Visualization of Vessel Traffic
We discuss methods to visualize large amounts of object movements described with so called multivariate trajectories, which are lists of records with multiple attribute values about the state of the object. In this chapter we focus on vessel traffic as one of the examples of this kind of data. The purpose of our visualizations is to reveal what has happened over a period of time. For vessel traffic, this is beneficial for surveillance operators and analysts, since current visualizations do not give an overview of normal behavior, which is needed to find abnormally behaving ships that can be a potential threat. Our approach is inspired by the technique of kernel density estimation and smooths trajectories to obtain an overview picture with a distribution of trajectories: a density map. Using knowledge about the attributes in the data, the user can adapt these pictures by setting parameters, filters, and expressions as means for rapid prototyping, required for quickly finding other types of behavior with our visualization approach. Furthermore, density maps are computationally expensive, which we address by implementing our tools on graphics hardware. We describe different variations of our techniques and illustrate them with real-world vessel traffic data.
This research has been carried out as a part of the Poseidon project at Thales under the responsibilities of the Embedded Systems Institute (ESI). This project is partially supported by the Dutch Ministry of Economic Affairs under the BSIK program.
We thank our industrial partners Thales and Noldus IT, and the management and research fellows at the Embedded Systems Institute. We thank our domain experts at the Dutch Maritime Research Institute (MARIN). We thank the Fraunhofer Institute IAIS and the partners in the Poseidon project in the preparation of this chapter. Maps in the figures are provided by OpenStreetMaps and National Geospatial-Intelligence Agency.
- 1.Hurter C, Tissoires B, Conversy S (2010) Accumulation as a tool for efficient visualization of geographical and temporal data. In: Proceedings of the AGILE workshop geospatial visual analytics: focus on time. GuimarãesGoogle Scholar
- 2.International Telecommunications Union (2001) Technical characteristics for a universal shipborne automatic identification system using time division multiple access in the VHF maritime mobile band, Recommendation ITU-R M.1371-1Google Scholar
- 3.Lampe OD, Hauser H (2011) Interactive visualization of streaming data with kernel density estimation. In: IEEE Pacific visualization symposium, Hong-Kong, pp 171–178Google Scholar
- 5.Scheepens R, Willems N, van de Wetering H, van Wijk JJ (2011) Interactive visualization of multivariate trajectory data with density maps. In: IEEE Pacific visualization symposium, Hong-Kong (PacificVis 2011), pp 147–154Google Scholar
- 7.Silverman BW (1992) Density estimation for statistics and data analysis. Monographs on statistics and applied probability no. 26. Chapman & Hall, LondonGoogle Scholar
- 8.Willems N (2011) Visualization of vessel traffic. PhD thesis, Eindhoven University of Technology. http://alexandria.tue.nl/extra2/719764.pdf
- 9.Willems N, van de Wetering H, Wijk JJ (2009) Visualization of vessel movements. Comput Graph Forum 28(3):959–966. Proceedings of EuroVis 2009Google Scholar
- 11.Willems N, van de Wetering H, Wijk JJ (2011) Evaluation of the visibility of vessel movement features in trajectory visualizations. Comput Graph Forum 30(3):801–810. Proceedings of EuroVis 2011Google Scholar