DevLan/vispubs huggingface dataset:
Visual Analysis of Proximal Temporal Relationships of Social and Communicative Behaviors
Authors: Yi Han 0005, Agata Rozga, Nevena Dimitrova, Gregory D. Abowd, John T. Stasko
Published at EuroVis, 2015

Developmental psychology researchers examine the temporal relationships of social and communicative behaviors, such as how a child responds to a name call, to understand early typical and atypical development and to discover early signs of autism and developmental delay. These related behaviors occur together or within close temporal proximity, forming unique patterns and relationships of interest. However, the task of finding these early signs, which are in the form of atypical behavioral patterns, becomes more challenging when behaviors of multiple children at different ages need to be compared with each other in search of generalizable patterns. The ability to visually explore the temporal relationships of behaviors, including flexible redefinition of closeness, over multiple social interaction sessions with children of different ages, can make such knowledge extraction easier. We have designed a visualization tool called TipoVis that helps psychology researchers visually explore the temporal patterns of social and communicative behaviors. We present two case studies to show how TipoVis helped two researchers derive new understandings of their data.
Visual Analysis of Spatio-Temporal Data: Applications in Weather Forecasting
Authors: Alexandra Diehl, Leandro Pelorosso, Claudio Delrieux, Celeste Saulo, Juan Ruiz 0002, M. Eduard Gröller, Stefan Bruckner
Published at EuroVis, 2015

Weather conditions affect multiple aspects of human life such as economy, safety, security, and social activities. For this reason, weather forecast plays a major role in society. Currently weather forecasts are based on Numerical Weather Prediction (NWP) models that generate a representation of the atmospheric flow. Interactive visualization of geo‐spatial data has been widely used in order to facilitate the analysis of NWP models. This paper presents a visualization system for the analysis of spatio‐temporal patterns in short‐term weather forecasts. For this purpose, we provide an interactive visualization interface that guides users from simple visual overviews to more advanced visualization techniques. Our solution presents multiple views that include a timeline with geo‐referenced maps, an integrated webmap view, a forecast operation tool, a curve‐pattern selector, spatial filters, and a linked meteogram. Two key contributions of this work are the timeline with geo‐referenced maps and the curve‐pattern selector. The latter provides novel functionality that allows users to specify and search for meaningful patterns in the data. The visual interface of our solution allows users to detect both possible weather trends and errors in the weather forecast model. We illustrate the usage of our solution with a series of case studies that were designed and validated in collaboration with domain experts.
Visual Analytics for Correlation-Based Comparison of Time Series Ensembles
Authors: Patrick Köthur, Carl Witt, Mike Sips, Norbert Marwan, Stefan Schinkel, Doris Dransch
Published at EuroVis, 2015

An established approach to studying interrelations between two non‐stationary time series is to compute the ‘windowed’ cross‐correlation (WCC). The time series are divided into intervals and the cross‐correlation between corresponding intervals is calculated. The outcome is a matrix that describes the correlation between two time series for different intervals and varying time lags. This important technique can only be used to compare two single time series. However, many applications require the comparison of ensembles of time series. Therefore, we propose a visual analytics approach that extends the WCC to support a correlation‐based comparison of two ensembles of time series. We compute the pairwise WCC between all time series from the two ensembles, which results in hundreds of thousands of WCC matrices. Statistical measures are used to derive a concise description of the time‐varying correlations between the ensembles as well as the uncertainty of the correlation values. We further introduce a visually scalable overview visualization of the computed correlation and uncertainty information. These components are combined with multiple linked views into a visual analytics system to support configuration of the WCC as well as detailed analysis of correlation patterns between two ensembles. Two use cases from very different domains, cognitive science and paleoclimatology, demonstrate the utility of our approach.
Visual Analytics for Exploring Local Impact of Air Traffic
Authors: Juri Buchmüller, Halldór Janetzko, Gennady L. Andrienko, Natalia V. Andrienko, Georg Fuchs, Daniel A. Keim
Published at EuroVis, 2015

The environmental and noise impact of airports often causes extensive political discussion which in some cases even lead to transnational tensions. Analyzing local approach and departure patterns around an airport is difficult since it depends on a variety of complex variables like weather, local and general regulations and many more. Yet, understanding these movements and the expected amount of flights during arrival and departure is of great interest to both casual and expert users, as planes have a higher impact on the areas beneath during these phases. We present a Visual Analytics framework that enables users to develop an understanding of local flight behavior through visual exploration of historical data and interactive manipulation of prediction models with direct feedback, as well as a classification quality visualization using a random noise metaphor. We showcase our approach using real world data from the Zurich International Airport region, where aircraft noise has led to an ongoing conflict between Germany and Switzerland. The use cases, findings and expert feedback demonstrate how our approach helps in understanding the situation and to substantiate the otherwise often subjective discourse on the topic.
Visual Analytics for the Exploration of Tumor Tissue Characterization
Authors: Renata Georgia Raidou, Uulke A. van der Heide, Cuong Viet Dinh, Ghazaleh Ghobadi, Jesper Kallehauge, Marcel Breeuwer, Anna Vilanova
Published at EuroVis, 2015

