帮助 关于我们

返回检索结果

基于滑动窗口的手机定位数据个体停留区域识别算法
Detecting Individual Stay Areas from Mobile Phone Location Data Based on Moving Windows

查看参考文献32篇

林楠 1,2   尹凌 1 *   赵志远 1,3  
文摘 手机的普及使手机定位数据成为分析个体时空行为特征的新兴重要数据源之一,并被逐渐应用到人口管理、城市规划、交通分析和流行病防控等众多领域的研究中。从手机定位数据中识别个体的停留区域是众多基于手机定位数据研究的重要基础环节。然而,当前常用的手机定位数据定位精度相对较低,且往往存在定位震荡和定位漂移导致的数据噪声,这些因素增加了从手机定位数据中识别停留区域的难度。为了提高从手机定位数据中识别个体停留区域的准确性,本研究结合个体行为的时空连续性,提出了一种基于滑动窗口的增长聚类算法。实验结果显示,相较常用的ST-DBSCAN算法和SMoT算法,对于采样时间间隔稀疏的手机定位数据,本研究提出的滑动窗口聚类算法在准确率方面的提升幅度最大可以达到35%。由于隐私问题,当前研究和应用中使用的大规模手机定位数据集中的时间分辨率往往较低,因此,本研究提出的滑动窗口聚类算法具有较为广泛的应用场景,可增强基于手机用户停留区域的众多研究结果的可靠性,为手机定位数据的广泛合理应用提供关键技术支撑。
其他语种文摘 With the development and popularization of mobile phones, mobile phone location data have become an important source of data for analyzing individual mobility characteristics. With these location data, many studies can be performed at a fine spatiotemporal scale in fields such as population management, urban planning, transportation analysis and health intervention. Detection of individual stay areas is an important and basic step in many studies based on mobile phone location data. However, the sparse spatial and temporal resolution of raw mobile phone location data and data noise caused by location oscillation and location drift introduce great challenges in effectively detecting individual stay areas from raw mobile phone location data. Considering the spatiotemporal continuity of individual behavior, this study proposes an incremental clustering algorithm based on a moving window to improve the accuracy of detecting individual stay areas from mobile phone location data. Specifically, the proposed algorithm first sorts the raw records in chronological order. Then, the algorithm consecutively examines the adjacent records with a given distance threshold. Records that satisfy the rule will be added to the current cluster. For each unqualified record, the algorithm extracts a series of records within a moving window and calculates the spatial distance of these records as a criterion for clustering. The time interval between the unqualified record and the selected records should be less than a given time threshold, which is also the width of the moving window in this proposed algorithm. In this step, the algorithm treats some unqualified records as location drift records or location oscillation records based on the detection rules and aggregates them into the current cluster, and unqualified records that do not fit the detection rules are excluded from the current cluster and the algorithm creates a new cluster for the unqualified records. Finally, the algorithm calculates the location and temporal information of each valid cluster as the parameters of the corresponding stay area and constructs a stay area sequence for each mobile user. We compared the results of the proposed algorithm with those obtained using the ST-DBSCAN and SMoT algorithms. The experiment applied the three algorithms to a mobile phone location dataset in Shenzhen that is a type of Call Detail Records, and the results show that the proposed algorithm significantly improves the accuracy by up to 35% for detecting individual stay areas from sparse mobile phone location data compared to the other two algorithms. Due to privacy issues associated with the government or telecom operators, the temporal resolution of large-scale mobile phone location data used in recent research is usually sparse, and thus the proposed algorithm can be used to improve the effectiveness of detecting individual stay areas and to provide reliable results for many studies based on mobile phone location data.
来源 地球信息科学学报 ,2018,20(6):762-771 【核心库】
DOI 10.12082/dqxxkx.2018.180087
关键词 手机定位数据 ; 数据噪声 ; 轨迹分析 ; 聚类 ; 停留区域识别
地址

1. 中国科学院深圳先进技术研究院, 深圳, 518055  

2. 中国科学院大学, 北京, 100049  

3. 武汉大学, 测绘遥感信息工程国家重点实验室, 武汉, 430079

语种 中文
文献类型 研究性论文
ISSN 1560-8999
学科 电子技术、通信技术;自动化技术、计算机技术
基金 国家自然科学基金 ;  深圳市科技创新委基础研究项目 ;  广东省自然科学基金
文献收藏号 CSCD:6258641

