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A Mobile Phone Data Visualization Tool for People Flow Analysis

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Web and Big Data (APWeb-WAIM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11642))

Abstract

Mobile phone data contain the information of each interaction between mobile phones and telecommunication infrastructures. These data provide a wealth of information about urban dynamics and human activities since each mobile phone can be seen as a sensor that senses the geographic position of the subscriber holder in real time. In this paper, we introduce an open-source and web-based data visualization tool for analyzing and displaying people flow information using mobile phone data. The developed tool provides users a user-friendly interface for data visualization. We demonstrate how to install and display this tool by using real mobile phone data.

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References

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Acknowledgments

The paper is partially supported by National Key R&D Program of China (2018YFB1003900) and the Fundamental Research Funds for the Central Universities, No. NS2017073.

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Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Liangjian Chen, Siyu Chen, Shengnan Guo & Jianqiu Xu

  2. Jiangsu Academy of Architectural Sciences, Nanjing, China

    Yue Yang

Authors
  1. Liangjian Chen
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  2. Siyu Chen
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  3. Shengnan Guo
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  4. Yue Yang
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  5. Jianqiu Xu
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Corresponding author

Correspondence to Jianqiu Xu .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Jie Shao

  2. Hong Kong Polytechnic University, Hong Kong, China

    Man Lung Yiu

  3. The University of Tokyo, Tokyo, Japan

    Masashi Toyoda

  4. Zhejiang University, Hangzhou, China

    Dongxiang Zhang

  5. National University of Singapore, Singapore, Singapore

    Wei Wang

  6. Peking University, Beijing, China

    Bin Cui

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Cite this paper

Chen, L., Chen, S., Guo, S., Yang, Y., Xu, J. (2019). A Mobile Phone Data Visualization Tool for People Flow Analysis. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11642. Springer, Cham. https://doi.org/10.1007/978-3-030-26075-0_32

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  • DOI: https://doi.org/10.1007/978-3-030-26075-0_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26074-3

  • Online ISBN: 978-3-030-26075-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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