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Visual Fingerprinting: A New Visual Mining Approach for Large-Scale Spatio-temporal Evolving Data

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Advanced Data Mining and Applications (ADMA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7713))

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Abstract

Spatio-temporal data analysis has important applications in transportation management, urban planning and other fields. However, spatio-temporal data are often highly dimensional, overly large and contain spatial and temporal attributes, which pose special challenges for analysts. In this paper, we propose a new visual aided mining approach, Visual Fingerprinting (VF) for extremely large-scale spatio-temporal feature extraction and analysis. It adopts a visual analytics approach for spatio-temporal data analysis that can generate fingerprints for temporal exploration while preserving the spatial distribution in a region or on a road. Fingerprinting has been proposed to display temporal changes in spatial distributions; for example fingerprints for a region grid can well display temporal changes in the traffic situations of significant spots of a city. These fingerprints integrate important statistical and historical information related to traffic and can be conveniently embedded into urban maps. The sophisticated design of the visualization can better reveal frequent or periodic patterns for temporal attributes. We have tested our approach with real-life vehicle data collected from thousands of taxis and some interesting findings about traffic patterns have been obtained. The experiments validate our methods and demonstrate that our approach can be used for analyzing vehicle trajectories on road networks.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, The Hong Kong University of Science and Technology, Hong Kong

    Jiansu Pu, Huamin Qu & Lionel Ni

  2. iLab, Heinz College, Carnegie Mellon University, USA

    Siyuan Liu

Authors
  1. Jiansu Pu
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  2. Siyuan Liu
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  3. Huamin Qu
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  4. Lionel Ni
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Editor information

Editors and Affiliations

  1. School of Computer Science, Fudan University, Handan Road 220, 200433, Shanghai, China

    Shuigeng Zhou

  2. Chinese Academy of Sciences, Academy of Mathematics and Systems Science, Dongguancun East Road 55, 100190, Beijing, China

    Songmao Zhang

  3. Department of Computer Science and Engineering, University of Minnesota, Union Street SE 200, 55455, Minneapolis, MN, USA

    George Karypis

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© 2012 Springer-Verlag Berlin Heidelberg

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Pu, J., Liu, S., Qu, H., Ni, L. (2012). Visual Fingerprinting: A New Visual Mining Approach for Large-Scale Spatio-temporal Evolving Data. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_42

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  • DOI: https://doi.org/10.1007/978-3-642-35527-1_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35526-4

  • Online ISBN: 978-3-642-35527-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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