Spatiotemporal Change Pattern Mining: A Multi-disciplinary Perspective

  • Xun Zhou
  • Shashi Shekhar
  • Pradeep Mohan


Given a definition of change and a dataset about spatiotemporal (ST) phenomena, change pattern mining is the process of identifying the location and/or time frame of shift in phenomenon. Due to the societal importance of ST change pattern mining for use cases such as disease outbreak, urban sprawl, and climate change, techniques for mining ST change patterns have emerged from a diverse set of research area ranging from time series analysis, remote sensing, to spatial statistics. Related work focus on surveying techniques in specific discipline. It is difficult to compare techniques across disciplines for cross-fertilization, due to the challenges such as homonym, synonym, and change definition diversity. This chapter attempts to provide an interdisciplinary perspective on change pattern mining techniques to promote cross-fertilization of ideas among researchers of diverse backgrounds. It proposes taxonomy of patterns from ST perspective and uses it to compare related concepts from different disciplines.


Time Series Analysis Change Pattern Spatial Cluster Spatial Region Generalize Extreme Value 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This material is based upon work supported by the National Science Foundation under Grant No. 1029711, IIS-1320580, 0940818 and IIS-1218168, the USDOD under Grant No. HM1582-08-1-0017 and HM0210-13-1-0005, and the University of Minnesota under the OVPR U-Spatial.


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Copyright information

© Springer Science+Business Media Dordrecht. 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of MinnesotaTwin Cities, MinneapolisUSA

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