期刊论文

Application of stay points spatial clustering in hot scenic spots analysis

中文

计算机工程与应用

1002-8331

2018

54

4

263-270

10.3778/j.issn.1002-8331.1608-0255

国家科技支撑计划课题

本校为第一机构

各种集成位置服务(LBS)的社交和旅游类APP的广泛应用,产生了大量轨迹空间数据,利用这些轨迹数据挖掘游客聚集密度高的热门景点区域,对景区的智慧服务和应急管理具有重要意义。为此,提出了一种基于轨迹停留点空间聚类的景区热点分析方法。重点研究了聚类速度快、能处理噪声、可以发现空间任意形状聚簇的DBSCAN算法,针对其参数需人工选择的不足,提出了一种根据数据统计分布特性来自适应确定参数的改进方法。分别采用人工合成二维数据集、四维Iris真实数据集和景区轨迹停留点三种不同的数据进行了DBSCAN聚类分析及对比实验,结果表明该方法可以自动产生合理的聚簇划分,优于传统DBSCAN和k-means等算法...

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, DBS...