• Journal of Geographical Sciences
  • Vol. 30, Issue 4, 04000515 (2020)
LUO Jing1、2、3, CHEN Siyun1、2、3, SUN Xuan4、*, ZHU Yuanyuan1、2、3, ZENG Juxin1、2、3, and CHEN Guangping5
Author Affiliations
  • 1College of Public Administration, Central China Normal University, Wuhan 430079, China
  • 2School of Management, Wuhan Institute of Technology, Wuhan 430205, China
  • 3Department of Urban Planning and Design, University of Hong Kong, Hong Kong 999077, China
  • 4Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 5School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
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    Abstract

    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.

    1 Introduction

    Christaller (1933), a German economic geographer, first proposed the concept of ‘centrality’. Centrality refers to the capacity of an area for providing various goods and services for local and other areas. Centrality is an index that gives a measure of the level of functions in a central region or city. Henri (2003) believes that the essence of the urban phenomenon lies in its centrality and that a city represents the best spatial arrangement for mechanization, technological transformation and economies of scale; it is a center of productive relationships and the center of ‘space production’ (Henri, 1968). Urban centrality reflects the ability of a city to provide services and is an important index for measuring the status of an urban center (Zhou et al., 2001.)