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 adequate supervision of groundwater pollution in industrial enterprises primarily focuses on post-pollution treatment, and there is an urgent need to develop methods that go beyond merely describing pollution conditions and thoroughly understand the characteristics of pollution entities. In response to the current challenges of groundwater pollution visualization and entity modeling, based on digital twin technology and multi-granularity spatiotemporal object models, this study innovatively proposes an entity modeling framework that integrates the spatial, temporal, and behavioral attributes of the enterprise-groundwater system, clarifying the definition and division basis of multi-granularity levels to support decision-making requirements at different scales. Taking the groundwater pollution of a specific chemical plant in Huizhou as an example, a digital twin of groundwater pollution with dynamic data assimilation capabilities for this site was constructed based on borehole data, historical monitoring records, and the deployed sensor network. The results demonstrate that the digital twin system enables the visualization and simulation of pollution dynamics by integrating near-real-time data. The multi-granularity spatio-temporal object model successfully captured the spatial heterogeneity, temporal evolution characteristics, and polluter-receptor interaction behavior of pollution. This research not only provides a more refined and dynamic cognitive tool for groundwater pollution but also lays scalable technical support for integrating multi-granularity spatiotemporal object models with digital twin technology for environmental supervision.