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Key Factors Assessment on Bird Strike Density Distribution in Airport Habitats: Spatial Heterogeneity and Geographically Weighted Regression Model

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Listed:
  • Quan Shao

    (College of Civil Aviation/College of Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Yan Zhou

    (College of Civil Aviation/College of Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Pei Zhu

    (College of Civil Aviation/College of Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Yan Ma

    (College of Urban and Environmental Sciences, Peking University, Beijing 100871, China)

  • Mengxue Shao

    (College of Civil Aviation/College of Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

Although the factors influencing bird strikes have been studied extensively, few works focused on the spatial variations in bird strikes affected by factors due to the difference in the geographical environment around the airport. In this paper, the bird strike density distribution of different seasons affected by factors in a rectangular region of 800 square kilometers centered on the Xi’an Airport runway was investigated based on collected bird strike data. The ordinary least square (OLS) model was used to analyze the global effects of different factors, and the Geographically Weighted Regression (GWR) model was used to analyze the spatial variations in the factors of bird strike density. The results showed that key factors on the kernel density of bird strikes showed evident spatial heterogeneity and the seasonal difference in the different habitats. Based on the results of the study, airport managers are provided with some specific defense measures to reduce the number of bird strikes from the two aspects of expelling birds on the airfield area and reducing the attractiveness of habitats outside the airport to birds, so that achieve the sustainable and safe development of civil aviation and the ecological environment.

Suggested Citation

  • Quan Shao & Yan Zhou & Pei Zhu & Yan Ma & Mengxue Shao, 2020. "Key Factors Assessment on Bird Strike Density Distribution in Airport Habitats: Spatial Heterogeneity and Geographically Weighted Regression Model," Sustainability, MDPI, vol. 12(18), pages 1-16, September.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7235-:d:408750
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    References listed on IDEAS

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    1. Su, Yongxian & Chen, Xiuzhi & Li, Yong & Liao, Jishan & Ye, Yuyao & Zhang, Hongou & Huang, Ningsheng & Kuang, Yaoqiu, 2014. "China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 231-243.
    2. Xu, Bin & Lin, Boqiang, 2017. "Factors affecting CO2 emissions in China’s agriculture sector: Evidence from geographically weighted regression model," Energy Policy, Elsevier, vol. 104(C), pages 404-414.
    3. Wang, Shaojian & Shi, Chenyi & Fang, Chuanglin & Feng, Kuishuang, 2019. "Examining the spatial variations of determinants of energy-related CO2 emissions in China at the city level using Geographically Weighted Regression Model," Applied Energy, Elsevier, vol. 235(C), pages 95-105.
    4. Gelfand A.E. & Kim H-J. & Sirmans C.F. & Banerjee S., 2003. "Spatial Modeling With Spatially Varying Coefficient Processes," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 387-396, January.
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    Cited by:

    1. Rui Wang & Qiang Zhao & Hui Sun & Xuedong Zhang & Yuyue Wang, 2022. "Risk Assessment Model Based on Set Pair Analysis Applied to Airport Bird Strikes," Sustainability, MDPI, vol. 14(19), pages 1-14, October.
    2. Yongxin Liu & Yiting Wang & Yiwen Lin & Xiaoqing Ma & Shifa Guo & Qianru Ouyang & Caige Sun, 2023. "Habitat Quality Assessment and Driving Factors Analysis of Guangdong Province, China," Sustainability, MDPI, vol. 15(15), pages 1-23, July.

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