Assessing isotropy for spatial point processes.
A common assumption while analyzing spatial point processes is direction invariance, i.e., isotropy. In this article, we propose a formal nonparametric approach to test for isotropy based on the asymptotic joint normality of the sample second-order intensity function. We derive an L(2) consistent subsampling estimator for the asymptotic covariance matrix of the sample second-order intensity function and use this to construct a test statistic with a chi(2) limiting distribution. We demonstrate the efficacy of the approach through simulation studies and an application to a desert plant data set, where our approach confirms suspected directional effects in the spatial distribution of the desert plant species.
Department of Management Science, University of Miami, Coral Gables, Florida 33124-6544, USA. yguan@miami.edu
Yongtao
Y
Guan
Michael
M
Sherman
James A
JA
Calvin
D016880:Anisotropy; D016009:Chi-Square Distribution; D003198:Computer Simulation; D003889:Desert Climate; D004463:Ecology / Q000379:methods / Q000706:statistics & numerical data; D010944:Plants; D018709:Statistics, Nonparametric