Exploring large data sets with the aim of extracting useful information for decision making can be challenging. If the data were collected at different locations and times, one important question is how to obtain reliable estimates for missing data in space or time. For example, measurements such as ozone concentrations are usually collected only by a limited number of monitoring stations and at different time instances. In order to estimate the values at unmeasured locations or time instances, interpolation in continuous space and time is needed. New and old interpolation methods for...