Recursive multi-frequency segmentation of movement trajectories (ReMuS)

Sean C. Ahearn, Somayeh Dodge

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


The quantity of GPS trajectories on the movement of individuals has far outstripped our ability to analyse them. Segmentation, a first step in trajectory analysis, is fundamental to imputing behaviour from patterns of movement. Behaviour occurs at multiple scales that are captured at different temporal frequencies. To identify behaviour from a complex trajectory it is essential to use a segmentation procedure that captures different frequency ranges. A limitation of existing segmentation algorithms is that they require training and parameterisation that can lead to special use segmentations that are not generalisable. This paper presents a new approach to the automated segmentation of movement trajectories at multiple scales. It has two distinguishing characteristics: it can capture movement patterns at multiple temporal frequencies through its recursive application; and requires no training or thresholding. The methodology is tested on two sets of simulated data and GPS tracking data of two species, the Turkey Vulture (Cathartes aura) and the Galapagos Albatross (Phoebastria irrorata). For the Turkey Vulture dataset, segments and transition points are evaluated using behavioural segmentations delineated by domain experts, and results are also compared with an existing approach. For the Albatross dataset, the segmentation results are evaluated using the geographic context of the movement tracks through visualisation. An Albatross trajectory is used to illustrate the application of recursive segmentation for extracting movement patterns at multiple temporal scales. The evaluation suggests that the segmentation technique is capable of extracting the right number of segments and in estimating transition points for most of the evaluated datasets. The proposed methodology has the potential to automate the segmentation process by enabling researchers to analyse a broad range of heterogeneous datasets without training. It will enable them to segment datasets recursively to derive multiple scales of movement patterns in a trajectory that may relate to different scales of behaviour.

Original languageEnglish (US)
Pages (from-to)1075-1087
Number of pages13
JournalMethods in Ecology and Evolution
Issue number4
StatePublished - Apr 2018


  • computational geometry
  • geographic information science
  • image processing
  • mobility
  • movement ecology
  • multi-scale
  • parameterless
  • signal processing
  • time-series analysis
  • trajectory analysis


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