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Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement
Wan, You1; Zhou, Chenghu2; Pei, Tao2,3,4
2017-07-01
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
ISSN2220-9964
Volume6Issue:7Pages:18
Corresponding AuthorPei, Tao(peit@lreis.ac.cn)
AbstractTrajectory 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.
Keywordtrajectory pattern semantic similarity geographic similarity pattern mining clustering
DOI10.3390/ijgi6070212
WOS KeywordMOVEMENT DATA ; DISTANCE ; TIME ; OBJECTS
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Key Research & Development Plan of China ; National Natural Science Foundation of China
WOS Research AreaPhysical Geography ; Remote Sensing
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000407506900029
PublisherMDPI AG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/61553
Collection中国科学院地理科学与资源研究所
Corresponding AuthorPei, Tao
Affiliation1.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
Recommended Citation
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.
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