面向轨迹起止特征点数据的多比例尺可视化方法

doi:10.3724/SP.J.1047.2017.01011

地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (8): 1011-1018.doi: 10.3724/SP.J.1047.2017.01011

• 地球信息科学理论与方法 • 上一篇下一篇

面向轨迹起止特征点数据的多比例尺可视化方法

金澄^1,³(), 陈瑗瑗², 杨敏^4,^5,^*()

1. 信息工程大学,郑州 450001
2. 北京大学遥感与地理信息系统研究所,北京 100871
3. 西安测绘研究所,西安 710054
4. 国土资源部城市土地资源监测与仿真重点实验室,深圳 518034
5. 武汉大学资源与环境科学学院,武汉 430079

收稿日期:2017-01-05 修回日期:2017-05-17 出版日期:2017-08-20 发布日期:2017-08-20
通讯作者: 杨敏 E-mail:jinchengno1@163.com;yangmin2003@whu.edu.cn
作者简介:
作者简介：金澄（1976-）,男,高级工程师,研究方向为地理信息服务。E-mail: jinchengno1@163.com
基金资助:
国家自然科学基金项目（41401447）;国土资源部城市土地资源监测与仿真重点实验室开放基金项目（KF-2016-02-020）;国家高技术研究发展计划（“863”）项目（2015AA124103）

A Multi-scale Visualization Method for the Trajectory Origin-Destination Data

JIN Cheng^1,³(), CHEN Yuanyuan², YANG Min^4,^5,^*()

1. Information Engineering University, Zhengzhou 450001, China
2. Institute of Remote Sensing & Geographical Information System, Peking University, Beijing 100871, China
3.Xi′an Research Institute of Surveying and Mapping, Xi’an 710054, China
4. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China
5. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China

Received:2017-01-05 Revised:2017-05-17 Online:2017-08-20 Published:2017-08-20
Contact: YANG Min E-mail:jinchengno1@163.com;yangmin2003@whu.edu.cn

摘要/Abstract

摘要：

本研究以北京市出租车GPS轨迹数据为例,建立了一种面向轨迹起止特征点(Origin-Destination, OD)的多比例尺可视化表达方法。首先,依据轨迹点描述信息提取OD特征点,并进行无效点清理与排除;然后,利用分布密度指标和辅助行政区划数据实施聚类分析,对OD数据分布空间进行区域划分;最后,定义参量统计各区域间OD数据隐含的流向特征,并设计专门符号进行可视化。其中,通过调整最小区域面积控制参数建立与街区、商圈、城区等不同层次地理单元相对应的区域划分,从而获得涵盖3种不同级别的OD数据多比例尺表达结果。试验结果表明,本文提出的方法能够对轨迹OD数据进行有效降维,获取不同尺度下区域间的车辆移动关系,对揭示车流人流时空交互模式及辅助决策有参考意义。

关键词: 轨迹数据, 多比例尺表达, 流向特征, 聚类分区

Abstract:

Based on the taxi trajectory data from the city of Beijing, this study proposes a multi-scale visualization approach for trajectory OD (Origin-Destination) data. First, we extract OD points from initial trajectory raw data eliminating invalid points. Then, the distribution space of OD data is subdivided by density analysis and administrative unit aggregation. Finally, we define relevant parameters to summarize inherent OD flow pattern and customize their presentation of multi-scale visualization. In the process above, three regionalization results, which correspond to block level, business district level and district level, are obtained by setting different values of the minimal area of the aggregated region. Therefore, representations at three different scales can be outputted. The experimental results confirmed that our method could effectively achieve the reduction of trajectory big data and reveal mobility pattern, which is helpful for future decision making.

Key words: trajectory data, multi-scale visualization, flow pattern, clustering and regionalization

金澄, 陈瑗瑗, 杨敏. 面向轨迹起止特征点数据的多比例尺可视化方法[J]. 地球信息科学学报, 2017, 19(8): 1011-1018.DOI:10.3724/SP.J.1047.2017.01011

JIN Cheng,CHEN Yuanyuan,YANG Min. A Multi-scale Visualization Method for the Trajectory Origin-Destination Data[J]. Journal of Geo-information Science, 2017, 19(8): 1011-1018.DOI:10.3724/SP.J.1047.2017.01011

