Analysis of the Temporal-Spatial Distribution Characteristics of the Geomagnetic Activity Index Vr

2012 ◽  
Vol 55 (2) ◽  
pp. 184-193
Author(s):  
Ya-Hong YUAN ◽  
Dong-Mei YANG ◽  
Hua-Ran CHEN ◽  
Yu-Fei HE ◽  
Chuan-Hua CHEN
Keyword(s):  
Spatial Distribution ◽  
Geomagnetic Activity ◽  
Activity Index ◽  
Distribution Characteristics ◽  
Spatial Distribution Characteristics ◽  
Geomagnetic Activity Index

scholarly journals Analysis on the Temporal-Spatial Distribution of the Geomagnetic Activity Index Vr

2011 ◽  
Vol 10 ◽  
pp. IAGA34-IAGA40 ◽  
Author(s):  
Ya-Hong Yuan ◽  
Dong-Mei Yang ◽  
Hua-Ran Chen ◽  
Yu-Fei He ◽  
Chuan-Hua Chen
Keyword(s):  
Spatial Distribution ◽  
Geomagnetic Activity ◽  
Activity Index ◽  
Geomagnetic Activity Index

ASSOCIATION BETWEEN VARIATIONS OF SOLAR WIND PARAMETERS AND GEOMAGNETIC ACTIVITY INDEX Ap IN 2003 - 2005

2011 ◽  
Vol 2 (3) ◽  
pp. 205-210 ◽  
Author(s):  
Igor Savel'evich Fal'kovich ◽  
M. R. Olyak ◽  
Nikolai Nikolaevich Kalinichenko ◽  
I. N. Bubnov
Keyword(s):  
Solar Wind ◽  
Geomagnetic Activity ◽  
Activity Index ◽  
Solar Wind Parameters ◽  
Geomagnetic Activity Index

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.

Sign in / Sign up

Export Citation Format

Share Document