Abstract: Urban wholesale markets are distribution centers that integrate the information flow, trade flow, and material flow; and their logistics activities belong to the urban logistics activities. Based on the data of an investigation in the wholesale markets of Guangzhou city, this paper examines the spatial patterns of supply logistics network by using social network analysis. Firstly, a 2-mode matrix comprising 19 supplying places and 21 receiving places is constructed, with the visualization of the relationship between places. Some conclusions can be drawn as follows. (1) The allocation of logistics nodes is characterized by surrounding traffic lines, concentrating in inner cities, and distributing to spheres. (2) Most of the goods in wholesale markets come from districts outside Guangzhou city, especially from foreign countries, other provinces, other parts of the Pearl River Delta, and northern Guangdong Province. (3) The 21 receiving places vary greatly in goods quantities, and form a reversed y-shaped pattern in the city. Secondly, applying the centrality analysis of the logistics network through measuring the indicators of degree centrality, closeness centrality and betweenness centrality, the core-edge places of the network are identified. Results indicate that the Pearl River Delta, other provinces and Baiyun District are the core supplying places, the wholesale markets of Liuhua and Zhongda are core receiving places; while Nansha District and Huangpu Maogang wholesale market are the edge places in the network. Finally, this paper discusses the formation mechanism of the spatial patterns in four aspects: the city character, the urban spatial disparity, the industrial cluster, and the logistics service. In brief, by analyzing the spatial patterns and their formation mechanism of supply logistics network of wholesale markets in Guangzhou, this paper is expected to provide helpful reference for the future optimization of the urban logistics space.

Key words: Guangzhou, wholesale markets, supply logistics, spatial patterns, social network analysis