Beyond Exploratory Visualization of Space–Time Paths

Kraak, M.J.; Huisman, Otto

doi:10.1201/9781420073980.ch17

Cited by 13 publications

(17 citation statements)

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“…Kraak and Huisman [21] use a combination of time graphs for two attributes (speed and heart rate), a map, and a space-time cube (representing a selected attribute by coloring trajectory segments) for identifying interesting events. However, this approach considers only single trajectories and not sets of them.…”

Section: Related Workmentioning

confidence: 99%

Stacking-Based Visualization of Trajectory Attribute Data

Tominski

Schumann

Andrienko

2012

IEEE Trans. Visual. Comput. Graphics

261

135

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Fig. 1. Visualization of radiation (CPM values) along the Tokio-Fukushima highway.Abstract-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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Section: Related Workmentioning

confidence: 99%

Stacking-Based Visualization of Trajectory Attribute Data

Tominski

Schumann

Andrienko

2012

IEEE Trans. Visual. Comput. Graphics

261

135

View full text Add to dashboard Cite

show abstract

“…Kraak and Huisman [35] used a combination of a time graph, scatter chart, cartographic map, and space-time cube to identify interesting events. The colored trajectory segments were used to represent the selected attributes through a map and a data cube.…”

Section: Related Workmentioning

confidence: 99%

4D Time Density of Trajectories: Discovering Spatiotemporal Patterns in Movement Data

Zou

Chen

et al. 2018

IJGI

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Abstract:Modern positioning and sensor technology enable the acquisition of movement positions and attributes on an unprecedented scale. Therefore, a large amount of trajectory data can be used to analyze various movement phenomena. In cartography, a common way to visualize and explore trajectory data is to use the 3D cube (e.g., space-time cube), where trajectories are presented as a tilted 3D polyline. As larger movement datasets become available, this type of display can easily become confusing and illegible. In addition, movement datasets are often unprecedentedly massive, high-dimensional, and complex (e.g., implicit spatial and temporal relations and interactions), making it challenging to explore and analyze the spatiotemporal movement patterns in space. In this paper, we propose 4D time density as a visualization method for identifying and analyzing spatiotemporal movement patterns in large trajectory datasets. The movement range of the objects is regarded as a 3D geographical space, into which the fourth dimension, 4D time density, is incorporated. The 4D time density is derived by modeling the movement path and velocity separately. We present a time density algorithm, and demonstrate it on the simulated trajectory and a real dataset representing the movement data of aircrafts in the Hong Kong International and the Macau International Airports. Finally, we consider wider applications and further developments of time density.

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“…Attribute values can be represented by colouring or shading of the segments [35][29] [48] or by symbols (glyphs) drawn on the segments [56]. Spretke et al [48] suggest an interactive tool for segmenting trajectories by dividing ranges of one or more position-related attributes into suitable intervals.…”

Section: Looking Inside Trajectories: Attributes Events and Patternsmentioning

confidence: 99%