An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables

Md Amiruzzaman (), Ye Zhao, Stefanie Amiruzzaman, Aryn C. Karpinski and Tsung Heng Wu
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Md Amiruzzaman: West Chester University
Ye Zhao: Kent State University
Stefanie Amiruzzaman: West Chester University
Aryn C. Karpinski: Kent State University
Tsung Heng Wu: Kent State University

Journal of Computational Social Science, 2023, vol. 6, issue 1, No 9, 315-337

Abstract: Abstract This study presents a framework to study quantitatively geographical visual diversities of urban neighborhood from a large collection of street-view images using an Artificial Intelligence (AI)-based image segmentation technique. A variety of diversity indices are computed from the extracted visual semantics. They are utilized to discover the relationships between urban visual appearance and socio-demographic variables. This study also validates the reliability of the method with human evaluators. The methodology and results obtained from this study can potentially be used to study urban features, locate houses, establish services, and better operate municipalities.

Keywords: Google street-view; Visual diversity; Urban neighborhood; Interrater reliability; Social phenomena; Semantic segmentation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s42001-022-00197-1

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