Integrating algebraic multigrid method in spatial aggregation of massive trajectory data

• Published in: International journal of geographical information science : IJGIS 2018-12, Vol.32 (12), p.2477-2496
• Main Authors: Wang, Siying, Du, Yunyan, Jia, Chen, Bian, Meng, Fei, Teng
• Format: Article
• Language: English
• Subjects: Agglomeration; Algebra; algebraic multigrid; Clutter; Data management; Flow mapping; Graphical representations; key node identification; Location based services; Multilayers; Spatial aggregation; Spatial discrimination; Trajectory analysis; trajectory visualization
• ISSN: 1365-8816; 1362-3087; 1365-8824
• DOI: 10.1080/13658816.2018.1512713

The advanced technologies in location-based services and telecom have yield large volumes of trajectory data. Understanding these data effectively requires intuitive yet accurate visual analysis. The visual analysis of massive trajectory data is challenged by the numerous interactions among different locations, which cause massive clutter. This paper presents a new methodology for visual analysis by integrating algebraic multigrid (AMG) method in data aggregation. The non-parametric method helps to build a multi-layer node representation from a graph which is extracted from trajectory data. The comparison with AMG and other methods shows that AMG method is more advanced in both the spatial representation and the importance of nodes. The new method is tested with real-world dataset of cell-phone signalling records in Beijing. The results show that our method is suitable for processing and creating abstraction of massive trajectory dataset, revealing inherent patterns and creating intuitive and vivid flow maps.