Yang Liu, Jing-ning Cheng, Wanqin Zhao, Jingjing Dai
{"title":"Structure characteristics of spatial correlation network of industrial digitalization","authors":"Yang Liu, Jing-ning Cheng, Wanqin Zhao, Jingjing Dai","doi":"10.1117/12.2673431","DOIUrl":null,"url":null,"abstract":"This study aims to reveal the spatial correlation effects of the digitization of regional industries. This study uses machine learning and text quantification to construct a digital index of listed companies in China's traditional industries, and to perform regional clustering. Based on the industrial digital development index from 2008 to 2021, this study choose representative annual features to fully demonstrate the spatial aggregation of digital industrial development. The findings of this paper provide the digital core position and the cohesive subgroups in different regions of China. Beijing, Shanghai, and their surrounding provinces are in the total core area of the spatial connection network of digital industrial development, while most of the provinces in the western region are in the absolute edge area, forming a typical distribution trend of \"strong in the east and weak in the west.\"","PeriodicalId":176918,"journal":{"name":"2nd International Conference on Digital Society and Intelligent Systems (DSInS 2022)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2nd International Conference on Digital Society and Intelligent Systems (DSInS 2022)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2673431","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This study aims to reveal the spatial correlation effects of the digitization of regional industries. This study uses machine learning and text quantification to construct a digital index of listed companies in China's traditional industries, and to perform regional clustering. Based on the industrial digital development index from 2008 to 2021, this study choose representative annual features to fully demonstrate the spatial aggregation of digital industrial development. The findings of this paper provide the digital core position and the cohesive subgroups in different regions of China. Beijing, Shanghai, and their surrounding provinces are in the total core area of the spatial connection network of digital industrial development, while most of the provinces in the western region are in the absolute edge area, forming a typical distribution trend of "strong in the east and weak in the west."