In recent years, interest has grown in modeling spatio-temporal data generated from
monitoring networks, satellite imaging, and climate models. Under Gaussianity, the …

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 …

Although isotropy, symmetry and separability are commonly assumed for practical reasons,
anisotropic, asymmetric and non-separable covariance functions are often more realistic; in …

In this paper, we set out general principles and develop geostatistical methods for the
analysis of data from spatio‐temporally referenced prevalence surveys. Our objective is to …

Rank and probability integral transform histograms are established tools to assess the
calibration of probabilistic forecasts. They not only check whether a forecast is calibrated, but …

This paper considers the problem of measuring spatial and temporal variation in driver
productivity on ride-sourcing trips. This variation is especially important from a driver's …

We focus on the discussion of modeling processes that are observed at fixed locations of a
region (geostatistics). A standard approach is to assume that the process of interest follows a …

Consider an affine field X: R 2→ R, that is a process equal in law to Z (A. t), where Z is
isotropic and A: R 2→ R 2 is a linear self-adjoint transformation. The field X and …

Peatlands in Indonesia are one of the primary global storages for terrestrial organic carbon.
Poor land management, drainage, and recurrent fires lead to the release of huge amounts of …

The concepts of isotropy/anisotropy and separability/non‐separability of a covariance
function are strictly related. If a covariance function is separable, it cannot be isotropic or …