Group: Visualization group, Department of Mathematics and Computer Science at the Eindhoven University of Technology.
I am a Ph.D. candidate at professor dr. Jack van Wijk's visualization group of the faculty of Mathematics and Computer Science at the Eindhoven University of Technology under the supervision of dr.ir. Huub van de Wetering. Our research is part of the Metis project of the Embedded Systems Institute, in cooperation with Thales Nederland BV. The project focuses on finding, analyzing, describing and visualizing uncertainty of heterogeneous information in systems-of-systems in the context of maritime safety and security systems. Our research focuses on the visualization of heterogeneous and uncertain information, specifically maritime data.
Before starting on Metis, I was involved, as master student and later as researcher, in the Poseidon project. Metis is the follow up project to Poseidon.
Visualization for Maritime Situational Awareness.
PhD Thesis, Eindhoven University of Technology, 2015.
How can operators be supported in attaining situational awareness using interactive visualization?
Visualization plays an essential role in aiding operators to acquire and maintain Situational Awareness. We have answered our research question in the following four parts.
Analysis: How can we provide tools to analyze and summarize patterns, enabling domain experts to find critical areas and to verify what normal or anomalous behavior is? We have presented a way to gain more domain knowledge by analyzing movement patterns. We do this by visualizing traffic flows and provide interaction tools to support their exploration. The user can intuitively select and filter traffic flows from an overview visualization. The dynamic behaviors of selected flows may then be shown in annotation windows in which they can be interactively explored and compared. We have demonstrated the effectiveness of our method through a number of use cases in the air traffic domain and the maritime safety and security domain.
Perception: How can we help the operator in perceiving a situation? We have shown how to support perception in situational awareness by reducing the cognitive overload and visual overlap caused by dense populations in the maritime picture. In moving object visualization in general, objects and their attributes are commonly represented by glyphs on a geographic map. In areas on the map densely populated by these objects, visual clutter and occlusion of glyphs occur. We have proposed a method to solve this problem and through a user study we found that, for a set of representative tasks, our method does not perform significantly worse than competitive visualizations with respect to correctness. Furthermore, it performs significantly better for density comparison tasks in high density data sets. We also found that the participants of the user study have a preference for our method. Additionally, we have shown how to support perception in situational awareness using a specialized multi-variate glyph designed in cooperation with domain experts. The glyph is designed to help the operator to perceive quickly which vessels are relevant and require more attention. Starting from maritime domain requirements, a number of design parameters and feasible design choices for them have been determined. We have determined the best choices by showing the glyphs in a sandbox environment and allowing the domain experts to vary the parameters.
Comprehension: How can we help the operator in comprehending a situation? We have shown how to support comprehension in situational awareness by visually explaining conclusions of a reasoning engine that raises an alarm if a certain situation is reached. We offer an improvement over current visualization methods, where only a list of evidence is shown. Two groups of domain and operational experts have been used to evaluate our system by testing a number of use cases in the maritime domain based on real data. Experts could easily follow the reasoning structure, and could quickly understand and find complicated patterns in an evidence matrix and relate these to real-world situations.
Projection: How can we help the operator in projecting a current situation into the future? We have shown how to support projection into the future of a current situation using a visualization method for the interactive exploration of predicted positions of moving objects, in particular, ocean-faring vessels. Users, investigating and exploring the possible development of a situation, can see where a vessel will be in the near future according to a given prediction model. Through a number of real-world use cases and a discussion with users, we have shown our methods can be used in monitoring traffic for collision avoidance, and detecting illegal activities, like piracy or smuggling. By applying our methods to pedestrian movements, we have shown that our methods can also be applied to a different domain.
Visualization for Maritime Situational Awareness. Roeland Scheepens. PhD Thesis, Eindhoven University of Technology, 2015.
Visualization, Selection, and Analysis of Traffic Flows.
Roeland Scheepens, Christophe Hurter, Huub van de Wetering, and Jarke J. van Wijk.
In IEEE Transactions on Visualization and Computer Graphics, 23(1):??-??, 2015.
