Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement | |
Wan, You1; Zhou, Chenghu2; Pei, Tao2,3,4 | |
刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
![]() |
2017-07-01 | |
卷号 | 6期号:7页码:18 |
关键词 | trajectory pattern semantic similarity geographic similarity pattern mining clustering |
ISSN号 | 2220-9964 |
DOI | 10.3390/ijgi6070212 |
通讯作者 | Pei, Tao(peit@lreis.ac.cn) |
英文摘要 | Trajectory pattern mining is becoming increasingly popular because of the development of ubiquitous computing technology. Trajectory data contain abundant semantic and geographic information that reflects people's movement patterns, i.e., who is performing a certain type of activity when and where. However, the variety and complexity of people's movement activity and the large size of trajectory datasets make it difficult to mine valuable trajectory patterns. Moreover, most existing trajectory similarity measurements only consider a portion of the information contained in trajectory data. The patterns obtained cannot be interpreted well in terms of both semantic meaning and geographic distributions. As a result, these patterns cannot be used accurately for recommendation systems or other applications. This paper introduces a novel concept of the semantic-geographic pattern that considers both semantic and geographic meaning simultaneously. A flexible density-based clustering algorithm with a new trajectory similarity measurement called semantic intensity is used to mine these semantic-geographic patterns. Comparative experiments on check-in data from the Sina Weibo service demonstrate that semantic intensity can effectively measure both semantic and geographic similarities among trajectories. The resulting patterns are more accurate and easy to interpret. |
资助项目 | National Key Research & Development Plan of China[2017YFB0503601] ; National Natural Science Foundation of China[41471327] ; National Natural Science Foundation of China[41525004] ; National Natural Science Foundation of China[41231171] |
WOS关键词 | MOVEMENT DATA ; DISTANCE ; TIME ; OBJECTS |
WOS研究方向 | Physical Geography ; Remote Sensing |
语种 | 英语 |
出版者 | MDPI AG |
WOS记录号 | WOS:000407506900029 |
资助机构 | National Key Research & Development Plan of China ; National Natural Science Foundation of China |
内容类型 | 期刊论文 |
源URL | [http://ir.igsnrr.ac.cn/handle/311030/61552] ![]() |
专题 | 中国科学院地理科学与资源研究所 |
通讯作者 | Pei, Tao |
作者单位 | 1.Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China |
推荐引用方式 GB/T 7714 | Wan, You,Zhou, Chenghu,Pei, Tao. Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2017,6(7):18. |
APA | Wan, You,Zhou, Chenghu,&Pei, Tao.(2017).Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,6(7),18. |
MLA | Wan, You,et al."Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 6.7(2017):18. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论