Spatio-temporal Point Process models on a global scale and their application to global lightning occurrences

Texas A&M University, College Station TX

Investigators

Abstract

With recent technological advances and increased network connectivity, many environmental, biological, and socio-economic datasets are collected on a global scale. However, many of the current statistical models and methods are not suitable for environmental applications where the data cover the surface of the sphere. The goal of this project is to develop spatial and spatio-temporal point process models on a global scale. The research is motivated by a study of global lightning occurrence, and the impact of climate change on global lightning occurrence patterns. The research will provide useful information for scientists, policy makers, and the general public. The proposed models will address the spatial inhomogeneity of the intensity functions as well as their nonstationarity, both of which are essential for environmental applications. In the spatio-temporal case, parametric covariance models for the stochastic intensity function that can represent complex spatio-temporal interactions will be studied, and univariate as well as multivariate point patterns that account for the cross-dependence between multiple point patterns will be considered.