近日，华南理工大学Qijing Tang团队研究了数字化转型下中国城市O2O零售空间的空间异质性。相关论文于2025年5月5日发表在《中国地理科学》杂志上。

数字技术的快速发展推动了线上到线下（O2O）零售的出现和普及，重塑了中国城市的零售格局。然而，在现有的研究中，新兴O2O零售的空间分布特征和影响机制尚未得到深入的研究。研究组以广州市中心城区为例，利用多源数据和机器学习方法探索O2O零售空间的分布特征，并进一步确定建筑环境、社会人口和经济因素对其分布的非线性影响。结果表明，O2O零售空间呈现出“单中心”的分布模式，与传统零售空间的“多中心”分布模式形成鲜明对比。这一发现支持了创新扩散假说，突显出O2O零售模式的扩张首先从传统发达的零售空间蔓延开来。 

此外，在不同类型的O2O零售空间中观察到了空间异质性，O2O店内展示了中心地理论所描述的“核心-外围”空间结构，而O2O交付则展示了中心流理论所描述出的“水平、非层次和多中心”的网络结构。与传统零售空间相比，O2O零售空间的分布更多地受到青年比例、教育水平和收入水平等社会人口因素的影响，但受办公和建筑密度等建筑环境因素的影响较小。此外，还确定了这些影响因素对O2O零售空间分布的非线性影响，通过突出有效范围和阈值效应丰富了现有文献。这些发现为数字化转型背景下的O2O零售空间发展提供了宝贵的见解。

附：英文原文

Title: Spatial Heterogeneities of O2O Retail Space in Urban China Under Digital Transformation: Evidence from Guangzhou, China

Author: Wei, Zongcai, Huang, Weichao, Tang, Qijing, Xie, Ruimin

Issue&Volume: 2025-05-05

Abstract: The rapid development of digital technologies has driven the emergence and popularization of online-to-offline (O2O) retail, reshaping the retail landscape in urban China. However, spatial distribution characteristics and influencing mechanisms of emerging O2O retail have not been thoroughly investigated in extant studies. Taking the central urban area of Guangzhou as the case, this study utilized multi-source data and machine learning methods to explore the distribution characteristics of O2O retail space and to further identify the nonlinear effects of the built environment, sociodemographic, and economic factors on its distribution. The results revealed that O2O retail space exhibited a ‘single-center’ distribution pattern, in contrast to the ‘multi-center’ distribution pattern of traditional retail space. This finding supported the diffusion of innovation hypothesis, highlighting that the expansion of O2O retail modes first spread from traditional developed retail space. Furthermore, spatial heterogeneities were observed across different types of O2O retail space, with O2O in-store showing a ‘core-periphery’ spatial structure as described by Central Place Theory, whereas O2O delivery displaying a ‘horizontal, non-hierarchical, and multi-centered’ network structure following Central Flow Theory. Compared to traditional retail space, the distribution of O2O retail space was more influenced by sociodemographic factors such as the proportion of youth, education level, and income level, but less affected by the built environment factors like office and building density. Furthermore, nonlinear effects of these influencing factors on the distribution of O2O retail space were identified, which enriched the existing literature by highlighting effective ranges and threshold effects. These findings provided valuable insights into O2O retail space development in the context of digital transformation.

DOI: 10.1007/s11769-025-1493-6

Source: https://link.springer.com/article/10.1007/s11769-025-1493-6