Research on the Influencing Mechanism of Urban Transportation Network on Recessive Transformation of Industrial Land:A Case Study of Wuhan City
The purpose of this study is to explore the influencing mechanism of urban transport network on the recessive transformation of industrial land based on the connotation of urban industrial land recessive transformation and its driving theory,to contribute to the optimization of the urban transport structure and the development of industrial novelty.The research methods are recessive morphological evaluation model of industrial land,kernel density estimation,geographically weighted regression model and logarithmic decay function.The research results show that:1)the development of recessive transformation of industrial land in the main urban area of Wuhan is highly unbalanced.2)The driving effect of highways,main roads,secondary roads and branch roads on the recessive transformation of industrial land in the main urban area of Wuhan is significant and an obviously spatial heterogeneity exists.3)The attractiveness of different levels of roads to the recessive transformation corridor effect of industrial land is as follows.Main roads are the highest,secondary roads are the second highest,while railways,motorways and side roads have a repulsive effect,and the effect preference for the recessive transformation of industrial land influenced by main roads and secondary roads is significant.In conclusion,to alleviate the internal structural optimization and transformation of urban industrial land,it is suggested to focus on the implementation of connotative construction and differentiated spatial control,coordinate the optimization of road network structure and the recessive transformation of industrial land,to optimize the urban transportation network with the goal of guiding the flow of economic factors,and to innovate the management and control of urban industrial land.
transportation networkindustrial landrecessive transformationgeographically weighted regressionWuhan City
万方数据
