Abstract
With its continuous development, location information acquisition technology is able to collect more and more trajectory data, and the rich information contained therein is gradually attracting attention from researchers. Trajectory data involves complex relationships among moving objects, time, space, which are hard to understand and be used directly. Nowadays, visual analysis of trajectory data is mainly focus on its representation and interaction, but fails to address the complex correlation contained in trajectory data. Hence, we propose TrajHIN, a heterogeneous information network model built on trajectory data, measure the meta path-based similarity and centrality, and use a visual analytics method to deeply understand trajectory data. The example of visual analysis of real trajectory data has been interpreted and given feedback from domain experts, which proves effectiveness of TrajHIN and feasibility of mining implicit semantic information from trajectory data.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grants 61572219, 61502192, 61671216, 61471408 and 51479157; by the China Postdoctoral Science Foundation under Grants 2017T100556; and by the Fundamental Research Funds for the Central Universities under Grant 2015QN073, 2016YXMS297, 2016JCTD118 and WUT:2016III028; by fund of Hubei Key Laboratory of Inland Ship** Technology under Grant NHHY2015005.
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Zhang, R., Ma, W., Zhong, L., **e, P., Jiang, H. (2018). Understanding Trajectory Data Based on Heterogeneous Information Network Using Visual Analytics. In: Zhu, L., Zhong, S. (eds) Mobile Ad-hoc and Sensor Networks. MSN 2017. Communications in Computer and Information Science, vol 747. Springer, Singapore. https://doi.org/10.1007/978-981-10-8890-2_23
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DOI: https://doi.org/10.1007/978-981-10-8890-2_23
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