Spatial statistics is concerned with the analysis of data that have spatial locations associated
with them, and those locations are used to model statistical dependence between the data …

Modern conflicts are characterized by an ever increasing use of information and sensing
technology, resulting in vast amounts of high resolution data. Modelling and prediction of …

FRK is an R software package for spatial/spatio-temporal modelling and prediction with
large datasets. It facilitates optimal spatial prediction (kriging) on the most commonly used …

Spatial processes with nonstationary and anisotropic covariance structure are often used
when modeling, analyzing, and predicting complex environmental phenomena. Such …

ABSTRACT The Log-Gaussian Cox process is a commonly used model for the analysis of
spatial point pattern data. Fitting this model is difficult because of its doubly stochastic …

Satellite remote sensing of trace gases such as carbon dioxide (CO 2) has increased our
ability to observe and understand Earth's climate. However, these remote sensing data …

Stochastic epidemic models which incorporate interactions between space and human
mobility are a key tool to inform prioritisation of outbreak control to appropriate locations …

Determining the Antarctic contribution to sea‐level rise from observational data is a complex
problem. The number of physical processes involved (such as ice dynamics and surface …

Multisensor state-space models underpin fusion applications in networks of sensors.
Estimation of latent parameters in these models has the potential to provide highly desirable …

Atmospheric trace-gas inversion refers to any technique used to predict spatial and temporal
fluxes using mole-fraction measurements and atmospheric simulations obtained from …