摘要
移动数据描述了大量的关于移动对象活动位置和时间变化的序列,反映出城市动态规划的语义知识。发现移动对象活动的异常区域,是发现移动对象时序变化的关键分析前提。因此,针对移动对象的活动轨迹分别从时间和空间的角度进行了研究,首先,从空间区域语义知识的角度分析,利用网格对移动对象的活动区域进行划分,并结合核函数和Top-k排序方法发现异常区域;接着,从时间角度分析,采用基于二进制序列的方法,发现移动对象活动周期;最后,在真实数据集上,验证了该方法的可行性和有效性。
Mobile data shows a large number of the changing sequence of location and time about the moving objects, reflec- ting semantic knowledge about the city dynamic planning. The discovery of anomalous regions visited by moving objects was a critical premise for the discovery of temporal changes about moving objects. Thus, this paper analyzed respectively the move- ment trajectories of moving objects from the temporal and the spatial. Firstly, from the perspective of spatial regional semantic knowledge, the grid divided the moving objects＇ entire activity space and then to find the anomalous regions by using the kernel function and Top-k sorting method. Secondly, from the perspective of the temporal, the paper proposed a method based on binary sequence to find activity periods of moving objects. Finally, experimental results validate accurately the feasibility and effectiveness of the above methods on the practical data sets.
出处
《计算机应用研究》
CSCD
北大核心
2017年第2期431-435,共5页
Application Research of Computers
基金
国家自然科学基金资助项目(61003031)
上海重点科技攻关资助项目(14511107902)
上海市工程中心建设资助项目(GCZX14014)
上海市一流学科建设资助项目(XTKX2012)
沪江基金研究基地专项资助项目(C14001)
关键词
移动数据
异常区域
网格索引
核函数
傅里叶变换
活动周期
mobile data
anomalous region
grid indexing
kernel function
Fourier transform
activity period
