Spatial functional data analysis (FDA) concerns the statistical analysis of spatial and spatiotemporal data.
Spatial FDA is used to better analyse, model and predict the complex dependence structure inherent in spatiotemporal processes and thus is able to provide
more accurate predictions at high temporal and spatial resolutions.
Spatial FDA also accounts for attributes of the geometry of the physical problem such as irregular shaped domains, external and internal boundary features and strong concavities.
Spatial FDA can also include a priori information about the spatial structure of the phenomenon described by a partial differential equation (PDE). This facilitates a causal explanation for the drivers and impediments of the underlying spatiotemporal process.