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Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing

Author

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  • Ruomu Miao

    (School of Media and Communication, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yuxia Wang

    (School of Geographic Sciences, Key Lab of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China)

  • Shuang Li

    (Institute of Chinese Historical Geography, Fudan University, Shanghai 200433, China)

Abstract

With the development of Web2.0 and mobile Internet, urban residents, a new type of “sensor”, provide us with massive amounts of volunteered geographic information (VGI). Quantifying the spatial patterns of VGI plays an increasingly important role in the understanding and development of urban spatial functions. Using VGI and social media activity data, this article developed a method to automatically extract and identify urban spatial patterns and functional zones. The method is put forward based on the case of Beijing, China, and includes the following three steps: (1) Obtain multi-source urban spatial data, such as Weibo data (equivalent to Twitter in Chinese), OpenStreetMap, population data, etc.; (2) Use the hierarchical clustering algorithm, term frequency-inverse document frequency (TF-IDF) method, and improved k-means clustering algorithms to identify functional zones; (3) Compare the identified results with the actual urban land uses and verify its accuracy. The experiment results proved that our method can effectively identify urban functional zones, and the results provide new ideas for the study of urban spatial patterns and have great significance in optimizing urban spatial planning.

Suggested Citation

  • Ruomu Miao & Yuxia Wang & Shuang Li, 2021. "Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:647-:d:478689
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    References listed on IDEAS

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    Cited by:

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    8. Yuewen Yang & Dongyan Wang & Zhuoran Yan & Shuwen Zhang, 2021. "Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China," Land, MDPI, vol. 10(11), pages 1-21, November.
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