2019, Kraus, Matthias, Buchmüller, Juri F., Schweitzer, Daniel, Keim, Daniel A., Fuchs, Johannes

3D heightmaps can be considered as an extension of heatmaps using the third dimension to encode the respective value by height, often in addition to encoding it by color. In contrast to 2D heatmaps, 3D heightmaps allow a superposition without aggregation. However, they also have the general disadvantages of 3D visualizations, such as occlusion and perceptual distortion. Previous research has revealed various advantages of stereoscopic displays and virtual reality (VR) in the context of 3D visualizations, for example, concerning memorization, depth perception, and collaboration. In this paper, we present a novel technique to compare heightmaps in VR by introducing a multi-layer approach of stacked heightmaps. We demonstrate the applicability and usefulness of our method by means of a use case on comparative crime data analysis.

2019, Buchmüller, Juri F., Cakmak, Eren, Andrienko, Natalia, Andrienko, Gennady, Jolles, Jolle, Keim, Daniel A.

While conventional applications for spatiotemporal datasets mostly focus on the relation between movers and environment, research questions in the analysis of collective movement typically focus more on relationships and dynamics between the moving entities themselves. Instead of concentrating on origin, destination and the way in between, this inter-mover perspective on spatiotemporal data allows to explain how moving groups are coordinating. Yet, only few visualization and Visual Analytics approaches focus on the relationships between movers. To illuminate this research gap, we propose initial steps towards a comprehensive formalization of coordination in collective movement based on temporal autocorrelation of distance matrices derived from basic movement characteristics. We exemplify how patterns can be encoded using autocorrelation cubes and outline the next steps towards an exhaustive formalization of coordination patterns.

2019, Jentner, Wolfgang, Buchmüller, Juri F., Sperrle, Fabian, Sevastjanova, Rita, Spinner, Thilo, Schlegel, Udo, Streeb, Dirk, Schäfer, Hanna

We present N.E.A.T. - a Visual Analytics approach to the collaborative management of large-scale emergencies. N.E.A.T. unifies the analysis and annotation of heterogeneous, uncertainty-afflicted data sources in a single, adjustable screen. Stakeholders can create individual or shared workspaces providing configurable views tailored to the needs of different emergency responders. Within each workspace, annotated findings are automatically shared in real-time for effective collaboration. We illustrate the functionality of the tool and showcase exemplary findings on the St. Himark incident.

2018, Cakmak, Eren, Castiglia, Giuliano, Jentner, Wolfgang, Buchmüller, Juri F., Keim, Daniel A.

Control centers for safety-critical infrastructures such as train systems rely on proven, time-tested visualizations to support the decision-making process of the operators. These systems are facing new challenges nowadays as traffic has increased. We describe an incremental visualization design process to adapt Train Management Systems to new tasks, while carefully building on existing techniques to ensure a continuous work environment for operators without or little additional training. The main focus of this work-in-progress is to provide additional contextual information to operators unobtrusively and to incorporate multi-perspective prediction models helping operators to make informed decisions efficiently.

2019, Meschenmoser, Philipp, Buchmüller, Juri F., Keim, Daniel A.

Airspace route planning relies on many regulations and individual factors that can be hard to understand for audiences without advanced domain knowledge. This aspect is problematic if regulations are discussed in complex debates about changing air traffic distributions, affecting the broad public in negative and positive ways. To increase accessibility and transparency, we propose a regulation-oriented scheme of trajectory filters that includes a fully automated detection component for regulation deviations. The scheme further includes filters by daytime, custom areas, MTOM, and is part of a client-independent web prototype. In this publication, we specify details on individual filters and their inter- play (1st contribution), while putting a particular emphasis on the deviation detector (2nd contribution).

2019-01, Buchmüller, Juri F., Jäckle, Dominik, Cakmak, Eren, Brandes, Ulrik, Keim, Daniel A.

