Sven Gedicke’s research while affiliated with University of Bonn and other places

Visualizing sets of elements and their relations is an important research area in information visualization. In this paper, we present MosaicSets : a novel approach to create Euler-like diagrams from non-spatial set systems such that each element occupies one cell of a regular hexagonal or square grid. The main challenge is to find an assignment of the elements to the grid cells such that each set constitutes a contiguous region. As use case, we consider the research groups of a university faculty as elements, and the departments and joint research projects as sets. We aim at finding a suitable mapping between the research groups and the grid cells such that the department structure forms a base map layout. Our objectives are to optimize both the compactness of the entirety of all cells and of each set by itself. We show that computing the mapping is NP-hard. However, using integer linear programming we can solve real-world instances optimally within a few seconds. Moreover, we propose a relaxation of the contiguity requirement to visualize otherwise non-embeddable set systems. We present and discuss different rendering styles for the set overlays. Based on a case study with real-world data, our evaluation comprises quantitative measures as well as expert interviews.

Visualizing sets of elements and their relations is an important research area in information visualization. In this paper, we present MosaicSets: a novel approach to create Euler-like diagrams from non-spatial set systems such that each element occupies one cell of a regular hexagonal or square grid. The main challenge is to find an assignment of the elements to the grid cells such that each set constitutes a contiguous region. As use case, we consider the research groups of a university faculty as elements, and the departments and joint research projects as sets. We aim at finding a suitable mapping between the research groups and the grid cells such that the department structure forms a base map layout. Our objectives are to optimize both the compactness of the entirety of all cells and of each set by itself. We show that computing the mapping is NP-hard. However, using integer linear programming we can solve real-world instances optimally within a few seconds. Moreover, we propose a relaxation of the contiguity requirement to visualize otherwise non-embeddable set systems. We present and discuss different rendering styles for the set overlays. Based on a case study with real-world data, our evaluation comprises quantitative measures as well as expert interviews.

Map applications on mobile devices such as smartphones and smartwatches have become ubiquitous. When visualizing spatial data on such small-screen devices, one major challenge is annotating the data with labels (e.g., small icons). The restricted space requires new visualization techniques as established strategies, such as maximizing the number of placed labels, easily lead to the omission of information. We propose an approach that distributes all labels contained in a temporarily fixed map section on multiple pages. Applying interaction techniques for navigating through the pages, a user can access all information both without any overlapping labels and without the need for zooming. We propose a method with two phases; a pre-processing phase and a query phase. We use an optimization approach to pre-compute labelings on the level of a whole city and provide the on-demand querying of individual labelings at a more local level. Our approach provides a consistent label-page assignment, meaning that labels do not appear and disappear when the user pans the map. Further, our model provides quick access to important information and a spatially balanced distribution of labels on pages. In experiments on real-world data we analyze different parameter settings and show that our model yields high-quality labelings.

Map generalization is the process of deriving small-scale target maps from a large-scale source map or database while preserving valuable information. In this paper we focus on topographic data, in particular areas of different land-use classes and line features representing the road network. When reducing the map scale, some areas need to be merged to larger composite regions. This process is known as area aggregation. Given a planar partition of areas, one usually aims to build geometrically compact regions of sufficient size while keeping class changes small. Since line features (e.g. roads) are perceived as separating elements in a map, we suggest integrating them into the process of area aggregation. Our aim is that boundaries of regions coincide with line features in such a way that strokes (i.e. chains of line features with small angles of deflection) are not broken into short sections. Complementing the criteria of compact regions and preserving land-use information, we consider this aim as a third criterion. Regarding all three criteria, we formalize an optimization problem and solve it with a heuristic approach using simulated annealing. Our evaluation is based on experiments with different parameter settings. In particular, we compare results of a baseline method that considers two criteria, namely compactness and class changes, with results of our new method that additionally considers our stroke-based criterion. Our results show that this third criterion can be substantially improved while keeping the quality with respect to the original two criteria on a similar level.

Visualizing spatial data on small-screen devices such as smartphones and smartwatches poses new challenges in computational cartography. The current interfaces for map exploration require their users to zoom in and out frequently. Indeed, zooming and panning are tools suitable for choosing the map extent corresponding to an area of interest. They are not as suitable, however, for resolving the graphical clutter caused by a high feature density since zooming in to a large map scale leads to a loss of context. Therefore, in this paper, we present new external labeling methods that allow a user to navigate through dense sets of points of interest while keeping the current map extent fixed. We provide a unified model, in which labels are placed at the boundary of the map and visually associated with the corresponding features via connecting lines, which are called leaders. Since the screen space is limited, labeling all features at the same time is impractical. Therefore, at any time, we label a subset of the features. We offer interaction techniques to change the current selection of features systematically and, thus, give the user access to all features. We distinguish three methods, which allow the user either to slide the labels along the bottom side of the map or to browse the labels based on pages or stacks. We present a generic algorithmic framework that provides us with the possibility of expressing the different variants of interaction techniques as optimization problems in a unified way. We propose both exact algorithms and fast and simple heuristics that solve the optimization problems taking into account different criteria such as the ranking of the labels, the total leader length as well as the distance between leaders. In experiments on real-world data we evaluate these algorithms and discuss the three variants with respect to their strengths and weaknesses proving the flexibility of the presented algorithmic framework.

Visualizing spatial data on small-screen devices such as smartphones and smartwatches poses new challenges in computational cartography. The current interfaces for map exploration require their users to zoom in and out frequently. Indeed, zooming and panning are tools suitable for choosing the map extent corresponding to an area of interest. They are not as suitable, however, for resolving the graphical clutter caused by a high feature density since zooming in to a large map scale leads to a loss of context. Therefore we present new external labeling methods that allow navigating through dense sets of points of interest while keeping the current map extent fixed. We provide a unified model, in which labels are placed at the boundary of the map and visually associated with the corresponding features via connecting lines, which are called leaders. Since the screen space is limited, labeling all features at the same time is impractical. Therefore, at any time, we label a subset of the features. We offer interaction techniques to change the current selection of features systematically and, thus, give the user access to all features. We distinguish three methods, which allow the user either to slide the labels along the bottom side of the map or to browse the labels based on pages or stacks. We present a generic algorithmic framework that provides us with the possibility of expressing the different variants of interaction techniques as optimization problems in a unified way. We propose both exact algorithms and fast and simple heuristics that solve the optimization problems taking into account different criteria such as the ranking of the labels, the total leader length and the distance between leaders. We experimentally evaluate these algorithms and discuss the three variants with respect to their strengths and weaknesses proving the flexibility of the presented algorithmic framework.