Environmental and climate processes are often distributed over large space-time domains. Their complexity and the amount of available data make modelling and analysis a challenging task. Statistical modelling of environment and climate data can have several different motivations including interpretation or characterisation of the data. Results from statistical analysis are often used as a integral part of larger environmental studies. Spatial statistics is an active and modern statistical field, concerned with the quantitative analysis of spatial data; their dependencies and uncertainties. Spatio-temporal statistics extends spatial statistics through the addition of time to the, two or three, spatial dimensions. The focus of this introductory paper is to provide an overview of spatial methods and their application to environmental and climate data. This paper also gives an overview of several important topics including large data sets and non-stationary covariance structures. Further, it is discussed how Bayesian hierarchical models can provide a flexible way of constructing models. Hierarchical models may seem to be a good solution, but they have challenges of their own such as, parameter estimation. Finally, the application of spatio-temporal models to the LANDCLIM data (LAND cover - CLIMate interactions in NW Europe during the Holocene) will be discussed.
- Pub Date:
- September 2019
- Statistics - Methodology;
- Statistics - Applications;
- Statistics - Other Statistics
- Book (this report/review of methodology and applications was written as an introductory paper for PhD study in Lund University)