(Leibniz University of Hannover)
In this talk, I will show the results of two different articles -- one about the morphological evolution of coastal profiles and one about the usage patterns of a bike-sharing system. Whereas the first data set is highly irregular and often has incomplete measurements, the second data set has a very detailed but regular temporal resolution. For both cases, we applied a functional model, which accounts for latent spatial and temporal effects, in combination with a spatial subsampling/bootstrap approach. I will show how this model can be used in two completely different situations and how we made the procedure scalable while accounting for the full spatial and temporal dependence. In general, beach profile data sets provide valuable insight into the morphological evolution of sandy shorelines, but to be precise, our aim was (1) to determine the temporal and spatial variability of beach profiles while accounting for autoregressive dependencies, (2) to identify effects of external influences, (3) to predict complete beach profiles at unknown locations, and (4) to forecast complete beach profiles accounting for external influences, such as storm events or nourishments. Regarding the second analysis, the station hire data is analysed in a spatiotemporal functional setting, where the number of bikes at a station is defined as a continuous function of the time of day. Understanding the usage patterns for bike-sharing systems is essential in terms of supporting and enhancing operational planning for such schemes. Studies have demonstrated how factors such as weather conditions influence the number of bikes that should be available at bike-sharing stations at certain times during the day. However, the influences of these factors usually vary over the course of a day, and if there is good temporal resolution, there could also be significant effects only for some hours/minutes (e.g., rush hours).