In the above sections the process of selection was illustrated. The application of TDFs is of course not limited to this process. All available statistical and other procedures can then be applied to the data in a very efficient way and changes in them can be discovered. The calculation of median center, dispersion indices or density matrices are only a few examples for this. Some interesting examples can be found in the prototype application described in Appendix A.
One of the major advantages of TDF is the speed at which data can be selected for analysis according to various temporal aspects. An application that could be available soon is closely linked to the technical developments of digital terrain models (DTM) where significant research efforts have been made in recent years. Creating DTMs with resolutions of one meter and below is now feasible at reasonable costs. Novel terrain data capture methods such as laser scanning even allow for the distinction between land surface and vegetation surface. With such data available highly refined analyses can be conducted on temporal phenomena. In wildlife research it was often speculated that shadows could influence the behavior of animals. Shadows from the sunlight or moonlight created at forest edges or other structures can be calculated with high resolution data. Such calculations increasingly important, because direct observations of animals, where such data can be recorded in the field, are replaced to a large extent by remote techniques as radio or satellite telemetry. Hence spatial databases are extremely important in these studies.
With changes in the spatial distribution in animals there are changes in environmental parameters taking place. The temporal data frame concept can be easily extended to a more general data frames concept where environmental parameters can serve as base criteria. Systems can implement such links between temporal and atemporal data and provide desired histograms or other plots instantly during the (exploratory) analysis phase.
Further developments are needed for spatially heterogenous temporal phenomena such as the tides and the much more complex weather. In wildlife studies the latter is very difficult to analyze as an influencing factor. This is due to the three facts that it is spatially heterogeneous, it changes all the time and there are time lag effects which by themselves are very hard to cope with.
There is a last topic to be mentioned here. In this chapter only phenomena with constant cycles are being considered. It is clear that some cyclic phenomena have some deviations from strictly constant periodicities, especially in biological systems. Methods need to be developed to cope with such data. The following chapter introduces a new method to analyze spatial periodicities in animal movements which are not strictly constant.