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Characterization of Behavioral Patterns Exploiting Description of Geographical Areas

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVII

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 9860))

Abstract

The enormous amount of recently available mobile phone data is providing unprecedented direct measurements of human behavior. Early recognition and prediction of behavioral patterns are of great importance in many societal applications like urban planning, transportation optimization, and health-care. Understanding the relationships between human behaviors and location’s context is an emerging interest for understanding human-environmental dynamics. Growing availability of Web 2.0, i.e. the increasing amount of websites with mainly user created content and social platforms opens up an opportunity to study such location’s contexts. This paper investigates relationships existing between human behavior and location context, by analyzing log mobile phone data records. First an advanced approach to categorize areas in a city based on the presence and distribution of categories of human activity (e.g., eating, working, and shopping) found across the areas, is proposed. The proposed classification is then evaluated through its comparison with the patterns of temporal variation of mobile phone activity and applying machine learning techniques to predict a timeline type of communication activity in a given location based on the knowledge of the obtained category vs. land-use type of the locations areas. The proposed classification turns out to be more consistent with the temporal variation of human communication activity, being a better predictor for those compared to the official land use classification.

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Notes

  1. 1.

    http://www.openstreetmap.org.

  2. 2.

    http://www.telecomitalia.com/tit/it/bigdatachallenge.html.

  3. 3.

    http://wiki.openstreetmap.org/wiki/Key:landuse.

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Acknowledgments

The authors would like to thank the Semantic Innovation Knowledge Lab - Telecom Italia for publicly sharing the mobile phone data records which were provided for Big Data Challenge organized in 2013, Italy. We also would like to thank MIT SENSEable City Lab Consortium partially for supporting the research.

Author information

Authors and Affiliations

  1. University of Trento, Via Sommarive, 9, Povo, TN, Italy

    Zolzaya Dashdorj

  2. SKIL LAB - Telecom Italia, Trento, Italy

    Zolzaya Dashdorj

  3. DKM - Fondazione Bruno Kessler, Trento, Italy

    Zolzaya Dashdorj

  4. SICT - Mongolian University of Science and Technology, Bayanzurkh District 22th, Khoroo, UB, Mongolia

    Zolzaya Dashdorj

  5. New York University, 1 MetroTech Center, Brooklyn, NY, USA

    Stanislav Sobolevsky

  6. Massachusetts Institute of Technology, MIT, 77 Massachusetts Avenue, Cambridge, MA, USA

    Stanislav Sobolevsky

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  1. Zolzaya Dashdorj
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Correspondence to Zolzaya Dashdorj .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

  4. MIT SENSEable City Lab, Cambridge, Massachusetts, USA

    Amin Anjomshoaa

  5. Faculty of Business and Information Technology, University of Ontario Institute of Technology (UOIT), Oshawa, Ontario, Canada

    Patrick C. K. Hung

  6. Trinity University, Plainview, Texas, USA

    Dominik Kalisch

  7. New York University, Brooklyn, New York, USA

    Stanislav Sobolevsky

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Dashdorj, Z., Sobolevsky, S. (2016). Characterization of Behavioral Patterns Exploiting Description of Geographical Areas. In: Hameurlain, A., et al. Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVII. Lecture Notes in Computer Science(), vol 9860. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53416-8_10

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  • DOI: https://doi.org/10.1007/978-3-662-53416-8_10

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