广州市零售业态空间分异影响因素识别与驱动力研究

以广州市47026个零售业网点为基本数据,通过梳理零售业空间分异的机制,构建包含人口密度、商务条件、公共交通便利性、业态丰富度与租金条件5个影响因子的零售业态空间分异影响因素评价体系,通过信息熵、核密度函数与空间回归模型分析零售业态的空间分异影响因素,对比不同城市圈层区位与不同零售业态集聚分异的因素差异。结果表明:①需求、区位、竞争与成本构成了广州市零售业态空间分异的主要驱动力,同时,零售的景观分异也由于业态异质性与城市的空间异质性而存在驱动力分异;②5个影响因素强度格局圈层差异明显,城市内圈层人口集聚度高,具备更好的公共交通便利性条件、商务条件与业态丰富度,同时也承受更高的地租;③人口密度是零售空间分异的核心要素,公共交通便利性条件、商务条件与业态丰富度对零售的集聚也有正向驱动作用,租金的影响较弱;不同圈层区位的零售空间分布与不同类型业态的空间分异的主要影响因素各不相同。

A Study on the Influencing Factors and Driving Forces of Spatial Differentiation of Retail Formats in Guangzhou

Exploration of the spatial differentiation of the retail industry based on large-scale geospatial data is of great significance for urban development. In the recent years, POI data has become an important data source for studying urban dynamics. POI data abstracts retail stores as a point on the map, and the data are of wide coverage and high fineness. These advantages make the POI data an ideal dataset for micro- analysis of urban retail commercial structure and their spatial distribution mechanism. Based on the data of 47 026 retail outlets in Guangzhou, we explored the driving mechanism of the spatial differentiation of the retail formats. By building an indicator system, we investigated the factors potentially affecting the spatial differentiation of the retail industry, which include population density, business conditions, public transportation convenience, format richness, and rent. Based on information entropy, kernel density function, and spatial regression, we analyzed the main influencing factors of the retail differentiation. Further, we divided the retail outlets by different urban areas and different retail formats, and conducted spatial regression analysis based on the same influencing factors to compare the main driving factors of different retail formats. Results shows that: (1) Demand, location, competition, and cost were the main driving force of the spatial differentiation of retail industry. Furthermore, because of the heterogeneity of the retail formats and the spatial heterogeneity of the city, there was also heterogeneity in the driving mechanism of the spatial differentiation in the retail industry. (2) There were significant spatial differences of the influencing factors. The inner circle of the city had higher population density, better accessibility, better business conditions, and higher format richness, and also higher land rent. There was a significant spatial differentiation between the old city area and the suburbs. (3) Compared with the traditional OLS regression method, the spatial regression method revealed the spatial distribution mechanism of the retail industry more accurately. The spatial error model revealed significant heterogeneity in the factors that influence the spatial agglomeration of the retail industry. Population density was the core driving force of retail spatial differentiation. Public transport convenience, business conditions, and format richness also had a positive effect on retail agglomeration, while the impact of rent was weak. The main driving factors of different retail formats and outlets located in different urban circle were different. Population density was the core factor, while the influence of other factors showed significant differences.