TY - GEN
T1 - Spatial-temporal analysis on bird habitat discovery in China
AU - Zhan, Xiaoming
AU - Ye, Yanming
AU - Zhuo, Yaoxin
AU - SHI, Benyun
AU - Ren, Yizhi
AU - Hu, Weitong
N1 - Funding Information:
ACKNOWLEDGMENT The authors would like to acknowledge the funding support from National Natural Science Foundation of China (Grant Nos. 81402760, 81573261), and the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20161563) for the research work being presented in this article. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Exploring migration patterns through uncovering migratory birds' habitat information is very important in biology, which has scientific significance in animal habitat conservation and avian influenza control. In this paper, we convert the traditional biology problem into a computational study and use data mining techniques to analyze the spatial and temporal distribution of bird-watching data in China. First, we present an improved hierarchical clustering algorithm (IHDBSCAN) to identify the habitats/stopovers of migrant birds. Then, we use a kernel smoothing method to fit the temporal distribution of bird observation in each spatial cluster. A hierarchical cluster tree is generated where the leaf nodes indicate different bird habitats/stopovers. Finally, the results is visualized on the map of China. Experimental results show that the proposed algorithm can effectively find the spatial and temporal distribution of Anseriformes' habitats.
AB - Exploring migration patterns through uncovering migratory birds' habitat information is very important in biology, which has scientific significance in animal habitat conservation and avian influenza control. In this paper, we convert the traditional biology problem into a computational study and use data mining techniques to analyze the spatial and temporal distribution of bird-watching data in China. First, we present an improved hierarchical clustering algorithm (IHDBSCAN) to identify the habitats/stopovers of migrant birds. Then, we use a kernel smoothing method to fit the temporal distribution of bird observation in each spatial cluster. A hierarchical cluster tree is generated where the leaf nodes indicate different bird habitats/stopovers. Finally, the results is visualized on the map of China. Experimental results show that the proposed algorithm can effectively find the spatial and temporal distribution of Anseriformes' habitats.
UR - http://www.scopus.com/inward/record.url?scp=85050609343&partnerID=8YFLogxK
U2 - 10.1109/SPAC.2017.8304343
DO - 10.1109/SPAC.2017.8304343
M3 - Conference proceeding
AN - SCOPUS:85050609343
T3 - 2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017
SP - 573
EP - 578
BT - 2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017
PB - IEEE
T2 - 2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017
Y2 - 15 December 2017 through 17 December 2017
ER -
