地理科学进展 ›› 2018, Vol. 37 ›› Issue (7): 880-889.doi: 10.18306/dlkxjz.2018.07.002

所属专题: 地理大数据

• 研究综述 • 上一篇    下一篇

移动定位大数据视角下的人群移动模式及城市空间结构研究进展

杨喜平1(), 方志祥2,*()   

  1. 1. 陕西师范大学地理科学与旅游学院, 陕西省旅游信息科学重点实验室,西安 710119
    2. 武汉大学测绘遥感信息工程国家重点实验室,武汉 430079
  • 收稿日期:2017-09-05 修回日期:2018-01-22 出版日期:2018-07-28 发布日期:2018-07-28
  • 通讯作者: 方志祥 E-mail:xpyang@snnu.edu.cn;zxfang@whu.edu.cn
  • 作者简介:

    作者简介:杨喜平(1986-),山西运城人,讲师,研究方向为时空轨迹大数据挖掘与人群移动行为,E-mail: xpyang@snnu.edu.cn

  • 基金资助:
    国家自然科学基金项目(41231171,41771473,41571135);中国博士后科学基金项目(2017M623112);中央高校基本科研业务费资助项目(GK201803049,2042017kf0235);国家重点研发计划项目(2017YFB0503802)

Recent progress in studying human mobility and urban spatial structure based on mobile location big data

Xiping YANG1(), Zhixiang FANG2,*()   

  1. 1. School of Geography and Tourism, Shaanxi Normal University, Shaanxi Key Laboratory of Tourism Informatics, Xi'an 710119, China
    2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • Received:2017-09-05 Revised:2018-01-22 Online:2018-07-28 Published:2018-07-28
  • Contact: Zhixiang FANG E-mail:xpyang@snnu.edu.cn;zxfang@whu.edu.cn
  • Supported by:
    National Natural Science Foundation of China, No.41231171, No.41771473, No.41571135;China Postdoctoral Science Foundation, No.2017M623112;Fundamental Research Funds for the Central Universities, No.GK201803049, No.2042017kf0235;National Key Research and Development Program of China, No.2017YFB0503802

摘要:

了解城市人群移动行为和空间结构对城市规划、交通管理、应急响应等具有重要的意义。近年来,随着信息技术(ICT)的快速发展,采集大规模、长时间序列的人群移动定位大数据变得容易,为人群移动行为研究带来了新的机遇和挑战。本文首先介绍了目前用于城市人群移动行为和空间结构研究的主要数据源及其特征,并分别从人群移动行为、城市空间结构2个方面对近3年国内外相关研究进行归纳总结。目前的研究主要从移动定位大数据中挖掘人群移动模式,理解人群移动时空规律,进一步透视城市的空间结构特征;而对城市空间结构与人群移动行为影响的研究较少。未来可通过融合多源时空数据,综合研究人群移动行为与城市空间结构之间的相互作用,发展大规模群体移动行为时空分析理论和模型,进一步深入理解人群移动行为与城市空间结构的耦合关系。

关键词: 移动定位大数据, 人群移动行为, 城市空间结构, 研究进展

Abstract:

Understanding human mobility patterns and spatial structure is of great important to urban planning, traffic management, emergence response, and so on. With the development of information and communication technologies, it is possible to collect large-scale, long-term human tracking data, which brings great opportunities and challenges for human mobility behavior studies. This article first introduces the main datasets being used for studying human mobility patterns, then reviews the recent progress from the perspectives of human travel behavior and urban spatial structure respectively. We found that most studies follow the route of "data-human travel behavior-urban spatial structure," mining and understanding human mobility patterns from the datasets and further giving insights on the characteristics of urban spatial structures. However, there exist few studies on the influence of urban spatial structure on human travel behavior. In the future, it is necessary to integrate multi-source spatiotemporal data to understand the interaction between human mobility and urban spatial structure, develop spatiotemporal analysis theory and models for dealing with mobile location big data, and focus on understanding the coupling relationship between human mobility and urban spatial structure.

Key words: mobile location big data, human mobility, urban spatial structure, progress