A grid-based clustering algorithm for wild bird distribution |
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Authors: | Yuwei Wang Yuanchun Zhou Ying Liu Ze Luo Danhuai Guo Jing Shao Fei Tan Liang Wu Jianhui Li Baoping Yan |
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Affiliation: | 12223. Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100190, China 22223. University of Chinese Academy of Sciences, Beijing, 100049, China 32223. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, China
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Abstract: | Advanced satellite tracking technologies provide biologists with long-term location sequence data to understand movement of wild birds then to find explicit correlation between dynamics of migratory birds and the spread of avian influenza. In this paper, we propose a hierarchical clustering algorithm based on a recursive grid partition and kernel density estimation (KDE) to hierarchically identify wild bird habitats with different densities. We hierarchically cluster the GPS data by taking into account the following observations: 1) the habitat variation on a variety of geospatial scales; 2) the spatial variation of the activity patterns of birds in different stages of the migration cycle. In addition, we measure the site fidelity of wild birds based on clustering. To assess effectiveness, we have evaluated our system using a large-scale GPS dataset collected from 59 birds over three years. As a result, our approach can identify the hierarchical habitats and distribution of wild birds more efficiently than several commonly used algorithms such as DBSCAN and DENCLUE. |
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