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

doi:10.3390/ijgi6070212

	Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement
	Wan, You 1; Zhou, Chenghu 2; Pei, Tao 2,3,4
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.