EuroVA18

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https://diglib.eg.org/handle/10.2312/2632398

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Browsing EuroVA18 by Subject "Graphical user interfaces (GUI)"

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    A Concept for Consensus-based Ordering of Views
    (The Eurographics Association, 2018) Jentner, Wolfgang; Jäckle, Dominik; Engelke, Ulrich; Keim, Daniel A.; Schreck, Tobias; Christian Tominski and Tatiana von Landesberger
    High-dimensional data poses a significant challenge for analysis, as patterns typically exist only in subsets of dimensions or records. A common approach to reveal patterns, such as meaningful structures or relationships, is to split the data and then to create a visual representation (views) for each data subset. This introduces the problem of ordering the views effectively because patterns can depend on the presented sequence. Existing methods provide metrics and heuristics to achieve an ordering of views based on their data characteristics. However, an effective ordering of subspace views is expected to rely on task- and data-dependent properties. Hence, heuristic-based ordering methods can be highly objective and not relevant to the task at hand, which is why the user involvement is key to find a meaningful ordering. We introduce a concept for a consensus-based ordering of views that learns to form sequences of subset views fitting the overall users' needs. This concept allows users to decide on the ordering freely and accumulates their preference into a global view that reflects the consensus. We showcase and discuss this concept based on ordering colored tiles from the controversially discussed rainbow color map.
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    polimaps: Supporting Predictive Policing with Visual Analytics
    (The Eurographics Association, 2018) Stoffel, Florian; Post, Hanna; Stewen, Marcus; Keim, Daniel A.; Christian Tominski and Tatiana von Landesberger
    Recently, predictive policing has gained a lot of attention, as the benefits, e.g., better crime prevention or an optimized resource planning are essential goals for law enforcement agencies. Commercial predictive policing systems commonly visualize predictions on maps but provide only little support for human analysts in the technical and methodological processes that constitute corresponding implementations. In this paper, we report on a project of bringing visual analytics to the field of predictive policing. We introduce a process model that includes machine learning as well as visualization and has been developed together with experts from a law enforcement agency. We also showcase a visual analytics tool, called polimaps, that is part of a real-world predictive policing project and implements elements of the proposed process.