Browsing by Author "Koch, Steffen"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Air Quality Temporal Analyser: Interactive Temporal Analyses with Visual Predictive Assessments
    (The Eurographics Association, 2021) Harbola, Shubhi; Koch, Steffen; Ertl, Thomas; Coors, Volker; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    This work presents Air Quality Temporal Analyser (AQTA), an interactive system to support visual analyses of air quality data with time. This interactive AQTA allows the seamless integration of predictive models and detailed patterns analyses. While previous approaches lack predictive air quality options, this interface provides back-and-forth dialogue with the designed multiple Machine Learning (ML) models and comparisons for better visual predictive assessments. These models can be dynamically selected in real-time, and the user could visually compare the results in different time conditions for chosen parameters. Moreover, AQTA provides data selection, display, visualisation of past, present, future (prediction) and correlation structure among air parameters, highlighting the predictive models effectiveness. AQTA has been evaluated using Stuttgart (Germany) city air pollutants, i:e:, Particular Matter (PM) PM10, Nitrogen Oxide (NO), Nitrogen Dioxide (NO2), and Ozone (O3) and meteorological parameters like pressure, temperature, wind and humidity. The initial findings are presented that corroborate the city’'s COVID lockdown (year 2020) conditions and sudden changes in patterns, highlighting the improvements in the pollutants concentrations. AQTA, thus, successfully discovers temporal relationships among complex air quality data, interactively in different time frames, by harnessing the user's knowledge of factors influencing the past, present and future behavior, with the aid of ML models. Further, this study also reveals that the decrease in the concentration of one pollutant does not ensure that the surrounding air quality would improve as other factors are interrelated.
  • Thumbnail Image
    Item
    Visual Analysis of Spatio-temporal Phenomena with 1D Projections
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Franke, Max; Martin, Henry; Koch, Steffen; Kurzhals, Kuno; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio-temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial context can be helpful for understanding details, but have proven to be less effective for overview and comparison tasks. We present an interactive approach for the exploration of spatio-temporal data, based on a set of neighborhood-preserving 1D projections which help identify patterns and support the comparison of numerous time steps and multivariate data. An important objective of the proposed approach is the visual description of local neighborhoods in the 1D projection to reveal patterns of similarity and propagation. As this locality cannot generally be guaranteed, we provide a selection of different projection techniques, as well as a hierarchical approach, to support the analysis of different data characteristics. In addition, we offer an interactive exploration technique to reorganize and improve the mapping locally to users' foci of interest. We demonstrate the usefulness of our approach with different real-world application scenarios and discuss the feedback we received from domain and visualization experts.
  • Thumbnail Image
    Item
    Visual Planning and Analysis of Latin Formation Dance Patterns
    (The Eurographics Association, 2023) Beck, Samuel; Doerr, Nina; Schmierer, Fabian; Sedlmair, Michael; Koch, Steffen; Gillmann, Christina; Krone, Michael; Lenti, Simone
    Latin formation dancing is a team sport in which up to eight couples perform a coordinated choreography. A central part are the patterns formed by the dancers on the dance floor and the transitions between them. Planning and practicing patterns are some of the most challenging aspects of Latin formation dancing. Interactive visualization approaches can support instructors as well as dancers in tackling these challenges. We present a web-based visualization prototype that assists with the planning, training, and analysis of patterns. Its design was iteratively developed with the involvement of experienced formation instructors. The interface offers views of the dancers' positions and orientations, pattern transitions, poses, and analytical information like dance floor utilization and movement distances. In a first expert study with formation instructors, the prototype was well received.
  • Thumbnail Image
    Item
    Visualizing Temporal-Thematic Patterns in Text Collections
    (The Eurographics Association, 2021) Knabben, Moritz; Baumann, Martin; Blascheck, Tanja; Ertl, Thomas; Koch, Steffen; Andres, Bjoern and Campen, Marcel and Sedlmair, Michael
    Visualizing the temporal evolution of texts is relevant for many domains that seek to gain insight from text repositories. However, existing visualization methods for text collections do not show fine-grained temporal-thematic patterns. Therefore, we developed and analyzed a new visualization method that aims at uncovering such patterns. Specifically, we project texts to one dimension, which allows positioning texts in a 2D diagram of projection space and time. For projection, we employed two manifold learning algorithms: the self-organizing map (SOM) and UMAP. To assess the utility of our method, we experimented with real-world datasets and discuss the resulting visualizations. We find our method facilitates relating patterns and extracting associated texts beyond what is possible with previous techniques. We also conducted interviews with historians to show that our prototypical system supports domain experts in their analysis tasks.