Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2833165.2833178acmconferencesArticle/Chapter ViewAbstractPublication PagesgisConference Proceedingsconference-collections
research-article

RendezView: Look at Meanings of an Encounter Region over Local Social Flocks

Published: 03 November 2015 Publication History
  • Get Citation Alerts
  • Abstract

    Social media data provide insight into people's opinions, thoughts, and reactions about real-world events such as hurricanes, infectious diseases, or urban crimes. In particular, the role of location-embedded social media is being emphasized to monitor surrounding situations and predict future effects by the geography of data shadows. However, it brings big challenges to find meaningful information about dynamic social phenomena from the mountains of fragmented, noisy data flooding. This paper proposes a data model to represent local flock phenomena as collective interests in geosocial streams and presents an interactive visual analysis process. In particular, we show a new visualization tool, called RendezView, composed of a three-dimensional map, word cloud, and Sankey flow diagram. RendezView allows a user to discern spatio-temporal and semantic contexts of local social flock phenomena and their co-occurrence relationships. An explanatory visual analysis of the proposed model is simulated by the experiments on a set of daily Twitter streams and shows the local patterns of social flocks with several visual results.

    References

    [1]
    L. Alex. Key concepts in classical social theory. SAGE Publications Ltd, London, 2011.
    [2]
    L. Anselin. Local indicators of spatial associationâĂŤlisa. Geographical Analysis, 27(2):93--115, 1995.
    [3]
    E. Antoine, A. Jatowt, S.Wakamiya, Y. Kawai, and T. Akiyama. Portraying collective spatial attention in twitter. In Proc. of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 39--48. ACM, 2015.
    [4]
    N. Cao, Y.-R. Lin, X. Sun, D. Lazer, S. Liu, and H. Qu. Whisper: Tracing the spatiotemporal process of information diffusion in real time. IEEE Transactions on Visualization and Computer Graphics, 18(12):2649--2658, 2012.
    [5]
    P. Earle, D. Bowden, and M. Guy. Twitter earthquake detection: earthquake monitoring in a social world. Annals of Geophysics, 54(6), 2012.
    [6]
    M. S. Gerber. Predicting crime using twitter and kernel density estimation. Decision Support Systems, 61(0):115--125, 2014.
    [7]
    J. Gomide, A. Veloso, W. Meira, Jr., V. Almeida, F. Benevenuto, F. Ferraz, and M. Teixeira. Dengue surveillance based on a computational model of spatio-temporal locality of twitter. In Proc. of the 3rd International Web Science Conference, pages 3:1--3:8, 2011.
    [8]
    I. Grabovitch-Zuyev, Y. Kanza, E. Kravi, and B. Pat. On the correlation between textual content and geospatial locations in microblogs. In Proc. of Workshop on Managing and Mining Enriched Geo-Spatial Data, pages 3:1--3:6, 2007.
    [9]
    T. Hagerstrand. What about people in regional science? Papers of the Regional Science Association, 24:7--21, 1975.
    [10]
    J.-Y. Jiang, Y.-S. Tzeng, P.-Y. Huang, and P.-J. Cheng. Analyzing the spatiotemporal effects on detection of rain event duration. In Information Retrieval Technology, volume 7675 of Lecture Notes in Computer Science, pages 506--517. Springer Berlin Heidelberg, 2012.
    [11]
    K. Y. Kamath, J. Caverlee, K. Lee, and Z. Cheng. Spatio-temporal dynamics of online memes: A study of geo-tagged tweets. In Proc. of the 22Nd International Conference on World Wide Web, pages 667--678. International World Wide Web Conferences Steering Committee, 2013.
    [12]
    D. A. Keim, F. Mansmann, J. Schneidewind, J. Thomas, and H. Ziegler. Visual data mining. chapter Visual Analytics: Scope and Challenges, pages 76--90. Springer-Verlag, 2008.
    [13]
    D. A. Keim, F. Mansmann, and J. Thomas. Visual analytics: How much visualization and how much analytics? SIGKDD Explor. Newsl., 11(2):5--8, 2010.
    [14]
    K.-S. Kim, H. Ogawa, A. Nakamura, and I. Kojima. Sophy: a Morphological Framework for Structuring Geo-referenced Social Media. In Proc. of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, 2014.
    [15]
    J. Li and C. Cardie. Early stage influenza detection from twitter. CoRR, abs/1309.7340, 2013.
    [16]
    A. MacEachren, A. Jaiswal, A. Robinson, S. Pezanowski, A. Savelyev, P. Mitra, X. Zhang, and J. Blanford. Senseplace2: Geotwitter analytics support for situational awareness. In 2011 IEEE Conference on Visual Analytics Science and Technology, pages 181--190, 2011.
    [17]
    A. Marcus, M. S. Bernstein, O. Badar, D. R. Karger, S. Madden, and R. C. Miller. Twitinfo: Aggregating and visualizing microblogs for event exploration. In Proc. of the SIGCHI Conference on Human Factors in Computing Systems, pages 227--236. ACM, 2011.
    [18]
    A. Pozdnoukhov and C. Kaiser. Space-time dynamics of topics in streaming text. In Proc. of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, pages 1--8, 2011.
    [19]
    J. Sankaranarayanan, H. Samet, B. E. Teitler, M. D. Lieberman, and J. Sperling. TwitterStand: News in Tweets. In Proc. of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 42--51, 2009.
    [20]
    M. Schmidt. The sankey diagram in energy and material flow management. Journal of industrial ecology, 12(1):82--94, 2008.
    [21]
    C. E. Shannon. A mathematical theory of communication. Bell System Technical Journal, 27(3):379--423, 1948.
    [22]
    T. Shelton, A. Poorthuis, M. Graham, and M. Zook. Mapping the data shadows of hurricane sandy: Uncovering the sociospatial dimensions of 'big data'. Geoforum, 52(0):167--179, 2014.
    [23]
    G.-D. Sun, Y.-C. Wu, R.-H. Liang, and S.-X. Liu. A survey of visual analytics techniques and applications: State-of-the-art research and future challenges. Journal of Computer Science and Technology, 28(5):852--867, 2013.
    [24]
    X. Zhou, S. Shekhar, and R. Y. Ali. Spatiotemporal change footprint pattern discovery: an inter-disciplinary survey. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery, pages 1--23, 2014.

    Index Terms

    1. RendezView: Look at Meanings of an Encounter Region over Local Social Flocks

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Conferences
        IWGS '15: Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming
        November 2015
        102 pages
        ISBN:9781450339711
        DOI:10.1145/2833165
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Sponsors

        In-Cooperation

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 03 November 2015

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. Geo-social morphology
        2. Interactive visual data analytics
        3. Local flock pattern
        4. Three dimensional visualization
        5. spatio-temporal processing

        Qualifiers

        • Research-article
        • Research
        • Refereed limited

        Funding Sources

        • JSPS KAKENHI

        Conference

        SIGSPATIAL'15
        Sponsor:

        Acceptance Rates

        Overall Acceptance Rate 7 of 9 submissions, 78%

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • 0
          Total Citations
        • 71
          Total Downloads
        • Downloads (Last 12 months)6
        • Downloads (Last 6 weeks)0
        Reflects downloads up to 26 Jul 2024

        Other Metrics

        Citations

        View Options

        Get Access

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Media

        Figures

        Other

        Tables

        Share

        Share

        Share this Publication link

        Share on social media