1,241 research outputs found

    Evaluation of two interaction techniques for visualization of dynamic graphs

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    Several techniques for visualization of dynamic graphs are based on different spatial arrangements of a temporal sequence of node-link diagrams. Many studies in the literature have investigated the importance of maintaining the user's mental map across this temporal sequence, but usually each layout is considered as a static graph drawing and the effect of user interaction is disregarded. We conducted a task-based controlled experiment to assess the effectiveness of two basic interaction techniques: the adjustment of the layout stability and the highlighting of adjacent nodes and edges. We found that generally both interaction techniques increase accuracy, sometimes at the cost of longer completion times, and that the highlighting outclasses the stability adjustment for many tasks except the most complex ones.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

    Designing visual analytics methods for massive collections of movement data

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    Exploration and analysis of large data sets cannot be carried out using purely visual means but require the involvement of database technologies, computerized data processing, and computational analysis methods. An appropriate combination of these technologies and methods with visualization may facilitate synergetic work of computer and human whereby the unique capabilities of each ā€œpartnerā€ can be utilized. We suggest a systematic approach to defining what methods and techniques, and what ways of linking them, can appropriately support such a work. The main idea is that software tools prepare and visualize the data so that the human analyst can detect various types of patterns by looking at the visual displays. To facilitate the detection of patterns, we must understand what types of patterns may exist in the data (or, more exactly, in the underlying phenomenon). This study focuses on data describing movements of multiple discrete entities that change their positions in space while preserving their integrity and identity. We define the possible types of patterns in such movement data on the basis of an abstract model of the data as a mathematical function that maps entities and times onto spatial positions. Then, we look for data transformations, computations, and visualization techniques that can facilitate the detection of these types of patterns and are suitable for very large data sets ā€“ possibly too large for a computer's memory. Under such constraints, visualization is applied to data that have previously been aggregated and generalized by means of database operations and/or computational techniques

    Motion of animated streamlets appears to surpass their graphical alterations in human visual detection of vector field maxima

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    Animations have become a frequently utilized illustration technique on maps but changes in their graphical loading remain understudied in empirical geovisualization and cartographic research. Animated streamlets have gained attention as an illustrative animation technique and have become popular on widely viewed maps. We conducted an experiment to investigate how altering four major animation parameters of animated streamlets affects peopleā€™s reading performance of field maxima on vector fields. The study involved 73 participants who performed reaction-time tasks on pointing maxima on vector field stimuli. Reaction times and correctness of answers changed surprisingly little between visually different animations, with only a few occasional statistical significances. The results suggest that motion of animated streamlets is such a strong visual cue that altering graphical parameters makes only little difference when searching for the maxima. This leads to the conclusion that, for this kind of a task, animated streamlets on maps can be designed relatively freely in graphical terms and their style fitted to other contents of the map. In the broader visual and geovisual analytics context, the results can lead to more generally hypothesizing that graphical loading of animations with continuous motion flux could be altered without severely affecting their communicative power

    Visualisation of Long in Time Dynamic Networks on Large Touch Displays

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    Any dataset containing information about relationships between entities can be modelled as a network. This network can be static, where the entities/relationships do not change over time, or dynamic, where the entities/relationships change over time. Network data that changes over time, dynamic network data, is a powerful resource when studying many important phenomena, across wide-ranging ļ¬elds from travel networks to epidemiology.However, it is very difļ¬cult to analyse this data, especially if it covers a long period of time (e.g, one month) with respect to its temporal resolution (e.g. seconds). In this thesis, we address the problem of visualising long in time dynamic networks: networks that may not be particularly large in terms of the number of entities or relationships, but are long in terms of the length of time they cover when compared to their temporal resolution.We ļ¬rst introduce Dynamic Network Plaid, a system for the visualisation and analysis of long in time dynamic networks. We design and build for an 84" touch-screen vertically-mounted display as existing work reports positive results for the use of these in a visualisation context, and that they are useful for collaboration. The Plaid integrates multiple views and we prioritise the visualisation of interaction provenance. In this system we also introduce a novel method of time exploration called ā€˜interactive timeslicingā€™. This allows the selection and comparison of points that are far apart in time, a feature not offered by existing visualisation systems. The Plaid is validated through an expert user evaluation with three public health researchers.To conļ¬rm observations of the expert user evaluation, we then carry out a formal laboratory study with a large touch-screen display to verify our novel method of time navigation against existing animation and small multiples approaches. From this study, we ļ¬nd that interactive timeslicing outperforms animation and small multiples for complex tasks requiring a compari-son between multiple points that are far apart in time. We also ļ¬nd that small multiples is best suited to comparisons of multiple sequential points in time across a time interval.To generalise the results of this experiment, we later run a second formal laboratory study in the same format as the ļ¬rst, but this time using standard-sized displays with indirect mouse input. The second study reafļ¬rms the results of the ļ¬rst, showing that our novel method of time navigation can facilitate the visual comparison of points that are distant in time in a way that existing approaches, small multiples and animation, cannot. The study demonstrates that our previous results generalise across display size and interaction type (touch vs mouse).In this thesis we introduce novel representations and time interaction techniques to improve the visualisation of long in time dynamic networks, and experimentally show that our novel method of time interaction outperforms other popular methods for some task types

    The Effectiveness of Interactive Visualization Techniques for Time Navigation of Dynamic Graphs on Large Displays

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    Dynamic networks can be challenging to analyze visually, especially if they span a large time range during which new nodes and edges can appear and disappear. Although it is straightforward to provide interfaces for visualization that represent multiple states of the network (i.e., multiple timeslices) either simultaneously (e.g., through small multiples) or interactively (e.g., through interactive animation), these interfaces might not support tasks in which disjoint timeslices need to be compared. Since these tasks are key for understanding the dynamic aspects of the network, understanding which interactive visualizations best support these tasks is important. We present the results of a series of laboratory experiments comparing two traditional approaches (small multiples and interactive animation), with a more recent approach based on interactive timeslicing. The tasks were performed on a large display through a touch interface. Participants completed 24 trials of three tasks with all techniques. The results show that interactive timeslicing brings benefit when comparing distant points in time, but less benefits when analyzing contiguous intervals of time

    Comparing Different Levels of Interactivity in the Visualization of Spatio-Temporal Data

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    The Internet and other advances in technology have dramatically affected cartography in recent decades and yet these new capabilities have not been adequately evaluated for effectiveness. Are dynamic maps more effective than traditional static paper maps in allowing users to visualize spatio-temporal patterns? How important is a higher level of interactivity in visualizing data? Which format is preferred? To examine these questions, human subject tests were conducted to evaluate different levels of interactivity as represented by 1) a static paper map series; 2) an animated map with \u27VCR\u27-type controls; and 3) a toggle map featuring an interactive temporal legend. Results indicate that while the level of interactivity did not affect accuracy of answers to questions regarding spatio-temporal patterns, the total amount of time in which these questions were answered lessened as the level of interactivity increased. Overall, test subjects were more enthusiastic towards the tools featuring greater interactivity

    Smooth Bundling of Large Streaming and Sequence Graphs

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    Smooth Bundling of Large Streaming and Sequence Graphs

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