Trajectory big data have significant applications in many areas, such as traffic management, urban planning and military reconnaissance. Traditional visualization methods, which are represented by contour maps, shading maps and hypsometric maps, are mainly based on the spatiotemporal information of trajectories, which can macroscopically study the spatiotemporal conditions of the entire trajectory set and microscopically analyze the individual movement of each trajectory; such methods are widely used in screen display and flat mapping. With the improvement of trajectory data quality, these data can generally describe information in the spatial and temporal dimensions and involve many other attributes (e.g., speed, orientation, and elevation) with large data amounts and high dimensions. Additionally, these data have relatively complicated internal relationships and regularities, whose analysis could cause many troubles; the traditional approaches can no longer fully meet the requirements of visualizing trajectory data and mining hidden information. Therefore, diverse visualization methods that present the value of massive trajectory information are currently a hot research topic. This paper summarizes the research status of trajectory data-visualization techniques in recent years and extracts common contemporary trajectory data-visualization methods to comprehensively cognize and understand the fundamental characteristics and diverse achievements of trajectory-data visualization.First, we should fully understand the conceptual distinction between "multidimensional" and "multivariate". Generally, "multidimensional" is used to describe the dimensionality of independent variables in physical space and concentrates on expressing spatial and temporal concepts, while "multivariate" is used for attribute-count description that indicates the attribute amount involved in the data. In fact, trajectory data are typically enormous, high-dimensional, spatiotemporal vector attribute data. We are interested not only in comprehending dimensional information for spatiotemporal-element depictions, but also in the visualization research with attribute-feature expression. Starting in Section 2, we present relevant visualization techniques and methods that function with multisource trajectory data. The chosen examples are for illustration and the list is not exhaustive-we only focus on small but representative choices. Universal Multivariate VisualizationThe trajectory visualization reflects the changing patterns of time-varying geolocations and corresponding attribute information, including basic-dimensional information and the attached thematic variables of space-time objects. However, these variables usually do not exist independently but are combined with each other, and the trajectory data of different sources, scales, or types are plotted in a hybrid manner. We separately list visualization techniques that integrate multiple attributes. As a powerful and portable carrier that encodes abundant information in methods of visual cha...

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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