论文
International Journal of Geographical Information Science
PublisherJournal
Multimodal
中文标题
基于遥感与社交媒体数据的人口构成高分辨率制图:一种多模态、多输出建模方法
English Title
High-resolution mapping of demographic compositions based on remote sensing and social media data: a multimodal, multi-output modeling approach
Ge Qiu Yuchen Li Shaoqing Dai Kun Qin Zhanpeng Wang Yaolin Liu Jiping Liu Peng Jia a School of Resource and Environmental Sciences, Center for Metabolic and Panvascular Diseases, Wuhan University, Wuhan, Chinab Medical Remote Sensing Information Research Institute (MRSIRI), Renmin Hospital of Wuhan University, Wuhan, Chinac Chinese Academy of Surveying and Mapping, Beijing, Chinad International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, Chinae School of Geography, University of Leeds, Leeds, UKf Duke Kunshan University, Kunshan, ChinaGe Qiu received his Ph.D. in Cartography and Geographic Information Systems from the School of Resource and Environmental Sciences, Wuhan University. His research interests are multi-source geospatial data fusion, spatial modeling and mapping, GeoAI, and health geography. His primary contributions to this study included conceptualization, methodology, investigation, formal analysis, software, validation, visualization, data curation, writing – original draft, and writing – review and editing.Yuchen Li is a Lecturer at the School of Geography, University of Leeds. His research interests are spatial analysis and modeling, spatial data mining, spatial statistics, transportation simulation, and public health modeling. His primary contributions to this study included methodology, validation, writing – original draft, and writing – review and editing.Shaoqing Dai is a postdoctoral researcher at the School of Resource and Environmental Sciences, Wuhan University. His research interests are health geography, spatial statistics, GeoAI, and urban visual intelligence. His primary contributions to this study included investigation, validation, visualization, and writing – review and editing.Kun Qin is a Ph.D. candidate at the School of Resource and Environmental Sciences, Wuhan University. His research interests include spatial modeling and mapping, spatiotemporal analysis, and health geography. His primary contributions to this study included software, visualization, data curation, and writing – review and editing.Zhanpeng Wang received his Ph.D. in Cartography and Geographic Information Systems from the School of Resource and Environmental Sciences, Wuhan University. His research interests are remote sensing, GeoAI, and health geography. His primary contributions to this study included investigation, visualization, data curation, and writing – review and editing.Yaolin Liu is a Professor at the School of Resource and Environmental Sciences, Wuhan University, and currently serves as the Chancellor of Duke Kunshan University. His research focuses on the application of geospatial technologies in land resource surveying, evaluation, planning, and management. His primary contributions to this study included conceptualization, validation, resources, supervision, project administration, and writing – review and editing.Jiping Liu is a Research Fellow and Deputy Director General at the Chinese Academy of Surveying and Mapping. His research interests are spatiotemporal big data analysis and mining, governmental geospatial information services, emergency geospatial information services, and 3D real-world modeling. His primary contributions to this study included conceptualization, investigation, validation, resources, supervision, funding acquisition, project administration, and writing – review and editing.Peng Jia is the founding director of the International Institute of Spatial Lifecourse Health (ISLE) and the Deputy Director of Medical Remote Sensing Information Research Institute (MRSIRI) at Renmin Hospital of Wuhan University. His research interests include health geography, spatial epidemiology, environmental health, and spatial science and technology. His primary contributions to this study included conceptualization, validation, resources, supervision, funding acquisition, project administration, and writing – review and editing.
发布时间
2026/7/27 17:32:01
来源类型
journal
语言
en
摘要
中文对照

精细尺度的人口构成(如性别与年龄结构)认知对公共卫生、城市规划及市场营销等多个领域具有基础性意义。然而,现有大多数精细尺度人口产品未能刻画行政单元内部人口构成的空间异质性。本研究首次提出一种新方法,利用人口普查数据、遥感影像及带地理标签的社交媒体数据等多源数据,在100 × 100米网格单元上估算完整的性别与年龄结构。该方法整合了遥感影像提取的关键环境变量(如人工灯光强度与植被指数)以及社交媒体文本中提取的人口相关变量,并采用多输出随机森林模型进行建模;该模型在考虑多种人口属性间相关性的前提下,估计上述变量与基于人口普查获得的人口构成(包括性别比与各年龄组占比)之间的关联关系。最终产品的均方根误差(RMSE)分别为:性别比(每100名女性对应的男性人数)6.18;0–14岁、15–59岁及≥60岁年龄组占总人口比例分别为1.89%、3.82%和3.40%。该方法具备开展精细化人口建模的潜力,可拓展至其他社会人口学因子,服务于多个学科领域。

English Original

Understanding fine-scale demographic compositions, such as sex and age structures, is fundamental to many fields, including public health, urban planning, and marketing. However, most existing fine-scale population products did not have spatial heterogeneities of demographic compositions within administrative units. This study, for the first time, developed a novel approach to estimate complete sex and age structures on 100 × 100 m grid cells, based on multimodal data including census, remote sensing imagery, and geotagged social media data. Key environmental variables derived from remote sensing imagery – such as artificial light intensity and vegetation index – and demographics-related variables extracted from social media texts were integrated using a multi-output random forest model, which, accounting for correlations among multiple demographic attributes, estimated the associations between the aforementioned variables and census-derived demographic compositions, including sex and age structures. The root mean square error (RMSE) of the final product was 6.18 for the sex ratio (the number of males per 100 females), and 1.89%, 3.82%, and 3.40% for the percentages over the total population of age groups 0–14, 15–59, and ≥60 years, respectively. This approach holds potential for detailed demographic modeling, which can be extended to other sociodemographic factors and benefit multiple disciplines.

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元数据
来源International Journal of Geographical Information Science
类型论文
抽取状态raw
关键词
PublisherJournal
Multimodal