Binary geospatial data is commonly analyzed with generalized linear mixed models,
specified with a linear fixed covariate effect and a Gaussian Process (GP)-distributed spatial …

Current approaches for modeling discrete-valued outcomes associated with spatially-
dependent areal units incur computational and theoretical challenges, especially in the …

Multivariate normal (MVN) probabilities arise in myriad applications, but they are analytically
intractable and need to be evaluated via Monte-Carlo-based numerical integration. For the …

Analysing non-Gaussian spatial-temporal data typically requires introducing spatial
dependence in generalised linear models through the link function of an exponential family …

We introduce a flexible and scalable class of Bayesian geostatistical models for discrete
data, based on nearest‐neighbor mixture processes (NNMP), referred to as discrete NNMP …

This special issue features eight articles on “Large-Scale Spatial Data Science.” Data
science for complex and large-scale spatial and spatio-temporal data has become essential …