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Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets

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Abstract

When mining large spatio-temporal datasets, interesting patterns typically emerge where the dataset is most dynamic. These dynamic regions can be characterized by a location or set of locations that exhibit different behaviors from their neighbors and the time periods where these differences are most pronounced. Examples include locally intense areas of precipitation, anomalous sea surface temperature (SST) readings, and locally high levels of water pollution, to name a few. The focus of this paper is to find and analyze the pattern of moving dynamic spatio-temporal regions in large sensor datasets. The approach presented in this paper uses a measure of local spatial autocorrelation over time to determine how pronounced the difference in measurements taken at a spatial location is with those taken at neighboring locations. Dynamic regions are analyzed both globally, in the form of spatial locations and time periods that have the largest difference in local spatial autocorrelation, and locally, in the form of dynamic spatial locations for a particular time period or dynamic time periods for a particular spatial node. Then, moving dynamic regions are identified by determining the spatio-temporal connectivity, extent, and trajectory for groups of locally dynamic spatial locations whose position has shifted from one time period to the next. The efficacy of the approach is demonstrated on two real-world spatio-temporal datasets (a) NEXRAD precipitation and (b) SST. Promising results were found in discovering highly dynamic regions in these datasets depicting several real environmental phenomenon which are validated as actual events of interest.

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Acknowledgments

This work has been funded in part by the United States National Oceanic and Atmospheric Administration Grants NA06OAR4310243, NA07OAR4170518, and NA10OAR310220. The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration or the Department of Commerce.

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Authors and Affiliations

  1. Department of Computer and Information Sciences, Towson University, 8000 York Rd, Baltimore, MD, 21252, USA

    M. P. McGuire

  2. Information Systems Department, University of Maryland, Baltimore County, Baltimore, MD, USA

    V. P. Janeja & A. Gangopadhyay

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  1. M. P. McGuire
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  2. V. P. Janeja
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  3. A. Gangopadhyay
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Correspondence to M. P. McGuire.

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Responsible editor: Srinivasan Parthasarathy.

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McGuire, M.P., Janeja, V.P. & Gangopadhyay, A. Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets. Data Min Knowl Disc 28, 961–1003 (2014). https://doi.org/10.1007/s10618-013-0324-z

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