AbstractWhen displaying thousands of aircraft trajectories on a screen, the visualization is spoiled by a tangle of trails. The visual analysis is therefore difficult, especially if a specific class of trajectories in an erroneous dataset has to be studied. We designed FromDaDy, a trajectory visualization tool that tackles the difficulties of exploring the visualization of multiple trails. This multidimensional data exploration is based on scatterplots, brushing, pick and drop, juxtaposed views and rapid visual design. Users can organize the workspace composed of multiple juxtaposed views. They can define the visual configuration of the views by connecting data dimensions from the dataset to Bertins visual variables. They can then brush trajectories, and with a pick and drop operation they can spread the brushed information across views. They can then repeat these interactions, until they extract a set of relevant data, thus formulating complex queries. Through two real-world scenarios, we show how FromDaDy supports iterative queries and the extraction of trajectories in a dataset that contains up to 5 million data. Index Termsvisualization, iterative exploration, direct manipulation, trajectories. In the Air Traffic Control (ATC) field, analyzing traffic or devising new ways of managing airspace requires trajectories analysis. An aircraft trajectory is a record of positions of an aircraft in a given airspace (3D+time plus other information such as identifier, speed etc). As such, trajectories are multidimensional data. Air Traffic stake-holders regularly analyze traffic to: understand past conflicts and then improve safety with adequate evolutions, assess new onboard and ground safety systems and the resulting aircraft trails, devise new air space organization and procedures to handle traffic increase, compare trails with environmental considerations (fuel consumption, noise pollution, vertical profile comparison), study profitability from a business trajectory point of view (number of aircraft on a specific Flight Route per day, number of aircraft that actually landed at a specific airport), filter and extract trajectories in order to re-use them (this task will be later illustrated in this paper in the section on trajectory extraction for Air Traffic Controllers training). Formulating queries over trajectories in a declarative, textuallanguage based manner, such as a SQL, is hard. Even if it is possible to select trajectories that flow over specific locations, it is very difficult to specify features like select trajectories where this part of the trajectory is straight or where this part has a constant climbing rate Thus, visual analysis remains the only way to detect relevant trajectory features efficiently. Trajectories are numerous and tangle: one-day's traffic over France for example, represents some 20000 trajectories ( Fig. 1 ). When dealing with trajectories, users must perform dynamic requests (response time < 100 ms [15]) on a huge multi-dimensional dataset (>1 million data). In addition to the data size problem, users have to deal with a dataset that contains many errors and uncertainties: recording is done in a periodic manner (in our database: a radar plot per aircraft every 4 minutes), but a plot can be missed, or have erroneous values because of physical problems that occurred at the time of recording. The problem we address in this paper is to find a way to express these queries, simply and accurately, given the constraints of size and uncertainty of the datasets. We have developed FromDaDy (which stands for FROM DAta to DisplaY), a visualization tool that tackles the challenge of representing, and interacting with, numerous trajectories involving uncertainties. FromDaDy employs a simple paradigm to explore multidimensional data based on scatterplots, brushing, pick and drop, juxtaposed views and rapid visual configuration. The fundamental new aspect of FromDaDy compared to existing visualization systems, is to enable users to spread data across views. Together with a finely tuned mix between design customization and simple interaction, users can filter, remove and add trajectories in an iterated manner until they extract a set of relevant data, thus formulating complex queries. The remainder of this paper is organized as follows. First, we present relevant related work. Then we list the design requirements to fulfill the trajectory analysis task. Next, we describe FromDaDy features and justify our implementation choices. Finally, we outline the strengths of FromDaDy with two specific data extraction scenarios.
There are hence no duplications of trajectories for a flight unit, but rather the trajectories are spread across views REF .
FromDaDy: Spreading Aircraft Trajectories Across Views to Support Iterative Queries
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Computer Science Medicine