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Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement

文献类型：期刊论文

作者Wan, You4; Zhou, Chenghu1; Pei, Tao1,2,3
刊名ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
出版日期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
DOI10.3390/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.
WOS关键词MOVEMENT DATA ; DISTANCE ; TIME ; OBJECTS
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研究方向Physical Geography ; Remote Sensing
语种英语
WOS记录号WOS:000407506900029
出版者MDPI AG
资助机构National Key Research & Development Plan of China ; National Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/61552]  
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Pei, Tao
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.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, 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.

入库方式： OAI收割

来源：地理科学与资源研究所

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