Sensing multiple semantics of urban space from crowdsourcing positioning data
文献类型：期刊论文
| 作者 | Cai, Ling1,2; Xu, Jun1 ; Liu, Ju1,2; Ma, Ting1,2; Pei, Tao1,2; Zhou, Chenghu1
|
| 刊名 | CITIES
 |
| 出版日期 | 2019-10-01 |
| 卷号 | 93页码:31-42 |
| 关键词 | Spatial function Urban dynamics Spatial-temporal pattern Tencent location big data Tensor factorization |
| ISSN号 | 0264-2751 |
| DOI | 10.1016/j.cities.2019.04.011 |
| 通讯作者 | Xu, Jun(xujun@lreis.ac.cn) |
| 英文摘要 | Urban spaces have multiple functions, and the main functions of these space change with human activities during a day; thus, there are dynamic semantics of spaces in a city. Knowing the dynamic semantics of urban spaces, which are implied in spatiotemporal patterns of human activities, can help urban planners and managers understand how a city performs over time and space. The very large amount of multidimensional user-generated data makes it possible to disclose the spatiotemporal patterns of human activities from multiple perspectives. In this paper, using Beijing as a case study, we extract the dynamic semantics of urban spaces through the spatiotemporal patterns of human activities discovered from crowdsourced positioning data. A high-order decomposition method, tensor factorization, is used to explore the crowdsourced positioning data. The decomposition results reveal five hourly patterns, four daily patterns and six spatial patterns of urban dynamics in Beijing, showing that urban dynamics in Beijing vary noticeably over different hours, days and space. The human activities implicated by hourly and daily patterns are inferred through empirical knowledge, and the activity semantics of spatial patterns are further disclosed by using the interaction relations among three dimensions stored in the core tensor. The k-means clustering method is executed to aggregate similar spatial units into one group. Five clusters of regions with similar activity semantics are discovered, the function semantics of clusters are clarified with point of interest (POI) data. |
| WOS关键词 | HUMAN MOBILITY PATTERNS ; FUNCTIONAL REGIONS ; LOCATION DATA ; PHONE ; TIME ; POINTS |
| 资助项目 | Key Programs of the Chinese Academy of Sciences[QYZDY-SSW-DQC007] ; NSFC[41771477] ; NSFC[41525004] ; National Science and Technology Key Project[2016YFB0502301] ; Innovation Project of LREIS[O88RA20BYA] |
| WOS研究方向 | Urban Studies |
| 语种 | 英语 |
| WOS记录号 | WOS:000488142900003 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | Key Programs of the Chinese Academy of Sciences ; NSFC ; National Science and Technology Key Project ; Innovation Project of LREIS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/129837]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Xu, Jun |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cai, Ling,Xu, Jun,Liu, Ju,et al. Sensing multiple semantics of urban space from crowdsourcing positioning data[J]. CITIES,2019,93:31-42. |
| APA | Cai, Ling,Xu, Jun,Liu, Ju,Ma, Ting,Pei, Tao,&Zhou, Chenghu.(2019).Sensing multiple semantics of urban space from crowdsourcing positioning data.CITIES,93,31-42. |
| MLA | Cai, Ling,et al."Sensing multiple semantics of urban space from crowdsourcing positioning data".CITIES 93(2019):31-42. |
入库方式： OAI收割
来源：地理科学与资源研究所
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