Abstract—Spatial temporal data refers to data with geographical location and time label. It has the characteristics of multi-source, massive quantity and fast update. It is a typical big data type. Spatial temporal data analysis is one of the core issues in the field of big data research. In order to better demonstrate the process and results of spatial temporal data analysis, visual processing has become one of the important ways of analysis. The analysis of spatial temporal big data through visualization technology can provide insight into the overall picture and main features of big data. However, when using the visualization technology to analyze large-scale spatial temporal data, the characteristics of spatial temporal big data are not considered, so there are often line-intensive and overlapping coverage problems in the visualization results. This paper proposes an improved space time cube visualization method to solve the above problems. First, cluster the spatial temporal data and then use the space time cube visualization method to display the clustered data. The clustering algorithm used is the sub-trajectory clustering. The experimental results show that the improved space time cube visualization technology has obvious visual effects and clear global features.
Index Terms—Spatial temporal data, visualization, space time cube, sub-trajectory clustering.
The authors are with Computer School, North China University of Technology, Beijing 100144 China (e-mail: sunjing8248@163.com, qingyun_huang@foxmail.com, zhaohq6625@sina.com).
Cite: Jing Sun, Qingyun Huang, and Huiqun Zhao, "Research on Improved Visualization Method of Space Time Cube," International Journal of Machine Learning and Computing vol. 10, no. 1, pp. 116-121, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).