Analysis of city centrality based on entropy weight TOPSIS and population mobility: A case study of cities in the Yangtze River Economic Belt

doi:10.1007/s11442-020-1740-9

Abstract

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.

Key words: city centrality, entropy weight TOPSIS, population mobility, Yangtze River Economic Belt, obstacle degree model

LUO Jing, CHEN Siyun, SUN Xuan, ZHU Yuanyuan, ZENG Juxin, CHEN Guangping. Analysis of city centrality based on entropy weight TOPSIS and population mobility: A case study of cities in the Yangtze River Economic Belt[J].Journal of Geographical Sciences, 2020, 30(4): 515-534.

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References 36

[1]	Christaller W , 1933. Central Place in Southern Germany. Baskin C W trans., 1966. Englewood Cliffs. NJ and London: Prentice Hall.
[2]	Fang D C, Sun M Y , 2015. Influence of core cities in Yangtze River Economic Belt. Economic Geography, 35(1):76-81, 20. (in Chinese)
[3]	Feng X H, Zhong Y X, Li Z R et al., 2017. Evolvement of spatial pattern of urban system in the economic belt of Yangtze River. Resources and Environment in the Yangtze Basin, 26(11):1721-1733. (in Chinese)
[4]	Gao J Z, Zheng H W, Liu Y Z , 2018. Diagnosis of the multi-functionality of land use based on an entropy weight TOPSIS model. Resources and Environment in the Yangtze Basin, 27(11):2496-2504. (in Chinese)
[5]	Henri L , 1966. The Sociology of Marx. Norbert G trans., 1968. New York: Columbia University Press.
[6]	Henri L , 1970. The Urban Revolution. Robert B trans., 2003. Twin Cities: University of Minnesota Press.
[7]	Hong H K, Liao H P, Wei C F et al., 2015. Health assessment of a land use system used in the ecologically sensitive area of the Three Gorges Reservoir Area, based on the improved TOPSIS method. Acta Ecologica Sinica, 35(24):8016-8027. (in Chinese)
[8]	Irwin M D, Hughes H L , 1992. Centrality and the structure of urban interaction: Measures, concepts, and applications. Social Forces, 71(1):17-51. doi: 10.2307/2579964
[9]	Jin G, Chen K, Wang P et al., 2019. Trade-offs in land-use competition and sustainable land development in the North China Plain. Technological Forecasting and Social Change, 141:36-46.
[10]	Jin G, Deng X Z, Chu X et al., 2017. Optimization of land-use management for ecosystem service improvement: A review. Physics and Chemistry of the Earth, 101:70-77.
[11]	Jin G, Deng X Z, Zhao X D et al., 2018. Spatiotemporal patterns in urbanization efficiency within the Yangtze River Economic Belt between 2005 and 2014. Journal of Geographical Sciences, 28(8):1113-1126. doi: 10.1007/s11442-018-1545-2
[12]	Jin G, Li Z H, Deng X Z et al., 2018. An analysis of spatiotemporal patterns in Chinese agricultural productivity between 2004 and 2014. Ecological Indicators, 105:591-600.
[13]	Lei X P, Robin Q, Liu Y , 2016. Evaluation of regional land use performance based on entropy TOPSIS model and diagnosis of its obstacle factors. Transactions of the Chinese Society of Agricultural Engineering, 32(13):243-253. (in Chinese)
[14]	Li D, You Y N, Ma C F , et al., 2018. Analysis on the differentiation characteristics of spatial poverty and its influencing factors in the oasis towns of arid areas: Taking three south Xinjiang districts as an example. World Regional Studies, 27(3):89-101. (in Chinese)
[15]	Li S C, Bing Z L, Jin G , 2019. Spatially explicit mapping of soil conservation service in monetary units due to land use/cover change for the Three Gorges Reservoir Area, China. Remote Sensing, 11(4):468. doi: 10.3390/rs11040468
[16]	Lu C Y, Wen F, Yang Q Y et al., 2011. An evaluation of urban land use performance based on the improved TOPSIS method and diagnosis of its obstacle indicators: A case study of Chongqing. Resources Science, 33(3):535-541. (in Chinese)
[17]	Luo M , 2017. Research on urban centricity of Yangtze River’s Midstream Urban Agglomeration [D]. Wuhan: Central China Normal University .(in Chinese)
[18]	Ma X Y, Shao J A, Xu X L , 2016. Rural transportation accessibility in mountainous areas based on the entropy-weight TOPSIS method: A case study of Shizhu County, Chongqing Municipality. Progress in Geography, 35(9):1144-1154. (in Chinese) doi: 10.18306/dlkxjz.2016.09.009
[19]	Marshall J U , 1989. The Structure of Urban Systems. Toronto: University of Toronto Press.
[20]	Ning Y M, Yan Z M , 1993. The uneven development and spatial diffusion of Chinese central cities. Acta Geographica Sinica, 48(2):97-104. (in Chinese)
[21]	Peng D Y, Xiao R J, Wang J et al., 2016. The competitiveness evaluation for the cities in the Yangtze River Economic Belt based on factor analysis. Journal of Nanchang University (Natural Science), 40(1):97-102. (in Chinese)
[22]	Phillip B , 1987. Power and centrality: A family of measures. American Journal of Sociology, 92(5):1170-1182.
[23]	Preston R E , 1970. Two centrality models. Yearbook of Association of Pacific Coast Geographers, 32:59-78.
[24]	Ren L, Zhang H T, Wei M Z , et al., 2019. Research on the evaluation of development level of smart city based on Entropy TOPSIS Model. Information Studies: Theory & Application, 1-12.[ 2019-01-05]. http://kns.cnki.net/kcms/detail/11.1762.g3.20190102.1825.002.html.(in Chinese)
[25]	Siewwuttanagul S, Inohae T, Mishima N , 2016. An investigation of urban gravity to develop a better understanding of the urbanization phenomenon using centrality analysis on GIS platform. Procedia Environmental Sciences, 36:191-198. doi: 10.1016/j.proenv.2016.09.032
[26]	Sun B D, Xu J H, Feng Z C , 2008. Analysis on the urban centricity and urban development of Liaoning Province. Human Geography, 23(2):77-81. (in Chinese)
[27]	Tian M L, Liu S M, Kou Y , 2013. Evaluation on the functions of national central cities and spatial temporal evaluation of their competitiveness. City Planning Review, 37(11):89-95. (in Chinese)
[28]	Wang Z B, Luo K, Song J et al., 2015. Characteristics of change and strategic considerations of the structure of urban functional divisions in the Yangtze River Economic Belt since 2000. Progress in Geography, 34(11):1409-1418. (in Chinese)
[29]	Wen J , 2009. A study on the urban centricity of Wuhan Urban Circle [D]. Wuhan: Central China Normal University. (in Chinese)
[30]	Wu Y L, Liu Z D , 2010. A study on city centricity and urban development in Shandong province. Territory & Natural Resources Study, ( 1):8-9. (in Chinese)
[31]	Xue L F, Ou X J, Tan H Q , 2009. Evaluation of urban centricity based on entropy method: A case study of Huaihai Economic Zone. Geography and Geo-Information Science, 25(3):63-66. (in Chinese)
[32]	Zhang G S, Ding Z W, Xu Y M et al., 2014. Study on urban system hierarchy level structure in Henan Province: Based on the analysis of the New Urbanization Strategy of Henan Province. Areal Research and Development, 33(1):46-51. (in Chinese)
[33]	Zhao Y , 2015. Research on the spatial poverty trap of concentrated contiguous areas with particular difficulties on basis of the geographic capital: Taking Longde County of Ningxia for example [D]. Yinchuan: Ningxia University. (in Chinese)
[34]	Zhong Y X, Feng X H , 2018. The evolution of urban functional structure from the perspective of multiscale in the Yangtze River Economic Belt. Journal of Nantong University (Social Sciences Edition), 34(1):34-40. (in Chinese)
[35]	Zhou Y X, Zhang L, Wu Y , 2001. Study of China’s urban centrality hierarchy. Regional Areal Research and Development, 20(4):1-5. (in Chinese)
[36]	Zou H, Duan X J , 2015. Summary reviews of studies on the Yangtze River Economic Belt. Resources and Environment in the Yangtze Basin, 24(10):1672-1682. (in Chinese)

