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

1, 2 and 2,3,4,*
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(7), 212;
Received: 4 May 2017 / Revised: 2 July 2017 / Accepted: 5 July 2017 / Published: 14 July 2017
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
PDF [3124 KB, uploaded 14 July 2017]


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. View Full-Text
Keywords: trajectory pattern; semantic similarity; geographic similarity; pattern mining; clustering trajectory pattern; semantic similarity; geographic similarity; pattern mining; clustering

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Wan, Y.; Zhou, C.; Pei, T. Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement. ISPRS Int. J. Geo-Inf. 2017, 6, 212.

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