With the rapid growth of trajectory big data, there is a need for more efficient methods to extract, analyze, and visualize these data. However, existing research on trajectory big data visualization mainly focuses on displaying trajectories for a specific period or showing spatial distribution characteristics of trajectory points in a single time slice using clustering, filtering, and other techniques. Therefore, this paper proposes a vector field visualization model for trajectory big data, aiming to effectively represent the inherent movement trends in the data and provide a more intuitive visualization of urban traffic congestion trends. The model utilizes the motion information of vehicles to create a travel vector grid and employs WebGL technology for vector field visualization rendering. The vector field effects are effectively displayed by generating many particles and simulating their movements. Furthermore, this research also designs and implements congestion trend point identification and hotspot congestion analysis, thus validating the practicality and effectiveness of trajectory big data vector field visualization. The results indicate that compared to traditional visualization methods, the vector field visualization method can demonstrate the direction and density changes in traffic flow and predict future traffic congestion. This work provides valuable data references and decision support for urban traffic management and planning.

The March 2021 dust storm in China degraded air quality across a wide area of Asia. Atmospheric circulation and meteorological factors play an important role in the occurrence of dust storms. To understand whether decreasing or increasing these factors can mitigate dust storms, this study utilizes remote sensing imagery data from the Himawari-8/-9 satellites to understand spatial and temporal variations in China’s 2016–2023 dust storms. Our findings are as follows: (1) in 2016–2023, dust storms covered northern China, with Xinjiang, Inner Mongolia, Gansu, and Ningxia being high-frequency areas; (2) the origins of the dust storms are northwest of Mongolia and Xinjiang, with upper air masses originating from Siberia and concentrating in central-west Inner Mongolia and northern Gansu; (3) dew point temperature, wind speed, cloud cover, and atmospheric circulation are important determinants of the occurrences of dust storms. Analyzing trends and influential factors of dust storms is important as this provides a scientific basis for decision-making in dust storm management.

Currently, China’s urbanization has entered a stage of high-quality development, and rapid urban growth has led to a series of environmental pollution issues, with haze pollution caused by delicate particulate matter (PM2.5) increasingly becoming a focal point for scholars. This paper focuses on Xi’an, Wuhan, Taiyuan, and Lanzhou, exploring the relationship between PM2.5 concentrations using methods such as the Pearson correlation coefficient (PCC), dominance analysis (DA), and ordinary least squares regression (OLSR). The results indicate that (1) Xi’an’s built environment is distributed radially, Wuhan is circular, Taiyuan is grid-like, and Lanzhou is strip-shaped; (2) Xi’an, Wuhan, Taiyuan, and Lanzhou exhibited different development patterns between 2014 and 2022, with Xi’an experiencing rapid urban expansion but lagging infrastructure, while Wuhan focused on improving post-urbanization quality. Taiyuan and Lanzhou advanced expansion and infrastructure construction simultaneously. (3) The regression coefficients of PM2.5 concentration concerning factors such as building density, green space density, road density, and water density in Xi’an are relatively high. In contrast, the regression coefficients of urban spatial structure factors in Wuhan, Taiyuan, and Lanzhou show consistency. This study provides a basis for reducing PM2.5 and explores the interaction and contribution relationship between urban spatial structure and PM2.5, offering a new research perspective for promoting urban sustainable development.

Amidst the increasing complexity of urban traffic networks and the frequent pedestrian-vehicle conflicts at intersections, this study addresses the challenges inherent in conventional methodologies, which often fail to accurately capture dynamic decision-making processes and are heavily reliant on empirical data. We propose an innovative simulation framework that synergistically integrates multi-agent behavioral modeling with dynamic non-cooperative game theory. This framework enables precise representation of pedestrian and vehicle behavioral strategies, conflict evolution, and spatial distribution patterns, complemented by conflict density mapping for enhanced visual risk assessment. Empirical results demonstrate the model’s capability to accurately identify high-risk zones and to substantially mitigate conflict frequency and severity through the modulation of vehicle yielding probabilities and the regulation of pedestrian behavior. By transcending the limitations of traditional static-rule-based approaches, the proposed framework offers a robust and theoretically grounded tool for traffic safety risk evaluation, thereby furnishing valuable insights for intersection safety management and the design of intelligent transportation systems.

The proliferation of the Internet and the widespread adoption of mobile devices have given rise to an immense volume of real-time trajectory big data. However, a single computer and conventional databases with limited scalability struggle to manage this data effectively. During the process of visual rendering, issues such as page stuttering and subpar visual outcomes often arise. This paper, founded on a distributed architecture, introduces a multi-level time encoding method using “minutes”, “hours”, and “days” as fundamental units, achieving a storage model for trajectory data at multi-scale time. Furthermore, building upon an improved DBSCAN clustering algorithm and integrating it with the K-means clustering algorithm, a novel density-based partitioning clustering algorithm has been introduced, which incorporates road coefficients to circumvent architectural obstacles, successfully resolving page stuttering issues and significantly enhancing the quality of visualization. The results indicate the following: (1) when data is extracted using the units of “minutes”, “hours”, and “days”, the retrieval efficiency of this model is 6.206 times, 12.475 times, and 18.634 times higher, respectively, compared to the retrieval efficiency of the original storage model. As the volume of retrieved data increases, the retrieval efficiency of the proposed storage model becomes increasingly superior to that of the original storage model. Under identical experimental conditions, this model’s retrieval efficiency also outperforms the space–time-coded storage model; (2) Under a consistent rendering level, the clustered trajectory data, when compared to the unclustered raw data, has shown a 40% improvement in the loading speed of generating heat maps. There is an absence of page stuttering. Furthermore, the heat kernel phenomenon in the heat map was also resolved while enhancing the visualization rendering speed.