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ISPRS Int. J. Geo-Inf. 2016, 5(10), 166; doi:10.3390/ijgi5100166

A Two-Step Clustering Approach to Extract Locations from Individual GPS Trajectory Data

1
School of Remote Sensing and Information Engineering, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, No. 129 Luoyu Road, Wuhan 430079, China
3
Department of Geography & Planning, University of Toledo, 2801 W. Bancroft, Toledo, OH 43606, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 26 July 2016 / Revised: 18 September 2016 / Accepted: 19 September 2016 / Published: 23 September 2016
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Abstract

High-accuracy location identification is the basis of location awareness and location services. However, because of the influence of GPS signal loss, data drift and repeated access in the individual trajectory data, the efficiency and accuracy of existing algorithms have some deficiencies. Therefore, we propose a two-step clustering approach to extract individuals’ locations according to their GPS trajectory data. Firstly, we defined three different types of stop points; secondly, we extracted these points from the trajectory data by using the spatio-temporal clustering algorithm based on time and distance. The experimental results show that the spatio-temporal clustering algorithm outperformed traditional extraction algorithms. It can avoid the problems caused by repeated access and can substantially reduce the effects of GPS signal loss and data drift. Finally, an improved clustering algorithm based on a fast search and identification of density peaks was applied to discover the trajectory locations. Compared to the existing algorithms, our method shows better performance and accuracy. View Full-Text
Keywords: GPS trajectory data; data mining; clustering algorithm; personal location GPS trajectory data; data mining; clustering algorithm; personal location
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Fu, Z.; Tian, Z.; Xu, Y.; Qiao, C. A Two-Step Clustering Approach to Extract Locations from Individual GPS Trajectory Data. ISPRS Int. J. Geo-Inf. 2016, 5, 166.

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