基于密度的轨迹时空聚类分析
Density-Based Spatiotemporal Clustering Analysis of Trajectories
查看参考文献23篇
|
文摘
|
通过轨迹聚类分析挖掘物体移动模式的空间分布和时间特征,对于认识运动的形成机制,预测运动的未来发展具有重要的意义。目前,轨迹聚类研究主要关注物体的空间位置变化,时空聚类中时间约束一般只是作为辅助信息,并不真正参与聚类。本文提出基于密度的轨迹时空聚类方法,在聚类过程中同时考虑轨迹包含的时空信息,在空间聚类的基础上提出了轨迹线段时间距离的度量方法和阈值确定原则,对时空邻域密度进行聚类分析,挖掘物体的时空移动模式。实验对南海涡旋轨迹进行时空聚类分析,得到了涡旋典型移动模式的空间分布和时间特征,验证了基于密度的轨迹时空聚类方法的有效性。加入时间约束后,移动通道主要发生缩短、分裂和消失的变化。和空间聚类相比,轨迹时空聚类可有效地划分发生在同一位置不同时间的轨迹,得到的聚类结果更加细化,移动模式更加准确,有利于物体的移动模式做更深入的分析。 |
|
其他语种文摘
|
Trajectory clustering, which aims to uncover the meaningful spatial distributions and temporal variations of moving objects, is of much importance in understanding potential dynamic mechanisms and predicting future development. However, placing many focuses on locational changes, many studies have made limited use of the time dimension in trajectories. This paper presents a density-based clustering method, which integrates time and space information in identifying significant migrating paths from trajectory datasets. Definition of temporal distances between any line segments decomposed from trajectories as well as the criterion of distance threshold selection is provided in detail. The experiments conducted on ocean eddies in the South China Sea demonstrate the effectiveness of this method in obtaining spatiotemporal migrating patterns. The migrating paths in the results are shortened, or separated into parts, or they turn insignificant as the effect of including time component in density clustering, which reveal more specific movement characteristics in the temporal domain covered by spatial clustering. This advantage facilitates the analysis of objects moving along the same path while displaying distinct time patterns. |
|
来源
|
地球信息科学学报
,2015,17(10):1162-1171 【核心库】
|
|
DOI
|
10.3724/SP.J.1047.2015.01162
|
|
关键词
|
轨迹聚类
;
时空数据挖掘
;
涡旋
;
南海
|
|
地址
|
中国科学院地理科学与资源研究所, 资源与环境信息系统国家重点实验室, 北京, 100101
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1560-8999 |
|
学科
|
测绘学 |
|
基金
|
国家自然科学基金项目
|
|
文献收藏号
|
CSCD:5524561
|
参考文献 共
23
共2页
|
|
1.
Han J. An overview of clustering methods in geographic data analysis.
Geographic data mining and knowledge discovery,2009:149-187
|
被引
3
次
|
|
|
|
|
2.
Jeung H. Trajectory pattern mining.
Computing with spatial trajectories,2011:143-177
|
被引
7
次
|
|
|
|
|
3.
Dykes J A. Seeking structure in records of spatio-temporal behaviour: visualization issues, efforts and applications.
Computational Statistics & Data Analysis,2003,43(4):581-603
|
被引
2
次
|
|
|
|
|
4.
Camargo S J. Cluster analysis of western North Pacific tropical cyclone tracks.
The 26th conference on hurricanes and tropical meteorology,2004:250-251
|
被引
1
次
|
|
|
|
|
5.
Lee J G. Trajectory clustering: a partition-and-group framework.
Proceedings of the the 2007 ACM SIGMOD international conference on Management of data,2007:593-604
|
被引
1
次
|
|
|
|
|
6.
Benkert M. Finding Popular Places.
Algorithms and Computation,2007:776-787
|
被引
1
次
|
|
|
|
|
7.
Shaw S L. A space-time GIS approach to exploring large individual-based spatiotemporal datasets.
Transactions in GIS,2008,12(4):425-441
|
被引
7
次
|
|
|
|
|
8.
Shoshany M. Evolution of clusters in dynamic point patterns: with a case study of Ants' simulation.
International Journal of Geographical Information Science,2007,21(7):777-797
|
被引
1
次
|
|
|
|
|
9.
D'auria M. Time-focused density-based clustering of trajectories of moving objects.
Proceedings of the Workshop on Mining Spatio-Temporal Data (MSTD-2005),2005:14
|
被引
1
次
|
|
|
|
|
10.
Nanni M. Time-focused clustering of trajectories of moving objects.
Journal of Intelligent Information Systems,2006,27(3):267-289
|
被引
36
次
|
|
|
|
|
11.
Spaccapietra S. A conceptual view on trajectories.
Data & Knowledge Engineering,2008,65(1):126-146
|
被引
36
次
|
|
|
|
|
12.
Pei T. Windowed nearest neighbour method for mining spatio-temporal clusters in the presence of noise.
International Journal of Geographical Information Science,2010,24(6):925-948
|
被引
13
次
|
|
|
|
|
13.
Giannotti F. Trajectory pattern mining.
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining,2007:330-339
|
被引
24
次
|
|
|
|
|
14.
Tan P N. Data.
Introduction to data mining,2006:5-18
|
被引
1
次
|
|
|
|
|
15.
Shannon C E. A mathematical theory of communication.
Bell System Technical Journal,1948,27(3):379-423
|
被引
1913
次
|
|
|
|
|
16.
Yi J. Enhancing the accuracy of automatic eddy detection and the capability of recognizing the multi-core structures from maps of sea level anomaly.
Ocean Science,2014,10(1):39-48
|
被引
13
次
|
|
|
|
|
17.
Cai S. The numerical study of the South China Sea upper circulation characteristics and its dynamic mechanism, in winter.
Continental Shelf Research,2002,22(15):2247-2264
|
被引
5
次
|
|
|
|
|
18.
Metzger E J. The nondeterministic nature of kuroshio penetration and eddy shedding in the South China Sea.
Journal of Physical Oceanography,2001,31(7):1712-1732
|
被引
39
次
|
|
|
|
|
19.
Su J. Overview of the South China Sea circulation and its influence on the coastal physical oceanography outside the Pearl River Estuary.
Continental Shelf Research,2004,24(16):1745-1760
|
被引
28
次
|
|
|
|
|
20.
Yuan D. Anti-cyclonic eddies northwest of Luzon in summer-fall observed by satellite altimeters.
Geophysical Research Letters,2007,34(L13610)
|
被引
3
次
|
|
|
|
|
了解气象卫星更多信息，请访问