Abstract
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.
Highlights
Current trajectory data are ubiquitous, involving abundant datasets with multiple dimensions and many variables; the relevant research has facilitated the comprehension of dynamically evolving behaviors and the prediction of future moving trends
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Summary
Current trajectory data are ubiquitous, involving abundant datasets with multiple dimensions and many variables; the relevant research has facilitated the comprehension of dynamically evolving behaviors and the prediction of future moving trends. Trajectory-data visualization involves modeling through computer graphics, computer vision, user interface and other techniques to visually represent trajectory data and provide effective interaction to support users’ data-exploration behavior [1]. The field has achieved considerable results worldwide, establishing appropriate visual representations for the dimensional information in trajectory data. The diversity of trajectory-visualization techniques could cause problems for researchers and designers in terms of clearly comprehending what has been accomplished, how to compare and choose visualization methods, and how much potential of space–time data visualization remains to be explored. We should fully understand the conceptual distinction between “multidimensional” and “multivariate”. Sutlatristionugrcine tSreacjteiocnto2ry, wdeatpar.eTsehnetcrheolesveanntexvaismuaplliezastaiorne ftoecrhinlliuqsutersatainodn amnedthtohdes ltihstatis not exhaufusnticvteio—n wwiethonmluyltfioscouusrcoentrsamjecatlolrbyudtartae.pTrheesecnhtoasteinveexcahmopicleess.are for illustration and the list is not exhaustive—we only focus on small but representative choices
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