地理学报

地理学报 ›› 2020, Vol. 75 ›› Issue (7): 1523-1538.doi: 10.11821/dlxb202007014

• 地理空间理论与应用 • 上一篇    下一篇

大数据时代的空间交互分析方法和应用再论

刘瑜1(), 姚欣1, 龚咏喜2, 康朝贵3,4, 施迅5, 王法辉6, 王姣娥7, 张毅1, 赵鹏飞1, 朱递1, 朱欣焰8   

  1. 1. 北京大学地球与空间科学学院遥感与地理信息系统研究所,北京 100871
    2. 哈尔滨工业大学（深圳） 深圳市城市规划与决策仿真重点实验室,深圳 518055
    3. 武汉大学遥感信息工程学院,武汉 430079
    4. 纽约大学城市科学与进步中心,美国 布鲁克林 11201
    5. 达特茅斯学院地理系,美国 汉诺威 03755
    6. 路易斯安那州立大学地理与人类学系,美国 巴吞鲁日 70803
    7. 中国科学院地理科学与资源研究所,北京 100101
    8. 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:2019-07-23 修回日期:2020-04-14 出版日期:2020-07-25 发布日期:2020-09-25
  • 作者简介:刘瑜(1971-), 男, 山东人, 教授, 中国地理学会会员(S110007302M), 主要研究方向为地理信息科学。E-mail: liuyu@urban.pku.edu.cn
  • 基金资助:
    国家自然科学基金项目(41830645);国家自然科学基金项目(41625003)

Analytical methods and applications of spatial interactions in the era of big data

LIU Yu1(), YAO Xin1, GONG Yongxi2, KANG Chaogui3,4, SHI Xun5, WANG Fahui6, WANG Jiao'e7, ZHANG Yi1, ZHAO Pengfei1, ZHU Di1, ZHU Xinyan8   

  1. 1. Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China
    2. Shenzhen Key Laboratory of Urban Planning and Decision Making, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, Guangdong, China
    3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
    4. Center for Urban Science and Progress, New York University, Brooklyn, NY 11201, USA
    5. Department of Geography, Dartmouth College, Hanover, NH 03755, USA
    6. Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
    7. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    8. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • Received:2019-07-23 Revised:2020-04-14 Published:2020-07-25 Online:2020-09-25
  • Supported by:
    National Natural Science Foundation of China(41830645);National Natural Science Foundation of China(41625003)

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摘要：

空间交互是理解地表人文过程的重要基础,与空间依赖一起共同体现了地理空间的独特性、关联性以及对嵌入该空间的地理分布格局的影响,具有鲜明的时空属性,因此对于地理学研究具有重要意义。大数据为空间交互研究带来了新的机遇,能够使我们在不同时空尺度感知和观察空间交互模式并对其动态演化特征进行模拟和预测,从而为揭示人类活动规律及区域空间结构提供有力支持。本文在探讨空间交互与地理空间模式关系的基础上,描述了利用地理大数据感知空间交互的方式和定量模型,介绍了空间交互分析方法的研究进展及其在空间规划与交通、公共卫生、旅游等领域的应用情况,并就一些基本问题进行了讨论,以期为大数据支持下空间交互相关研究提供指导。

关键词: 空间交互, 大数据, 模型, 分析方法, 应用, 社会感知

Abstract:

Spatial interaction is a critical basis of understanding human processes on the land surface. Together with spatial dependence, it embodies the uniqueness and relatedness of geographical space, as well as the impact on the embedded geographical distribution patterns. Spatial interaction also has distinctive space-time attributes, and thus it is significant to geographical research. Big data bring new opportunities for the studies of spatial interaction, which enables us to sense and observe spatial interaction patterns at different spatial scales, and simulate and predict their dynamic evolution. This provides great support for the research of human activity regularities and regional spatial structures. In this article, we first demonstrated the relationship between spatial interaction and geospatial patterns, and introduced how to sense spatial interaction with big geodata. Then, we generalized the progress of relevant models and analytical methods, and introduced the corresponding applications in fields of spatial planning, urban transportation, public health and tourism. Some key issues were also discussed. We hope this review can provide guidance for the studies of spatial interaction supported by big data.

Key words: spatial interaction, big data, model, analytical method, application, social sensing