Visualization of the trajectories of moving objects leads to dense and cluttered images, which hinders exploration and understanding. It also hinders adding additional visual information, such as direction, and makes it difficult to interactively extract traffic flows, i.e., subsets of trajectories. In this paper we present our approach to visualize traffic flows and provide interaction tools to support their exploration. We show an overview of the traffic using a density map. The directions of traffic flows are visualized using a particle system on top of the density map. The user can extract traffic flows using a novel selection widget that allows for the intuitive selection of an area, and filtering on a range of directions and any additional attributes. Using simple, visual set expressions, the user can construct more complicated selections. The dynamic behaviors of selected flows may then be shown in annotation windows in which they can be interactively explored and compared. We validate our approach through use cases where we explore and analyze the temporal behavior of aircraft and vessel trajectories, e.g., landing and takeoff sequences, or the evolution of flight route density. The aircraft use cases have been developed and validated in collaboration with domain experts.
Figure: (a) Traffic flows are visualized using a density map and a particle system. They can be selected using our novel selection widget, and are visible through colored particles. (b) Two traffic flows of aircraft moving in opposite directions have been selected. The green traffic flow is moving south-west, and the orange traffic flow is heading north-east. Their dynamics can be explored and compared through movable windows, which show distributions of direction, and density over time. The gray window shows the difference between the two traffic flows.
Visualization, Selection, and Analysis of Traffic Flows. Roeland Scheepens, Christophe Hurter, Huub van de Wetering, and Jarke J. van Wijk. In IEEE Transactions on Visualization and Computer Graphics, 23(1):??-??, 2015.
Rationale visualization for safety and security.
Roeland Scheepens, Steffen Michels, Huub van de Wetering, and Jarke J. van Wijk.
In Computer Graphics Forum, 34(3):191-200, 2015.
In safety and security domains where objects of interest (OOI), such as people, vessels, or transactions, are continuously monitored, automated reasoning is required due to their sheer number and volume of information. We present a method to visually explain the rationale of a reasoning engine that raises an alarm if a certain situation is reached. Based both on evidence from heterogeneous and possibly unreliable sources, and on a domain specific reasoning structure, this engine concludes with a certain probability that, e.g., the OOI is suspected of smuggling. To support decision making, we visualize the rationale, an abstraction of the complicated reasoning structure. The evidence is displayed in a color-coded matrix that easily reveals if and where observations contradict. In it, domain and operational experts can quickly understand and find complicated patterns and relate them to real-world situations. Also, two groups of these experts evaluate our system through maritime use cases based on real data.
Figure: A rationale visualization of a maritime use case in which an alarm has risen due to possible Reckless behavior. By hovering the mouse over the name attribute, the operator is investigating the possible names of the vessel and the effect on the reasoning. All hypotheses that depend on the name attribute are highlighted in green.
Rationale visualization for safety and security. Roeland Scheepens, Steffen Michels, Huub van de Wetering, and Jarke J. van Wijk. In Computer Graphics Forum, 34(3):191-200, 2015.
Design of Glyphs for Uncertain Maritime Data [Poster].
Roeland Scheepens, Huub van de Wetering, and Jarke J. van Wijk.
At Eurographics Conference on Visualization (EuroVis), June 2014.
Through a case study we show how to design a specialized multi-variate glyph in cooperation with domain experts. We take a maritime use case, requiring a mission-independent glyph that visualizes attributes with varying properties. From domain requirements, a design process is started in which a number of design parameters and feasible choices for them are determined. We then determine the best choices effectively and efficiently by showing the glyphs in a sandbox environment and allowing the domain experts to play with the parameters.
Figure: The final glyph design in a sandbox environment.
Design of Glyphs for Uncertain Maritime Data [Poster]. R. Scheepens, H. van de Wetering, and J. J. van Wijk. Eurographics Conference on Visualization (EuroVis), June 2014.
Design of Glyphs for Uncertain Maritime Data (Abstract).
Design of Glyphs for Uncertain Maritime Data (Poster).
Non-overlapping Aggregated Multivariate Glyphs for Moving Objects.
In Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), 2014, p. 17-24, 2014.
In moving object visualization, objects and their attributes are commonly represented by glyphs on a geographic map. In areas on the map densely populated by these objects, visual clutter and occlusion of glyphs occur. We propose a method to solve this problem by partitioning the set of all objects into subsets that are each visualized using an aggregated multivariate glyph that shows the distribution of several attributes of its objects, such as heading, type and velocity. We choose the combination of subsets and glyph design such that the glyphs do not overlap and the number of subsets is approximately maximal. The partition is maintained and updated while the objects move. We use examples from the maritime domain, but our method is applicable to a wider range of dynamic data. Through a user study we find that, for a set of representative tasks, our method does not perform significantly worse than competitive visualizations with respect to correctness. Furthermore, it performs significantly better for density comparison tasks in high density data sets. We also find that the participants of the user study have a preference for our method.
