2016 | OriginalPaper | Buchkapitel
Mining Persistent and Dynamic Spatio-Temporal Change in Global Climate Data
verfasst von : Jie Lian, Michael P. McGuire
Erschienen in: Information Technology: New Generations
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The potential impacts of climate change on natural and man-made systems can have a drastic effect on life on Earth. The application of data mining algorithms on global climate data can result in a better understanding of the climate system. Of which, change detection has proven to be a very useful approach when mining climate data. Understanding spatio-temporal change can give insight to interesting patterns that can be used to predict climate events. This paper proposes a method to generate spatial homogeneous regions that uses a novel indexing structure for the analysis of spatial change including homogeneous change and heterogeneous change. The resulting regions are then used to analyze persistent and dynamic regions at longer time scales. The efficacy of the approach was demonstrated on a real-world climate dataset and the results suggest interesting patterns that are explained by known climate phenomena.