Deep Learning-Based Spatial Governance of Groundswell Economy

DOI

10.2991/978-94-6463-200-2_146How to use a DOI?

Keywords

stall economy; spatial governance; deep learning; simulation

Abstract

As a new form of economy in the new era, the stall economy has brought new vitality to urban development. However, the rapid development of the stall economy has also brought a series of problems, among which spatial governance is one of the key factors affecting the healthy development of the stall economy. This paper proposes a research method of spatial governance of stall economy based on deep learning. Firstly, this paper analyzes the current situation and existing problems of spatial governance of stall economy from a theoretical perspective, and puts forward the application ideas of deep learning in spatial governance of stall economy. Secondly, taking the stall economy of a certain city as the research object, this paper collects the relevant data of the city and preprocesses the data. Finally, based on the deep learning model, this paper simulates the spatial governance of the stall economy and analyzes the simulation results. The experimental results show that the research method of spatial governance of stall economy based on deep learning proposed in this paper has high feasibility and practicality.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Enjue Zhao
PY  - 2023
DA  - 2023/07/26
TI  - Deep Learning-Based Spatial Governance of Groundswell Economy
BT  - Proceedings of the 2023 3rd International Conference on Public Management and Intelligent Society (PMIS 2023)
PB  - Atlantis Press
SP  - 1359
EP  - 1365
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-200-2_146
DO  - 10.2991/978-94-6463-200-2_146
ID  - Zhao2023
ER  -