Indicator	Symbol	Indicator	Symbol
Total permanent residential population	C1	Number of authorized patents	C10
GDP	C2	High-tech output value	C11
Added value of secondary and tertiary industries	C3	Number of high-speed railway and express railway stations	C12
Total fixed assets investment	C4	Number of civil airports	C13
Total retail sales of consumer goods	C5	Total amount of cargo transported	C14
Actual use of foreign investment	C6	Total number of passengers transported	C15
Amount of imports and exports	C7	Number of colleges and universities	C16
Tourism income	C8	Number of students in colleges and universities	C17
R&D expenditure	C9

Indicators	Data sources
C1, C2, C3, C4, C5, C6, C14, C15, C16, C17	China Urban Statistical Yearbook (2017)
C1, C2, C3, C4, C5, C6, C7, C8, C9, C10, C11, C14, C15, C16, C17	Statistical Yearbooks (2017) of the cities in the YREB
C1, C2, C3, C4, C5, C6, C7, C8, C9, C10, C11, C14, C15, C16, C17	2016 National Economic and Social Development Statistics Bulletin of the cities in the YREB
C12	12306 China Railway (https://www.12306.cn/mormhweb/czyd_2143/)
C13	2016 Civil Aviation Airport Production Statistics Bulletin of China (http://www.caac.gov.cn/XXGK/XXGK/TJSJ/201702/t20170224_42760.html)

Indicator	Weight	Indicator	Weight
C1	0.0691	C10	0.0572
C2	0.1039	C11	0.0708
C3	0.0781	C12	0.0480
C4	0.0581	C13	0.0403
C5	0.0659	C14	0.0408
C6	0.0654	C15	0.0423
C7	0.0514	C16	0.0527
C8	0.0363	C17	0.0616
C9	0.0581	—	—

Classification	Correlation coefficient	P
The correlation coefficient between population inflows and the centrality scores of cities in the YREB	0.9030	8.56E-49
The correlation coefficient between population outflows and the centrality scores of cities in the YREB	0.9257	6.66E-56
The correlation coefficient between population entering the YREB from other cities in China and the centrality scores of cities in the YREB	0.9508	5.48E-67
The correlation coefficient between population flowing out of the YREB to other cities in China and the centrality scores of cities in the YREB	0.8613	1.84E-39

The source city for population inflow from first-class cities		The source city for population inflow from second-class cities		The source city for population inflow from third-class cities		The source city for population inflow from fourth-class cities		The source city for population inflow for fifth-class cities
Class	Proportion (%)	Class	Proportion (%)	Class	Proportion (%)	Class	Proportion (%)	Class	Proportion (%)
First	55.01	First	35.85	Second	43.09	Second	33.81	First	43.15
Second	23.65	Third	23.43	First	28.25	First	31.06	Fourth	22.34
Fourth	13.78	Second	19.91	Third	14.86	Fourth	18.83	Second	19.76
Fifth	4.96	Fourth	17.71	Fourth	12.20	Third	11.58	Fifth	8.38
Third	2.59	Fifth	3.10	Fifth	1.60	Fifth	4.73	Third	6.38