Teetool -- a probabilistic trajectory analysis tool

Eerland, Willem; Box, Simon; Fangohr, Hans; Sóbester, András

doi:10.5334/jors.163

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Although outside the scope of this work, there are a variety of algorithms available for sub-trajectory clustering (e.g. Lee et al 2007;Eerland et al 2017) Throughout, we have focussed upon clustering movement data based purely upon the geographic locations, which is typical of most trajectory similarity studies. However, trajectories can be compared using a variety of movement parameters other than, or in conjunction with, geographic location.…”

Section: Discussionmentioning

confidence: 99%

Using time-series similarity measures to compare animal movement trajectories in ecology

Cleasby

Wakefield

Morrissey

et al. 2019

Behav Ecol Sociobiol

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Identifying and understanding patterns in movement data are amongst the principal aims of movement ecology. By quantifying the similarity of movement trajectories, inferences can be made about diverse processes, ranging from individual specialisation to the ontogeny of foraging strategies. Movement analysis is not unique to ecology however, and methods for estimating the similarity of movement trajectories have been developed in other fields but are currently under-utilised by ecologists. Here, we introduce five commonly used measures of trajectory similarity: dynamic time warping (DTW), longest common subsequence (LCSS), edit distance for real sequences (EDR), Fréchet distance and nearest neighbour distance (NND), of which only NND is routinely used by ecologists. We investigate the performance of each of these measures by simulating movement trajectories using an Ornstein-Uhlenbeck (OU) model in which we varied the following parameters: (1) the point of attraction, (2) the strength of attraction to this point and (3) the noise or volatility added to the movement process in order to determine which measures were most responsive to such changes. In addition, we demonstrate how these measures can be applied using movement trajectories of breeding northern gannets (Morus bassanus) by performing trajectory clustering on a large ecological dataset. Simulations showed that DTW and Fréchet distance were most responsive to changes in movement parameters and were able to distinguish between all the different parameter combinations we trialled. In contrast, NND was the least sensitive measure trialled. When applied to our gannet dataset, the five similarity measures were highly correlated despite differences in their underlying calculation. Clustering of trajectories within and across individuals allowed us to easily visualise and compare patterns of space use over time across a large dataset. Trajectory clusters reflected the bearing on which birds departed the colony and highlighted the use of well-known bathymetric features. As both the volume of movement data and the need to quantify similarity amongst animal trajectories grow, the measures described here and the bridge they provide to other fields of research will become increasingly useful in ecology. Significance statement As the use of tracking technology increases, there is a need to develop analytical techniques to process such large volumes of data. One area in which this would be useful is the comparison of individual movement trajectories. In response, a variety of measures Communicated by L. Z. Garamszegi

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Section: Discussionmentioning

confidence: 99%

Using time-series similarity measures to compare animal movement trajectories in ecology

Cleasby

Wakefield

Morrissey

et al. 2019

Behav Ecol Sociobiol

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“…Velocities were computed using a smoothing, differentiating Savitzky-Golay filter (2nd order polynomial, 21 ms smoothing). Hand trajectory confidence intervals were calculated using Teetool, 98 which models the 2d trajectories as a Gaussian process, producing an area that encompasses the 1 Σ covariance around the mean path. Reach endpoints were labeled where the hand speed fell below 50 mm/sec, and the 95% covariance ellipses were computed.…”

Section: Methodsmentioning

confidence: 99%

Direct neural perturbations reveal a dynamical mechanism for robust computation

O’Shea

Duncker

Goo

et al. 2022

Preprint

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The rich repertoire of skilled mammalian behavior is the product of neural circuits that generate robust and flexible patterns of activity distributed across populations of neurons. Decades of associative studies have linked many behaviors to specific patterns of population activity, but association alone cannot reveal the dynamical mechanisms that shape those patterns. Are local neural circuits high- dimensional dynamical reservoirs able to generate arbitrary superpositions of patterns with appropriate excitation? Or might circuit dynamics be shaped in response to behavioral context so as to generate only the low-dimensional patterns needed for the task at hand? Here, we address these questions within primate motor cortex by delivering optogenetic and electrical microstimulation perturbations during reaching behavior. We develop a novel analytic approach that relates measured activity to theoretically tractable, dynamical models of excitatory and inhibitory neurons. This computational model captures the dynamical effects of these perturbations and demonstrates that motor cortical activity during reaching is shaped by a self-contained, low-dimensional dynamical system. The subspace containing task-relevant dynamics proves to be oriented so as to be robust to strong non-normal amplification within cortical circuits. This task dynamics space exhibits a privileged causal relationship with behavior, in that stimulation in motor cortex perturb reach kinematics only to the extent that it alters neural states within this subspace. Our results resolve long-standing questions about the dynamical structure of cortical activity associated with movement, and illuminate the dynamical perturbation experiments needed to understand how neural circuits throughout the brain generate complex behavior.

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“…The approach is data-driven, and requires minimum human interaction to construct the flight corridors and complexity maps from the trajectory data, save selecting the resolution of the evaluation. The flight corridors are generated via an open-source Python toolbox, developed specifically for generating corridors in two and three dimensions [19]. We provided evidence of the effectiveness of this toolbox in the analysis of rocket trajectories generated by a stochastic rocket simulator [20,21].…”

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confidence: 99%

A Gaussian process based decision support tool for air traffic management

Eerland

Box

Fangohr

et al. 2017

17th AIAA Aviation Technology, Integration, and Operations Conference

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Technological developments in the last decade have shifted challenges in traffic flow management from obtaining and storing data, to analysing and presenting the enormous amount of available trajectory data in a comprehensible manner. This paper introduces a novel approach to visualising air-traffic, shifting the focus from displaying traffic density, towards directly visualising the flight corridors used by air-traffic. Such an approach is suitable for visualising air-traffic in three dimensions, which is particularly helpful in the vicinity of an airport where the air-traffic often changes level. Furthermore, the approach is data-driven, allowing the comparison of multiple trajectory datasets in order to identify changes in traffic corridors related to changing air-traffic and weather conditions. Finally, by using the probabilistic nature of the approach, it is possible to quantify the air-traffic complexity in terms of the traffic structure. The results presented in this paper show the approach applied to a trajectory dataset as measured by ground-radar near Denver airport (DEN

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Teetool -- a probabilistic trajectory analysis tool

Cited by 5 publications

References 12 publications

Using time-series similarity measures to compare animal movement trajectories in ecology

Using time-series similarity measures to compare animal movement trajectories in ecology

Direct neural perturbations reveal a dynamical mechanism for robust computation

A Gaussian process based decision support tool for air traffic management

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