Introduction of TDF: the Basic Form

I shall use data on two badgers^4.1 in the Sihlwald area near Zurich (Switzerland) as an introductory example. It shall make the understanding easier.

Tracking an animal by means of direct observations or radio-telemetry gives at any instant a certain location of the animal. To summarize the whereabouts in a certain time period, for example within two hours, we can either try to keep it in our memory or writing all locations down on paper. Then we select the appropriate observations and draw them on a map. This is quite a time consuming procedure, but sometimes we are interested enough to repeat that procedure for another time period. This procedure is continued for at most a few dozens of times. Computer programs such as spreadsheets or databases help speeding it up a bit, but it still remains a lot of work. Although this may appear to be a very simple task, we have to acknowledge that this is exactly what wildlife researchers are doing with spatially referenced observations in 95 percent of the cases!

So what are they exactly doing? They define an interval (e.g. 2 hours) and select all observations at a certain instant within that interval. Then they move to another instant (normally shifted by the amount of time equal to the observation interval) and then use the resulting observations. A classic example are the seasonal homeranges often used in wildlife studies.

Figure 4.1: Basic form of a temporal data frame. Data are selected by a frame defined on the continuous time axis by specifying the position and width of the frame (period).

The procedures used hereby can be generalized to the concept of temporal data frames. It is a selection mechanism providing access to the data using the temporal domain. The general concept is illustrated in figure 4.1. The so called temporal data frame is defined by its position (e.g. 1.6.1998 16:00:00) and width (e.g. $\pm$ 1 hour). It can be used to select data within a defined period of time.

This now provides two parameters which can be changed dynamically: (1) the position, which means that one can drive through time selecting the data which are contained within the temporal data frame (figure 4.2 left). (2) The width of the frame. By changing the width of the data frame one can add observations which were just outside the edge of the frame (figure 4.2 right).

This is the easiest form of a temporal data frame (TDF) where the continuous time is used as the temporal aspect. Starting from here I will now continue with more complex temporal aspects in the next section.

Figure 4.2: Methods of how to change a temporal data frame. Left: Change of position. Right: Change of width of the frame.