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

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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.

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Data availability statement

Data sharing is not applicable to this article as datasets used in this study came from Google Street-View (GSV) images and other publicly available datasets. Google does allow sharing of GSV images publicly, anyone can download the GSV images using GSV API (e.g., https://maps.googleapis.com/maps/api/streetview?size=400x400 &location=47.5763831,-122.4211769&fov=80 &heading=70&pitch=0 &key=YOUR_API_KEY &signature=YOUR_SIGNATURE). Statistical data used in this study are available at https://www.zillow.com/howto/api/APIOverview.htm, and at https://www.niche.com/about/data/.

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Acknowledgements

This work was supported by the U.S. National Science Foundation under Grant 1739491. Md Amiruzzaman was also supported by West Chester University Faculty Startup Grant.

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Authors and Affiliations

  1. Department of Computer Science, West Chester University, West Chester, PA, USA

    Md Amiruzzaman

  2. Department of Computer Science, Kent State University, Kent, OH, USA

    Ye Zhao & Tsung Heng Wu

  3. Department of Languages and Cultures, West Chester University, West Chester, PA, USA

    Stefanie Amiruzzaman

  4. Research, Measurement & Statistics, Kent State University, Kent, OH, USA

    Aryn C. Karpinski

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  1. Md Amiruzzaman
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Amiruzzaman, M., Zhao, Y., Amiruzzaman, S. et al. An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables. J Comput Soc Sc 6, 315–337 (2023). https://doi.org/10.1007/s42001-022-00197-1

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