Pinpointing spatio-temporal interactions in wildfire patterns

被引:0
作者
P. Juan
J. Mateu
M. Saez
机构
[1] Universitat Jaume I,Department of Mathematics
[2] University of Girona,Research Group on Statistics, Applied Economics and Health (GRECS)
[3] CIBER of Epidemiology and Public Health (CIBERESP),undefined
来源
Stochastic Environmental Research and Risk Assessment | 2012年 / 26卷
关键词
Environmental covariates; Intensity; Second-order characteristics; Spatial and spatio-temporal point processes; Wildfires;
D O I
暂无
中图分类号
学科分类号
摘要
The spatial and spatio-temporal patterns of wildfire incidence and their relationship to various geographical and environmental variables are analyzed. Such relationships may be treated as components in particular point process models for wildfire activity. We show some of the techniques for the analysis of point patterns that have become available due to recent developments in point process modeling software. These developments permit convenient exploratory data analysis, model fitting, and model assessment. The discussion of these techniques is conducted jointly with and in the context of the analyses of a collection of data sets which are of considerable interest in their own right. These data sets consist of the complete records of wildfires occurred in Catalonia (north-eastern Spain) during the years 2004–2008.
引用
收藏
页码:1131 / 1150
页数:19
相关论文
共 55 条
[1]  
Anselin L(1995)Local indicators of spatial association—LISA Geogr Anal 27 93-115
[2]  
Baddeley A(2000)Practical maximum pseudolikelihood for spatial point processes N Z J Stat 42 283-322
[3]  
Turner R(2005)Spatstat: an R package for analyzing spatial point patterns J Stat Softw 12 1-42
[4]  
Baddeley A(1995)Area-interaction point processes Ann Inst Stat Math 47 601-619
[5]  
Turner R(1992)Approximanting point process likelihoods with GLIM Appl Stat 41 31-38
[6]  
Baddeley A(1974)Spatial interaction and the statistical analysis of lattice systems (with discussion) J R Stat Soc B 36 192-236
[7]  
van Lieshout MNM(1976)Some methods of statistical analysis for spatial data Biometrika 47 77-92
[8]  
Berman M(1982)Point process limits of lattice processes Ann Appl Prob 19 210-216
[9]  
Tarner TR(1993)Approximate inference in generalized linear mixed models J Am Stat Assoc 88 9-25
[10]  
Besag J(2011)Statistical inference for Gibbs point processes based on field observations Stoch Environ Res Risk Assess, 25 287-300