Tumors are heterogeneous tissues consisting of multiple regions with distinct characteristics. Characterization of these intra‐tumor regions can improve patient diagnosis and enable a better targeted treatment. Ideally, tissue characterization could be performed non‐invasively, using medical imaging data, to derive per voxel a number of features, indicative of tissue properties. However, the high dimensionality and complexity of this imaging‐derived feature space is prohibiting for easy exploration and analysis ‐ especially when clinical researchers require to associate observations from the feature space to other reference data, e.g., features derived from histopathological data. Currently, the exploratory approach used in clinical research consists of juxtaposing these data, visually comparing them and mentally reconstructing their relationships. This is a time consuming and tedious process, from which it is difficult to obtain the required insight. We propose a visual tool for: (1) easy exploration and visual analysis of the feature space of imaging‐derived tissue characteristics and (2) knowledge discovery and hypothesis generation and confirmation, with respect to reference data used in clinical research. We employ, as central view, a 2D embedding of the imaging‐derived features. Multiple linked interactive views provide functionality for the exploration and analysis of the local structure of the feature space, enabling linking to patient anatomy and clinical reference data. We performed an initial evaluation with ten clinical researchers. All participants agreed that, unlike current practice, the proposed visual tool enables them to identify, explore and analyze heterogeneous intra‐tumor regions and particularly, to generate and confirm hypotheses, with respect to clinical reference data.
Visual Assessment of Alleged Plagiarism Cases
Authors: Patrick Riehmann, Martin Potthast, Benno Stein 0001, Bernd Fröhlich 0001
Published at EuroVis, 2015

We developed a visual analysis tool to support the verification, assessment, and presentation of alleged cases of plagiarism. The analysis of a suspicious document typically results in a compilation of categorized “finding spots”. The categorization reveals the way in which the suspicious text fragment was created from the source, e.g. by obfuscation, translation, or by shake and paste. We provide a three‐level approach for exploring the finding spots in context. The overview shows the relationship of the entire suspicious document to the set of source documents. A glyph‐based view reveals the structural and textual differences and similarities of a set of finding spots and their corresponding source text fragments. For further analysis and editing of the finding spot's assessment, the actual text fragments can be embedded side‐by‐side in the diffline view. The different views are tied together by versatile navigation and selection operations. Our expert reviewers confirm that our tool provides a significant improvement over existing static visualizations for assessing plagiarism cases.
Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections
Authors: Shusen Liu 0001, Bei Wang 0001, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Valerio Pascucci
Published at EuroVis, 2015

We introduce a novel interactive framework for visualizing and exploring high‐dimensional datasets based on subspace analysis and dynamic projections. We assume the high‐dimensional dataset can be represented by a mixture of low‐dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real‐world examples to demonstrate the novelty and usability of our proposed framework.
Visualization of Coherent Structures of Light Transport
Authors: Tobias Zirr, Marco Ament, Carsten Dachsbacher
Published at EuroVis, 2015

Inspired by vector field topology, an established tool for the extraction and identification of important features of flows and vector fields, we develop means for the analysis of the structure of light transport. For that, we derive an analogy to vector field topology that defines coherent structures in light transport. We also introduce Finite‐Time Path Deflection (FTPD), a scalar quantity that represents the deflection characteristic of all light transport paths passing through a given point in space. For virtual scenes, the FTPD can be computed directly using path‐space Monte Carlo integration. We visualize the FTPD field for several example scenes and discuss the revealed structures. Lastly, we show that the coherent regions visualized by the FTPD are closely related to the coherent regions in our new topologically‐motivated analysis of light transport. FTPD visualizations are thus also visualizations of the structure of light transport.
Visualization of Object-Centered Vulnerability to Possible Flood Hazards
Authors: Daniel Cornel, Artem Konev, Bernhard Sadransky, Zsolt Horváth, M. Eduard Gröller, Jürgen Waser
Published at EuroVis, 2015

