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Information Filtering and Retrieval pp 35–61Cite as

Mining Movement Data to Extract Personal Points of Interest: A Feature Based Approach

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 668))

Abstract

Due to the widespread of mobile devices in recent years, records of the locations visited by users are common and growing, and the availability of such large amounts of spatio-temporal data opens new challenges to automatically discover valuable knowledge. One aspect that is being studied is the identification of important locations, i.e. places where people spend a fair amount of time during their daily activities; we address it with a novel approach. Our proposed method is organised in two phases: first, a set of candidate stay points is identified by exploiting some state-of-the-art algorithms to filter the GPS-logs; then, the candidate stay points are mapped onto a feature space having as dimensions the area underlying the stay point, its intensity (e.g. the time spent in a location) and its frequency (e.g. the number of total visits). We conjecture that the feature space allows to model aspects/measures that are more semantically related to users and better suited to reason about their similarities and differences than simpler physical measures (e.g. latitude, longitude, and timestamp). An experimental evaluation on the GeoLife public dataset confirms the effectiveness of our approach and sheds some light on the peculiar features and critical issues of location based systems.

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Notes

  1. 1.

    \( Accuracy \le 30\) is a parameter set empirically, by observing the raw data.

  2. 2.

    \(p_j.acc \le 10\) is a parameter set empirically, by observing a set of GPS detections in several signal acquisition conditions.

  3. 3.

    Otherwise, activities of a single day may escape from the usual routine and could easily hinder the recognition process.

  4. 4.

    \(aT \ge 3\) is a parameter set empirically, by observing user movements during our preliminary experiment described in Sect. 3.3.

  5. 5.

    For instance, the user slightly changes speed while driving along a highway.

  6. 6.

    \(Overlap\,{\ge }50\,\%\) is a parameter set empirically, by observing user movements during the preliminary experiment.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Udine, Via Delle Scienze 206, Udine, Italy

    Marco Pavan, Stefano Mizzaro & Ivan Scagnetto

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  1. Marco Pavan
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  2. Stefano Mizzaro
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  3. Ivan Scagnetto
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Correspondence to Marco Pavan .

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Editors and Affiliations

  1. Parco Scientifico e Technologico della Sardegna, Center of Advanced Studies, Research and Development in Sardinia (CRS4), Pula, Italy

    Cristian Lai

  2. Dept. of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy

    Alessandro Giuliani

  3. Dept. of Computer Science , University of Bari Aldo Moro, Bari, Italy

    Giovanni Semeraro

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Pavan, M., Mizzaro, S., Scagnetto, I. (2017). Mining Movement Data to Extract Personal Points of Interest: A Feature Based Approach. In: Lai, C., Giuliani, A., Semeraro, G. (eds) Information Filtering and Retrieval. Studies in Computational Intelligence, vol 668. Springer, Cham. https://doi.org/10.1007/978-3-319-46135-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-46135-9_3

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-46135-9

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