Cross-Correlation Measure for Mining Spatio-Temporal Patterns

Authors:
James Ma;Daniel Zeng;Huimin Zhao;Chunyang Liu
Affiliations:
Hasan School of Business, Colorado State University, Pueblo, CO, USA;State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China & Department of MIS, Eller College of Management, Universit ...;Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI, USA;National Computer Network Emergency Response Technical Team Coordination Center of China, Beijing, China
Venue:
Journal of Database Management
Year:
2013

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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 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.