Analysis and Optimization of the Spatial Patterns of Commercial Service Facilities Based on Multisource Spatiotemporal Data and Graph Neural Networks: A Case Study of Beijing, China
As a crucial component of urban economic activities, the layout and optimization of urban commercial spaces directly influence the economic prosperity and quality of life of residents. Therefore, comprehensively and accurately characterizing the distribution characteristics and evolutionary patterns...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/1/23 |
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| author | Yihang Xiao Cunzhi Li Zhiwu Zhou Dongyang Hou Xiaoguang Zhou |
| author_facet | Yihang Xiao Cunzhi Li Zhiwu Zhou Dongyang Hou Xiaoguang Zhou |
| author_sort | Yihang Xiao |
| collection | DOAJ |
| description | As a crucial component of urban economic activities, the layout and optimization of urban commercial spaces directly influence the economic prosperity and quality of life of residents. Therefore, comprehensively and accurately characterizing the distribution characteristics and evolutionary patterns of urban commercial spaces is essential for improving the efficiency of urban spatial allocation and achieving scientific spatial planning and governance. This paper utilizes multisource spatiotemporal data, employing geographic spatial analysis methods and graph neural network models to explore the spatial structure of commercial service facilities in Beijing and their relationships with population density and land use, thereby achieving a detailed classification of the commercial service patterns at the natural neighborhood scale. The research findings indicate a significant association between commercial service facilities and population, as well as land use, with a strong spatial heterogeneity. There exists a dissonance between the layout of commercial service facilities and population distribution, and the differences in commercial service development across various regions pose challenges to balanced urban development. Based on this, this paper provides specific recommendations for optimizing the urban commercial spatial structure, offering reference points for future urban planning and development. |
| format | Article |
| id | doaj-art-12f71396247f4f8597b2ce58dc3034ac |
| institution | Kabale University |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-12f71396247f4f8597b2ce58dc3034ac2025-01-24T13:35:00ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-01-011412310.3390/ijgi14010023Analysis and Optimization of the Spatial Patterns of Commercial Service Facilities Based on Multisource Spatiotemporal Data and Graph Neural Networks: A Case Study of Beijing, ChinaYihang Xiao0Cunzhi Li1Zhiwu Zhou2Dongyang Hou3Xiaoguang Zhou4School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaNational Geomatics Center of China, Beijing 100830, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaAs a crucial component of urban economic activities, the layout and optimization of urban commercial spaces directly influence the economic prosperity and quality of life of residents. Therefore, comprehensively and accurately characterizing the distribution characteristics and evolutionary patterns of urban commercial spaces is essential for improving the efficiency of urban spatial allocation and achieving scientific spatial planning and governance. This paper utilizes multisource spatiotemporal data, employing geographic spatial analysis methods and graph neural network models to explore the spatial structure of commercial service facilities in Beijing and their relationships with population density and land use, thereby achieving a detailed classification of the commercial service patterns at the natural neighborhood scale. The research findings indicate a significant association between commercial service facilities and population, as well as land use, with a strong spatial heterogeneity. There exists a dissonance between the layout of commercial service facilities and population distribution, and the differences in commercial service development across various regions pose challenges to balanced urban development. Based on this, this paper provides specific recommendations for optimizing the urban commercial spatial structure, offering reference points for future urban planning and development.https://www.mdpi.com/2220-9964/14/1/23commercial facilities distributionspatial patternsGraphSAGEmultisource spatiotemporal dataBeijing |
| spellingShingle | Yihang Xiao Cunzhi Li Zhiwu Zhou Dongyang Hou Xiaoguang Zhou Analysis and Optimization of the Spatial Patterns of Commercial Service Facilities Based on Multisource Spatiotemporal Data and Graph Neural Networks: A Case Study of Beijing, China ISPRS International Journal of Geo-Information commercial facilities distribution spatial patterns GraphSAGE multisource spatiotemporal data Beijing |
| title | Analysis and Optimization of the Spatial Patterns of Commercial Service Facilities Based on Multisource Spatiotemporal Data and Graph Neural Networks: A Case Study of Beijing, China |
| title_full | Analysis and Optimization of the Spatial Patterns of Commercial Service Facilities Based on Multisource Spatiotemporal Data and Graph Neural Networks: A Case Study of Beijing, China |
| title_fullStr | Analysis and Optimization of the Spatial Patterns of Commercial Service Facilities Based on Multisource Spatiotemporal Data and Graph Neural Networks: A Case Study of Beijing, China |
| title_full_unstemmed | Analysis and Optimization of the Spatial Patterns of Commercial Service Facilities Based on Multisource Spatiotemporal Data and Graph Neural Networks: A Case Study of Beijing, China |
| title_short | Analysis and Optimization of the Spatial Patterns of Commercial Service Facilities Based on Multisource Spatiotemporal Data and Graph Neural Networks: A Case Study of Beijing, China |
| title_sort | analysis and optimization of the spatial patterns of commercial service facilities based on multisource spatiotemporal data and graph neural networks a case study of beijing china |
| topic | commercial facilities distribution spatial patterns GraphSAGE multisource spatiotemporal data Beijing |
| url | https://www.mdpi.com/2220-9964/14/1/23 |
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