An information theory framework for movement path segmentation and analysis

Improved animal tracking technologies provide opportunities for novel
segmentation of movement tracks/paths into behavioral activity modes
(BAMs) critical to understanding the ecology of individuals and the
functioning of ecosystems. Current BAM segmentation includes biological
change point analyses and hidden Markov models. Here we use an elemental
approach to segmenting tracks into µ‐step-long "base segments"
and m-base-segment-long "words". These are respectively
clustered into n statistical movement elements (StaMEs) and k
"raw" canonical activity modes (CAMs). Once the words are coded
using m extracted StaME symbols, those encoded by the same string of
symbols, after a rectification processes has been implemented to minimize
misassigned words, are identified with particular "rectified"
CAM types. The percent of reassignment errors, along with information
theory measures, are used to compare the efficiencies of coding both
simulated and empirical barn owl data for a selection of parameter values
and approaches to clustering.