Xiaorui Yang , Rui Li , Jing Xia , Junhao Wang , Hongyan Li , Nixiao Zou
{"title":"人口分析单元的异构多尺度时空相互作用网络模型","authors":"Xiaorui Yang , Rui Li , Jing Xia , Junhao Wang , Hongyan Li , Nixiao Zou","doi":"10.1016/j.jag.2025.104565","DOIUrl":null,"url":null,"abstract":"<div><div>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 (R<sup>2</sup> = 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.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"140 ","pages":"Article 104565"},"PeriodicalIF":7.6000,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"HMS-PAU-IN: A heterogeneous multi-scale spatiotemporal interaction network model for population analysis units\",\"authors\":\"Xiaorui Yang , Rui Li , Jing Xia , Junhao Wang , Hongyan Li , Nixiao Zou\",\"doi\":\"10.1016/j.jag.2025.104565\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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 (R<sup>2</sup> = 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.</div></div>\",\"PeriodicalId\":73423,\"journal\":{\"name\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"volume\":\"140 \",\"pages\":\"Article 104565\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2025-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1569843225002122\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of applied earth observation and geoinformation : ITC journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1569843225002122","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REMOTE SENSING","Score":null,"Total":0}
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, 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.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.