机构

• 地理科学与资源研究... [27]
• 深圳先进技术研究院 [11]
• 自动化研究所 [3]
• 心理研究所 [1]

采集方式

• OAI收割 [42]

内容类型

• 期刊论文 [38]
• SCI/SSCI论文 [2]
• 会议论文 [1]
• 学位论文 [1]

发表日期

• 2023 [1]
• 2022 [1]
• 2021 [6]
• 2020 [5]
• 2019 [10]
• 2018 [5]
• 更多

学科主题

• Physics [1]

筛选

浏览/检索结果: 共42条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择提交时间升序提交时间降序发表日期升序发表日期降序题名升序题名降序作者升序作者降序 How does socioeconomic status influence social relations? A perspective from mobile phone data 期刊论文  OAI收割 PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2023, 卷号: 615, 页码: 128612 作者:  Wang, Xi;  Pei, Tao;  Song, Ci;  Chen, Jie;  Shu, Hua   |  收藏  |  浏览/下载：13/0  |  提交时间：2023/07/14 Computational social science  Mobile phone data  Social network analysis  Socioeconomic status  Human mobility pattern   青少年问题性手机使用路径形成及干预研究 学位论文  OAI收割 中国科学院心理研究所: 中国科学院大学, 2022 作者:  马骋   |  收藏  |  浏览/下载：23/0  |  提交时间：2022/09/15 青少年  问题性手机使用  链式中介  焦点解决   Assessment of the Dynamic Exposure to PM2.5 Based on Hourly Cell Phone Location and Land Use Regression Model in Beijing 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2021, 卷号: 18, 期号: 11, 页码: 14 作者:  Liu, Junli;  Cai, Panli;  Dong, Jin;  Wang, Junshun;  Li, Runkui   |  收藏  |  浏览/下载：21/0  |  提交时间：2021/08/19 cell phone  activity pattern  exposure assessment  fine particulate matter  land use regression model   Assessment of the Dynamic Exposure to PM2.5 Based on Hourly Cell Phone Location and Land Use Regression Model in Beijing 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2021, 卷号: 18, 期号: 11, 页码: 14 作者:  Liu, Junli;  Cai, Panli;  Dong, Jin;  Wang, Junshun;  Li, Runkui   |  收藏  |  浏览/下载：14/0  |  提交时间：2021/08/19 cell phone  activity pattern  exposure assessment  fine particulate matter  land use regression model   Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2021, 页码: 28 作者:  Li, Mingxiao;  Gao, Song;  Lu, Feng;  Liu, Kang;  Zhang, Hengcai   |  收藏  |  浏览/下载：56/0  |  提交时间：2021/06/10 Human activity intensity prediction  graph convolutional networks  social interaction  mobile phone data  human mobility   Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks 期刊论文  OAI收割 INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2021, 页码: 28 作者:  Li, Mingxiao;  Gao, Song;  Lu, Feng;  Liu, Kang;  Zhang, Hengcai   |  收藏  |  浏览/下载：9/0  |  提交时间：2021/06/10 Human activity intensity prediction  graph convolutional networks  social interaction  mobile phone data  human mobility   Using Mobile Phone Data to Examine Point-of-Interest Urban Mobility 期刊论文  OAI收割 JOURNAL OF URBAN TECHNOLOGY, 2021, 页码: 16 作者:  Chen, Hao;  Song, Xianfeng;  Xu, Changhui;  Zhang, Xiaoping   |  收藏  |  浏览/下载：20/0  |  提交时间：2021/04/25 Mobility pattern  appearance probability estimation  frequent location detection  population classification  radius of gyration   Using Mobile Phone Data to Examine Point-of-Interest Urban Mobility 期刊论文  OAI收割 JOURNAL OF URBAN TECHNOLOGY, 2021, 页码: 16 作者:  Chen, Hao;  Song, Xianfeng;  Xu, Changhui;  Zhang, Xiaoping   |  收藏  |  浏览/下载：7/0  |  提交时间：2021/04/25 Mobility pattern  appearance probability estimation  frequent location detection  population classification  radius of gyration   Predicting future locations of moving objects with deep fuzzy-LSTM networks 期刊论文  OAI收割 TRANSPORTMETRICA A-TRANSPORT SCIENCE, 2020, 卷号: 16, 期号: 1, 页码: 119-136 作者:  Li, Mingxiao;  Lu, Feng;  Zhang, Hengcai;  Chen, Jie   |  收藏  |  浏览/下载：85/0  |  提交时间：2020/05/19 Location prediction  fuzzy space partition  mobile phone data  trajectory data mining  deep learning   Predicting future locations of moving objects with deep fuzzy-LSTM networks 期刊论文  OAI收割 TRANSPORTMETRICA A-TRANSPORT SCIENCE, 2020, 卷号: 16, 期号: 1, 页码: 119-136 作者:  Li, Mingxiao;  Lu, Feng;  Zhang, Hengcai;  Chen, Jie   |  收藏  |  浏览/下载：19/0  |  提交时间：2020/05/19 Location prediction  fuzzy space partition  mobile phone data  trajectory data mining  deep learning