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This paper proposes a solution that integrates data acquisition, storage, GIS visualization, deep learning, and statistical correlation analysis to deeply analyze the distribution data of companies collected in the past 40 years in the Yangtze River Delta. Through deep learning, we predict the spatial distribution of the company after the opening of the train stations. Through statistical and correlation analysis of the company\u2019s registered capital and quantity, the urban development relationship under the influence of the opening of the railway is explored. Going forward, the use and application of such analysis can be tested for use and application in the context of other smart cities for specific aspects or scale.<\/jats:p>","DOI":"10.1155\/2020\/5192861","type":"journal-article","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T18:31:15Z","timestamp":1579631475000},"page":"1-25","source":"Crossref","is-referenced-by-count":5,"title":["In-Depth Analysis of Railway and Company Evolution of Yangtze River Delta with Deep Learning"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5696-1220","authenticated-orcid":true,"given":"Renzhou","family":"Gui","sequence":"first","affiliation":[{"name":"The Department of Electronic and Communication Engineering, Tongji University, Shanghai 201804, 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