Spaceprint: a Mobility-based Fingerprinting Scheme for Spaces

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    Abstract

    In this paper, we address the problem of how automated situational awareness in a specifi c location can be achieved by characterizing the fingerprint of recurrent situations from ubiquitously generated mobility data. Without semantic input about the time and space (location) where situations take place, this turns out to be a fundamental challenging problem. Uncertainties in data also introduce technical challenges when data is generated in irregular time intervals, being mixed with noise, and errors. Purely relying on temporal patterns observable in mobility data, in this paper, we propose Spaceprint, a fully automated algorithm for fi nding the repetitive pattern of similar situations in spaces. We evaluate this technique by showing how the latent variables describing the actual identity of a space can be discovered from the extracted situation patterns.
    Original languageEnglish
    Title of host publicationSIGSPATIAL '17
    Subtitle of host publicationProceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
    PublisherAssociation for Computing Machinery (ACM)
    ISBN (Electronic)978-1-4503-5490-5
    DOIs
    Publication statusPublished - 7 Nov 2017
    Event25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2017 - Redondo Beach, United States
    Duration: 7 Nov 201710 Nov 2017
    Conference number: 25

    Conference

    Conference25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2017
    Abbreviated titleSIGSPATIAL 2017
    CountryUnited States
    CityRedondo Beach
    Period7/11/1710/11/17

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