Figure: Single moving objects are grouped into larger visual representations to reduce visual clutter and overlap. On the left we show individual vessels in a harbor visualized using vessel glyphs, while on the right we show them grouped and visualized using aggregated multivariate glyphs.
Figure: Our subset glyph (D) is composed of a Type Pie (A) showing the distribution of object types, a heading ring (B) showing the types and number of objects moving in a select number of direction ranges, and a Stationary Disc (C) showing the proportion of stationary objects.
Non-overlapping Aggregated Multivariate Glyphs for Moving Objects. Roeland Scheepens, Huub van de Wetering, and Jarke J. van Wijk. In Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), 2014, p. 17-24, 2014.
Contour based visualization of vessel movement predictions.
In International Journal of Geographical Information Science, vol. 28, no. 5, p. 891-909, 2014.
We present a visualization method for the interactive exploration of predicted positions of moving objects, in particular, ocean-faring vessels. Two simple prediction models, one based on similarity to historical trajectories and one on Monte Carlo simulation, are presented. The prediction models generate temporal probability density fields starting from a known situation. We use contours to visualize spatio-temporal zones of these density fields. Predictions are split into a configurable number of segments for which we render one or more contours. Users, investigating and exploring the possible development of a situation, can see where a vessel will be in the near future according to a given prediction model. Through a number of real-world use cases and a discussion with users, we show our methods can be used in monitoring traffic for collision avoidance, and detecting illegal activities, like piracy or smuggling. By applying our methods to pedestrian movements, we show that our methods can also be applied to a different domain.
Figure: Left: The interaction between predictions of two vessels. Both predictions are over 12 minutes, divided into two time intervals. The interaction chance is shown using a discrete color map ranging from blue (low chance), to red (high chance). We see a large area of potential interaction in the second time interval. The range of the color map can be changed to suppress low probabilities. Right: Prediction of the interaction between a tanker and a vessel suspected of piracy. The prediction is divided into three time instances, 6 minutes apart. There are two areas where the pirate vessel may strike; around 12 minutes and around 18 minutes.
Contour based visualization of vessel movement predictions. Roeland Scheepens, Huub van de Wetering, and Jarke J. van Wijk. In International Journal of Geographical Information Science, vol. 28, no. 5, p. 891-909, 2014.
Composite Density Maps for Multivariate Trajectories.
Roeland Scheepens, Niels Willems, Huub van de Wetering, Gennady Andrienko, Natalia Andrienko, and Jarke J. van Wijk.
In Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2011), p. 147-154, 2011.
Convolution: Computes a density field of a subset of the trajectory data where both the convolution function and the filter functions can be parameterized by an arbitrary function on the trajectory attributes.
Counter: Counts the number of unique vessels per area where both the counting expressing and the trajectory filter function can be parameterized by an arbitrary function on the trajectory attributes.
Composition: An operator that can combine multiple density fields using some arbitrary function.
Enhancement: A set of parameterizable, image-based filters such as: Gaussian blur, edge detection and discretization.
Enrichment: A function that adds an attribute to the trajectory data that can be parameterized by an arbitrary function on the attributes.
Iteration: A meta-function that peforms a bounded number of iterations of a block network.
An analyst can build networks for a large variety of use cases such as the extraction of busy shipping lanes from a large set of trajectories (see above: a), making clearer images of areas with slow moving vessels by inversely scaling the kernel size by an underlying density map (see above: b) or by finding complex movement patterns such as areas with vessel movements that are moving against the 'main flow' (see above: c).
Composite Density Maps for Multivariate Trajectories. Roeland Scheepens, Niels Willems, Huub van de Wetering, Gennady Andrienko, Natalia Andrienko, and Jarke J. van Wijk. In Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2011), p. 147-154, 2011.
Interactive Density Maps for Moving Objects.
Roeland Scheepens, Niels Willems, Huub van de Wetering, and Jarke J. van Wijk.
In IEEE Computer Graphics and Applications 32, 1 (jan.-feb. 2012), 56-66.
In this research we focused on visualizing large scale, multivariate, moving object data, in particular maritime data. These moving objects generally have a large amount of attributes besides time and position, i.e., size, type, velocity, etc. The challenge is how to convey these additional attributes to a user. We researched a method to interactively explore multiple attributes in trajectory data using density maps, i.e., images that show an aggregate overview of massive amounts of data.
Opacity: Each density field is assigned a weight and the color mapped images are blended.
Maximum: For each pixel the color associated with the density field that has the maximum value is chosen.