参考文献 共 32 共2页

1.  中国工业和信息化部. 2017年通信运营业统计公报,2018 CSCD被引 1 次    
2.  Zheng Y. Trajectory data mining: An overview. ACM Transactions on Intelligent Systems and Technology,2015,6(3):29 CSCD被引 111 次    
3.  Yue Y. Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies. Travel Behaviour & Society,2014,1(2):69-78 CSCD被引 14 次    
4.  刘瑜. 社会感知视角下的若干人文地理学基本问题再思考. 地理学报,2016,71(4):564-575 CSCD被引 49 次    
5.  郑宇. 城市计算概述. 武汉大学学报·信息科学版,2015,40(1):1-13 CSCD被引 46 次    
6.  陆锋. 大数据时代的人类移动性研究. 地球信息科学学报,2014,16(5):665-672 CSCD被引 56 次    
7.  Schneider C M. Unravelling daily human mobility motifs. Journal of the Royal Society Interface,2013,10(84):20130246 CSCD被引 19 次    
8.  Phithakkitnukoon S. Activity-aware map: Identifying human daily activity pattern using mobile phone data. Human Behavior Understanding, First International Workshop, HBU 2010, Istanbul, Turkey, August 22, 2010. Proceedings,2010:14-25 CSCD被引 1 次    
9.  Pei T. A new insight into land use classification based on aggregated mobile phone data. International Journal of Geographical Information Science,2014,28(9):1988-2007 CSCD被引 38 次    
10.  尹凌. 利用手机通话位置数据估计城市24 h人口分布误差. 地球信息科学学报,2017,19(6):763-771 CSCD被引 3 次    
11.  Calabrese F. Estimating origin-destination flows using mobile phone location data. IEEE Pervasive Computing,2011,10(4):36-44 CSCD被引 20 次    
12.  Fang Z X. Spatiotemporal model for assessing the stability of urban human convergence and divergence patterns. International Journal of Geographical Information Science,2017,31(11):2119-2141 CSCD被引 10 次    
13.  Brdar S. Unveiling spatial epidemiology of HIV with mobile phone data. Scientific reports,2016,6:19342 CSCD被引 1 次    
14.  Isdory A. The impact of human mobility on HIV transmission in kenya. Plos One,2015,10(11):e0142805 CSCD被引 2 次    
15.  Mao L. Mapping intra-urban transmission risk of dengue fever with big hourly cellphone data. Acta Tropica,2016,162:188-195 CSCD被引 2 次    
16.  Spaccapietra S. A conceptual view on trajectories. Data & Knowledge Engineering,2008,65(1):126-146 CSCD被引 36 次    
17.  Zheng Y. GeoLife2.0: A Location-Based Social Networking Service. Tenth International Conference on Mobile Data Management: Systems, Services and Middleware,2009:357-358 CSCD被引 1 次    
18.  Zheng Y. GeoLife: A collaborative social networking service among user, location and trajectory. Bulletin of the Technical Committee on Data Engineering,2011,33(2):32-39 CSCD被引 6 次    
19.  Bao J. Recommendations in location-based social networks: A survey. Geoinformatica,2015,19(3):525-565 CSCD被引 36 次    
20.  Lian D F. Mining check-in history for personalized location naming. Acm Transactions on Intelligent Systems & Technology,2014,5(2):1-25 CSCD被引 2 次    
引证文献 5 篇

1 王培晓 ST-CFSFDP:快速搜索密度峰值的时空聚类算法 测绘学报,2019,48(11):1380-1390
CSCD被引 4 次

2 杨鸿海 西宁市居民工作日和休息日时空活动差异研究 地理与地理信息科学,2022,38(1):57-62
CSCD被引 3 次

显示所有5篇文献

论文科学数据集
PlumX Metrics
相关文献

 作者相关
 关键词相关
 参考文献相关

版权所有 ©2008 中国科学院文献情报中心 制作维护：中国科学院文献情报中心
地址：北京中关村北四环西路33号 邮政编码：100190 联系电话：(010)82627496 E-mail:cscd@mail.las.ac.cn 京ICP备05002861号-4 | 京公网安备11010802043238号