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.
KeywordsKernel Density Estimation Density Field Kernel Size Graphic Hardware Automatic Identification System
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.
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