HMS-PAU-IN: A heterogeneous multi-scale spatiotemporal interaction network model for population analysis units
Population analysis units (PAUs), as fundamental spatial units accommodating population-related activities, hold significance in constructing spatiotemporal interaction networks to understand intra-unit population distribution and activity patterns as well as inter-unit interactions. However, existi...
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| Format: | Article |
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Elsevier
2025-06-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225002122 |
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| author | Xiaorui Yang Rui Li Jing Xia Junhao Wang Hongyan Li Nixiao Zou |
| author_facet | Xiaorui Yang Rui Li Jing Xia Junhao Wang Hongyan Li Nixiao Zou |
| author_sort | Xiaorui Yang |
| collection | DOAJ |
| description | Population analysis units (PAUs), as fundamental spatial units accommodating population-related activities, hold significance in constructing spatiotemporal interaction networks to understand intra-unit population distribution and activity patterns as well as inter-unit interactions. However, existing networks are constrained by fixed scales and the absence of temporal dynamics, with insufficient consideration of multi-scale features, thereby limiting their semantic representation and dynamic analysis capabilities. Thus, we proposed a heterogeneous multi-scale PAU interaction network (HMS-PAU-IN) model that integrates spatial, temporal, and semantic representations, enabling HMS-PAU-IN modeling and semantic analysis based on spatiotemporal knowledge graph. In the spatial dimension, spatiotemporal interactions are classified into explicit interactions driven by population flows and potential interactions shaped by spatial relationships. In the temporal dimension, the changes of PAUs are captured through the evolutionary relationships of nodes between different time windows. To validate the model, we developed a population prediction model that integrates the multi-scale features of PAUs and introduced Leiden-IES-PMS, a community detection method based on the Leiden algorithm, which integrates internal and external environmental semantics and adopts a proximity merging strategy. Experimental results demonstrate that the proposed model and method effectively characterize spatiotemporal interactions among multi-scale PAUs, enhancing the accuracy of population distribution prediction (R2 = 0.77) at the community scale, and improving the interpretability of temporal community analysis at the building scale. This study develops a multi-scale spatiotemporal framework for analyzing population distribution, activity patterns, and community evolution within PAUs, providing actionable insights for urban planning, resource optimization, and sustainable management. |
| format | Article |
| id | doaj-art-91ffc71c8258408e9c7b8c0ec808ca47 |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-91ffc71c8258408e9c7b8c0ec808ca472025-08-20T02:34:31ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-06-0114010456510.1016/j.jag.2025.104565HMS-PAU-IN: A heterogeneous multi-scale spatiotemporal interaction network model for population analysis unitsXiaorui Yang0Rui Li1Jing Xia2Junhao Wang3Hongyan Li4Nixiao Zou5State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; Corresponding author.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaPopulation analysis units (PAUs), as fundamental spatial units accommodating population-related activities, hold significance in constructing spatiotemporal interaction networks to understand intra-unit population distribution and activity patterns as well as inter-unit interactions. However, existing networks are constrained by fixed scales and the absence of temporal dynamics, with insufficient consideration of multi-scale features, thereby limiting their semantic representation and dynamic analysis capabilities. Thus, we proposed a heterogeneous multi-scale PAU interaction network (HMS-PAU-IN) model that integrates spatial, temporal, and semantic representations, enabling HMS-PAU-IN modeling and semantic analysis based on spatiotemporal knowledge graph. In the spatial dimension, spatiotemporal interactions are classified into explicit interactions driven by population flows and potential interactions shaped by spatial relationships. In the temporal dimension, the changes of PAUs are captured through the evolutionary relationships of nodes between different time windows. To validate the model, we developed a population prediction model that integrates the multi-scale features of PAUs and introduced Leiden-IES-PMS, a community detection method based on the Leiden algorithm, which integrates internal and external environmental semantics and adopts a proximity merging strategy. Experimental results demonstrate that the proposed model and method effectively characterize spatiotemporal interactions among multi-scale PAUs, enhancing the accuracy of population distribution prediction (R2 = 0.77) at the community scale, and improving the interpretability of temporal community analysis at the building scale. This study develops a multi-scale spatiotemporal framework for analyzing population distribution, activity patterns, and community evolution within PAUs, providing actionable insights for urban planning, resource optimization, and sustainable management.http://www.sciencedirect.com/science/article/pii/S1569843225002122Population analysis unitSpatiotemporal knowledge graphSpatiotemporal interactionPopulation estimationCommunity detectionHeterogeneous network |
| spellingShingle | Xiaorui Yang Rui Li Jing Xia Junhao Wang Hongyan Li Nixiao Zou HMS-PAU-IN: A heterogeneous multi-scale spatiotemporal interaction network model for population analysis units International Journal of Applied Earth Observations and Geoinformation Population analysis unit Spatiotemporal knowledge graph Spatiotemporal interaction Population estimation Community detection Heterogeneous network |
| title | HMS-PAU-IN: A heterogeneous multi-scale spatiotemporal interaction network model for population analysis units |
| title_full | HMS-PAU-IN: A heterogeneous multi-scale spatiotemporal interaction network model for population analysis units |
| title_fullStr | HMS-PAU-IN: A heterogeneous multi-scale spatiotemporal interaction network model for population analysis units |
| title_full_unstemmed | HMS-PAU-IN: A heterogeneous multi-scale spatiotemporal interaction network model for population analysis units |
| title_short | HMS-PAU-IN: A heterogeneous multi-scale spatiotemporal interaction network model for population analysis units |
| title_sort | hms pau in a heterogeneous multi scale spatiotemporal interaction network model for population analysis units |
| topic | Population analysis unit Spatiotemporal knowledge graph Spatiotemporal interaction Population estimation Community detection Heterogeneous network |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225002122 |
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