Simulating inter-city population flows based on graph neural networks
Inter-city population mobility, a critical phenomenon in the modern urbanisation process, is closely related to urban industrial structure and socioeconomic development. This paper aims to investigate the dynamics of population flows and their intricate ties to industrial structure, so we employ the...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-01-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2024.2331223 |
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| author | Miao Luo Yimin Chen |
| author_facet | Miao Luo Yimin Chen |
| author_sort | Miao Luo |
| collection | DOAJ |
| description | Inter-city population mobility, a critical phenomenon in the modern urbanisation process, is closely related to urban industrial structure and socioeconomic development. This paper aims to investigate the dynamics of population flows and their intricate ties to industrial structure, so we employ the graph neural networks (GNNs) method to simulate inter-city population flows in China, which efficiently integrates demographic and socioeconomic data with Tencent migration big data while accounts for geographical relationships between cities. The results show that the model’s predictive accuracy using the CPC index was high for road and rail traffic and moderate for air transportation. A comparison with real-world data verified the model’s effectiveness in predicting the urban hierarchy and regional aggregation of flows. Using GNNExplainer, the results indicated that population size positively influenced population flow, while developed manufacturing reduced population mobility for road and rail traffic but increased it for air transportation. By conducting scenario simulations in Northeast China, we found that enhancing the region’s industry and consumer service industry could mitigate negative population outflows. The conclusions drawn from this study offer valuable perspectives to policymakers and urban planners, enabling them to make well-informed and judicious choices concerning urban planning, transportation, and resource allocation. |
| format | Article |
| id | doaj-art-6bb66f99f3224353b30b7149539124eb |
| institution | OA Journals |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-6bb66f99f3224353b30b7149539124eb2025-08-20T01:59:21ZengTaylor & Francis GroupGeocarto International1010-60491752-07622024-01-0139110.1080/10106049.2024.2331223Simulating inter-city population flows based on graph neural networksMiao Luo0Yimin Chen1School of Geography and Planning, and Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, PR ChinaSchool of Geography and Planning, and Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, PR ChinaInter-city population mobility, a critical phenomenon in the modern urbanisation process, is closely related to urban industrial structure and socioeconomic development. This paper aims to investigate the dynamics of population flows and their intricate ties to industrial structure, so we employ the graph neural networks (GNNs) method to simulate inter-city population flows in China, which efficiently integrates demographic and socioeconomic data with Tencent migration big data while accounts for geographical relationships between cities. The results show that the model’s predictive accuracy using the CPC index was high for road and rail traffic and moderate for air transportation. A comparison with real-world data verified the model’s effectiveness in predicting the urban hierarchy and regional aggregation of flows. Using GNNExplainer, the results indicated that population size positively influenced population flow, while developed manufacturing reduced population mobility for road and rail traffic but increased it for air transportation. By conducting scenario simulations in Northeast China, we found that enhancing the region’s industry and consumer service industry could mitigate negative population outflows. The conclusions drawn from this study offer valuable perspectives to policymakers and urban planners, enabling them to make well-informed and judicious choices concerning urban planning, transportation, and resource allocation.https://www.tandfonline.com/doi/10.1080/10106049.2024.2331223Inter-city population flowsindustrial structuregraph neural networksscenario simulationexplainable AI |
| spellingShingle | Miao Luo Yimin Chen Simulating inter-city population flows based on graph neural networks Geocarto International Inter-city population flows industrial structure graph neural networks scenario simulation explainable AI |
| title | Simulating inter-city population flows based on graph neural networks |
| title_full | Simulating inter-city population flows based on graph neural networks |
| title_fullStr | Simulating inter-city population flows based on graph neural networks |
| title_full_unstemmed | Simulating inter-city population flows based on graph neural networks |
| title_short | Simulating inter-city population flows based on graph neural networks |
| title_sort | simulating inter city population flows based on graph neural networks |
| topic | Inter-city population flows industrial structure graph neural networks scenario simulation explainable AI |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2024.2331223 |
| work_keys_str_mv | AT miaoluo simulatingintercitypopulationflowsbasedongraphneuralnetworks AT yiminchen simulatingintercitypopulationflowsbasedongraphneuralnetworks |