{"title":"空间格局& &;山东半岛城市群产业结构的数量关系","authors":"Shan Baoyan, Wang Li-e","doi":"10.1109/ICISCE.2016.140","DOIUrl":null,"url":null,"abstract":"By using the data of 19 industries of 8 cities in Shandong peninsula urban agglomeration (SDPUA) in 2009 and 2014, the industrial structure coefficient, the coefficient of variation, the similarity coefficient and Shannon's diversity index were calculated to find the quantitative relationship of the industries of the 8 cities, the BP artificial neural network and fuzzy mathematics were used to study the comprehensive level of the industries, and GIS were applied to see the spatial pattern of industrial structure of SDPUA. We got these results and conclusions: The secondary industry as percentage of GDP reduced and the tertiary industry increased from 2009 to 2014, the industry structure coefficient of the 8 cities had an obvious spatial differentiation. The coefficients of variation of most industries of the 8 cities in SDPUA in 2014 are more than these in 2009. Most of the coefficients of variation of industries as percentage of GDP are less than the that of industries production value both in 2009 and 2014. The industry structure of the 8 cities in SDPUA was similar. The comprehensive level of industries of the 8 cities had an obvious spatial differentiation, and Jinan and Qingdao had the highest level, and Rizhao and Weihai had the lowest level both in 2009 and 2014.","PeriodicalId":6882,"journal":{"name":"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)","volume":"70 1","pages":"631-635"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Spatial Pattern & Quantitative Relationship of Industrial Structure of Shandong Peninsula Urban Agglomeration\",\"authors\":\"Shan Baoyan, Wang Li-e\",\"doi\":\"10.1109/ICISCE.2016.140\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"By using the data of 19 industries of 8 cities in Shandong peninsula urban agglomeration (SDPUA) in 2009 and 2014, the industrial structure coefficient, the coefficient of variation, the similarity coefficient and Shannon's diversity index were calculated to find the quantitative relationship of the industries of the 8 cities, the BP artificial neural network and fuzzy mathematics were used to study the comprehensive level of the industries, and GIS were applied to see the spatial pattern of industrial structure of SDPUA. We got these results and conclusions: The secondary industry as percentage of GDP reduced and the tertiary industry increased from 2009 to 2014, the industry structure coefficient of the 8 cities had an obvious spatial differentiation. The coefficients of variation of most industries of the 8 cities in SDPUA in 2014 are more than these in 2009. Most of the coefficients of variation of industries as percentage of GDP are less than the that of industries production value both in 2009 and 2014. The industry structure of the 8 cities in SDPUA was similar. The comprehensive level of industries of the 8 cities had an obvious spatial differentiation, and Jinan and Qingdao had the highest level, and Rizhao and Weihai had the lowest level both in 2009 and 2014.\",\"PeriodicalId\":6882,\"journal\":{\"name\":\"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)\",\"volume\":\"70 1\",\"pages\":\"631-635\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISCE.2016.140\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISCE.2016.140","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Spatial Pattern & Quantitative Relationship of Industrial Structure of Shandong Peninsula Urban Agglomeration
By using the data of 19 industries of 8 cities in Shandong peninsula urban agglomeration (SDPUA) in 2009 and 2014, the industrial structure coefficient, the coefficient of variation, the similarity coefficient and Shannon's diversity index were calculated to find the quantitative relationship of the industries of the 8 cities, the BP artificial neural network and fuzzy mathematics were used to study the comprehensive level of the industries, and GIS were applied to see the spatial pattern of industrial structure of SDPUA. We got these results and conclusions: The secondary industry as percentage of GDP reduced and the tertiary industry increased from 2009 to 2014, the industry structure coefficient of the 8 cities had an obvious spatial differentiation. The coefficients of variation of most industries of the 8 cities in SDPUA in 2014 are more than these in 2009. Most of the coefficients of variation of industries as percentage of GDP are less than the that of industries production value both in 2009 and 2014. The industry structure of the 8 cities in SDPUA was similar. The comprehensive level of industries of the 8 cities had an obvious spatial differentiation, and Jinan and Qingdao had the highest level, and Rizhao and Weihai had the lowest level both in 2009 and 2014.