We describe a procedure for analyzing trajectory attributes that in-cludes spatial clustering of the trajectories and visual analysis of the distribution of attribute values in the temporal and spatial dimensions

Andrienko, G.

Andrienko, N.

English

City Research Online

Andrienko, G. & Andrienko, N. (2013). Exploring Trajectory Attributes in Brest Harbor. Paper presented at the COST MOVE Workshop on Moving Objects at Sea, 27 - 28 Jun 2013, Brest, France.City Research OnlineOriginal citation: Andrienko, G. & Andrienko, N. (2013). Exploring Trajectory Attributes in Brest Harbor. Paper presented at the COST MOVE Workshop on Moving Objects at Sea, 27 - 28 Jun 2013, Brest, France.Permanent City Research Online URL: http://openaccess.city.ac.uk/2911/ Copyright & reuseCity  University  London has developed City  Research Online  so that  its  users  may access the research outputs of City University London's staff. Copyright © and Moral Rights for this paper are retained by the individual author(s) and/ or other copyright holders.  All material in City Research Online is checked for eligibility for copyright before being made available in the live archive. URLs from City Research Online may be freely distributed and linked to from other web pages. Versions of researchThe version in City Research Online may differ from the final published version. Users are advised to check the Permanent City Research Online URL above for the status of the paper.EnquiriesIf you have any enquiries about any aspect of City Research Online, or if you wish to make contact  with the author(s) of this paper, please email the team at publications@city.ac.uk.adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011 Exploring Trajectory Attributes in Brest Harbor  Gennady Andrienko and Natalia Andrienko Abstract. We describe a procedure for analyzing trajectory attributes that in-cludes spatial clustering of the trajectories and visual analysis of the distribution of attribute values in the temporal and spatial dimensions. 1 Introduction We extract trips from the tracks of the vessels by removing stationary points and then dividing the tracks by temporal gaps of more than 30 minutes and spatial gaps of more than 2km. Knowing the positions of the major ports, we use them for further splitting. In the result, we obtain 7,702 trajectories. To detect clusters of repeated trips, we use the density-based clustering method Optics with a similarity function that assesses the similarity of two trajectories based on the geographic distances be-tween their starting and ending points [1]. Our visual analytics toolkit supports a rich library of similarity measures [1,2] and can be applied to data in main memory or in a database [2] for scalable processing. More sophisticated similarity measures support-ed by moving object databases are also available [3].  Fig. 1. Two clusters of trajectories sailing from Ecole Navale to Ile Longue (220 trajectories, yellow) and back (56 trajectories, red) We demonstrate the possibilities for analysis by an example of two clusters of tra-jectories connecting Ecole Navale and Ile Longue in the southern part of the harbor (Figure 1). Figure 2 shows the temporal distribution of these trajectories. We can observe that these trips mostly occur in morning and evening hours of Saturday and Sunday (days 6 and 7) and in the middle of day during the working days.  Fig. 2. Distribution of the trips for the days of week and hours of day  Fig. 3. Temporal variation of the speed values in the trips from Ile Longue to Ecole Navale.  Fig. 4. Variation of the speed values along the selected trajectories.   Fig. 5. Variation of the speed values along the trajectories from Ecole Navale to Ile Longue. We select one attribute of these trajectories, speed, for further analysis (paper [5] includes an example of analyzing another attribute, tortuosity). The temporal posi-tions of 56 trips and the temporal variation of the speed values during these trips are shown in a time bar display [4] in Figure 3. We can see that a usual trip lasts for about 20 minutes and that all trips start either in the interval 5:35-5:40AM or bin the interval 6:45-6:50AM. All trips have quite similar dynamics of the speeds. Figure 4 presents a visualization of the same data by means of the trajectory wall technique [5]. The time query panel in the bottom right corner shows that most of these trips happen either on Saturday or on Sunday. Figure 5 applies the same visualization to 220 trips in the opposite direction. It is noticeable that higher speed values occur more often during the working days than during the weekends and more often in the middle of the day than in the morning or in the evening. Further inspection of these trajectories on the time bar display (Figure 6) reveals that the morning trips have been shifted by one hour back during the period from the 29th of March 2009 till the 24th of October 2009. This period corresponds to the summer time period in France in 2009.  Fig. 6. A fragment of trajectory time bar for trips from Ecole Navale to Ile Longue. Further analysis of the attributes of vessel traffic can be performed by combining additional visual analytics tools and methods, as suggested by [6,7]. Paper [6] pre-sents an example of a multi-perspective analysis of another vessel traffic data set. Different parts of the analysis focus on trajectories, events, places of interest, and time intervals. These possibilities for extended analysis will be demonstrated and discussed at the workshop. Acknowledgements This work has been done in cooperation with Dr. C.Tominski and Prof. H.Schumann from Univ. Rostock.  References 1. S.Rinzivillo, D.Pedreschi, M.Nanni, F.Giannotti, N.Andrienko, G.Andrienko. Visually–driven analysis of movement data by progressive clustering. Information Visualization, 2008, v.7 (3/4), pp. 225-239 2. G.Andrienko, N.Andrienko, S.Rinzivillo, M.Nanni, D.Pedreschi, F.Giannotti. Interactive Visual Clustering of Large Collections of Trajectories. IEEE Visual Analytics Science and Technology (VAST 2009). Proceedings, IEEE Computer Society Press, 2009, pp.3-10 3. N.Pelekis, G.Andrienko, N.Andrienko, I.Kopanakis, G.Marketos, Y.Theodoridis. Visually Exploring Movement Data via Similarity-based Analysis. Journal of Intelligent Infor-mation Systems, 2012, v.38 (2), pp.343-391 4. G.Andrienko, N.Andrienko, M.Heurich. An Event-Based Conceptual Model for Context-Aware Movement Analysis. International Journal Geographical Information Science, 2011, v.25 (9), pp.1347-1370 5. C.Tominski, H.Schumann, G.Andrienko, N.Andrienko. Stacking-Based Visualization of Trajectory Attribute Data. IEEE Transactions on Visualization and Computer Graphics (Proceedings IEEE Information Visualization 2012), vol. 18(12), pp.2565-2574, Dec. 2012 6. N.Andrienko, G.Andrienko. Visual Analytics of Movement: an Overview of Methods, Tools, and Procedures. Information Visualization, vol. 12(1), pp.3-24, 2013 7. G.Andrienko, N.Andrienko, P.Bak, D.Keim, S.Wrobel. Visual Analytics of Movement. Springer, 2013.  

Exploring Trajectory Attributes in Brest Harbor

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