2020
DOI: 10.1007/s11442-020-1740-9
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Analysis of city centrality based on entropy weight TOPSIS and population mobility: A case study of cities in the Yangtze River Economic Belt

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… Show more

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Cited by 27 publications
(21 citation statements)
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“…The EWM is widely used in the field of economic management and decision control [ 28 , 29 ], and weight is determined by the size of the information of each evaluation indicator. When the information of the indicator directly changes markedly, the smaller the entropy value is, the greater the entropy weight is, indicating that the indicator is more important in the evaluation system.…”
Section: Comprehensive Evaluation Model Of Sports Smart Braceletsmentioning
confidence: 99%
“…The EWM is widely used in the field of economic management and decision control [ 28 , 29 ], and weight is determined by the size of the information of each evaluation indicator. When the information of the indicator directly changes markedly, the smaller the entropy value is, the greater the entropy weight is, indicating that the indicator is more important in the evaluation system.…”
Section: Comprehensive Evaluation Model Of Sports Smart Braceletsmentioning
confidence: 99%
“…Zhu et al [28] explored the potential links between urban smartness and resilience for Chinese cities. Luo et al [27] measured the centrality together with the factors influencing centrality using data for the population flow of cities in the Yangtze River Economic Belt. Further, Rana et al [26] prioritized barriers to recognize the most important barrier category and ranking of specific barriers within the categories to the development of Indian smart cities.…”
Section: Discussionmentioning
confidence: 99%
“…Source: author's elaboration on the basis of[13,[15][16][17][18][19][20][21][22][23][24][26][27][28][29][30][31][32][33][34].…”
mentioning
confidence: 99%
“…The key factors include population, area, urbanization, traveling mode, and climate. Source: authors' elaboration on the basis of [33,[41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59].…”
Section: Literature Reviewmentioning
confidence: 99%