Time-series data mining in a geospatial decision support system

Authors:
Dan Li;Sherri Harms;Steve Goddard;William Waltman;Jitender Deogun
Affiliations:
University of Nebraska-Lincoln, Lincoln NE;University of Nebraska-Lincoln, Lincoln NE;University of Nebraska-Lincoln, Lincoln NE;University of Nebraska-Lincoln, Lincoln NE;University of Nebraska-Lincoln, Lincoln NE
Venue:
dg.o '03 Proceedings of the 2003 annual national conference on Digital government research
Year:
2003

Citing 4
Cited 1

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ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Interpolation techniques for geo-spatial association rule mining

RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing

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Abstract

This paper presents an overview of the motivation for, and the use of time-series data mining in, a Geospatial Decision Support System (GDSS). Our approach is based on a combination of time-series data mining algorithms and spatial interpolation techniques. The initial focus of the system is to facilitate drought risk management. We develop two association rule mining algorithms and two interpolation methods, which help drought experts predict local weather conditions or potential yield impact based on the global weather patterns.