基于密度的停留点识别方法

doi:10.11959/j.issn.2096-0271.2018052

大数据 ›› 2018, Vol. 4 ›› Issue (5): 80-93.doi: 10.11959/j.issn.2096-0271.2018052

基于密度的停留点识别方法

李毓瑞,陈红梅(),王丽珍,肖清

云南大学信息学院，云南昆明 650091

出版日期:2018-09-15 发布日期:2018-12-10
作者简介:李毓瑞（1989-），男，云南大学信息学院硕士生，主要研究方向为空间数据挖掘。|陈红梅（1976-），女，博士，云南大学信息学院副教授，主要研究方向为数据挖掘、空间数据挖掘等。|王丽珍（1962-），女，博士，云南大学信息学院教授，博士生导师，主要研究方向为数据库、数据挖掘、计算机算法等。|肖清（1975-），女，云南大学信息学院讲师，主要研究方向为数据挖掘、空间数据挖掘等。
基金资助:
国家自然科学基金资助项目(61662086);国家自然科学基金资助项目(61472346);国家自然科学基金资助项目(61762090);云南省自然科学基金资助项目(2015FB14);云南省自然科学基金资助项目(2016FA026);云南大学“东陆中青年骨干教师”培养计划(WX069051)

Stay point identification based on density

Yurui LI,Hongmei CHEN(),Lizhen WANG,Qing XIAO

School of Information Science and Engineering,Yunnan University,Kunming 650091,China

Online:2018-09-15 Published:2018-12-10
Supported by:
The National Natural Science Foundation of China(61662086);The National Natural Science Foundation of China(61472346);The National Natural Science Foundation of China(61762090);The National Natural Science Foundation of Yunnan Province(2015FB14);The National Natural Science Foundation of Yunnan Province(2016FA026);The Program for Young and Middle-aged Skeleton Teachers of Yunnan University(WX069051)

摘要/Abstract

摘要：

从GPS轨迹点序列中识别停留点，是轨迹分析的重要预处理步骤，是用户行为分析、个性化兴趣点推荐等位置服务的基础，停留点识别方法的识别能力对位置服务的可用性和可靠性有根本性的影响。针对现有方法未考虑轨迹点的时间连续性或仅考虑时间连续性的一个方向所导致的停留点识别能力不足的问题，提出一种新的基于密度的停留点识别方法。该方法考虑了轨迹点的时空聚集，兼顾了轨迹点的时间连续性和方向性。在GeoLife数据集上的实验结果验证了该方法的识别能力强于基准方法，可以进一步识别基准方法不能识别的两类停留点。

关键词: 停留点；密度, 时间连续性, 时间方向性

Abstract:

Identifying stay points from GPS trajectory is an important preprocessing procedure of trajectory analysis and the foundation of location based service such as user behavior analysis and personal POI recommendation,and the capability of the stay point identification method has a fundamental impact on the availability and reliability of location based service.Existing methods for identifying stay points have some shortcomings due to not considering time continuity or only considering one direction of time continuity.A new method called stay point identification based on density (SPID) was proposed.SPID takes into account the spatial-temporal clustering of trajectory points,and the time directions and time continuity of trajectory points.The experimental results on Geolife dataset verify that SPID is better than the baseline methods,and can identify two kinds of stay points which can’t be found by the baseline methods.

Key words: stay point, density, time continuity, time direction

中图分类号:

TP311.13

李毓瑞, 陈红梅, 王丽珍, 肖清. 基于密度的停留点识别方法[J]. 大数据, 2018, 4(5): 80-93.

Yurui LI, Hongmei CHEN, Lizhen WANG, Qing XIAO. Stay point identification based on density[J]. Big Data Research, 2018, 4(5): 80-93.

