关键词: 停留点, 空间聚类, 热点分析, DBSCAN算法, 轨迹, 景区

Abstract: With broad applications of all kinds of smart phone APP containing Location Based Services（LBS）, a large amount of trajectory data left behind tourists can be collected, and mining hot spots from such data will contribute to intelligent service and emergency management. A clustering-based approach for discovering hot spots in spatial trajectories is proposed. Firstly, the DBSCAN（Density-Based Spatial Clustering of Applications with Noise） algorithm is applied to spatial clustering due to its simplicity, robustness against noise and ability to discover clusters of arbitrary shapes. However, DBSCAN is sensitive to its two initial input parameters and it is hard to determine them a priori. This paper presents an improved method to choose DBSCAN parameters automatically according to the data statistics distribution. Experimental results are obtained from two-dimensional artificial data sets, four-dimensional UCI Iris data sets and stay points data sets. The final results show that the improved algorithm gets good results with respect to the original DBSCAN and k-means algorithms. Finally, the Getis-Ord Gi* hot spot analysis and mapping based on clustering results are conducted in ArcGIS software, and the grading heats of different scenic spots are given, which are equivalent to the information held by the tourism management.

Key words: stay point, spatial clustering, hot spot analysis, Density-Based Spatial Clustering of Applications with Noise（DBSCAN） algorithm, trajectory, scenic spot