摘要
Based on statistical data and population flow data for 2016,and using entropy weight TOPSIS and the obstacle degree model,the centrality of cities in the Yangtze River Economic Belt(YREB)together with the factors influencing centrality were measured.In addition,data for the population flow were used to analyze the relationships between cities and to verify centrality.The results showed that:(1)The pattern of centrality conforms closely to the pole-axis theory and the central geography theory.Two axes,corresponding to the Yangtze River and the Shanghai-Kunming railway line,interconnect cities of different classes.On the whole,the downstream cities have higher centrality,well-defined gradients and better development of city infrastructure compared with cities in the middle and upper reaches.(2)The economic scale and size of the population play a fundamental role in the centrality of cities,and other factors reflect differences due to different city classes.For most of the coastal cities or the capital cities in the central and western regions,factors that require long-term development such as industrial facilities,consumption,research and education provide the main competitive advantages.For cities that are lagging behind in development,transportation facilities,construction of infrastructure and fixed asset investment have become the main methods to achieve development and enhance competitiveness.(3)The mobility of city populations has a significant correlation with the centrality score,the correlation coefficients for the relationships between population mobility and centrality are all greater than 0.86(P<0.01).The population flow is mainly between high-class cities,or high-class and low-class cities,reflecting the high centrality and huge radiating effects of high-class cities.Furthermore,the cities in the YREB are closely linked to Guangdong and Beijing,reflecting the dominant economic status of Guangdong with its geographical proximity to the YREB and Beijing's enormous influence as the national political and cultural center,respectively.
作者
罗静
陈四云
孙璇
朱媛媛
曾菊新
陈广平
LUO Jing;CHEN Siyun;SUN Xuan;ZHU Yuanyuan;ZENG Juxin;CHEN Guangping(Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province,Central China Normal University,Wuhan 430079,China;The College of Urban&Environmental Sciences,Central China Normal University,Wuhan 430079,China;Academy of Wuhan Metropolitan Area,Hubei Provincial Development and Reform Commission/Central China Normal University,Wuhan 430079,China;Institute of Geographic Sciences and Natural Resources Research,CAS,Beijing 100101,China;School of Earth Sciences,Zhejiang University,Hangzhou 310027,China)
基金
National Natural Science Foundation of China,No.41871176
The“Hua Bo”Plan of Central China Normal University
Postgraduate Education Innovation Subsidy Project of Central China Normal University,No.2018CXZZ004。