As flood events tend to happen more frequently, there is a growing demand for understanding the vulnerability of infrastructure to flood‐related hazards. Such demand exists both for flood management personnel and the general public. Modern software tools are capable of generating uncertainty‐aware flood predictions. However, the information addressing individual objects is incomplete, scattered, and hard to extract. In this paper, we address vulnerability to flood‐related hazards focusing on a specific building. Our approach is based on the automatic extraction of relevant information from a large collection of pre‐simulated flooding events, called a scenario pool. From this pool, we generate uncertainty‐aware visualizations conveying the vulnerability of the building of interest to different kinds of flooding events. On the one hand, we display the adverse effects of the disaster on a detailed level, ranging from damage inflicted on the building facades or cellars to the accessibility of the important infrastructure in the vicinity. On the other hand, we provide visual indications of the events to which the building of interest is vulnerable in particular. Our visual encodings are displayed in the context of urban 3D renderings to establish an intuitive relation between geospatial and abstract information. We combine all the visualizations in a lightweight interface that enables the user to study the impacts and vulnerabilities of interest and explore the scenarios of choice. We evaluate our solution with experts involved in flood management and public communication.
Visualization of Particle-based Data with Transparency and Ambient Occlusion
Authors: Joachim Staib, Sebastian Grottel, Stefan Gumhold
Published at EuroVis, 2015

Particle‐based simulation techniques, like the discrete element method or molecular dynamics, are widely used in many research fields. In real‐time explorative visualization it is common to render the resulting data using opaque spherical glyphs with local lighting only. Due to massive overlaps, however, inner structures of the data are often occluded rendering visual analysis impossible. Furthermore, local lighting is not sufficient as several important features like complex shapes, holes, rifts or filaments cannot be perceived well.To address both problems we present a new technique that jointly supports transparency and ambient occlusion in a consistent illumination model. Our approach is based on the emission‐absorption model of volume rendering. We provide analytic solutions to the volume rendering integral for several density distributions within a spherical glyph. Compared to constant transparency our approach preserves the three‐dimensional impression of the glyphs much better. We approximate ambient illumination with a fast hierarchical voxel cone‐tracing approach, which builds on a new real‐time voxelization of the particle data.Our implementation achieves interactive frame rates for millions of static or dynamic particles without any preprocessing. We illustrate the merits of our method on real‐world data sets gaining several new insights.
Visualizing Time-Specific Hurricane Predictions, with Uncertainty, from Storm Path Ensembles
Authors: Le Liu 0007, Mahsa Mirzargar, Robert M. Kirby, Ross T. Whitaker, Donald H. House
Published at EuroVis, 2015

The U.S. National Hurricane Center (NHC) issues advisories every six hours during the life of a hurricane. These advisories describe the current state of the storm, and its predicted path, size, and wind speed over the next five days. However, from these data alone, the question “What is the likelihood that the storm will hit Houston with hurricane strength winds between 12:00 and 14:00 on Saturday?” cannot be directly answered. To address this issue, the NHC has recently begun making an ensemble of potential storm paths available as part of each storm advisory. Since each path is parameterized by time, predicted values such as wind speed associated with the path can be inferred for a specific time period by analyzing the statistics of the ensemble. This paper proposes an approach for generating smooth scalar fields from such a predicted storm path ensemble, allowing the user to examine the predicted state of the storm at any chosen time. As a demonstration task, we show how our approach can be used to support a visualization tool, allowing the user to display predicted storm position – including its uncertainty – at any time in the forecast. In our approach, we estimate the likelihood of hurricane risk for a fixed time at any geospatial location by interpolating simplicial depth values in the path ensemble. Adaptivelysized radial basis functions are used to carry out the interpolation. Finally, geometric fitting is used to produce a simple graphical visualization of this likelihood. We also employ a non‐linear filter, in time, to assure frame‐to‐frame coherency in the visualization as the prediction time is advanced. We explain the underlying algorithm and definitions, and give a number of examples of how our algorithm performs for several different storm predictions, and for two different sources of predicted path ensembles.
Visualnostics: Visual Guidance Pictograms for Analyzing Projections of High-dimensional Data
Authors: Dirk J. Lehmann, Fritz Kemmler, Tatsiana Zhyhalava, Marco Kirschke, Holger Theisel
Published at EuroVis, 2015

The visual analysis of multivariate projections is a challenging task, because complex visual structures occur. This causes fatigue or misinterpretations, which distorts the analysis. In fact, the same projection can lead to different analysis results. We provide visual guidance pictograms to improve objectivity of the visual search. A visual guidance pictogram is an iconic visual density map encoding the visual structure of certain data properties. By using them to guide the analysis, structures in the projection can be better understood and mentally linked to properties in the data. We introduce a systematic scheme for designing such pictograms and provide a set of pictograms for standard visual tasks, such as correlation and distribution analysis, for standard projections like scatterplots, RadVis, and Star Coordinates. We conduct a study that compares the visual analysis of real data with and without the support of guidance pictograms. Our tests show that the training effort for a visual search can be decreased and the analysis bias can be reduced by supporting the user's visual search with guidance pictograms.
"Everyone Is Talking about It!": A Distributed Approach to Urban Voting Technology and Visualisations
Authors: Lisa Koeman, Vaiva Kalnikaité, Yvonne Rogers
Published at CHI, 2015

