The focus of this paper is the discovery of spatiotemporal neighborhoods in sensor datasets where a time series of data is collected at many spatial locations. The purpose of the spatiotemporal neighborhoods is to provide regions in the data where knowledge discovery tasks such as outlier detection, can be focused. As building blocks for the spatiotemporal neighborhoods, we have developed a method to generate spatial neighborhoods and a method to discretize temporal intervals. These methods were tested on real life datasets including (a) sea surface temperature data from the Tropical Atmospheric Ocean Project (TAO) array in the Equatorial Pacific Ocean and (b)highway sensor network data archive. We have found encouraging results which are validated by real life phenomenon.
M. P. McGuire, V. Janeja, A. Gangopadhyay
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