Correlation analysis of spatial time series datasets: A filter-and-refine approach

Pusheng Zhang, Yan Huang, Shashi Shekhar, Vipin Kumar

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

2 Scopus citations


A spatial time series dataset is a collection of time series, each referencing a location in a common spatial framework. Correlation analysis is often used to identify pairs of potentially interacting elements from the cross product of two spatial time series datasets. However, the computational cost of correlation analysis is very high when the dimension of the time series and the number of locations in the spatial frameworks are large. The key contribution of this paper is the use of spatial autocorrelation among spatial neighboring time series to reduce computational cost. A filter-and-refine algorithm based on coning, i.e. grouping of locations, is proposed to reduce the cost of correlation analysis over a pair of spatial time series datasets. Cone-level correlation computation can be used to eliminate (filter out) a large number of element pairs whose correlation is clearly below (or above) a given threshold. Element pair correlation needs to be computed for remaining pairs. Using experimental studies withe Earth science datasets, we show that the filter-and-refine approach can save a large fraction of the computational cost, particularly when the minimal correlation threshold is high.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 7th Pacific-Asia Conference, PAKDD 2003, Proceedings
EditorsKyu-Young Wang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivastava
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783540047605
StatePublished - 2003
Event7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 - Seoul, Korea, Republic of
Duration: Apr 30 2003May 2 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003
Country/TerritoryKorea, Republic of

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2003.


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