Spatio-temporal trajectory big data analysis based on HNCORS

• Published in: 2022 5th International Conference on Data Science and Information Technology (DSIT) pp. 1 - 7
• Main Authors: Zhang, Yubing, Zeng, Xiangqiang, Ao, Minsi, Jin, Wenping
• Format: Conference Proceeding
• Language: English
• Published: IEEE 22.07.2022
• Subjects: Big Data; HNCORS; kernel density analysis; Lakes; Planning; Predictive models; real-time kinematic; Real-time systems; spatial-temporal analysis; Trajectory; Trajectory big data; Urban areas
• DOI: 10.1109/DSIT55514.2022.9943937

The analysis based on spatio-temporal trajectory big data is of great significance for mining user operation rules, predicting behavior trends, and exploring application expansion. Hunan continuously operating reference station network, which provides the official real-time kinematic service to the public, is one of the most important regional geospatial datum infrastructures in Hunan province, China. Using the location based big data of users, the multi-scale and multi-time characteristics of trajectory operation data and fixed solution ratio are analyzed by preprocessing, interpolation, kernel density analysis and other methods. The results show that the distribution characteristics of trajectory data in Hunan province are related to the working habit in field surveying and mapping, and the overall performance is reduced from the Middle East to the West, from economically developed areas to economically poor areas, and from plain hilly areas to mountain / lake areas. Among them, the suburbs of towns are relatively dense areas of trajectory data. The amount of trajectory operations is also significantly correlated with the local economy. By analyzing the spatio-temporal sequence diagram of the trajectory data, the evolution process is divided into six stages, which is manifested in the continuous growth and decline around Changsha-Zhuzhou-Xiangtan region and the eleven urban areas in a natural day.