Understanding the movement patterns of collectives, such as flocks of birds or fish swarms, is an interesting open research question. The collectives are driven by mutual objectives or react to individual direction changes and external influence factors and stimuli. The challenge in visualizing collective movement data is to show space and time of hundreds of movements at the same time to enable the detection of spatiotemporal patterns. In this paper, we propose MotionRugs, a novel space efficient technique for visualizing moving groups of entities. Building upon established space-partitioning strategies, our approach reduces the spatial dimensions in each time step to a one-dimensional ordered representation of the individual entities. By design, MotionRugs provides an overlap-free, compact overview of the development of group movements over time and thus, enables analysts to visually identify and explore group-specific temporal patterns. We demonstrate the usefulness of our approach in the field of fish swarm analysis and report on initial feedback of domain experts from the field of collective behavior.

2019, Jeitler, Astrik Veronika, Türkoglu, Alpin, Makarov, Denis, Jockers, Timo, Buchmüller, Juri F., Schlegel, Udo, Keim, Daniel A.

This paper presents RescueMark, a web-based visual analytics tool for analyzing disaster situations and guiding emergency response. In disaster situations operators must take quick and effective decisions to solve critical problems. RescueMark provides spatial, topic and temporal event exploration supporting decision making for resource allocation and determine damaged areas of the city. We describe the data analysis and visualization process of the social media data applied to extract the relevant information.

2019, Angelini, Marco, Buchmüller, Juri F., Keim, Daniel A., Meschenmoser, Philipp, Santucci, Giuseppe

Visualizing contextual information to a map often comes at the expense of overplotting issues. Especially for use cases with relevant map features in the immediate vicinity of an information to add, occlusion of the relevant map context should be avoided. We present SurgeryCuts, a map manipulation technique for the creation of additional canvas area for contextual visualizations on maps. SurgeryCuts is occlusion-free and does not shift, zoom or alter the map viewport. Instead, relevant parts of the map can be cut apart. The affected area is controlledly distorted using a parameterizable warping function fading out the map distortion depending on the distance to the cut. We define extended metrics for our approach and compare to related approaches. As well, we demonstrate the applicability of our approach at the example of tangible use cases and a comparative user study.

2019, Majumdar, Arjun, Ymeri, Gent, Strumbelj, Sebastian, Buchmüller, Juri F., Schlegel, Udo, Keim, Daniel A.

This paper presents our tool for the Vast Challenge 2019 Mini Challenge 1 (MC1). It will give an overview of the approach of data preprocessing techniques used for the given dataset and it will introduce our application which is built considering the requirements and questions to be answered for the MC1. This application consists of Machine Learning techniques and Information Visualization techniques such as Integrated Spatial Uncertainty Visualization as shown in this paper [1] to convey the needed information to the end users. To show the usefulness of this application we give examples of analysis.

2019, Nagel, Fabian, Castiglia, Giuliano, Ademaj, Gemza, Buchmüller, Juri F., Schlegel, Udo, Keim, Daniel A.

The goal of the VAST challenge 2019 Mini Challenge 2 was to visualize radioactive contaminations measured by mobile and static sensors and their changes over time, allowing city officials to determine the severity of the leakage at the city's nuclear power plant. We propose cpmViz, a web-based tool that allows for interactive data exploration of the sensor readings in both of the spatial and temporal dimensions. The tool consists out of three views that are connected via linking and scrolling. We visualize static sensor uncertainty by introducing Voronoi cells to illustrate how much space is covered by an individual measurement unit. For mobile sensors, we showcase their activity periods and introduce the concept of sensor streaks as periods of uninterrupted recordings as a temporal uncertainty measure. As for spatial uncertainty, we color individual districts based on the amount of data that was recorded inside the user's selected time window. Using our system, we were able to easily spot major events like the city's initial earthquake in the sensor readings. Certain southern districts are clearly visible as areas of concern that we consider in need of more static sensors. Furthermore, we were also able to identify static as well as moving contaminations.