Block: The screen is divided into a number of blocks, for each block one of the colored images is chosen at random, proportional to the density of the fields.
piechart: A number of piecharts is rendered on the screen to convey the proportion between the underlying density fields for a number of select locations.
One of the things we can do with the above method is visual, spatial anomaly detection. We take a large historic set of trajectories as reference and a live data set and aggregate these. A spatial anomaly is then defined as a 'live' vessel moving in an area where there is little or no traffic in the historic set. We use a density field of the historic data only for the illumination to serve as context and show anomalies with a green to red color map as shown in the image above.
Interactive Visualization of Multivariate Trajectory Data with Density Maps. Roeland Scheepens, Niels Willems, Huub van de Wetering, and Jarke J. van Wijk. In Proceedings of PacificVis 2011, p. 147-154, 2011.
Density Based, Visual Anomaly Detection. Roeland Scheepens, Niels Willems, Huub van de Wetering, and Jarke J. van Wijk. In Proceedings of the international workshop on Maritime Anomaly Detection (MAD) 2011, p. 11-12, 2011.
Interactive Density Maps for Moving Objects. Roeland Scheepens, Niels Willems, Huub van de Wetering, and Jarke J. van Wijk. In Computer Graphics and Applications, IEEE 32, 1 (jan.-feb. 2012), 56-66.
Density maps show trends in objects' trajectories. Density map creation involves aggregating smoothed trajectories in a density field and visualizing the field. Using an interactive distribution map, users can define subsets and, supported by graphics hardware, get fast feedback for these computationally expensive density field calculations.
We consider moving objects as multivariate time-series. By visually analyzing the attributes, patterns may appear that explain why certain movements have occurred. Density maps as proposed by Scheepens et al. are a way to reveal these patterns by means of aggregations of filtered subsets of trajectories. Since filtering is often not sufficient for analysts to express their domain knowledge, we propose to use expressions instead. We present a flexible architecture for density maps to enable custom, versatile exploration using multiple density fields. The flexibility comes from a script, depicted in this paper as a block diagram, which defines an advanced computation of a density field. We define six different types of blocks to create, compose, and enhance trajectories or density fields. Blocks are customized by means of expressions that allow the analyst to model domain knowledge. The versatility of our architecture is demonstrated with several maritime use cases developed with domain experts. Our approach is expected to be useful for the analysis of objects in other domains.
We present a method to interactively explore multiple attributes in trajectory data using density maps, i.e., images that show an aggregate overview of massive amounts of data. So far, density maps have mainly been used to visualize single attributes. Density maps are created in a two-way procedure; first smoothed trajectories are aggregated in a density field, and then the density field is visualized. In our approach, the user can explore attributes along trajectories by calculating a density field for multiple subsets of the data. These density fields are then either combined into a new density field or first visualized and then combined. Using a widget, called a distribution map, the user can interactively define subsets in an effective and intuitive way, and, supported by high-end graphics hardware the user gets fast feedback for these computationally expensive density field calculations. We show the versatility of our method with use cases in the maritime domain: to distinguish between periods in the temporal aggregation, to find anomalously behaving vessels, to solve ambiguities in density maps via drill down in the data, and for risk assessments. Given the generic framework and the lack of domain-specific assumptions, we expect our concept to be applicable for trajectories in other domains as well.
Anomaly detection in a visual setting allows a user to more easily recognize and interactively investigate anomalies. We show how density maps by Scheepens et al. can support the analysis. Density maps are created in a four-way procedure-see Figure 1: First, the user divides the data into one or more subsets using interactive controls. Second, for each subset, smoothed trajectories are aggregated in a density field. Third, the density fields are combined into a single field using an aggregation operator and, finally, the resulting density field is rendered and composed into a density map. With modern graphics hardware, this process can be performed at interactive speeds.
Moving object data visualization and analysis is a growing field of research. In this thesis we present a continuation of previous work where paths of moving vessels are convolved with a kernel onto a den-sity map to aid operators of coastal surveillance systems and analysts. The density map is visualized as a shaded height field and reveals hotspots of vessel activity.
Our improvements are twofold: First, we increase the computation speed of the density map by roughly a factor using modern graphics hardware and second, we introduce several visualization methods to reveal spatial and temporal distributions as well as relations between subsets of the data that are otherwise not visible in the traditional density map.
We present a geographic information system that allows an operator to analyze data sets containing moving objects. While the main focus in this thesis is data sets of ocean faring vessels, we show that our method is applicable to other types of moving object data sets as well.