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VisualCommunity: a platform for archiving and studying communities

Author

Listed:
  • Suphanut Jamonnak

    (Kent State University)

  • Deepshikha Bhati

    (Kent State University)

  • Md Amiruzzaman

    (West Chester University)

  • Ye Zhao

    (Kent State University)

  • Xinyue Ye

    (Texas A&M University)

  • Andrew Curtis

    (Case Western Reserve University)

Abstract

VisualCommunity is a platform designed to support community or neighborhood scale research. The platform integrates mobile, AI, visualization techniques, along with tools to help domain researchers, practitioners, and students collecting and working with spatialized video and geo-narratives. These data, which provide granular spatialized imagery and associated context gained through expert commentary have previously provided value in understanding various community-scale challenges. This paper further enhances this work AI-based image processing and speech transcription tools available in VisualCommunity, allowing for the easy exploration of the acquired semantic and visual information about the area under investigation. In this paper we describe the specific advances through use case examples including COVID-19 related scenarios.

Suggested Citation

  • Suphanut Jamonnak & Deepshikha Bhati & Md Amiruzzaman & Ye Zhao & Xinyue Ye & Andrew Curtis, 2022. "VisualCommunity: a platform for archiving and studying communities," Journal of Computational Social Science, Springer, vol. 5(2), pages 1257-1279, November.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:2:d:10.1007_s42001-022-00170-y
    DOI: 10.1007/s42001-022-00170-y
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    References listed on IDEAS

    as
    1. Andrew Curtis & Jason K. Blackburn & Sarah L. Smiley & Minmin Yen & Andrew Camilli & Meer Taifur Alam & Afsar Ali & J. Glenn Morris, 2016. "Mapping to Support Fine Scale Epidemiological Cholera Investigations: A Case Study of Spatial Video in Haiti," IJERPH, MDPI, vol. 13(2), pages 1-13, February.
    2. Md Amiruzzaman & Andrew Curtis & Ye Zhao & Suphanut Jamonnak & Xinyue Ye, 2021. "Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach," Journal of Computational Social Science, Springer, vol. 4(2), pages 813-837, November.
    3. Andrew Curtis & Chaz Felix & Susanne Mitchell & Jayakrishnan Ajayakumar & Peter R. Kerndt, 2018. "Contextualizing Overdoses in Los Angeles's Skid Row between 2014 and 2016 by Leveraging the Spatial Knowledge of the Marginalized as a Resource," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 108(6), pages 1521-1536, November.
    Full references (including those not matched with items on IDEAS)

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