Visual analytics of delays and interaction in movement data

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dc.contributor.authorKonzack, Maximilian
dc.contributor.authorMcKetterick, Thomas
dc.contributor.authorOphelders, Tim
dc.contributor.authorBuchin, Maike
dc.contributor.authorGiuggioli, Luca
dc.contributor.authorLong, Jed
dc.contributor.authorNelson, Trisalyn
dc.contributor.authorWestenberg, Michel A.
dc.contributor.authorBuchin, Kevin
dc.date.accessioned2018-07-09T19:08:18Z
dc.date.available2018-07-09T19:08:18Z
dc.date.copyright2017en_US
dc.date.issued2017
dc.description.abstractThe analysis of interaction between movement trajectories is of interest for various domains when movement of multiple objects is concerned. Interaction often includes a delayed response, making it difficult to detect interaction with current methods that compare movement at specific time intervals. We propose analyses and visualizations, on a local and global scale, of delayed movement responses, where an action is followed by a reaction over time, on trajectories recorded simultaneously. We developed a novel approach to compute the global delay in subquadratic time using a fast Fourier transform (FFT). Central to our local analysis of delays is the computation of a matching between the trajectories in a so-called delay space. It encodes the similarities between all pairs of points of the trajectories. In the visualization, the edges of the matching are bundled into patches, such that shape and color of a patch help to encode changes in an interaction pattern. To evaluate our approach experimentally, we have implemented it as a prototype visual analytics tool and have applied the tool on three bidimensional data sets. For this we used various measures to compute the delay space, including the directional distance, a new similarity measure, which captures more complex interactions by combining directional and spatial characteristics. We compare matchings of various methods computing similarity between trajectories. We also compare various procedures to compute the matching in the delay space, specifically the Frechet distance, dynamic time warping (DTW), and edit distance (ED). Finally, we demonstrate how to validate the consistency of pairwise matchings by computing matchings between more than two trajectories.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipThis work was supported by the Netherlands Organisation for Scientific Research (NWO) under [grant no. 612.001.207 and grant no. 639.023.208].en_US
dc.identifier.citationKonzack, M.; McKetterick, T.; Ophelders, T.; Buchin, M.; Giuggioli, L.; Long, J.; … & Buchin, K. (2017). Visual analytics of delays and interaction in movement data. International Journal of Geographical Information Science, 31(2), 320-345. https://doi.org/10.1080/13658816.2016.1199806en_US
dc.identifier.urihttps://doi.org/10.1080/13658816.2016.1199806
dc.identifier.urihttp://hdl.handle.net/1828/9639
dc.language.isoenen_US
dc.publisherInternational Journal of Geographical Information Scienceen_US
dc.subjecttrajectory analysis
dc.subjectvisual analytics
dc.subjectsimilarity measures
dc.subject.departmentDepartment of Geography
dc.titleVisual analytics of delays and interaction in movement dataen_US
dc.typeArticleen_US

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