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Detection of Points of Interest from Crowdsourced Tourism Data

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Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11620))

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Abstract

Availability of the big data on human mobility raised a lot of expectations regarding the possibility to have a more detailed insights into daily and seasonal mobility patterns. However, this is not a trivial task and often noisy positioning data pose a great challenge among researchers and practitioners. In this paper, we tackle the detection of the Points of Interest (PoI) locations from the mobile sensed tourist data gathered in Zeeland (Netherlands) region. We consider different clustering approaches to detect individuals and collective PoI locations and find that OPTICS proved to be the most robust against initial parameters choices and k-means the most sensitive. K-means also seemed not appropriate to use to extract individual places but it indicates promising to extract areas of city which are often visited.

Supported by Province of Zeeland, VVV Zeeland and Urban Innovative Actions program.

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Author information

Authors and Affiliations

  1. Department of Industrial Systems Engineering and Product Design, Ghent University, Technologiepark 903, 9052, Gent-Zwijnaarde, Belgium

    Ivana Semanjski & Sidharta Gautama

  2. Industrial Systems Engineering (ISyE), Flanders Make, Ghent University, 9052, Gent-Zwijnaarde, Belgium

    Ivana Semanjski & Sidharta Gautama

  3. Department of Telecommunications and Information Processing, Ghent University, St-Pietersnieuwstraat 41, 9000, Gent, Belgium

    Moustapha Ramachi

Authors
  1. Ivana Semanjski
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  2. Moustapha Ramachi
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  3. Sidharta Gautama
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Corresponding author

Correspondence to Ivana Semanjski .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg, Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Semanjski, I., Ramachi, M., Gautama, S. (2019). Detection of Points of Interest from Crowdsourced Tourism Data. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11620. Springer, Cham. https://doi.org/10.1007/978-3-030-24296-1_18

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  • DOI: https://doi.org/10.1007/978-3-030-24296-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24295-4

  • Online ISBN: 978-3-030-24296-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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