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<p>Understanding the vitality of commercial centers is essential for urban planning and economic development. This study introduces a novel framework to quantitatively assess the vitality of commercial centers in Shanghai, leveraging multi-source geospatial big data, including the rarely utilized UnionPay consumption data, alongside mobile signaling data and Points of Interest (POI). By integrating these datasets, a multi-dimensional vitality index was developed, encompassing population, goods, and market dynamics. Dimensionality reduction techniques such as Principal Component Analysis and Factor Analysis were applied, followed by K-means clustering to classify commercial centers into vitality clusters. Unlike prior studies which relied primarily on POI and mobility data, our inclusion of granular consumption metrics provides a deeper understanding of market vitality. Results reveal a spatial gradient, with higher vitality concentrated in central Shanghai and diminishing towards the periphery, validating the “commercial gravity” theory. This framework not only enhances the methodological toolkit for evaluating urban commercial vitality but also offers valuable insights for optimizing commercial resources and addressing spatial inequalities in urban planning. The approach is adaptable for other metropolitan regions.</p>

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Evaluation of comprehensive vitality of Shanghai’s commercial centers using multi-dimensional geospatial big data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Hengzhi Hu,
  • Jingbo Yan,
  • Bolin Wang,
  • Xuefei Sun

摘要

Understanding the vitality of commercial centers is essential for urban planning and economic development. This study introduces a novel framework to quantitatively assess the vitality of commercial centers in Shanghai, leveraging multi-source geospatial big data, including the rarely utilized UnionPay consumption data, alongside mobile signaling data and Points of Interest (POI). By integrating these datasets, a multi-dimensional vitality index was developed, encompassing population, goods, and market dynamics. Dimensionality reduction techniques such as Principal Component Analysis and Factor Analysis were applied, followed by K-means clustering to classify commercial centers into vitality clusters. Unlike prior studies which relied primarily on POI and mobility data, our inclusion of granular consumption metrics provides a deeper understanding of market vitality. Results reveal a spatial gradient, with higher vitality concentrated in central Shanghai and diminishing towards the periphery, validating the “commercial gravity” theory. This framework not only enhances the methodological toolkit for evaluating urban commercial vitality but also offers valuable insights for optimizing commercial resources and addressing spatial inequalities in urban planning. The approach is adaptable for other metropolitan regions.