Spatial Distribution Analysis of Urban Retail Industry Using POI Big Data


  • Hongwei Su Graduate School, University of the East, Manila, Philippines and China Urban Construction Desig&Research Institute CO.,LTD, Beijing,China Author
  • Maria Amelia E. Damian Faculty College of Engineering,University of the East, Manila, Philippines Author



With the development of social economy, it is very important to identify urban functional areas and understand the spatial distribution characteristics of urban functional areas for urban scientific planning and government decision-making. Reasonable spatial distribution of urban retail industry is of great significance to promote urban economic development, optimize urban structure, and meet the consumption needs of urban residents. Based on the data of retail industry's POI, GDP, population and transportation in Nanning, China, this paper analyzes the spatial distribution characteristics and formation mechanism of different retail industry clusters in the city, and studies the spatial correlation between retail industry and population, economy, transportation, etc., using the methods of kernel density method, spatial correlation analysis, Getis-Ord G * index statistics, and average nearest neighbor distance. The results show that:(1)On the whole, the retail industry is concentrated in the central urban area of Nanning, spreading from the middle to the surrounding areas. (2)The retail hot spots have formed a multi-center development and surrounding spread pattern in spatial distribution. (3) The spatial distribution of the retail industry is highly related to the main traffic lines. At the same time, the concentration and distribution of the retail industry have a strong spatial correlation with the economy and population.


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Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.  

How to Cite

H. Su and M. A. E. . Damian, “Spatial Distribution Analysis of Urban Retail Industry Using POI Big Data”, ijetaa, vol. 1, no. 2, pp. 1–7, Mar. 2024, doi: 10.62677/IJETAA.2402105.