Trajectory similarity measures

Toohey, Kevin; Duckham, Matt

doi:10.1145/2782759.2782767

Cited by 160 publications

(82 citation statements)

References 23 publications

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“…SimilarityMeasures (2015, CRAN, inactive) (Toohey, 2015) assesses similarity between trajectories using metrics such as the longest common subsequence (LCSS), Fréchet distance, edit distance and dynamic time warping (DTW). Magdy et al (2015) provides a brief review on trajectory similarity measures.…”

Section: Movement Similaritymentioning

confidence: 99%

“…Six R packages focus on the analysis of human accelerometry data, mainly to describe periodicity and levels of activity. accelerometry (Van Domelen, 2018), GGIR (van Hees et al, 2014, 2015, 2019 and PhysicalActivity computes descriptive statistics such as interdaily stability, intradaily variability and relative amplitude of activity (Blume, Santhi & Schabus, 2016). acc (Song & Cox, 2016), GGIR and pawacc (Geraci et al, 2012;Geraci, 2017) classify wear data into different levels of activity (e.g.…”

Section: Analysis Of Biologging But Not Tracking Datamentioning

confidence: 99%

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Navigating through therpackages for movement

Joo

Boone

Clay

et al. 2019

Journal of Animal Ecology

109

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1. The advent of miniaturized biologging devices has provided ecologists with unprecedented opportunities to record animal movement across scales, and * led to the collection of ever-increasing quantities of tracking data. In parallel, sophisticated tools have been developed to process, visualize and analyze tracking data, however many of these tools have proliferated in isolation, making it challenging for users to select the most appropriate method for the question in hand. Indeed, within the R software alone, we listed 58 packages created to deal with tracking data or 'tracking packages'.2. Here we reviewed and described each tracking package based on a workflow centered around tracking data (i.e. spatio-temporal locations (x, y, t)), broken down into three stages: pre-processing, post-processing and analysis, the latter consisting of data visualization, track description, path reconstruction, behavioral pattern identification, space use characterization, trajectory simulation and others. Supporting documentation is key to render a package accessible for users.Based on a user survey, we reviewed the quality of packages' documentation, and identified 11 packages with good or excellent documentation. 4. Links between packages were assessed through a network graph analysis. Although a large group of packages showed some degree of connectivity (either depending on functions or suggesting the use of another tracking package), one third of the packages worked in isolation, reflecting a fragmentation in the R movement-ecology programming community.5. Finally, we provide recommendations for users when choosing packages, and for developers to maximize the usefulness of their contribution and strengthen the links within the programming community.2 Keywords biologging, movement ecology, R project for statistical computing, spatial, tracking data

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Section: Movement Similaritymentioning

confidence: 99%

Section: Analysis Of Biologging But Not Tracking Datamentioning

confidence: 99%

Navigating through therpackages for movement

Joo

Boone

Clay

et al. 2019

Journal of Animal Ecology

109

View full text Add to dashboard Cite

show abstract

“…Different methods for measuring similarity between time‐dependent data exist. In Toohey and Duckham (), the authors compare four of the most common measures of trajectory similarity—longest common subsequence, Fréchet distance, dynamic time warping, and edit distance. The authors highlight some differences between these four similarity measures using real trajectory data.…”

Section: The Visual Analytics Frameworkmentioning

confidence: 99%

“…In Toohey and Duckham (2015), the authors compare four of the most common measures of trajectory similarity-longest common subsequence, Fréchet distance, dynamic time warping, and edit distance.…”

Section: Trajectory Similarity Measuresmentioning

confidence: 99%

Visual analytics of educational time‐dependent data using interactive dynamic visualization

Géryk

2016

Expert Systems

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A major challenge in the modern educational research is the large amount of time‐dependent data, which requires suitable methods to provide an efficient decision making. The fields of visual analytics and interactive visualization can help to cope with the related issues by involving the analyst in the exploration process. The time is an important variable that needs to be modeled in visual analytics. Methods like motion charts show the changes over time by presenting animations in the two‐dimensional space and by changing the element appearances. In this paper, we present a novel web‐based visual analytics framework, which is primarily designed for exploratory analysis of academic analytics and employs a combination of improved motion charts methods, trajectory‐based visualizations, and automated analytic methods for the purpose of analyzing data elements based on their relations with the time dimension and examining data dynamics. We evaluate the usefulness and the general applicability of the framework with a controlled experiment to assess the efficacy of the methods. To interpret the experiment results, we utilize one‐way repeated measures analysis of variance. Also, a questionnaire survey with experts was conducted to evaluate the usability of the methods.

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“…There are several possibilities for measuring the similarity between two trajectories [84]. However those metrics normally require time-stamped positions, or they involve complex computation and do not compile with the requirements.…”

Section: Comparing Two Trajectoriesmentioning

confidence: 99%

Tracking and intercepting pedestrians: a robotic approach to surveillance of critical infrastructures

Oviedo¹

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Trajectory similarity measures

Cited by 160 publications

References 23 publications

Navigating through therpackages for movement

Navigating through therpackages for movement

Visual analytics of educational time‐dependent data using interactive dynamic visualization

Tracking and intercepting pedestrians: a robotic approach to surveillance of critical infrastructures

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