Discovering spatial co-location patterns: A summary of results

Shashi Shekhar, Yan Huang

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

194 Scopus citations

Abstract

Given a collection of boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. For example, the analysis of an ecology dataset may reveal the frequent co-location of a fire ignition source feature with a needle vegetation type feature and a drought feature. The spatial co-location rule problem is different from the association rule problem. Even though boolean spatial feature types (also called spatial events) may correspond to items in association rules over market-basket datasets, there is no natural notion of transactions. This creates difficulty in using traditional measures (e.g. support, confidence) and applying association rule mining algorithms which use support based pruning. We propose a notion of user-specified neighborhoods in place of transactions to specify groups of items. New interest measures for spatial co-location patterns are proposed which are robust in the face of potentially infinite overlapping neighborhoods. We also propose an algorithm to mine frequent spatial co-location patterns and analyze its correctness, and completeness. We plan to carry out experimental evaluations and performance tuning in the near future.

Original languageEnglish (US)
Title of host publicationAdvances in Spatial and Temporal Databases - 7th International Symposium, SSTD 2001, Proceedings
EditorsChristian S. Jensen, Markus Schneider, Bernhard Seeger, Vassilis J. Tsotras
PublisherSpringer Verlag
Pages236-256
Number of pages21
ISBN (Print)9783540423010
DOIs
StatePublished - 2001
Event7th International Symposium on Spatial and Temporal Databases, SSTD 2001 - Redondo Beach, United States
Duration: Jul 12 2001Jul 15 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2121
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Symposium on Spatial and Temporal Databases, SSTD 2001
CountryUnited States
CityRedondo Beach
Period7/12/017/15/01

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.

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