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.

As a frontier research topic in the field of scientific visualization, trajectory data visualization extracts valuable patterns and knowledge from trajectory data for decision support via spatiotemporal trajectory visualization techniques. We propose the concept of multivariate trajectory data and interpret two categories of attributes that are based on geographical space and abstract space. Properly analyzing multivariate trajectory data depends on many factors such as visualization task and data sparsity. Therefore, we generalize rich interactions to explore the evolution of trajectory events and transform the issue into a more intelligibly perceptual task, which derives our discussion regarding advantages and limitations of the analytical methods. This review endeavors to provide a quick and thorough cognition and comprehension with regard to fundamental features and numerous outcomes in visual analytics for trajectory data, seeks to promote comparisons and criticisms about the descriptive framework for multivariate spatiotemporal trajectory data visualization, and aims to encourage the exploration of emerging methods and techniques.

The Coronavirus Disease 2019 (COVID-19) has proved a globally prevalent outbreak since December 2019. As a focused country to alleviate the epidemic impact, China implemented a range of public health interventions to prevent the disease from further transmission, including the pandemic lockdown in Wuhan and other cities. This paper establishes China’s mobility network by a flight dataset and proposes a model without epidemiological parameters to indicate the spread risks through the network, which is termed as epidemic strength. By simply adjusting an intervention parameter, traffic volumes under different travel-restriction levels can be simulated to analyze how the containment strategy can mitigate the virus dissemination through traffic. This approach is successfully applied to a network of Chinese provinces and the epidemic strength is smoothly interpreted by flow maps. Through this node-to-node interpretation of transmission risks, both overall and detailed epidemic hazards are properly analyzed, which can provide valuable intervention advice during public health emergencies.

One approach used in GIScience to investigate movement data is adopting time-geography theory and its important principles/methods: space-time paths and space-time cube. Thus, the movement of a moving object can be represented as a 3D polyline in a space-time cube. However, with the advent of larger movement datasets, this type of display can easily become cluttered and incomprehensible. In this article, we propose a new space-time aggregation algorithm, i.e., directional-layered space-time densities (DLSTDs), to solve the problem of trajectory clutter in a space-time cube. The approach is an extension of the pixel (2D raster cell) density around a point in two-dimensional space to the voxel density around a trajectory line segment in three-dimensional space-time $(x-y+t)$ . In the algorithm, the horizontal or vertical voxel layers are first calculated around the trajectory based on the direction of each trajectory line segment. Subsequently, the voxel density is calculated by substituting the distance of the voxel from the trajectory segment into a Gaussian-based attenuation function. Finally, the resulting layered densities are presented using volume rendering techniques. We demonstrate the DLSTDs on 30-day movement data of transport trucks in a mine in China and compare the algorithm with two other algorithms. At the end of the article, we summarize the research and consider further developments of the proposed approach.

There are many sources of geographic big data, and most of them come from heterogeneous environments. The data sources obtained in this case contain attribute information of different spatial scales, different time scales and different complexity levels. It is worth noting that the emergence of new high-dimensional trajectory data types and the increasing number of details are becoming more difficult. In this case, visualizing high-dimensional spatiotemporal trajectory data is extremely challenging. Therefore, i-tStar and its extension i-tStar (3D) proposed, a trajectory behavior feature for moving objects that are integrated into a view with less effort to display and extract spatiotemporal conditions, and evaluate our approach through case studies of an open-pit mine truck dataset. The experimental results show that this method is easier to mine the interaction behavior of multi-attribute trajectory data and the correlation and influence of various indicators of moving objects.

Rapidly advancing location-awareness technologies and services have collected and stored massive amounts of moving object trajectory data with attribute information that involves various degrees of spatial scales, timescales, and levels of complexity. Unfortunately, interesting behaviors regarding combinations of attributes are scarcely extracted from datasets. Further, trajectories are typically dependent on the environment of three-dimensional space, and another issue of interest to us is to preserve spatial-location visualization while guaranteeing the description of temporal information. Therefore, we developed a novel analytics tool that combines visual and interactive components to enable a dynamic visualization of three-dimensional trajectory multi-attribute behaviors. Under the context of spatiotemporal analysis, this approach integrates multiple attributes into one view to efficiently explore the attribute visualization problem of multi-attribute combination without over-plotting. To assess the feasibility of our solution, we visualized and analyzed multi-attribute information of moving object trajectories using a real mining truck dataset as a case study.