What matters in the e-commerce era? Modelling and mapping shop rents in Guangzhou, China

Abstract

The rise of e-commerce is changing consumer behaviours and the value of retail space. Tracking the changes of shop rents under the impact of e-commerce and understanding the logic behind those changes is important for urban management. By identifying the factors that reflect the impacts of e-commerce and applying the Pearson correlation coefficient and the LASSO model, this study uses geospatial big data to quantify and evaluate the changing factors that impact shop rents. The result of the rising importance of trade area and neighbourhood services challenges the traditional emphasis on locations for shop rents. Using the random forest regression algorithm, this study also maps the shop rent distribution in Guangzhou, China, and compares the result with the land value distribution in 2015. The scattered and smaller highest-rent centres indicate the decreasing influence of central place logic. The decline of some traditional spots with the highest commercial values and rise of new catering services centres in suburban areas have been observed, suggesting the changing impacts of the agglomerated externalities.