VisualCommunity: a platform for archiving and studying communities

Suphanut Jamonnak (), Deepshikha Bhati, Md Amiruzzaman, Ye Zhao, Xinyue Ye and Andrew Curtis
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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

Journal of Computational Social Science, 2022, vol. 5, issue 2, No 6, 1257-1279

Abstract: 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.

Keywords: AI processing; Community study; Geo-narrative; Spatial video; Visualization system (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s42001-022-00170-y

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