scholarly journals Analysis of the Spatial Distribution Characteristics of Abnormal Bone Density in Male Long-Distance Runners from Different Regions in China

2021 ◽  
Vol 11 (01) ◽  
pp. 80-91
Author(s):  
Longxing Fan ◽  
Wenjie Zhang ◽  
Huanhuan Cui ◽  
Yanqing Liu ◽  
Ziquan Liu
Keyword(s):  
Spatial Distribution ◽  
Bone Density ◽  
Distribution Characteristics ◽  
Distance Runners ◽  
Long Distance ◽  
Spatial Distribution Characteristics

scholarly journals The spatial distribution characteristics of the centrality advantage stations in Hangzhou metro planning (2005-2022)

2021 ◽  
Vol 634 (1) ◽  
pp. 012110
Author(s):  
Fang Wu ◽  
Yue Zhu ◽  
Wei Zhu ◽  
Kaiqiang Wang ◽  
Peng Shen ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Distribution Characteristics ◽  
Spatial Distribution Characteristics

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.

Spatial distribution characteristics of reconstructed soil bulk density of opencast coal-mine in the loess area of China

CATENA ◽  
2021 ◽  
Vol 199 ◽  
pp. 105116
Author(s):  
Yuhan Huang ◽  
Yingui Cao ◽  
Marcin Pietrzykowski ◽  
Wei Zhou ◽  
Zhongke Bai
Keyword(s):  
Spatial Distribution ◽  
Bulk Density ◽  
Coal Mine ◽  
Soil Bulk Density ◽  
Distribution Characteristics ◽  
Loess Area ◽  
Opencast Coal Mine ◽  
Spatial Distribution Characteristics ◽  
Reconstructed Soil
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