1.Key Laboratory for Geographical Process Analysis & Simulation, Hubei Province, Central China Normal University, Wuhan 430079, Hubei,China 2. Academy of Wuhan Metropolitan Area, Hubei Development and Reform Commission & Central China Normal University, Wuhan 430079,Hubei,China 3. Chongqing Geomatics Center, Chongqing 401121,China
Study in regional economic linkages is one of the most important aspects of economic geography and regional research. The approach of social network analysis (SNA) has emerged as a key technique in study of regional economic linkages. Taking the 78 county areas in Anhui Province as network nodes to constructed the economic linkage network, the connections between nodes were evaluated by the revised economic relation intensity in 1996, 2004 and 2013 in this article. Firstly, we used GIS tools to map the structure of economic linkage network at county level in Anhui Province in 1996, 2004 and 2013. Then, under the support of social network analysis software UCINET, our study analyzed the centrality from three aspects: degree centrality, closeness centrality and betweenness centrality. Furthermore, we marked out four cohesive subgroups of economic linkage network at county level in Anhui Province. For the influencing factors, this article constructed a spatial markov matrix for county-level GDP per capita in Anhui Province to examine spatial adjacency effect, and the evolution of traffic accessibility at county level was calculated by average traffic time from a node to all other nodes. The results show that: 1) The density of economic linkage network in Anhui Province has been continuously boosting in 2013, from 0.307 in 1996 to 0.712. Besides, the economic linkages between counties have been developing toward multi-direction, densification and deepened way, which was good to the formation of economic linkage network; 2) The central city of Hefei, the capital city of Anhui Province, was the center of economic linkage networkwith its capacity of economic spread has been increasingly enlarging. Simultaneously, Wuwei, Huaiyuan and Feidong have gradually became the portal nodes that play an important role in promoting regional economic connection; 3) Cohesive subgroup is an effective way to the construction of inter-county economic linkage network. The small group analysis can more availably reveal the source of development and competitiveness improvement of the less important nodes. 4) Hierarchical agglomeration was a characteristic of the structure of economic linkage network and mainly embodied in the spatial pattern called “four main regions and eight sub regions”. 5) The factors influenced the evolution of the structure of economic linkage network mainly included the agglomeration and diffusion of elements, spatial adjacency effect, the improvement of traffic accessibility and the policy motivation and guidance. Based on all of the above, the policy recommendations for promoting regional coordinate and integrated development were also discussed.
. 安徽省县际经济联系网络结构演变及影响因素[J]. 地理科学,
2016, 36(2): 265-273.
Rongrong Zhuo et al
. Evolution and Influencing Factors of the Structure of Economic Linkage Network at County Level in Anhui Province[J]. SCIENTIA GEOGRAPHICA SINICA,
2016, 36(2): 265-273.
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BaglerG.Analysis of the Airport Network of India as a Complex Weighted Network[J]. , 2008, 387(12): 2972-2980.http://www.sciencedirect.com/science/article/pii/S0378437108001131
Transportation infrastructure of a country is one of the most important indicators of its economic growth. Here we study the Airport Network of India (ANI), which represents India's domestic civil aviation infrastructure, as a complex network. We find that ANI, a network of domestic airports connected by air links, is a small-world network characterized by a truncated power-law degree distribution, and has a signature of hierarchy. We investigate ANI as a weighted network to explore its various properties and compare them with their topological counterparts. The traffic in ANI, as in the World-wide Airport Network (WAN), is found to be accumulated on interconnected groups of airports and is concentrated between large airports. In contrast to WAN, ANI is found to be having disassortative mixing which is offset by the traffic dynamics. The analysis indicates toward possible mechanism of formation of a national transportation network, which is different from that on a global scale.