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

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 seas...

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Main Authors: Quan Shao, Yan Zhou, Pei Zhu, Yan Ma, Mengxue Shao
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/12/18/7235
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spelling doaj-a5d7a3467bee4575b72f768f5390ff842020-11-25T02:30:43ZengMDPI AGSustainability2071-10502020-09-01127235723510.3390/su12187235Key Factors Assessment on Bird Strike Density Distribution in Airport Habitats: Spatial Heterogeneity and Geographically Weighted Regression ModelQuan Shao0Yan Zhou1Pei Zhu2Yan Ma3Mengxue Shao4College of Civil Aviation/College of Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Civil Aviation/College of Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Civil Aviation/College of Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, ChinaCollege of Civil Aviation/College of Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaAlthough 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.https://www.mdpi.com/2071-1050/12/18/7235bird strike densityspatial heterogeneitykey factorsairport habitatsGWR model
collection DOAJ
language English
format Article
sources DOAJ
author Quan Shao
Yan Zhou
Pei Zhu
Yan Ma
Mengxue Shao
spellingShingle Quan Shao
Yan Zhou
Pei Zhu
Yan Ma
Mengxue Shao
Key Factors Assessment on Bird Strike Density Distribution in Airport Habitats: Spatial Heterogeneity and Geographically Weighted Regression Model
Sustainability
bird strike density
spatial heterogeneity
key factors
airport habitats
GWR model
author_facet Quan Shao
Yan Zhou
Pei Zhu
Yan Ma
Mengxue Shao
author_sort Quan Shao
title Key Factors Assessment on Bird Strike Density Distribution in Airport Habitats: Spatial Heterogeneity and Geographically Weighted Regression Model
title_short Key Factors Assessment on Bird Strike Density Distribution in Airport Habitats: Spatial Heterogeneity and Geographically Weighted Regression Model
title_full Key Factors Assessment on Bird Strike Density Distribution in Airport Habitats: Spatial Heterogeneity and Geographically Weighted Regression Model
title_fullStr Key Factors Assessment on Bird Strike Density Distribution in Airport Habitats: Spatial Heterogeneity and Geographically Weighted Regression Model
title_full_unstemmed Key Factors Assessment on Bird Strike Density Distribution in Airport Habitats: Spatial Heterogeneity and Geographically Weighted Regression Model
title_sort key factors assessment on bird strike density distribution in airport habitats: spatial heterogeneity and geographically weighted regression model
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2020-09-01
description 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.
topic bird strike density
spatial heterogeneity
key factors
airport habitats
GWR model
url https://www.mdpi.com/2071-1050/12/18/7235
work_keys_str_mv AT quanshao keyfactorsassessmentonbirdstrikedensitydistributioninairporthabitatsspatialheterogeneityandgeographicallyweightedregressionmodel
AT yanzhou keyfactorsassessmentonbirdstrikedensitydistributioninairporthabitatsspatialheterogeneityandgeographicallyweightedregressionmodel
AT peizhu keyfactorsassessmentonbirdstrikedensitydistributioninairporthabitatsspatialheterogeneityandgeographicallyweightedregressionmodel
AT yanma keyfactorsassessmentonbirdstrikedensitydistributioninairporthabitatsspatialheterogeneityandgeographicallyweightedregressionmodel
AT mengxueshao keyfactorsassessmentonbirdstrikedensitydistributioninairporthabitatsspatialheterogeneityandgeographicallyweightedregressionmodel
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