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Exploring the Built Environment Factors Influencing Town Image Using Social Media Data and Deep Learning Methods

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Listed:
  • Weixing Xu

    (School of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, China)

  • Peng Zeng

    (School of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, China)

  • Beibei Liu

    (School of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, China)

  • Liangwa Cai

    (School of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, China)

  • Zongyao Sun

    (School of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, China)

  • Sicheng Liu

    (School of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, China)

  • Fengliang Tang

    (School of Architecture, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, China)

Abstract

The representational image of the city has attracted people’s long-term attention. Nevertheless, the mechanism of interaction between the image and the built environment (BE) and image studies at the town scale have not been fully explored. In this study, we collected multi-source data from 26 characteristic towns in Tianjin, China. We explored a deep learning approach to recognize social media data, which led to the development of quantifiable town uniqueness image (UI) variables. We studied the influence of the BE on the town UI and the moderating effects of positive emotions on the relationship between the two. The results showed that positive emotions had significantly positive moderating effects on the water system ratio’s effect on UI, but weakened sidewalk density and tourist attraction density. They also inhibited the negative effects of road connectivity but could strengthen the negative effects of the sky view factor and points of interest (POI) mix. The moderating effects on other variables are relatively mediocre. This study helps to reveal the inner mechanism of BE and town image. It is conducive to accurately coordinating the relationship between planning policies and design strategies, optimizing resource allocation, and promoting sustainable town development.

Suggested Citation

  • Weixing Xu & Peng Zeng & Beibei Liu & Liangwa Cai & Zongyao Sun & Sicheng Liu & Fengliang Tang, 2024. "Exploring the Built Environment Factors Influencing Town Image Using Social Media Data and Deep Learning Methods," Land, MDPI, vol. 13(3), pages 1-21, February.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:3:p:291-:d:1346001
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    References listed on IDEAS

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    3. Hernández-Moreno, Ángela & Reyes-Paecke, Sonia, 2018. "The effects of urban expansion on green infrastructure along an extended latitudinal gradient (23°S–45°S) in Chile over the last thirty years," Land Use Policy, Elsevier, vol. 79(C), pages 725-733.
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    5. Andrew Mondschein & Steven T. Moga, 2018. "New Directions in Cognitive-Environmental Research," Journal of the American Planning Association, Taylor & Francis Journals, vol. 84(3-4), pages 263-275, October.
    6. Lei Su & Weifeng Chen & Yan Zhou & Lei Fan, 2023. "Exploring City Image Perception in Social Media Big Data through Deep Learning: A Case Study of Zhongshan City," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
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