High-dimensional urban dynamic patterns perception under the perspective of human activity semantics and spatiotemporal coupling
文献类型：期刊论文
| 作者 | Lv, Yunshuo3,4,5; Yang, Jiaqi2; Xu, Jun2; Guan, Xuyuan1; Zhang, Jing3,4,5 |
| 刊名 | SUSTAINABLE CITIES AND SOCIETY
 |
| 出版日期 | 2025-03-01 |
| 卷号 | 121页码:106192 |
| 关键词 | Urban dynamic patterns Activity semantics High-dimensional pattern mining Pre-trained language model Tensor decomposition Social media data |
| ISSN号 | 2210-6707 |
| DOI | 10.1016/j.scs.2025.106192 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | As urbanization accelerates, megacities are emerging globally. Various human activities shape dynamic urban spaces, understanding dynamic performance implicit within them is essential for developing smart cities. Previous studies on urban dynamic patterns mainly focused on the spatiotemporal dimensions, unable to explain the joint effects of higher-dimensional patterns. In fact, large-scale social media data encapsulate human activity features across multiple dimensions, including semantics, space, and time, whose combined effects drive the formation of high-dimensional urban dynamic patterns. This study proposes a framework that expands the activity semantics dimension on top of spatiotemporal dimensions and perceive these patterns through highdimensional feature coupling. Activity semantics are extracted from social media texts using ERNIE 3.0, a large-scale knowledge-enhanced pre-trained model. Data with three features dimensions are coupled into highorder tensors, and tensor decomposition uncovers key patterns. A case study using Weibo check-in records within Beijing's Sixth Ring Road extracted ten distinct activity semantics, and interpretable patterns along each dimension. Through core tensors, we identified eight urban dynamic patterns under various states and their corresponding activity complexity changes. Additionally, correlations between activity semantics (dynamic attributes) and fixed facility configurations (static attributes) were explored using Point of Interest (POI) data. The results confirm the advantages of our method in exploring high-dimensional urban dynamic patterns. |
| URL标识 | 查看原文 |
| WOS关键词 | SPARSE ; ALGORITHMS |
| WOS研究方向 | Construction & Building Technology ; Science & Technology - Other Topics ; Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:001427243100001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/213308]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Zhang, Jing |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 3.Capital Normal Univ, Informat Collect & Applicat Key Lab Educ Minist 3D, Beijing 100048, Peoples R China; 4.Capital Normal Univ, Beijing State Key Lab Incubat Base Urban Environm, Beijing 100048, Peoples R China; 5.Capital Normal Univ, Coll Resource Environm & Tourism, 105 West Third Ring Rd North, Beijing 100048, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Lv, Yunshuo,Yang, Jiaqi,Xu, Jun,et al. High-dimensional urban dynamic patterns perception under the perspective of human activity semantics and spatiotemporal coupling[J]. SUSTAINABLE CITIES AND SOCIETY,2025,121:106192. |
| APA | Lv, Yunshuo,Yang, Jiaqi,Xu, Jun,Guan, Xuyuan,&Zhang, Jing.(2025).High-dimensional urban dynamic patterns perception under the perspective of human activity semantics and spatiotemporal coupling.SUSTAINABLE CITIES AND SOCIETY,121,106192. |
| MLA | Lv, Yunshuo,et al."High-dimensional urban dynamic patterns perception under the perspective of human activity semantics and spatiotemporal coupling".SUSTAINABLE CITIES AND SOCIETY 121(2025):106192. |
入库方式： OAI收割
来源：地理科学与资源研究所
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