基于中国观鸟数据的移动对象周期模式发现

Periodic Pattern Discovery of Moving Objects Based on China Birding Data

移动对象的轨迹数据中包含大量时空信息,挖掘时空信息背后隐藏的周期模式对掌握移动对象变化规律具有重要作用。为此,提出一种三阶段移动对象周期模式检测算法,通过研究轨迹点的时空特征识别并剔除重复数据,利用密度聚类算法发现轨迹点密集区域并找出密集区域中每一类移动对象的周期模式,解决移动对象轨迹周期模式挖掘中轨迹数据重复、采样数据不连续及潜在周期模式发现问题。基于2003年—2015年中国观鸟记录中心、中国观鸟年报等公开数据的实验结果表明,该算法可有效处理轨迹数据并准确挖掘出规律性移动对象的周期模式。

The trajectory data of moving objects contains a large amount of spatio-temporal information,and mining the periodic pattern hidden behind the spatio-temporal information is of great significance. In this paper, an algorithm for detecting the periodic pattern of the moving objects based on three stages is proposed. Through the study of the temporal and spatial characteristics of the trajectory points,it identifies and eliminates duplicate data. Density clustering algorithm is used to find the dense region of the locus and the periodic pattern of each moving object in the dense region, which solves the problem of the repetition of the trajectory data, the incontinuity of sampling data and the finding of the periodic pattern period of the moving objects. Experimental results based on 2003--2015 China birding record center, China Birding Report（CBR） and other public data show that this algoithm can process the trajectory data effectively and dig out the periodic pattern of the moving objects with regularity accurately.