Use of kernel density estimation and maximum curvature to set Marine Protected Area boundaries: Identifying a Special Protection Area for wintering red-throated divers in the UK

2012 ◽  
Vol 156 ◽  
pp. 15-21 ◽  
Author(s):  
Susan H. O’Brien ◽  
Andrew Webb ◽  
Mark J. Brewer ◽  
James B. Reid
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Protected Area ◽  
Marine Protected Area ◽  
Kernel Density ◽  
Maximum Curvature ◽  
Protection Area ◽  
The Uk ◽  
Special Protection

scholarly journals Identifying the Birds Diversity Hot Spots in Teeb Protected Area South-eastern Iraq by Using Kernel Density Estimation

2021 ◽  
Vol 790 (1) ◽  
pp. 012084
Author(s):  
Thaer Kareem Hassan ◽  
Widad Fadhullah ◽  
Salwan Ali Abed ◽  
Mudhafar A. Salim ◽  
Kudhur A. Al-Kenani ◽  
...  
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Hot Spots ◽  
Protected Area ◽  
Kernel Density ◽  
South Eastern

scholarly journals Author Correction: An extended Weight Kernel Density Estimation model forecasts COVID-19 onset risk and identifies spatiotemporal variations of lockdown effects in China

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Wenzhong Shi ◽  
Chengzhuo Tong ◽  
Anshu Zhang ◽  
Bin Wang ◽  
Zhicheng Shi ◽  
...  
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Spatiotemporal Variations ◽  
Estimation Model

A Correction to this paper has been published: https://doi.org/10.1038/s42003-021-01924-6

scholarly journals Identifying the density of grassland fire points with kernel density estimation based on spatial distribution characteristics

2021 ◽  
Vol 13 (1) ◽  
pp. 796-806
Author(s):  
Zhen Shuo ◽  
Zhang Jingyu ◽  
Zhang Zhengxiang ◽  
Zhao Jianjun
Keyword(s):  
Spatial Distribution ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Function Method ◽  
Kernel Density ◽  
Fire Occurrence ◽  
Distribution Characteristics ◽  
Fire Point ◽  
Spatial Distribution Characteristics ◽  
Grassland Fire

Abstract Understanding the risk of grassland fire occurrence associated with historical fire point events is critical for implementing effective management of grasslands. This may require a model to convert the fire point records into continuous spatial distribution data. Kernel density estimation (KDE) can be used to represent the spatial distribution of grassland fire occurrences and decrease the influences historical records in point format with inaccurate positions. The bandwidth is the most important parameter because it dominates the amount of variation in the estimation of KDE. In this study, the spatial distribution characteristic of the points was considered to determine the bandwidth of KDE with the Ripley’s K function method. With high, medium, and low concentration scenes of grassland fire points, kernel density surfaces were produced by using the kernel function with four bandwidth parameter selection methods. For acquiring the best maps, the estimated density surfaces were compared by mean integrated squared error methods. The results show that Ripley’s K function method is the best bandwidth selection method for mapping and analyzing the risk of grassland fire occurrence with the dependent or inaccurate point variable, considering the spatial distribution characteristics.

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