A filtering-based clustering algorithm for improving spatio-temporal kriging interpolation accuracy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Geostatistical interpolation is the process that uses existing data and statistical models as inputs to predict data in unobserved spatio-temporal contexts as output. Kriging is a well-known geostatistical interpolation method that minimizes mean square error of prediction. The result interpolated by Kriging is accurate when consistency of statistical properties in data is assumed. However, without this assumption, Kriging interpolation has poor accuracy. To address this problem, this paper presents a new filtering-based clustering algorithm that partitions data into clusters such that the interpolation error within each cluster is significantly reduced, which in turn improves the overall accuracy. Comparisons to traditional Kriging are made with two real-world datasets using two error criteria: normalized mean square error(NMSE) and χ2 test statistics for normalized deviation measurement. Our method has reduced NMSE by more than 50% for both datasets over traditional Kriging. Moreover, χ2 tests have also shown significant improvements of our approach over traditional Kriging.

Original languageEnglish (US)
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2209-2214
Number of pages6
ISBN (Electronic)9781450340731
DOIs
StatePublished - Oct 24 2016
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: Oct 24 2016Oct 28 2016

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
Volume24-28-October-2016

Other

Other25th ACM International Conference on Information and Knowledge Management, CIKM 2016
CountryUnited States
CityIndianapolis
Period10/24/1610/28/16

Keywords

  • Kriging
  • Spatio-temporal clustering
  • Spatio-temporal interpolation

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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