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dc.contributor.authorBuchmüller, Jurien_US
dc.contributor.authorCakmak, Erenen_US
dc.contributor.authorAndrienko, Nataliaen_US
dc.contributor.authorAndrienko, Gennadyen_US
dc.contributor.authorJolles, Jolle W.en_US
dc.contributor.authorKeim, Daniel A.en_US
dc.contributor.editorLandesberger, Tatiana von and Turkay, Cagatayen_US
dc.date.accessioned2019-06-02T18:19:22Z
dc.date.available2019-06-02T18:19:22Z
dc.date.issued2019
dc.identifier.isbn978-3-03868-087-1
dc.identifier.urihttps://doi.org/10.2312/eurova.20191122
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurova20191122
dc.description.abstractWhile conventional applications for spatiotemporal datasets mostly focus on the relation between movers and environment, research questions in the analysis of collective movement typically focus more on relationships and dynamics between the moving entities themselves. Instead of concentrating on origin, destination and the way in between, this inter-mover perspective on spatiotemporal data allows to explain how moving groups are coordinating. Yet, only few visualization and Visual Analytics approaches focus on the relationships between movers. To illuminate this research gap, we propose initial steps towards a comprehensive formalization of coordination in collective movement based on temporal autocorrelation of distance matrices derived from basic movement characteristics. We exemplify how patterns can be encoded using autocorrelation cubes and outline the next steps towards an exhaustive formalization of coordination patterns.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisualization theory
dc.subjectconcepts and paradigms
dc.titleMoving Together: Towards a Formalization of Collective Movementen_US
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.description.sectionheadersAnalyzing Movement and Events
dc.identifier.doi10.2312/eurova.20191122
dc.identifier.pages37-41


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