Abstract
We present a method to create empirically informed, and thus realistic, random trajectories between two endpoints. The method used relies on empirical distribution functions, which define a dynamic drift expressed in a stepwise joint probability surface. We create random discrete time-step trajectories that connect spatiotemporal points while maintaining a predefined geometry, often based on real observed trajectories. The resulting trajectories have multiple uses, such as to generate null models for hypotheses testing, to serve as a basis for resource selection models, through the integration of spatial context and to quantify space use intensity.
Technitis, Georgios