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

Joint Authors

Gui, Renzhou
Chen, Tongjie
Nie, Han

Source

Complexity

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-25, 25 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-01-21

Country of Publication

Egypt

No. of Pages

25

Main Subjects

Philosophy

Abstract EN

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.

American Psychological Association (APA)

Gui, Renzhou& Chen, Tongjie& Nie, Han. 2020. In-Depth Analysis of Railway and Company Evolution of Yangtze River Delta with Deep Learning. Complexity،Vol. 2020, no. 2020, pp.1-25.
https://search.emarefa.net/detail/BIM-1142307

Modern Language Association (MLA)

Gui, Renzhou…[et al.]. In-Depth Analysis of Railway and Company Evolution of Yangtze River Delta with Deep Learning. Complexity No. 2020 (2020), pp.1-25.
https://search.emarefa.net/detail/BIM-1142307

American Medical Association (AMA)

Gui, Renzhou& Chen, Tongjie& Nie, Han. In-Depth Analysis of Railway and Company Evolution of Yangtze River Delta with Deep Learning. Complexity. 2020. Vol. 2020, no. 2020, pp.1-25.
https://search.emarefa.net/detail/BIM-1142307

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1142307