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参考文献 21

[1]	陆锋,刘康,陈洁.大数据时代的人类移动性研究[J].地球信息科学学报,2014,16(5):665-672. doi: 10.3724/SP.J.1047.2014.00665
	[ Lu F, Liu K, Chen J.Research on human mobility in big data era[J]. Journal of Geo-information Science, 2014,16(5):665-672. ] doi: 10.3724/SP.J.1047.2014.00665
[2]	Zheng Y, Xie X, Ma W Y.GeoLife: A collaborative social networking service among user, location and trajectory[J]. IEEE Data Engineering Bulletin, 2010,33(2):32-39.
[3]	龙瀛,张宇,崔承印.利用公交刷卡数据分析北京职住关系和通勤出行[J].地理学报, 2012,67(10):1339-1352. doi: 10.11821/xb201210005
	[ Long Y, Zhang Y, Cui C Y. Identifying commuting pattern of Beijing using bus smart card data[J]. Acta Geographica Sinica, 2012,67(10):1339-1352. ] doi: 10.11821/xb201210005
[4]	Andrienko G, Andrienko N, Fuchs G.Understanding movement data quality[J]. Journal of location Based services, 2016,10(1):31-46. doi: 10.1080/17489725.2016.1169322
[5]	Yue Y, Zhuang Y, Li Q Q, et al.Mining time-dependent attractive areas and movement patterns from taxi trajectory data[C]. Proceedings of the 17th International Conference on Geoinformatics. 2009:1-6.
[6]	Zhang W S, Li S J, Pan G.Mining the semantics of origin-destination flows using taxi traces[C]. Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 2012:943-949.
[7]	Guo D S, Zhu X, Jin H, et al.Discovering spatial patterns in origin-destination mobility data[J]. Transactions in GIS, 2012,16(3):411-429. doi: 10.1111/j.1467-9671.2012.01344.x
[8]	Pan G, Qi G D, Wu Z H, et al.Land-use classification using taxi GPS traces[J]. IEEE Transactions on Intelligent Transportation Systems, 2013,14(1):113-123. doi: 10.1109/TITS.2012.2209201
[9]	Phan D, Xiao L, Yeh R, et al.Flow Map Layout[C]. IEEE Symposium on information visualization. 2005:219-224.
[10]	Tobler W R.Spatial Interaction Patterns[J]. Journal of Environmental Systems, 1976,6(4):271-301. doi: 10.2190/VAKC-3GRF-3XUG-WY4W
[11]	Tobler W R.Experiments in Migration Mapping by Computer[J]. American Cartographer, 1987,14:155-163. doi: 10.1559/152304087783875273
[12]	Guo D, Zhu X.Origin-destination flow data smoothing and mapping[J]. IEEE transactions on visualization and computer graphics, 2014,20(12):2043-2052. doi: 10.1109/TVCG.2014.2346271 pmid: 26356918
[13]	Ullman E L, Dacey M F.The minimum requirements approach to the urban economic base[J]. Papers in Regional Science, 1960,6(1):175-94. doi: 10.1111/j.1435-5597.1960.tb01712.x
[14]	Corcoran J, Chhetri P, Stimson R.Using circular statistics to explore the geography of the journey to work[J]. Papers in Regional Science, 2009,88(1):119-32 doi: 10.1111/j.1435-5957.2008.00164.x
[15]	Adrienko N, Adrienko G.Spatial generalization and aggregation of massive movement data[J]. IEEE Transactions on visualization and computer graphics, 2011,17(2): 205-219. doi: 10.1109/TVCG.2010.44 pmid: 21149876
[16]	Guo D.Flow mapping and multivariate visualization of large spatial interaction data[J]. IEEE Transactionson Visualization and Computer Graphics, 2009,15:1041-48. doi: 10.1109/TVCG.2009.143 pmid: 19834170
[17]	Zhu X, Guo D.Mapping large spatial flow data with hierarchical clustering[J]. Transactions in GIS, 2014,18(3):421-435. doi: 10.1111/tgis.12100
[18]	王亮,胡琨元,库涛,等.基于多尺度空间划分与路网建模的城市移动轨迹模式挖掘[J].自动化学报,2015,41(1):47-58. doi: 10.16383/j.aas.2015.c130804
	[ Wang L, Hu K Y, Ku T, et al.Mining urban moving trajectory patterns based on multi-scale space partition and road network modeling[J]. Acta Automatica Sinica, 2015,41(1):47-58. ] doi: 10.16383/j.aas.2015.c130804
[19]	Cui W, Zhou H, Qu H, et al.Geometry-based edge clustering for graph visualization[J]. IEEE Transactions on Visualization and Computer Graphics, 2008,14:1277-84. doi: 10.1109/TVCG.2008.135 pmid: 18988974
[20]	Holten D, van Wijk J J. Force-directed edge bundling for graph visualization[J]. Computer Graphics Forum, 2009,28(3):983-90. doi: 10.1111/j.1467-8659.2009.01450.x
[21]	艾廷华. 大数据驱动下的地图学发展[J].测绘地理信息,2016,41(2):1-7. doi: 10.14188/j.2095-6045.2016.02.001
	[ AI T.Development of cartography driven by big data[J]. Journal of Geomatics, 2016,41(2):1-7. ] doi: 10.14188/j.2095-6045.2016.02.001

字段名称	数据说明	数据示例
V_ID	车辆标识	206400
Longitude	GPS经度/°	116.4243011
Latitude	GPS纬度/°	40.0727348
Time	GPS时间	20121101095636
Event	触发事件（0=变空车,1=变载客,3=其它）	1
SerState	运营状态（0=空车,1=载客,2=驻车,3=停运,4=其他）	1
Speed	GPS速度/（km/h）	43
GPSState	GPS状态（0=无效,1=有效）	1

面向轨迹起止特征点数据的多比例尺可视化方法

A Multi-scale Visualization Method for the Trajectory Origin-Destination Data

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