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Studying geospatial urban visual appearance and diversity to understand social phenomena

Amiruzzaman, Md
http://orcid.org/0000-0002-2292-5798
http://rave.ohiolink.edu/etdc/view?acc_num=kent1618904789316283

Abstract Details

2021, PHD, Kent State University, College of Arts and Sciences / Department of Computer Science.
The complex interrelationship between the built environment and social problems is often described but frequently lacks the data and analytical framework for advances leading to widespread application. First, this study addresses this gap by presenting a machine learning (ML) approach to study whether street-level built environment visuals can be used to classify locations with high-crime and lower-crime activities. In training the model, spatialized expert narratives are used to classify locations (and therefore place types) as being linked to high crime. Google Street View (GSV) images are then extracted into semantic categories (e.g., road, sky, greenery, and building) through a deep learning image segmentation algorithm. From these local visual representatives are generated and used to train a classification model to identify similar potential high crime environments. The model is applied to multiple cities in the US. Results of the first part of the study show our model can predict high-and lower-crime areas with more than 98\% in the first test city, and above 95\% accuracy in the second test city. However, when the urban environment changes across regions, so the predictive capacity of the model falls. Second, this study presents a method to compute urban visual diversity using an Artificial Intelligence (AI)-based technique and shows its relationship with the social phenomenon. The developed framework show computational method to find single category and multiple category indices. A process to find important features from GSV images that can help to compute geospatial visual diversity. The second part of the study shows the reliability and validity of the method using Inter-Rater Reliability Indices (IRR). The results of the second part of the study indicate GSV images can be used to compute geospatial visual diversity and assess relationships between social phenomena and visual diversity indices.
Ye Zhao, Ph.D. (Advisor)
Xiang Lian, Ph.D. (Committee Member)
Qiang Guan, Ph.D. (Committee Member)
Aryn Karpinski, Ph.D. (Committee Member)
He Yin, Ph.D. (Other)
134 p.
Computer Science
Computational framework,deep learning,inter-rater reliability,machine learning,statistics,street view images,visual appearance,visual diversity

Recommended Citations

Citations

  • Amiruzzaman, M. (2021). Studying geospatial urban visual appearance and diversity to understand social phenomena [Doctoral dissertation, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1618904789316283

    APA Style (7th edition)

  • Amiruzzaman, Md. Studying geospatial urban visual appearance and diversity to understand social phenomena. 2021. Kent State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1618904789316283.

    MLA Style (8th edition)

  • Amiruzzaman, Md. "Studying geospatial urban visual appearance and diversity to understand social phenomena." Doctoral dissertation, Kent State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=kent1618904789316283

    Chicago Manual of Style (17th edition)

kent1618904789316283
318
© 2021, some rights reserved.Creative Commons License Studying geospatial urban visual appearance and diversity to understand social phenomena by Md Amiruzzaman is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Kent State University and OhioLINK.