Document Type
Article
Publication Date
12-2022
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.
Publication Title
Journal of Computational Social Science
ISSN
2432-2717
Publisher
Springer Nature
Volume
6
First Page
315
Last Page
337
DOI
10.1007/s42001-022-00197-1
Recommended Citation
Amiruzzaman, M., Zhao, Y., Amiruzzaman, S., Karpinski, A. C., & Wu, T. H. (2022). An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables. Journal of Computational Social Science, 6, 315-337. http://dx.doi.org/10.1007/s42001-022-00197-1