Cross-correlation measure for mining spatio-temporal patterns

James Ma, Daniel Zeng, Huimin Zhao, Chunyang Liu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Spatio-temporal data mining is finding applications in many domains, such as public health, public safety, financial fraud detection, transportation, and product lifecycle management. Correlation analysis is an important spatio-temporal mining technique for unveiling spatial and temporal relationships among multiple event types. This paper presents a new measure for assessing and analyzing spatio-temporal cross-correlations. This measure extends Ripley's K(r), a widely used measure of spatial correlation, with an additional temporal dimension. Empirical studies using real-world data show that the new measure can lead to a more discriminating and flexible spatio-temporal data analysis framework. In contrast with its predecessor, this measure also allows the discovery of leading (and potentially causal) event types whose occurrences precede those of other event types. Findings from analyses employing this measure may bear important managerial implications.

Original languageEnglish (US)
Pages (from-to)13-34
Number of pages22
JournalJournal of Database Management
Volume24
Issue number2
DOIs
StatePublished - Apr 1 2013

Keywords

  • Cross-correlation
  • Data mining
  • Public health
  • Public safety
  • Spatio-temporal data analysis

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture

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