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Urban area characterization based on crowd behavioral lifelogs over Twitter

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Abstract

Recent location-based social networking sites are attractively providing us with a novel capability of monitoring massive crowd lifelogs in the real-world space. In particular, they make it easier to collect publicly shared crowd lifelogs in a large scale of geographic area reflecting the crowd’s daily lives and even more characterizing urban space through what they have in minds and how they behave in the space. In this paper, we challenge to analyze urban characteristics in terms of crowd behavior by utilizing crowd lifelogs in urban area over the social networking sites. In order to collect crowd behavioral data, we exploit the most famous microblogging site, Twitter, where a great deal of geo-tagged micro lifelogs emitted by massive crowds can be easily acquired. We first present a model to deal with crowds’ behavioral logs on the social network sites as a representing feature of urban space’s characteristics, which will be used to conduct crowd-based urban characterization. Based on this crowd behavioral feature, we will extract significant crowd behavioral patterns in a period of time. In the experiment, we conducted the urban characterization by extracting the crowd behavioral patterns and examined the relation between the regions of common crowd activity patterns and the major categories of local facilities.

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Acknowledgments

This research was supported in part by the 7th Microsoft Research IJARC Core Project.

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Authors and Affiliations

  1. University of Hyogo, Himeji, Japan

    Ryong Lee, Shoko Wakamiya & Kazutoshi Sumiya

  2. National Institute of Information and Communications Technology (NICT), Kyoto, Japan

    Ryong Lee

Authors
  1. Ryong Lee
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  2. Shoko Wakamiya
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  3. Kazutoshi Sumiya
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Correspondence to Ryong Lee.

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Lee, R., Wakamiya, S. & Sumiya, K. Urban area characterization based on crowd behavioral lifelogs over Twitter. Pers Ubiquit Comput 17, 605–620 (2013). https://doi.org/10.1007/s00779-012-0510-9

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  • DOI: https://doi.org/10.1007/s00779-012-0510-9

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