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- PublikationToward flexible visual analytics augmented through smooth display transitions(Elsevier, 2021) Tominski, Christian; Andrienko, Gennady; Andrienko, Natalia; Bleisch, Susanne; Fabrikant, Sara Irina; Mayr, Eva; Miksch, Silvia; Pohl, Margit; Skupin, André [in: Visual Informatics]Visualizing big and complex multivariate data is challenging. To address this challenge, we propose flexible visual analytics (FVA) with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics, while maintaining the strengths of multiple perspectives on the studied data. At the heart of our proposed approach are transitions that fluidly transform data between user-relevant views to offer various perspectives and insights into the data. While smooth display transitions have been already proposed, there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas. As a call to further action, we argue that future research is necessary to develop a conceptual framework for flexible visual analytics. We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them, and consider the display user for whom such depictions are produced and made available for visual analytics. With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationRelationship of critical dynamics, functional connectivity, and states of consciousness in large-scale human brain networks(Elsevier, 2019) Lee, Heonsoo; Golkowski, Daniel; Jordan, Denis; Berger, Sebastian; Ilg, Rüdiger; Lee, Joseph; Mashour, George A.; Lee, UnCheol; Avidan, Michael S.; Blain-Moraes, Stefanie; Golmirzaie, Goodarz; Hardie, Randall; Hogg, Rosemary; Janke, Ellen; Kelz, Max B.; Maier, Kaitlyn; Mashour, George A.; Maybrier, Hannah; McKinstry-Wu, Andrew; Muench, Maxwell; Ochroch, Andrew; Palanca, Ben J.A.; Picton, Paul; Schwarz, E. Marlon; Tarnal, Vijay; Vanini, Giancarlo; Vlisides, Phillip E. [in: NeuroImage]Recent modeling and empirical studies support the hypothesis that large-scale brain networks function near a critical state. Similar functional connectivity patterns derived from resting state empirical data and brain network models at criticality provide further support. However, despite the strong implication of a relationship, there has been no principled explanation of how criticality shapes the characteristic functional connectivity in large-scale brain networks. Here, we hypothesized that the network science concept of partial phase locking is the underlying mechanism of optimal functional connectivity in the resting state. We further hypothesized that the characteristic connectivity of the critical state provides a theoretical boundary to quantify how far pharmacologically or pathologically perturbed brain connectivity deviates from its critical state, which could enable the differentiation of various states of consciousness with a theory-based metric. To test the hypothesis, we used a neuroanatomically informed brain network model with the resulting source signals projected to electroencephalogram (EEG)-like sensor signals with a forward model. Phase lag entropy (PLE), a measure of phase relation diversity, was estimated and the topography of PLE was analyzed. To measure the distance from criticality, the PLE topography at a critical state was compared with those of the EEG data from baseline consciousness, isoflurane anesthesia, ketamine anesthesia, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state. We demonstrate that the partial phase locking at criticality shapes the functional connectivity and asymmetric anterior-posterior PLE topography, with low (high) PLE for high (low) degree nodes. The topographical similarity and the strength of PLE differentiates various pharmacologic and pathologic states of consciousness. Moreover, this model-based EEG network analysis provides a novel metric to quantify how far a pharmacologically or pathologically perturbed brain network is away from critical state, rather than merely determining whether it is in a critical or non-critical state.01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationAccurate visual localization in outdoor and indoor environments exploiting 3D image spaces as spatial reference(International Society for Photogrammetry and Remote Sensing, 2018) Rettenmund, Daniel; Fehr, Markus; Cavegn, Stefan; Nebiker, Stephan [in: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences]In this paper, we present a method for visual localization and pose estimation based on 3D image spaces. The method works in indoor and outdoor environments and does not require the presence of control points or markers. The method is evaluated with different sensors in an outdoor and an indoor test field. The results of our research show the viability of single image localization with absolute position accuracies at the decimetre level for outdoor environments and 5 cm or better for indoor environments. However, the evaluation also revealed a number of limitations of single image visual localization in real-world environments. Some of them could be addressed by an alternative AR-based localization approach, which we also present and compare in this paper. We then discuss the strengths and weaknesses of the two approaches and show possibilities for combining them to obtain accurate and robust visual localization in an absolute coordinate frame.04B - Beitrag Konferenzschrift
- PublikationExploratory geovisualizations for supporting the qualitative analysis and synthesis of place-related emotion data(North American Cartographic Information Society, 2019) Bleisch, Susanne; Hollenstein, Daria [in: Cartographic Perspectives]Locations become places through personal significance and experience. While place data are not emotion data, per se, personal significance and experience are often emotional. In this paper, we explore the potential of using visual data exploration to support the qualitative analysis of place-related emotion data. To do so, we draw upon Creswell’s (2009) definition of place to define a generic data model that contains emotion data for a given location and its locale. For each data dimension in our model, we present symbolization options that can be combined to create a range of interactive visualizations, specifically supporting re-expression. We discuss the usefulness of example visualizations, created based on a data set from a pilot study on how elderly women experience their neighborhood. We find that the visualizations support four broad qualitative data analysis tasks: revising categorizations, making connections and relationships, aggregating for synthesis, and corroborating evidence by combining sense of place with locale information to support a holistic interpretation of place data. In conclusion, the paper contributes to the literature in three ways. It provides a generic data model and associated symbolization options, and uses examples to show how place-related emotion data can be visualized. Further, the example visualizations make explicit how re-expression, the combination of emotion data with locale information, and visualization of vagueness and linked data support the analysis of emotion data. Finally, we advocate for visualization-supported qualitative data analysis in interdisciplinary teams so that more suitable maps are used and so that cartographers can better understand and support qualitative data analysis.01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationExploratory bivariate and multivariate geovisualizations of a social vulnerability index(North American Cartographic Information Society, 2020) Strode, Georgianna; Mesev, Victor; Bleisch, Susanne; Ziewitz, Kathryn; Reed, Fennis; Morgan, John Derek [in: Cartographic Perspectives]In the United States, the Centers for Disease Control and Prevention (CDC) is the national agency that conducts and supports public health research and practice. Among the CDC’s many achievements is the development of a social vulnerability index (SVI) to aid planners and emergency responders when identifying vulnerable segments of the population, especially during natural hazard events. The index includes an overall social vulnerability ranking as well as four individual themes: socioeconomic, household composition & disability, ethnicity & language, and housing & transportation. This makes the SVI dataset multivariate, but it is typically viewed via maps that show one theme at a time. This paper explores a suite of cartographic techniques that can represent the SVI beyond the univariate view. Specifically, we recommend three techniques: (1) bivariate mapping to illustrate overall vulnerability and population density, (2) multivariate mapping using cartographic glyphs to disaggregate levels of the four vulnerability themes, and (3) visual analytics using Euler diagrams to depict overlap between the vulnerability themes. The CDC’s SVI, and by extension, vulnerability indices in other countries, can be viewed in a variety of cartographic forms that illustrate the location of vulnerable groups of society. Viewing data from various perspectives can facilitate the understanding and analysis of the growing amount and complexity of data.01A - Beitrag in wissenschaftlicher Zeitschrift