Buchmüller, JuriCakmak, ErenAndrienko, NataliaAndrienko, GennadyJolles, Jolle W.Keim, Daniel A.Landesberger, Tatiana von and Turkay, Cagatay2019-06-022019-06-022019978-3-03868-087-1https://doi.org/10.2312/eurova.20191122https://diglib.eg.org:443/handle/10.2312/eurova20191122While 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.Humancentered computingVisualization theoryconcepts and paradigms10.2312/eurova.2019112237-41