The deployment of technology interventions, such as public displays and mobile apps, in community settings has been found to engage people in sharing and comparing their opinions. Our research is concerned with how to extend this to community-wide participation by devising and deploying multiple voting devices and visualisations. We present an in-the-wild study where a number of shopkeepers along a street participated by placing a novel voting device in their shops to collect locals' opinions. Results were displayed outside the shops, on the pavement. This distributed set-up was found to promote public debate on local issues, particularly around the perceived divide between people on either end of the street. We outline our design process and describe the impact of distributing voting devices and situated visualisations in a local community. \
(s|qu)eries: Visual Regular Expressions for Querying and Exploring Event Sequences
Authors: Emanuel Zgraggen, Steven Mark Drucker, Danyel Fisher, Robert DeLine
Published at CHI, 2015

Many different domains collect event sequence data and rely on finding and analyzing patterns within it to gain meaningful insights. Current systems that support such queries either provide limited expressiveness, hinder exploratory workflows or present interaction and visualization models which do not scale well to large and multi-faceted data sets. In this paper we present (s|qu)eries (pronounced "Squeries"), a visual query interface for creating queries on sequences (series) of data, based on regular expressions. (s|qu)eries is a touch-based system that exposes the full expressive power of regular expressions in an approachable way and interleaves query specification with result visualizations. Being able to visually investigate the results of different query-parts supports debugging and encourages iterative query-building as well as exploratory work-flows. We validate our design and implementation through a set of informal interviews with data scientists that analyze event sequences on a daily basis.
An Evaluation of Interactive Map Comparison Techniques
Authors: María-Jesús Lobo, Emmanuel Pietriga, Caroline Appert
Published at CHI, 2015

Geovisualization applications typically organize data into layers. These layers hold different types of geographical features, describe different characteristics of the same features, or represent those features at different points in time. Layers can be composited in various ways, most often employing a juxtaposition or superimposition strategy, to produce maps that users can explore interactively. From an HCI perspective, one of the main challenges is to design interactive compositions that optimize the legibility of the resulting map and that ease layer comparison. We characterize five representative techniques, and empirically evaluate them using a set of real-world maps in which we purposefully introduce six types of differences amenable to inter-layer visual comparison. We discuss the merits of these techniques in terms of visual interference, user attention and scanning strategy. Our results can help inform the design of map-based visualizations for supporting geo-analysis tasks in many application areas.
An Interactive System for Data Structure Development
Authors: Jibin Ou, Martin T. Vechev, Otmar Hilliges
Published at CHI, 2015

Data structure algorithms are of fundamental importance in teaching and software development, yet are difficult to understand. We propose a new approach for understanding, debugging and developing heap manipulating data structures. The key technical idea of our work is to combine deep parametric abstraction techniques emerging from the area of static analysis with interactive abstraction manipulation. Our approach bridges program analysis with HCI and enables new capabilities not possible before: i) online automatic visualization of the data structure in a way which captures its essential operation, thus enabling powerful local reasoning, and ii) fine grained pen and touch gestures allowing for interactive control of the abstraction -- at any point the developer can pause the program, graphically interact with the data, and continue program execution. These features address some of the most pressing challenges in developing data structures. We implemented our approach in a Java-based system called FluiEdt and evaluated it with $27$ developers. The results indicate that FluiEdt is more effective in helping developers find data structure errors than existing state of the art IDEs (e.g. Eclipse) or pure visualization based approaches.
BodyVis: A New Approach to Body Learning Through Wearable Sensing and Visualization
Authors: Leyla Norooz, Matthew Louis Mauriello, Anita Jorgensen, Brenna McNally, Jon E. Froehlich
Published at CHI, 2015

Internal organs are hidden and untouchable, making it difficult for children to learn their size, position, and function. Traditionally, human anatomy (body form) and physiology (body function) are taught using techniques ranging from worksheets to three-dimensional models. We present a new approach called BodyVis, an e-textile shirt that combines biometric sensing and wearable visualizations to reveal otherwise invisible body parts and functions. We describe our 15-month iterative design process including lessons learned through the development of three prototypes using participatory design and two evaluations of the final prototype: a design probe interview with seven elementary school teachers and three single-session deployments in after-school programs. Our findings have implications for the growing area of wearables and tangibles for learning.