Version 1
: Received: 21 March 2024 / Approved: 21 March 2024 / Online: 22 March 2024 (05:10:56 CET)
Version 2
: Received: 22 March 2024 / Approved: 5 April 2024 / Online: 7 April 2024 (13:58:27 CEST)
How to cite:
Yang, D.; Zhao, J.; Xu, P.; Du, B.; Dong, J. Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks. Preprints2024, 2024031325. https://doi.org/10.20944/preprints202403.1325.v1
Yang, D.; Zhao, J.; Xu, P.; Du, B.; Dong, J. Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks. Preprints 2024, 2024031325. https://doi.org/10.20944/preprints202403.1325.v1
Yang, D.; Zhao, J.; Xu, P.; Du, B.; Dong, J. Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks. Preprints2024, 2024031325. https://doi.org/10.20944/preprints202403.1325.v1
APA Style
Yang, D., Zhao, J., Xu, P., Du, B., & Dong, J. (2024). Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks. Preprints. https://doi.org/10.20944/preprints202403.1325.v1
Chicago/Turabian Style
Yang, D., Bin Du and Jiahua Dong. 2024 "Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks" Preprints. https://doi.org/10.20944/preprints202403.1325.v1
Abstract
Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.
Keywords
commercial space; points of interest; deep learning; BP Neural Network
Subject
Business, Economics and Management, Econometrics and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
