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Place identity: a generative AI’s perspective

Author

Listed:
  • Kee Moon Jang

    (Massachusetts Institute of Technology)

  • Junda Chen

    (University of California San Diego)

  • Yuhao Kang

    (Massachusetts Institute of Technology
    The University of Texas at Austin
    University of South Carolina)

  • Junghwan Kim

    (Virginia Tech)

  • Jinhyung Lee

    (Western University)

  • Fabio Duarte

    (Massachusetts Institute of Technology)

  • Carlo Ratti

    (Massachusetts Institute of Technology)

Abstract

Do cities have a collective identity? The latest advancements in generative artificial intelligence (AI) models have enabled the creation of realistic representations learned from vast amounts of data. In this study, we test the potential of generative AI as the source of textual and visual information in capturing the place identity of cities assessed by filtered descriptions and images. We asked questions on the place identity of 64 global cities to two generative AI models, ChatGPT and DALL·E2. Furthermore, given the ethical concerns surrounding the trustworthiness of generative AI, we examined whether the results were consistent with real urban settings. In particular, we measured similarity between text and image outputs with Wikipedia data and images searched from Google, respectively, and compared across cases to identify how unique the generated outputs were for each city. Our results indicate that generative models have the potential to capture the salient characteristics of cities that make them distinguishable. This study is among the first attempts to explore the capabilities of generative AI in simulating the built environment in regard to place-specific meanings. It contributes to urban design and geography literature by fostering research opportunities with generative AI and discussing potential limitations for future studies.

Suggested Citation

  • Kee Moon Jang & Junda Chen & Yuhao Kang & Junghwan Kim & Jinhyung Lee & Fabio Duarte & Carlo Ratti, 2024. "Place identity: a generative AI’s perspective," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-16, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03645-7
    DOI: 10.1057/s41599-024-03645-7
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    References listed on IDEAS

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    1. Wang, Suosheng & Chen, Joseph S., 2015. "The influence of place identity on perceived tourism impacts," Annals of Tourism Research, Elsevier, vol. 52(C), pages 16-28.
    2. Yong Gao & Yuanyuan Chen & Lan Mu & Shize Gong & Pengcheng Zhang & Yu Liu, 2022. "Measuring urban sentiments from social media data: a dual-polarity metric approach," Journal of Geographical Systems, Springer, vol. 24(2), pages 199-221, April.
    3. Yingjie Hu & Chengbin Deng & Zhou Zhou, 2019. "A Semantic and Sentiment Analysis on Online Neighborhood Reviews for Understanding the Perceptions of People toward Their Living Environments," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 109(4), pages 1052-1073, July.
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