The identification of transformation trajectories is also very important, i.e. on the one hand huge decreases of grassland, and on the other hand increasing successional areas, arable land, and built-up area, as well as areas of transport infrastructure.
To confirm that the transformation was successful, points were selected around the boundary of the transformed trajectory and an ellipse was again fit.
As a result of this data-driven transformation, where trajectories are generalized from sequences of points to sequences of cells, the re-identification probability already drops significantly.
An privacy transformation of the trajectories consists of the following steps: 1. characteristic points are extracted from the original trajectories: starting points, ending points, points of significant turn, points of significant stop (Figure 1(b)); 2.
AmberTools's ptraj was used for the DSSP calculations and for all the transformation of the trajectories.
To achieve this k-anonymous database, the generalized trajectories, obtained after the data-driven transformation, are transformed in such a way that all the k-harmful sub-trajectories in D are not k-harmful in D ∗.
We found that an in silico transformation reverts a tangled trajectory to a symmetrical linear form with one transition point, similar to the one we observe in bacteria.
Each alignment gave us an estimate of the transformation that aligns a trajectory to its reference.
Four alternative contexts are described: endogenous renewal; re-orientation of trajectories; emergent transformation and purposive transitions.
