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ISPRS Int. J. Geo-Inf. 2018, 7(6), 212; https://doi.org/10.3390/ijgi7060212

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

1
College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
2
Changjiang Chongqing Waterway Engineering Bureau, Chongqing 401121, China
*
Authors to whom correspondence should be addressed.
Received: 10 April 2018 / Revised: 23 May 2018 / Accepted: 27 May 2018 / Published: 4 June 2018
(This article belongs to the Special Issue Cognitive Aspects of Human-Computer Interaction for GIS)
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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. View Full-Text
Keywords: 4D time density; 3D data cube; movement data; trajectory datasets; visual data exploration; space use intensity; spatiotemporal movement patterns 4D time density; 3D data cube; movement data; trajectory datasets; visual data exploration; space use intensity; spatiotemporal movement patterns
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Zou, Y.; Chen, Y.; He, J.; Pang, G.; Zhang, K. 4D Time Density of Trajectories: Discovering Spatiotemporal Patterns in Movement Data. ISPRS Int. J. Geo-Inf. 2018, 7, 212.

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