Skip to content
DraconiticResearch
Search Sources API
203,203 studies31 sourcesUpdated just now
Peer-reviewedCC BY 4.0Mathematics

BioTraj: Information-Geometric Mapping of Spatial and Temporal Heterogeneity in Biological Trajectories

  1. 1School of Engineering and Physical Sciences, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK

Abstract

Quantitative trajectory analysis commonly summarizes biological motion using global geometric or dynamical descriptors, but these do not directly quantify how movement statistics vary across space or time. Here, we present BioTraj, an open-source Python framework integrating video-based trajectory extraction, conventional movement descriptors, and information-geometric analysis. Local and time-resolved turning angle distributions are represented on the probability simplex and compared using the Fisher–Rao geodesic distance. Spatial and temporal Fisher–Rao measures thereby quantify distributional heterogeneity without predefined behavioral states. The method is validated using simulated trajectories with a deliberately imposed stationary, spatial, temporal, and spatiotemporal structure, while sampling controls characterize finite-sample sensitivity. The workflow is then applied to ant recordings using independently defined scene annotations. In the Yellow ant recording, Fisher–Rao heterogeneity differed broadly inside and outside the historical footprint of a removed rock after controlling for tracking domain edges, whereas small candidate nest openings were unresolved at the selected grid scale. In the Wall recording, annotated surfaces exhibited different local heterogeneity, while distance from surface transitions showed no universal monotonic response. BioTraj therefore provides a descriptive, reproducible framework for identifying where movement statistics change and relating these changes to an independently annotated environmental context, rather than automatically assigning behavioral states.

Related

  1. Information-Geometric Grammar Principle: A Statistical-Geometric Framework for Adaptive Systems
    Angelo Plastino
    AxiomsPeer-reviewedCC BY 4.0General mathematicsCrossref
  2. Maximum Entropy Probability Distributions on Spheres with Fixed Mean Busemann Function and Holomorphic-Information-Geometric Model of Cognition
    Vladimir Jacimovic
    arXivPreprintCC BY 4.0Full textProbabilityarXiv
  3. Information-Geometric Blind Spots of Conformal Test Martingales
    Johan Hallberg Szabadváry
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  4. An information-geometric formulation of distributed computation
    Alberto Liardi, George Blackburne, Pedro A. M. Mediano
    arXivPreprintCC BY 4.0Full textGeneral mathematicsarXiv
  5. Information Rate Decomposition for Noisy Nanopore Channels with Geometric Duplication
    Brendon McBain, Emanuele Viterbo
    arXivPreprintCC BY 4.0Full textGeneral mathematicsarXiv
BibTeX
@article{roccatano2026biotraj,
  title = {{BioTraj: Information-Geometric Mapping of Spatial and Temporal Heterogeneity in Biological Trajectories}},
  author = {Danilo Roccatano},
  journal = {Mathematics},
  year = {2026},
  month = nov,
  doi = {10.3390/math14214025},
  url = {https://www.mdpi.com/2227-7390/14/21/4025},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - BioTraj: Information-Geometric Mapping of Spatial and Temporal Heterogeneity in Biological Trajectories
AU  - Danilo Roccatano
PY  - 2026
DA  - 2026/11/04
JO  - Mathematics
DO  - 10.3390/math14214025
UR  - https://www.mdpi.com/2227-7390/14/21/4025
AB  - Quantitative trajectory analysis commonly summarizes biological motion using global geometric or dynamical descriptors, but these do not directly quantify how movement statistics vary across space or time. Here, we present BioTraj, an open-source Python framework integrating video-based trajectory extraction, conventional movement descriptors, and information-geometric analysis. Local and time-resolved turning angle distributions are represented on the probability simplex and compared using the Fisher–Rao geodesic distance. Spatial and temporal Fisher–Rao measures thereby quantify distributional heterogeneity without predefined behavioral states. The method is validated using simulated trajectories with a deliberately imposed stationary, spatial, temporal, and spatiotemporal structure, while sampling controls characterize finite-sample sensitivity. The workflow is then applied to ant recordings using independently defined scene annotations. In the Yellow ant recording, Fisher–Rao heterogeneity differed broadly inside and outside the historical footprint of a removed rock after controlling for tracking domain edges, whereas small candidate nest openings were unresolved at the selected grid scale. In the Wall recording, annotated surfaces exhibited different local heterogeneity, while distance from surface transitions showed no universal monotonic response. BioTraj therefore provides a descriptive, reproducible framework for identifying where movement statistics change and relating these changes to an independently annotated environmental context, rather than automatically assigning behavioral states.
SN  - 2227-7390
ER  -