skip to main content
10.1145/3347146.3363465acmconferencesArticle/Chapter ViewAbstractPublication PagesgisConference Proceedingsconference-collections
extended-abstract

Spatial Cluster Detection with Social Media Mobility Patterns

Published: 05 November 2019 Publication History

Abstract

We proposed a set of new spatial scan methods for detecting spatial clusters of disease infection that use movement data from case and control individuals, rather than a single location per individual, in order to identify areas with a high relative risk of infection. We illustrate the use of these methods to detect spatial clusters of dengue infection risk using geo-located data from Twitter classified into infected cases and non-infected controls.

References

[1]
Martin Kulldorff, Richard Heffernan, Jessica Hartman, Renato Assunção, and Farzad Mostashari. 2005. A Space-Time Permutation Scan Statistic for Disease Outbreak Detection. PLoS Med (2005).
[2]
Daniel B. Neill. 2012. Fast subset scan for spatial pattern detection. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 74, 2 (2012), 337--360.
[3]
R. CSNP. Souza, R. Assunção, D. M. Oliveira, D. E. F. Brito, and W. Meira Jr. 2016. Infection Hot Spot Mining from Social Media Trajectories. In ECML/PKDD.
[4]
Roberto CSNP Souza, Renato M Assunção, Derick M Oliveira, Daniel B Neill, and Wagner Meira Jr. 2019. Where did I get dengue? Detecting spatial clusters of infection risk with social network data. SSTE 29 (2019), 163--175.
[5]
Steven Stoddard et al. 2013. House-to-house human movement drives dengue virus transmission. PNAS 110, 3 (2013), 994--999.

Index Terms

  1. Spatial Cluster Detection with Social Media Mobility Patterns

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
    November 2019
    648 pages
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 05 November 2019

    Check for updates

    Author Tags

    1. social media data
    2. spatial cluster detection
    3. spatial scan statistics

    Qualifiers

    • Extended-abstract
    • Research
    • Refereed limited

    Conference

    SIGSPATIAL '19
    Sponsor:

    Acceptance Rates

    SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;
    Overall Acceptance Rate 257 of 1,238 submissions, 21%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 110
      Total Downloads
    • Downloads (Last 12 months)1
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 25 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media