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WalkingStreet: Understanding Human Mobility Phenomena Through a Mobile Application

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Intelligent Data Engineering and Automated Learning – IDEAL 2021 (IDEAL 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13113))

Abstract

Understanding human mobility patterns requires access to timely and reliable data for an adequate policy response. This data can come from several sources, such as mobile devices. Additionally, the wide availability of communications networks enables applications (mobile apps) to generate data anytime and anywhere thanks to their general adoption by individuals. Although data is generated from personal devices, if a relevant set of metrics is applied to it, it can become useful for the authorities and the community as a whole. This paper explores new methods for gathering and analyzing location-based data using a mobile application called WalkingStreet. The article also illustrates the great potential of human mobility metrics for moving spatial measures beyond census units, key measures of individual, collective mobility and a mix of the two, investigating a range of important social phenomena, the heterogeneity of activity spaces and the dynamic nature of spatial segregation.

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Acknowledgments

This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been supported by national funds through FCT – Fundação para a Ciência e Tecnologia through project UIDB/04728/2020.

Author information

Authors and Affiliations

  1. Department of Informatics, University of Minho, ALGORITMI Center, Braga, Portugal

    Luís Rosa & Cesar Analide

  2. CIICESI, ESTG, Politécnico do Porto, Felgueiras, Portugal

    Fábio Silva

Authors
  1. Luís Rosa
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  2. Fábio Silva
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  3. Cesar Analide
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Corresponding authors

Correspondence to Luís Rosa , Fábio Silva or Cesar Analide .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Universidad Politecnica de Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

  4. University of Manchester, Manchester, UK

    Richard Allmendinger

  5. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  6. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  7. Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

  8. University of Minho, Braga, Portugal

    Paulo Novais

  9. NOVA University of Lisbon, Lisbon, Portugal

    Susana Nascimento

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Rosa, L., Silva, F., Analide, C. (2021). WalkingStreet: Understanding Human Mobility Phenomena Through a Mobile Application. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2021. IDEAL 2021. Lecture Notes in Computer Science(), vol 13113. Springer, Cham. https://doi.org/10.1007/978-3-030-91608-4_58

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  • DOI: https://doi.org/10.1007/978-3-030-91608-4_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91607-7

  • Online ISBN: 978-3-030-91608-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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