PROGRESS IN GEOGRAPHY ›› 2013, Vol. 32 ›› Issue (12): 1732-1741.doi: 10.11820/dlkxjz.2013.12.002

• Special Column: The 14th National Symposium for Young Geographers • Previous Articles     Next Articles

Characteristics of commercial bank branch networks based on complex networks theory:A case study on Bank of China in Beijing

ZHEN Maocheng1, ZHANG Jingqiu2, YANG Guanglin2   

  1. 1. College of Resource, Environment and Tourism, Capital Normal University, Beijing 100048, China;
    2. College of Arts and Science, Beijing Union University, Beijing 100191, China
  • Received:2013-10-01 Revised:2013-11-01 Online:2013-12-25 Published:2013-12-25
  • Contact: 张景秋(1967-),女,甘肃兰州人,博士,教授,主要从事城市地理、城市与区域规划研究。E-mail:jingqiu@buu.edu.cn E-mail:jingqiu@buu.edu.cn

Abstract: Complex network theory, combined with social network technology, has been more and more widely used in the researches and applications related to realistically existing large irregular networks to find the rules of network operations and to improve operation efficiency. In human geography research, using complex network technology to analyze city economic activities can help improve the analysis and interpretation of the pattern, and the change of the pattern, of the distribution of geographic phenomena to a certain extent, and can be applied to the studies of the essential characteristics of geographic networks. In this paper, taking Bank of China as an example, using the theory of complex network and with the principle of proximity, based on data of bank branch distribution networks in the six administrative districts of Beijing, we constructed a complex network model of bank branch distribution networks with the service radius of 400 meters and 800 meters, and used GIS spatial visualization technology to analyze and interpret the network characteristics. The research shows that: (1) the bank branch networks showed a flat structure with sparsity and small world network characteristics. The flat management pattern of the bank branch networks shortens the length of typical routes, which enhances the clustering coefficient, improves network efficiency, and at the same time increases intensifies the degree of the market competition. (2) The convergence of the layout of bank branch distribution increases with the expansion of the service radius, and stability decreases. For bank branch spots in the network, best service radius is 400 meters to 800 meters; the network spots accessible by walking are more stable than the network spots accessible by public transportations. Specifically, for the bank branches with service radius less than 400 meters, since the sharing of the customers is weak, and also faced with the challenges of increasing land rent and intensifying competition, they should community-focused bank branches in order to enhance the cohesion and competitiveness of each community area. However, for the bank branches with service radius less than 800 meters, the increase of the length of typical routes causes the increase of the cost of the process and the decrease of operation efficiency. Therefore, the bank branches should diversify service features, improve the area's networking degree, enhance the survivability, and improve stability. (3) The bank branch distribution exhibit area differences. The high level nodes are showing high level of agglomeration in the areas within the 4th beltway, mainly concentrated in Financial Street, Sanyuanqiao, China World Trade Center, and CBD area. On the other hand, the node degrees show big differences at the street level. For example, on the streets of well-developed inner city areas, the network and node degree are high, while on the other streets, especially the streets close to suburban areas, there few bank branch networks, or even no nodes, indicative of financial exclusion. In the future the network planning should focus on cultivating a number of edge nodes to play leading roles, expanding the network coverage, and improving the area network service functions.

Key words: Beijing, commercial bank, complex network, network characteristics, spatial distribution