Identification of source and sink points of population flow based on POI-KG embedding
The source and sink points of urban population flow represent key areas of population aggregation and dispersion, crucial for optimizing resource allocation and urban development planning. Identifying these points heavily relies on high-quality population flow data, which presents challenges due to...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2512062 |
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| _version_ | 1849224310009364480 |
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| author | Yanhao Li Rui Li Xinrui Liu Bosen Li |
| author_facet | Yanhao Li Rui Li Xinrui Liu Bosen Li |
| author_sort | Yanhao Li |
| collection | DOAJ |
| description | The source and sink points of urban population flow represent key areas of population aggregation and dispersion, crucial for optimizing resource allocation and urban development planning. Identifying these points heavily relies on high-quality population flow data, which presents challenges due to data acquisition difficulties and limited coverage. This study proposes a method using knowledge graph embedding that incorporates the semantic and spatial information of points of interest (POI) to represent factors influencing population flow, allowing for source and sink point identification with minimal reliance on population flow data. First, a knowledge graph is constructed to represent factors like socio-economic location, semantic aggregation, semantic similarity, and spatial aggregation of POIs influencing population flow. Then, POI semantic and spatial information is aligned in a unified space to construct the integrated semantics and spatial information-knowledge graph embedding (ISS-KGE). By integrating total population flow and net inflow/outflow rates, the urban grid is divided into strong sources, strong sinks, and stable points. Weakly supervised learning is applied using ISS-KGE to identify these types within the urban grid. Experimental results, in Wuchang District, Wuhan, show that ISS-KGE achieves an F1-score of 0.69 in identifying population flow source and sink points, outperforming baseline models Word2Vec and GloVe. |
| format | Article |
| id | doaj-art-9e270d18b4bf4445849660cced0af07a |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-9e270d18b4bf4445849660cced0af07a2025-08-25T11:25:09ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2512062Identification of source and sink points of population flow based on POI-KG embeddingYanhao Li0Rui Li1Xinrui Liu2Bosen Li3State Key Laboratory of lnformation Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, People’s Republic of ChinaState Key Laboratory of lnformation Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, People’s Republic of ChinaState Key Laboratory of lnformation Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, People’s Republic of ChinaState Key Laboratory of lnformation Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, People’s Republic of ChinaThe source and sink points of urban population flow represent key areas of population aggregation and dispersion, crucial for optimizing resource allocation and urban development planning. Identifying these points heavily relies on high-quality population flow data, which presents challenges due to data acquisition difficulties and limited coverage. This study proposes a method using knowledge graph embedding that incorporates the semantic and spatial information of points of interest (POI) to represent factors influencing population flow, allowing for source and sink point identification with minimal reliance on population flow data. First, a knowledge graph is constructed to represent factors like socio-economic location, semantic aggregation, semantic similarity, and spatial aggregation of POIs influencing population flow. Then, POI semantic and spatial information is aligned in a unified space to construct the integrated semantics and spatial information-knowledge graph embedding (ISS-KGE). By integrating total population flow and net inflow/outflow rates, the urban grid is divided into strong sources, strong sinks, and stable points. Weakly supervised learning is applied using ISS-KGE to identify these types within the urban grid. Experimental results, in Wuchang District, Wuhan, show that ISS-KGE achieves an F1-score of 0.69 in identifying population flow source and sink points, outperforming baseline models Word2Vec and GloVe.https://www.tandfonline.com/doi/10.1080/17538947.2025.2512062POI-KGknowledge graph embeddingsource and sink points of population flow |
| spellingShingle | Yanhao Li Rui Li Xinrui Liu Bosen Li Identification of source and sink points of population flow based on POI-KG embedding International Journal of Digital Earth POI-KG knowledge graph embedding source and sink points of population flow |
| title | Identification of source and sink points of population flow based on POI-KG embedding |
| title_full | Identification of source and sink points of population flow based on POI-KG embedding |
| title_fullStr | Identification of source and sink points of population flow based on POI-KG embedding |
| title_full_unstemmed | Identification of source and sink points of population flow based on POI-KG embedding |
| title_short | Identification of source and sink points of population flow based on POI-KG embedding |
| title_sort | identification of source and sink points of population flow based on poi kg embedding |
| topic | POI-KG knowledge graph embedding source and sink points of population flow |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2512062 |
| work_keys_str_mv | AT yanhaoli identificationofsourceandsinkpointsofpopulationflowbasedonpoikgembedding AT ruili identificationofsourceandsinkpointsofpopulationflowbasedonpoikgembedding AT xinruiliu identificationofsourceandsinkpointsofpopulationflowbasedonpoikgembedding AT bosenli identificationofsourceandsinkpointsofpopulationflowbasedonpoikgembedding |