地理科学进展 ›› 2020, Vol. 39 ›› Issue (8): 1397-1411.doi: 10.18306/dlkxjz.2020.08.013

• 研究综述 • 上一篇    下一篇

基于大数据的流空间研究进展

杨延杰1,2(), 尹丹1,2, 刘紫玟1,2, 黄庆旭1,2,*(), 何春阳1,2, 吴康3   

  1. 1. 北京师范大学地表过程与资源生态国家重点实验室,人与环境系统可持续研究中心,北京 100875
    2. 北京师范大学地理科学学部自然资源学院,土地资源与区域发展研究中心,北京 100875
    3. 首都经济贸易大学城市群系统演化与可持续发展的决策模拟研究北京市重点实验室,北京 100070
  • 收稿日期:2019-07-15 修回日期:2019-10-31 出版日期:2020-08-28 发布日期:2020-10-28
  • 通讯作者: 黄庆旭
  • 作者简介:杨延杰(1992— ),男,山西太原人,硕士生,主要从事大数据和城市景观过程模拟研究。E-mail: yjyang@mail.bnu.edu.cn
  • 基金资助:
    北京市科技新星项目(Z181100006218049)

Research progress on the space of flow using big data

YANG Yanjie1,2(), YIN Dan1,2, LIU Ziwen1,2, HUANG Qingxu1,2,*(), HE Chunyang1,2, WU Kang3   

  1. 1. Center for Human-Environment System Sustainability (CHESS), State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China
    2. School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    3. Beijing Key Laboratory of Megaregions Sustainable Development Simulation, Capital University of Economics and Business, Beijing 100070, China
  • Received:2019-07-15 Revised:2019-10-31 Online:2020-08-28 Published:2020-10-28
  • Contact: HUANG Qingxu
  • Supported by:
    Beijing Nova Program(Z181100006218049)

摘要:

流空间是认识城市网络结构和演化的重要手段。近年来大数据的快速发展为流空间研究提供了新的机遇和挑战。论文系统综述了基于大数据的流空间研究进展。首先,论文梳理了基于大数据流空间研究的背景和历史,然后总结了基于大数据的流空间研究的主题、数据类型、方法和主要发现,最后展望了未来的研究挑战。2011年以后,基于大数据的流空间研究呈指数增长趋势,中英文论文年均发表量从2010年的11篇增长到2018年的106篇。大数据主要从提供新的数据源、激发新的分析方法和提供新的研究视角三方面推进了流空间研究。常用于流空间研究的大数据主要包括手机信令数据、社交媒体签到数据、公共交通刷卡数据和出租车轨迹数据,它们比传统统计数据更能直接提供人流、物流和信息流的时空动态信息。研究方法也从传统的基于距离的重力模型发展为网络分析方法。未来在交叉学科研究、大数据和传统数据的耦合、大数据与深度学习和云计算等新方法的结合方面仍需进一步探索,从理论、数据和方法上全面深化流空间研究。

关键词: 流空间, 大数据, 城市网络, 人员移动, 城市可持续性

Abstract:

The studies on the space of flow play an important role in understanding the structure and change of urban networks. The rapid development of big data provides new opportunities and challenges for the studies on the space of flow in recent years. This article systematically reviewed the research progress on the space of flow based on big data. First, we retrospect the background and history of the studies on the space of flow, then summarized the themes, the types of big data, the methods used for the studies and the major findings, as well as discussed the research challenges. We found an exponential growth of studies on the space of flow using big data after 2011. The annual number of published papers increased from 11 to 106 during 2011-2018. Big data deepen the research on the space of flow by providing new data sources, inspiring new analytical methods, and new research perspectives. Four types of big data—mobile phone, social media, smart card, and taxi trajectory data are commonly used in the studies on the space of flow, which can provide information on spatiotemporal flows (such as population flow, material flow, and information flow) directly. Research methods have also evolved from distance-based gravity models to network analysis. In the future, the research on the space of flow using big data can be further improved by validating the effectiveness and representativeness of the big data, the integration of big data and traditional data, and the information mining from big data using new methods such as deep learning and cloud computing.

Key words: space of flow, big data, urban network, human mobility, urban sustainability