
  • Yuning Fu
    • 1
  • Kurt S. Panter
    • 1
  • Jeffrey Snyder
    • 1
  • Asanga M. Ramanayake
    • 1
  1. 1.School of Earth, Environment and SocietyBowling Green State UniversityBowling GreenUSA

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