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; Tsung Heng Wu

doi:10.1007/s42001-022-00197-1

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

J Comput Soc Sci. 2023;6(1):315-337. doi: 10.1007/s42001-022-00197-1. Epub 2022 Dec 28.

Authors

Md Amiruzzaman¹, Ye Zhao², Stefanie Amiruzzaman³, Aryn C Karpinski⁴, Tsung Heng Wu²

Affiliations

¹ Department of Computer Science, West Chester University, West Chester, PA USA.
² Department of Computer Science, Kent State University, Kent, OH USA.
³ Department of Languages and Cultures, West Chester University, West Chester, PA USA.
⁴ Research, Measurement & Statistics, Kent State University, Kent, OH USA.

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; Interrater reliability; Semantic segmentation; Social phenomena; Urban neighborhood; Visual diversity.

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