From Paths to Routes: A Method for Path Classification
Many animals establish, learn and optimize routes between locations to commute efficiently. One step in understanding route following is defining measures of similarities between the paths taken by the animals. Paths have commonly been compared by using several descriptors (e.g., the speed, distance...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-01-01
|
| Series: | Frontiers in Behavioral Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbeh.2020.610560/full |
| _version_ | 1818662238236442624 |
|---|---|
| author | Andrea Gonsek Manon Jeschke Silvia Rönnau Olivier J. N. Bertrand |
| author_facet | Andrea Gonsek Manon Jeschke Silvia Rönnau Olivier J. N. Bertrand |
| author_sort | Andrea Gonsek |
| collection | DOAJ |
| description | Many animals establish, learn and optimize routes between locations to commute efficiently. One step in understanding route following is defining measures of similarities between the paths taken by the animals. Paths have commonly been compared by using several descriptors (e.g., the speed, distance traveled, or the amount of meandering) or were visually classified into categories by the experimenters. However, similar quantities obtained from such descriptors do not guarantee similar paths, and qualitative classification by experimenters is prone to observer biases. Here we propose a novel method to classify paths based on their similarity with different distance functions and clustering algorithms based on the trajectories of bumblebees flying through a cluttered environment. We established a method based on two distance functions (Dynamic Time Warping and Fréchet Distance). For all combinations of trajectories, the distance was calculated with each measure. Based on these distance values, we grouped similar trajectories by applying the Monte Carlo Reference-Based Consensus Clustering algorithm. Our procedure provides new options for trajectory analysis based on path similarities in a variety of experimental paradigms. |
| first_indexed | 2024-12-17T04:57:46Z |
| format | Article |
| id | doaj.art-14f4cfbbfa8c47609956e09b08de880a |
| institution | Directory Open Access Journal |
| issn | 1662-5153 |
| language | English |
| last_indexed | 2024-12-17T04:57:46Z |
| publishDate | 2021-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Behavioral Neuroscience |
| spelling | doaj.art-14f4cfbbfa8c47609956e09b08de880a2022-12-21T22:02:39ZengFrontiers Media S.A.Frontiers in Behavioral Neuroscience1662-51532021-01-011410.3389/fnbeh.2020.610560610560From Paths to Routes: A Method for Path ClassificationAndrea GonsekManon JeschkeSilvia RönnauOlivier J. N. BertrandMany animals establish, learn and optimize routes between locations to commute efficiently. One step in understanding route following is defining measures of similarities between the paths taken by the animals. Paths have commonly been compared by using several descriptors (e.g., the speed, distance traveled, or the amount of meandering) or were visually classified into categories by the experimenters. However, similar quantities obtained from such descriptors do not guarantee similar paths, and qualitative classification by experimenters is prone to observer biases. Here we propose a novel method to classify paths based on their similarity with different distance functions and clustering algorithms based on the trajectories of bumblebees flying through a cluttered environment. We established a method based on two distance functions (Dynamic Time Warping and Fréchet Distance). For all combinations of trajectories, the distance was calculated with each measure. Based on these distance values, we grouped similar trajectories by applying the Monte Carlo Reference-Based Consensus Clustering algorithm. Our procedure provides new options for trajectory analysis based on path similarities in a variety of experimental paradigms.https://www.frontiersin.org/articles/10.3389/fnbeh.2020.610560/fullbumblebeeclusteringrouteclassificationclutternavigation |
| spellingShingle | Andrea Gonsek Manon Jeschke Silvia Rönnau Olivier J. N. Bertrand From Paths to Routes: A Method for Path Classification Frontiers in Behavioral Neuroscience bumblebee clustering route classification clutter navigation |
| title | From Paths to Routes: A Method for Path Classification |
| title_full | From Paths to Routes: A Method for Path Classification |
| title_fullStr | From Paths to Routes: A Method for Path Classification |
| title_full_unstemmed | From Paths to Routes: A Method for Path Classification |
| title_short | From Paths to Routes: A Method for Path Classification |
| title_sort | from paths to routes a method for path classification |
| topic | bumblebee clustering route classification clutter navigation |
| url | https://www.frontiersin.org/articles/10.3389/fnbeh.2020.610560/full |
| work_keys_str_mv | AT andreagonsek frompathstoroutesamethodforpathclassification AT manonjeschke frompathstoroutesamethodforpathclassification AT silviaronnau frompathstoroutesamethodforpathclassification AT olivierjnbertrand frompathstoroutesamethodforpathclassification |