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With the increasing availability of GPS-enabled devices, a huge amount of GPS trajectories recording people's location traces havebeen accumulated and shared freely on the Web. In this area, one of the most important research topics is to exploittrajectory-movement pattern about where and when people clustered based on the raw GPS data. In order to solve this problem,clustering is a good way to perform data mining tasks on trajectory data.  This paper provides a clustering algorithm which aims at mining people’s movement pattern about the clustered location andtheir temporal evolution characteristics. Firstly, the characteristic points of GPS trajectories were chosen. Based on the characteristicpoints, a trajectory has been partitioned into a group of line segments. These line segments can represent the movement pattern oftrajectories much better than that of track points. Secondly, an improved density-based line clustering method was used for theindividual partitioned line segments to find out individual clusters with similar track segments. In this step, the absolute time spot ofpeople’s trajectories was taking into account as a characteristic for the temporal evolution of people’s trajectories. Finally, therepresentative clustered hot spots of multiple users’ line segments achieved by above steps were output. Experiments were conductedwith GPS trajectories data downloaded from the web to verify the effectiveness of the algorithm in this paper. According to theresults, the spatial distribution and temporal evolution characteristics of people’s stay hot spots were effectively discovered frompeople’s GPS trajectories data

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Trajectory Clustering for People's Movement Pattern Based on Crowd Souring Data