2,900 research outputs found
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Stacking-based visualization of trajectory attribute data
Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well
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Exploring Trajectory Attributes in Brest Harbor
We describe a procedure for analyzing trajectory attributes that in-cludes spatial clustering of the trajectories and visual analysis of the distribution of attribute values in the temporal and spatial dimensions
Visual analytics of movement: An overview of methods, tools and procedures
Analysis of movement is currently a hot research topic in visual analytics. A wide variety of methods and tools for analysis of movement data has been developed in recent years. They allow analysts to look at the data from different perspectives and fulfil diverse analytical tasks. Visual displays and interactive techniques are often combined with computational processing, which, in particular, enables analysis of a larger number of data than would be possible with purely visual methods. Visual analytics leverages methods and tools developed in other areas related to data analytics, particularly statistics, machine learning and geographic information science. We present an illustrated structured survey of the state of the art in visual analytics concerning the analysis of movement data. Besides reviewing the existing works, we demonstrate, using examples, how different visual analytics techniques can support our understanding of various aspects of movement
Contributions to the cornerstones of interaction in visualization: strengthening the interaction of visualization
Visualization has become an accepted means for data exploration and analysis. Although interaction is an important component of visualization approaches, current visualization research pays less attention to interaction than to aspects of the graphical representation. Therefore, the goal of this work is to strengthen the interaction side of visualization. To this end, we establish a unified view on interaction in visualization. This unified view covers four cornerstones: the data, the tasks, the technology, and the human.Visualisierung hat sich zu einem unverzichtbaren Werkzeug für die Exploration und Analyse von Daten entwickelt. Obwohl Interaktion ein wichtiger Bestandteil solcher Werkzeuge ist, wird der Interaktion in der aktuellen Visualisierungsforschung weniger Aufmerksamkeit gewidmet als Aspekten der graphischen Repräsentation. Daher ist es das Ziel dieser Arbeit, die Interaktion im Bereich der Visualisierung zu stärken. Hierzu wird eine einheitliche Sicht auf Interaktion in der Visualisierung entwickelt
Fine-grained traffic state estimation and visualisation
Tools for visualising the current traffic state are used by local authorities for strategic monitoring of the traffic network and by everyday users for planning their journey. Popular visualisations include those provided by Google Maps and by Inrix. Both employ a traffic lights colour-coding system, where roads on a map are coloured green if traffic is flowing normally and red or black if there is congestion. New sensor technology, especially from wireless sources, is allowing resolution down to lane level. A case study is reported in which a traffic micro-simulation test bed is used to generate high-resolution estimates. An interactive visualisation of the fine-grained traffic state is presented. The visualisation is demonstrated using Google Earth and affords the user a detailed three-dimensional view of the traffic state down to lane level in real time
Interactive Visual Analytics for Agent-Based Simulation: Street-Crossing Behavior at Signalized Pedestrian Crossing
To design a pedestrian crossing area reasonably can be a demanding task for traffic planners. There are several challenges, including determining the appropriate dimensions, and ensuring that pedestrians are exposed to the least risks. Pedestrian safety is especially obscure to analyze, given that many people in Stockholm cross the street illegally by running against the red light. To cope with these challenges, computational approaches of trajectory data visual analytics can be used to support the analytical reasoning process. However, it remains an unexplored field regarding how to visualize and communicate the street-crossing spatio-temporal data effectively. Moreover, the rendering also needs to deal with a growing data size for a more massive number of people. This thesis proposes a web-based interactive visual analytics tool for pedestrians' street-crossing behavior under various flow rates. The visualization methodology is also presented, which is then evaluated to have achieved satisfying communication and rendering effectiveness for maximal 180 agents over 100 seconds. In terms of the visualization scenario, pedestrians either wait for the red light or cross the street illegally; all people can choose to stop by a buffer island before they finish crossing. The visualization enables the analysis under multiple flow rates for 1) pedestrian movement, 2) space utilization, 3) crossing frequency in time-series, and 4) illegal frequency. Additionally, to acquire the initial trajectory data, Optimal Reciprocal Collision Avoidance (ORCA) algorithm is engaged in the crowd simulation. Then different visualization techniques are utilized to comply with user demands, including map animation, data aggregation, and time-series graph
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