Case Report
Version 1
Preserved in Portico This version is not peer-reviewed
Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks
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)
Version 2 : Received: 22 March 2024 / Approved: 5 April 2024 / Online: 7 April 2024 (13:58:27 CEST)
A peer-reviewed article of this Preprint also exists.
Yang, D.; Zhao, J.; Xu, P. Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks. Appl. Sci. 2024, 14, 3845. Yang, D.; Zhao, J.; Xu, P. Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks. Appl. Sci. 2024, 14, 3845.
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.
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