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Identification of Spatial and Symbolic City Image Elements Through Social Media Data: A Case Study of Hangzhou

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  • Jiaqi Wang

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
    The Architectural Design & Research Institute of Zhejiang University Co., Ltd., Hangzhou 310028, China)

  • Yu Shi

    (The Architectural Design & Research Institute of Zhejiang University Co., Ltd., Hangzhou 310028, China)

  • Weishun Xu

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
    Zhejiang University Center for Balance Architecture, Zhejiang University, Hangzhou 310058, China)

  • Yue Wu

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

Abstract

Despite emerging empirical findings and computational tools that extend city image research to include social dimensions beyond visual perception, methodologies for effectively identifying and analyzing the relationships between the five city image elements remain underdeveloped. This paper addresses the gap by proposing a big data-driven method, integrating Weibo check-in data, Baidu Map POI, and ArcGIS algorithms to identify city image elements and further reveal a city’s overall morphological characteristics. Based on different modes of observation, city image elements are categorized as spatial descriptors (“districts”, “nodes”, and “paths”) and symbolic descriptors (“landmarks” and “edges”). Taking Hangzhou as a case study, the findings show a strong alignment between urban development achievements and the distribution patterns of city image elements. “Districts” and “landmarks” stand out as the most prominent, reflecting functional zoning and urban maturity, while “nodes” emphasize the city’s polycentric structure. “Paths” offer clear insight into the city’s development trajectory, while “edges” appear to be legible only in relation to other elements. This method innovates cognitive mapping by merging real-world perceptions with algorithmic precision, offering a valuable tool for understanding urban morphology, monitoring development changes, and fostering participatory urban design.

Suggested Citation

  • Jiaqi Wang & Yu Shi & Weishun Xu & Yue Wu, 2024. "Identification of Spatial and Symbolic City Image Elements Through Social Media Data: A Case Study of Hangzhou," Land, MDPI, vol. 13(12), pages 1-22, December.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:12:p:2194-:d:1544605
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    References listed on IDEAS

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    3. 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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