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In-Depth Analysis of Railway and Company Evolution of Yangtze River Delta with Deep Learning

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  • Renzhou Gui
  • Tongjie Chen
  • Han Nie

Abstract

The coordinated development of smart cities has become the goal of world urban development, and the railway network plays an important role in this progress. 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’s 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.

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  • Renzhou Gui & Tongjie Chen & Han Nie, 2020. "In-Depth Analysis of Railway and Company Evolution of Yangtze River Delta with Deep Learning," Complexity, Hindawi, vol. 2020, pages 1-25, January.
    • Handle: RePEc:hin:complx:5192861
    DOI: 10.1155/2020/5192861
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    Cited by:

    1. Rui Ding & Jun Fu & Yiming Du & Linyu Du & Tao Zhou & Yilin Zhang & Siwei Shen & Yuqi Zhu & Shihui Chen, 2022. "Structural Evolution and Community Detection of China Rail Transit Route Network," Sustainability, MDPI, vol. 14(19), pages 1-19, September.

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