Spatial Heterogeneity Of Transportation Networks On Economic Drivers

Transportation network is the cornerstone of economic development and plays a pivotal role in promoting regional economic development.The outline of the National 14 th Five-Year Plan points out that urban clusters should be developed and expanded,and their integrated development should be promoted.It is of great significance for the development,growth,and integrated construction of urban agglomerations to strength the coordination between transportation and economic development.The study of the spatial pattern of transportation network and economy of the Yangtze River Delta city cluster and the mechanism of the role of transportation network on economic development is of guiding significance to the rational allocation of regional resources and the rational layout of industries.In turn,this effectively promotes the high-quality economic development of the Yangtze River Delta city cluster.This paper takes the Yangtze River Delta city cluster as the research object.Firstly,based on the theory of complex networks,a calculation model for the centrality of transportation networks was constructed.The similarities and differences between railway and highway networks were compared from three aspects: overall network characteristics,node characteristics,and connection strength.Thus the network structure of the Yangtze River Delta urban agglomeration was explored.Secondly,the indicators of gross regional product,rationalization of industrial structure and advanced industrial structure were measured.And the spatial differences and spatial clustering characteristics of the overall economic and industrial structures were analyzed by using the methods of global spatial autocorrelation and local spatial autocorrelation,revealing the economic spatial pattern of the Yangtze River Delta city cluster.Finally,using three economic indicators as explanatory variables,four transportation network indicators as explanatory variables,and four other indicators as control variables,three spatial regression models,such as ordinary least squares(OLS),geographically weighted regression(GWR),and multi-scale geographically weighted regression(MGWR)were constructed to compare their fitting results and analyze the modeling advantages of each model in terms of spatial heterogeneity.Then the optimal model was selected to explore the mechanism of traffic network on economic development,and the spatial heterogeneity of the effect of different transportation networks on economic development was identified.Research findings:(1)The transportation network of the Yangtze River Delta city cluster has obvious small-world characteristics.And there is an obvious distribution pattern of multiple cores,with Shanghai as the main core and Nanjing and Hangzhou as the secondary cores.(2)Both the overall economy and industrial structure among cities show positive spatial correlation with obvious spatial clustering phenomenon.The overall economy of Shanghai,Suzhou and Wuxi show high-high agglomeration phenomenon,while the edge cities in Anhui province show low-low agglomeration phenomenon.The rationalization of industrial structure in Shanghai,SuXi-Chang metropolitan area and northern Zhejiang province show high-high agglomeration,while northern Anhui province shows low-low agglomeration.The advanced industrial structure of Shanghai,Suzhou and southwestern Zhejiang show high-high agglomeration,while the area to the northern Anhui and northern Jiangsu show low-low agglomeration.(3)Among the three spatial regression models,ordinary least squares,geographically weighted regression,and multi-scale geographically weighted regression,the multi-scale geographically weighted regression has the best fitting results and the strongest explanatory power.Both regional railroad network and highway network can have significant positive driving effects on the development of urban economy.And the driving effect of railroad network on the economy is significantly stronger than that of highway network,which verifies the spatial heterogeneity of different transportation networks on economic driving effects.