Outdoor air pollution costs millions of premature deaths annually, mostly due to
anthropogenic fine particulate matter (or PM2. 5). Quito, the capital city of Ecuador, is no …

We review recent advances in Object Oriented Spatial Statistics, a system of ideas,
algorithms and methods that allows the analysis of high dimensional and complex data …

The series aims to: present current and emerging innovations in GIScience; describe new
and robust GIScience methods for use in transdisciplinary problem solving and decision …

Random function models and kriging constitute the core of the geostatistical methods
created by Georges Matheron in the 1960s and further developed at the research center he …

A fundamental decision to make during the analysis of geostatistical data is the modeling of
the spatial dependence structure as stationary or non-stationary. Although second-order …

Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants produced by
anthropogenic activities that contaminate all environmental spheres, including soils. This …

Peaks-over-threshold analysis using the generalised Pareto distribution is widely applied in
modelling tails of univariate random variables, but much information may be lost when …

Gaussian processes are valuable tools for non-parametric modelling, where typically an
assumption of stationarity is employed. While removing this assumption can improve …

Geophysical and other natural processes often exhibit nonstationary covariances and this
feature is important for statistical models that attempt to emulate the physical process. A …

In multivariate spatio-temporal analysis, we are faced with the formidable challenge of
specifying a valid spatio-temporal cross-covariance function, either directly or through the …