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. 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 Cheng1,3(), CHEN Yuanyuan2, YANG Min4,5,*()   

  1. 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:

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