Spatiotemporal Outlier Detection: Did Buoys Tell Where the Hurricanes Were?

Abstract

ABSTRACTData points that exhibit abnormal behavior, either spatially, temporally, or both, are considered spatiotemporal outliers. Spatiotemporal outlier detection is important for the discovery of exceptional events due to the rapidly increasing amount of spatiotemporal data available, and the need to understand such data. A tropical cyclone system or a hurricane can be considered an abnormal activity of the atmosphere system. Discovery of such an abnormality usually leverages data from a satellite or radar. Not many people have thought about using a weather buoy, a floating device that provides meteorological and environmental information in real time for open ocean and coastal zones. The aim of this research is to see if a spatiotemporal outlier approach can help to discover the evolution and movement of the hurricane system from weather buoy observations. This article leverages an algorithm, spatiotemporal local density-based clustering of applications with noise (ST-LDBCAN), which has been developed and used by the authors to detect outliers in various scenarios. The ST-LDBCAN has a novel way of defining spatiotemporal context and can handle multivariate data, which is its advantage over existing algorithms. The results show a good correlation between detected spatiotemporal outliers and the paths and evolution of Hurricanes Katrina and Gustav.