A grid-based clustering algorithm for wild bird distribution
A grid-based clustering algorithm for wild bird distribution作者机构：Computer Network Information Center Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China
出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)
年 卷 期：2013年第7卷第4期
页 面：475-485页
核心收录：
学科分类：090603[农学-临床兽医学] 0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 080203[工学-机械设计及理论] 09[农学] 0906[农学-兽医学] 0802[工学-机械工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：国家自然科学基金 Strategic Priority Research Program of Chinese Academy of Sciences "12th Five-Year" Plan for Science & Technology Support
主 题：hierarchical clustering bird migration kerneldensity estimation grid partition
摘 要：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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