Spatial-temporal analysis on bird habitat discovery in China

Xiaoming Zhan; Yanming Ye; Yaoxin Zhuo; Benyun SHI; Yizhi Ren; Weitong Hu

doi:10.1109/SPAC.2017.8304343

Spatial-temporal analysis on bird habitat discovery in China

Xiaoming Zhan
, Yanming Ye^*
, Yaoxin Zhuo
, Benyun SHI
, Yizhi Ren
, Weitong Hu

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017
Publisher	IEEE
Pages	573-578
Number of pages	6
ISBN (Electronic)	9781538630167
DOIs	https://doi.org/10.1109/SPAC.2017.8304343
Publication status	Published - 2 Jul 2017
Event	2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017 - Shenzhen, China Duration: 15 Dec 2017 → 17 Dec 2017

Publication series

Name	2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017
Volume	2018-January

Conference

Conference	2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017
Country/Territory	China
City	Shenzhen
Period	15/12/17 → 17/12/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 15 Life on Land

Access to Document

10.1109/SPAC.2017.8304343

Cite this

Zhan, X., Ye, Y., Zhuo, Y., SHI, B., Ren, Y., & Hu, W. (2017). Spatial-temporal analysis on bird habitat discovery in China. In 2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017 (pp. 573-578). (2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017; Vol. 2018-January). IEEE. https://doi.org/10.1109/SPAC.2017.8304343

@inproceedings{5538a519e0d44b56b99139c87289f264,

title = "Spatial-temporal analysis on bird habitat discovery in China",

abstract = "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.",

author = "Xiaoming Zhan and Yanming Ye and Yaoxin Zhuo and Benyun SHI and Yizhi Ren and Weitong Hu",

note = "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.; 2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017 ; Conference date: 15-12-2017 Through 17-12-2017",

year = "2017",

month = jul,

day = "2",

doi = "10.1109/SPAC.2017.8304343",

language = "English",

series = "2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017",

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booktitle = "2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017",

address = "United States",

}

Zhan, X, Ye, Y, Zhuo, Y, SHI, B, Ren, Y & Hu, W 2017, Spatial-temporal analysis on bird habitat discovery in China. in 2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017. 2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017, vol. 2018-January, IEEE, pp. 573-578, 2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017, Shenzhen, China, 15/12/17. https://doi.org/10.1109/SPAC.2017.8304343

Spatial-temporal analysis on bird habitat discovery in China. / Zhan, Xiaoming; Ye, Yanming; Zhuo, Yaoxin et al.
2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017. IEEE, 2017. p. 573-578 (2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017; Vol. 2018-January).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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 - https://www.scopus.com/pages/publications/85050609343

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 -

Spatial-temporal analysis on bird habitat discovery in China

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