PROGRESS IN GEOGRAPHY ›› 2013, Vol. 32 ›› Issue (11): 1612-1621.

• Industrial Economy and Regional Development •

Distribution of centrality of traffic network and its relationship with economic density of tertiary industry in Shenyang

CHEN Chen, CHENG Lin, XIU Chunliang

1. School of Geographical Science, Northeast Normal University, Changchun 130024, China
• Received:2013-06-01 Revised:2013-08-01 Online:2013-11-25 Published:2013-11-25

Abstract: With the development of network science, many scholars abroad begin to focus on the research of centrality of traffic network based on MCA(Multiple Centrality Assessment) and its relationship with economic activities. Centrality of traffic network is calibrated in a MCA model composed of multiple measures such as closeness, betweenness, and straightness. MCA model is a very important indicator that measures the rate of land development and utilization, and is widely used both in the theoretical and empirical inquiries. In this paper, by using the tools developed by MIT to calculate centrality of traffic network and its relationship with economic activities precisely and efficiently, we investigated the geography of three centralities of traffic network and their correlations with economic density of tertiary industry in Shenyang City, and then applied the KDE method to both centralities of traffic network and economic density to examine the correlations between them. Since economic density is regional data based on subdistricts, we created fishnet in ArcGIS and then did spatial interpolation. The results indicated that centralities of traffic network are correlated with the spatial distribution of economic density of tertiary industry in Shenyang. Spatial distribution of economic activity density correlates highly with the betweenness of traffic network, which means that the multiple centers of the streets lead to multiple centralities of economic activities. But we found that only betweenness and straightness show clear multi-centricity. Closeness, however, just has single centrality. This also means closeness has less impact on economic activities than betweenness and straightness. The major contributions made by this research can be summarized as follows: (1) Improving overall understanding of the spatial distribution of street centralities in Shenyang, which can be one of the most powerful determinants for urban planners and designers to understand how a city works and to decide where renovation and redevelopment need to be placed, to guide economic layout. (2) The concept that central urban arterials should be conceived as the cores, not the borders, of neighborhoods has the importance of directing in the theory and practice of city planning. (3) By drawing lessons from foreign research experiences, this research can enrich the theory, methods, and practice of the street network centrality in our country. If we take into account the relative properties of different street grades and types, vehicle flow rate and capacity, one-way or two-way streets, and so on, and give them appropriate weights based on their properties, the results will be more actual and practical and can help us understand the centrality of traffic network and its relationship with economic activities more precisely. More works need be done in order to study centrality of traffic network and its relationship with economic activities more comprehensively: (1) By looking into centrality of traffic network and its relationship with every kind of economic activity, we can get clear dependent relationship between centrality of traffic network and economic activities profoundly, and better understand the different relationships between them. (2) If we can get different attributes of every traffic level and use them as weights when we do KDE analysis, the research results will be much more practical.