Mining sensor datasets with spatiotemporal neighborhoods
| dc.contributor.author | Patrick McGuire, Michael | |
| dc.contributor.author | Janeja, Vandana | |
| dc.contributor.author | Gangopadhyay, Aryya | |
| dc.date.accessioned | 2020-11-16T19:06:54Z | |
| dc.date.available | 2020-11-16T19:06:54Z | |
| dc.date.issued | 2013-11-06 | |
| dc.description.abstract | Many spatiotemporal data mining methods are dependent on how relationships between a spatiotemporal unit and its neighbors are defined. These relationships are often termed the neighborhood of a spatiotemporal object. The focus of this paper is the discovery of spatiotemporal neighborhoods to find automatically spatiotemporal sub-regions in a sensor dataset. This research is motivated by the need to characterize large sensor datasets like those found in oceanographic and meteorological research. The approach presented in this paper finds spatiotemporal neighborhoods in sensor datasets by combining an agglomerative method to create temporal intervals and a graph-based method to find spatial neighborhoods within each temporal interval. These methods were tested on real-world datasets including (a) sea surface temperature data from the Tropical Atmospheric Ocean Project (TAO) array in the Equatorial Pacific Ocean and (b) NEXRAD precipitation data from the Hydro-NEXRAD system. The results were evaluated based on known patterns of the phenomenon being measured. Furthermore, the results were quantified by performing hypothesis testing to establish the statistical significance using Monte Carlo simulations. The approach was also compared with existing approaches using validation metrics namely spatial autocorrelation and temporal interval dissimilarity. The results of these experiments show that our approach indeed identifies highly refined spatiotemporal neighborhoods. | en_US |
| dc.description.sponsorship | 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. This work was also partially supported by the FDRC grant of Towson University | en_US |
| dc.description.uri | http://www.josis.org/index.php/josis/article/view/94 | en_US |
| dc.format.extent | 42 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.identifier | doi:10.13016/m2i0dk-e7hz | |
| dc.identifier.citation | Patrick McGuire, Michael; Janeja, Vandana; Gangopadhyay, Aryya; Mining sensor datasets with spatiotemporal neighborhoods; Journal of Spatial Information Science, No 6 (2013); http://www.josis.org/index.php/josis/article/view/94 | en_US |
| dc.identifier.issn | https://doi.org/10.5311/JOSIS.2013.6.94 | |
| dc.identifier.uri | http://hdl.handle.net/11603/20064 | |
| dc.identifier.uri | https://doi.org/10.5311/JOSIS.2013.6.94 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | Attribution 3.0 Unported (CC BY 3.0) | * |
| dc.rights.uri | https://creativecommons.org/licenses/by/3.0/ | * |
| dc.title | Mining sensor datasets with spatiotemporal neighborhoods | en_US |
| dc.type | Text | en_US |