Abstract
The industrial division of labor and the degree of convergence of the industrial structure directly affect the core competitiveness of the urban agglomeration, and it is of great practical significance to measure the industrial division of labor and the degree of structural convergence of the urban agglomeration scientifically and rationally. The similarity coefficient method and grey correlative analysis method more detailed classification based on the study of 22 urban agglomerations in the city in 2007 and 2012 in Yunnan, the whole city and urban agglomeration between the convergence of industrial structure and regional division of different industries and the evolution of city. The results show that: Yunnan city group 22 inter city assimilation degree has obvious upward trend; the industrial structure of Kunming and Yunnan city group of the overall industrial structure is the most similar, belong to the comprehensive development, Yuxi, Chuxiong and Qujing belong to the traditional agricultural and mineral resource type structure, and Yuxi is the agricultural and mineral resource type structure to the integrated development from the industrial transformation; Yunnan city group regional division, the first and the second industry and the third industry in the circulation department and production and Living Services Department of city industrial division of labor between the strengthening of the degree of specialization of the third industry of social public service between the City declined. As a whole, the industrial structure of the central Yunnan Urban Agglomeration tends to be higher and higher, while the industrial division of labor among cities is still not obvious, but it has begun to take shape.
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This paper was funded by National Natural Science Foundation of China (71463067; 41301180), Social Philosophy Foundation of Yunnan Province (YB201504D).
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Li, Y., Yan, X. (2018). Study on the Evolution of Industrial Division of Labor and Structure in Central Yunnan Urban Agglomeration. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_54
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DOI: https://doi.org/10.1007/978-981-13-0893-2_54
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