图/表 11

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参考文献 22

[1]	HERDER E , SIEHNDEL P , KAWASE R . Predicting user locations and trajectories[M]. Heidelberg: Springer International PublishingPress, 2014: 86-97.
[2]	YANG Y , ZHENG Y , CHEN Y K ,et al. Mining individual life pattern based on location history[C]// The 2009 10th International Conference on Mobile Data Management:Systems,Services and Middleware,May 18-20,2009,Taipei,China. Washington,DC:IEEE Computer Society, 2009: 1-10.
[3]	HORANONT T , PHITHAKKITNUKOON S , SHIBASAKI R . Sensing urban densityusing mobile phone gps locations:a case study of Odaiba Area,Japan[M]// Nature of Computation and Communication. Heidelberg:Springer International Publishing, 2014: 146-155.
[4]	ZHENG V W , ZHENG Y , XIE X ,et al. Collaborative location and activity recommendations with GPS history data[C]// The 19th International Conference on World Wide Web,April 26-30,2010,Raleigh,USA. New York:ACM Press, 2010: 1029-1038.
[5]	LEE C , YOON G , HAN D . A probabilistic place extraction algorithm based on a superstate model[J]. IEEE Transactions on Mobile Computing, 2013,12(5): 945-956.
[6]	ESTER M , KRIEGEL H P , XU X W . A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise[C]// The 2nd International Conference on Knowledge Discovery and Data Mining,August 2-4,1996,Portland,Oregon. Palo Alto:AAAI Press, 1996: 226-231.
[7]	ZHOU C Q , FRANKOWSKI D , LUDFORD P ,et al. Discovering personally meaningful places[J]. ACM Transactions on Information Systems, 2007,25(3):12.
[8]	PALMA A T , BOGORNY V , KUIJPERS B ,et al. A clustering-based approach for discovering interesting places in trajectories[C]// The 2008 ACM Symposium on Applied Computing,March 16-20,2008,Fortaleza,Brazil. New York:ACM Press, 2008: 863-868.
[9]	ZHANG K S , LI H F , TORKKOLA K ,et al. Adaptive learning of semantic locations and routes[C]// The 3rd International Conference on Location-And ContextAwareness,September 20-21,2007,Oberpfaffenhofen,Germany. Heidelberg:Springer-Verlag, 2007: 193-210.
[10]	NURMI P , BHATTACHARYA S . Identifying meaningful places:the non-parametric way[C]// The 6th International Conference on Pervasive Computing,May 19-22,2008,Sydney,Australia. Heidelberg:Springer-Verlag, 2008: 111-127.
[11]	LIAO Z X , LI S C , PENG W C ,et al. On the feature discovery for App usage prediction in smartphones[C]// The 13th International Conference on Data Mining,December 7-10,2013,Dallas,USA. Piscataway:IEEE Press, 2013: 1127-1132.
[12]	HARIHARAN R , TOYAMA K . Project lachesis:parsing and modeling location histories[C]// Geographic Information Science,Third International Conference,GIScience 2004,October 20-23,2004,Adelphi,USA. Heidelberg:Springer, 2004: 106-124.
[13]	LIU J H , WOLFSON O , YIN H B . Extracting semantic location from outdoor positioning systems[C]// The 7th International Conference on Mobile Data Management,May 10-12,2006,Nara,Japan. Washington,DC:IEEE Computer Society, 2006:73.
[14]	LI Q N , ZHENG Y , XIE X ,et al. Mining user similarity based on location history[C]// The 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems,November 5-7,2008,Irvine,USA. New York:ACM Press, 2008: 1-10.
[15]	PéREZTORRES R , TORRES-HUITZIL C , GALEANAZAPIéN H . Full ondevice stay points detection in smartphones for location-based mobile applications[J]. Sensors, 2016,16(10):1693.
[16]	PAVAN M , MIZZARO S , SCAGNETTO I ,et al. Finding important locations:a feature-based approach[C]// IEEE International Conference on Mobile Data Management,June 15-18,2015,Pittsburgh,USA. Washington,DC:IEEE Computer Society, 2015: 110-115.
[17]	ASHBROOK D , STARNER T . Using GPS to learn significant locations and predict movement across multiple users[J]. Personal ＆ Ubiquitous Computing, 2003,7(5): 275-286.
[18]	TOYAMA N , OTA T , KATO F ,et al. Exploiting multiple radii to learn significant locations[C]// The 1st International Workshop,International Symposium on Location- and ContextAwareness,May 12-13,2005,Oberpfaffenhofen,Germany. Heidelberg:Springer, 2005: 157-168.
[19]	ZIMMERMANN M , KIRSTE T , SPILIOPOULOU M . Finding stops in error-prone trajectories of moving objects with time-based clustering[C]// International Conference on Intelligent Interactive Assistance and Mobile Multimedia Computing,November 9-11,2009,Rostock-Warnemünde,Germany. Heidelberg:Springer, 2009: 275-286.
[20]	CAO X , CONG G , JENSEN C S . Mining significant semantic locations from GPS data[J]. Proceedings of the Vldb Endowment, 2010,3(1): 1009-1020.
[21]	ZHANG K S , LI H F , TORKKOLA K ,et al. Adaptive learning of semantic locations and routes[C]// The 3rd International Conference on LocationAnd Context-Awareness,September 20-21,2007,Oberpfaffenhofen,Germany. Heidelberg:Springer Verlag, 2007: 193-210.
[22]	LIAO L , FOX D , KAUTZ H . Extracting places and activities from gps traces using hierarchical conditional random fields[J]. International Journal of Robotics Research, 2007,26(1): 119-134.

序号	纬度	经度	时间
0	40.007 219	116.319 595	2009-05-18 06:46:48
…	…	…	…
209	39.991 537	116.331 743	2009-05-18 08:29:04
210	39.991 524	116.331 918	2009-05-18 08:29:09
211	39.991 540	116.332 044	2009-05-18 08:29:14
…	…	…	…
219	39.991 312	116.332 921	2009-05-18 08:29:54
220	39.991 304	116.332 948	2009-05-18 08:29:59
221	39.991 312	116.332 960	2009-05-18 08:30:04
…	…	…	…
256	39.991 530	116.331 946	2009-05-18 09:19:06
257	39.991 562	116.331 887	2009-05-18 09:19:11
258	39.991 557	116.331 789	2009-05-18 09:19:16
…	…	…	…
409	40.001 156	116.326 798	2009-05-18 09:37:51

锚点	起点	终点
220	210	257
…	…	…
242	212	257
…	…	…
367	358	404
…	…	…
69	60	98
…	…	…
101	74	106

锚点	起点	终点
367	358	404
…	…	…
69	60	98
…	…	…
101	74	106

代表点	坐标	停留时间	密度区间
p₂₂₀	<39.991 304,116.332 948>	2009-05-18 08:29:9,2009-05-18 09:19:11]	210,257]
p₃₆₇	<39.999 844,116.326 921>	2009-05-18 09:27:32,2009-05-18 09:37:26]	358,404]
p₆₉	<40.000 392,116.326 610>	2009-05-18 06:51:48,2009-05-18 08:17:54]	60,98]

基于密度的停留点识别方法

Stay point identification based on density

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