科研之友
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摘要
By establishing a network with related indices in urban system as nodes, urbanization quality can be defined as the structural features of the network. According to maximum information entropy principle (MIEP), structural parameter 4 of the index network and its dynamic equation have been derived. The dynamic equation can be calculated by self organizing feature map (SOFM) artificial neural network algorithm in MATLAB platform. In this way, a new index network model (INM) of urbanization quality assessment (UQA) is constructed. The new INM is applied to the UQA of 37 major cities in China. Values of xi quantitatively give the rank of the cities, which show that Shenzhen, Beijing and Suzhou have the highest quality scores more than 10 both in 2010 and 2014, reflecting much higher urbanization quality than other cities. The following cities are Xiamen, Tianjin, Nanjing, Hangzhou etc., then Chengdu, Zhengzhou, Shenyang and Tangshan etc. The quality scores of Guiyang, Kunming and Chongqing etc. are the lowest. With Xi'an and Wenzhou as sample cities, the contributions of each index to the urbanization quality are simultaneously revealed. Based on the structural parameter xi of each mesoscope indexes derived from the new INM, we use the analysis method of factor contribution rate to reveal the contributions of each index to the urbanization quality. By recognizing the key factors, which influence the urbanization quality, the new INM can guide us take targeted measures for the improvement of urbanization quality. Beyond the application cases given, we finally state that the new INM is very universal and advantageous, which can be conveniently applied to the UQA of many other cities.
- 出版日期2018-10-15
- 单位天津大学
