地理学报 ›› 2018, Vol. 73 ›› Issue (8): 1397-1406.doi: 10.11821/dlxb201808001

所属专题: 地理大数据

• 理论前沿 •    下一篇

地理大数据为地理复杂性研究提供新机遇

程昌秀1,2,3(),史培军1,2,3,宋长青1,3,高剑波1,3   

  1. 1. 北京师范大学地表过程与资源生态国家重点实验室,北京 100875
    2. 北京师范大学环境演变与自然灾害教育部重点实验室,北京 100875
    3. 北京师范大学地理科学学部,北京 100875
  • 收稿日期:2018-06-02 出版日期:2018-08-15 发布日期:2018-07-31
  • 基金资助:
    国家自然科学基金项目(41771537);北京师范大学人才启动项目

Geographic big-data: A new opportunity for geography complexity study

CHENG Changxiu1,2,3(),SHI Peijun1,2,3,SONG Changqing1,3,GAO Jianbo1,3   

  1. 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
    2. Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China
    3. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • Received:2018-06-02 Online:2018-08-15 Published:2018-07-31
  • Supported by:
    National Natural Science Foundation of China, No.41771537;Talent Start Project of Beijing Normal University

摘要:

大数据之风自2010年席卷全球,已在科学、工程和社会等领域产生深远影响。本文首先从地理大数据、第四范式以及非线性复杂地理系统3组基本概念出发,剖析上述3组概念之间的科学联系与相互支撑作用,提出大数据和第四范式为地理复杂性研究提供新机遇。其后,探讨如何利用大数据和复杂性科学的理论方法开展地理复杂性研究。基于地理大数据,可以通过统计物理学的系列指标描述现实地理世界的复杂非线性特征,同时,还可利用深度学习、复杂网络、多智能体等方法,实现复杂非线性地理系统的推演和模拟。上述方法对认知地理现象和过程的复杂性,对复杂地理系统的分析、模拟、反演与预测有重要作用。最后,提出地理大数据和复杂性科学相互支撑可能成为21世纪地理学的主流科学方法。

关键词: 地理大数据, 第四范式, 非线性, 地理复杂性

Abstract:

Since 2010, big data has played a significant role in various fields of science, engineering and society. The paper introduces the concepts of geographic big-data, the fourth paradigm and nonlinear complex geographic system, and discusses interactive relationships of these concepts. It is proposed that geographic big-data and the fourth paradigm would become a new opportunity to research on geography complexity. Then the paper discusses how to use the methods of geographic big-data and complexity science to examine geography complexity. For example, based on big-data, a series of indicators of statistical physics fields could be constructed to describe the complex nonlinear characteristics of the real geographic world. Deep learning, complex network and multi-agent methods can be used to model and simulate the complex nonlinear geographic systems. These methods are important for a better understanding of the complexity of geographic phenomena and processes, as well as the analysis, simulation, inversion and prediction of complex geographic systems. Finally, the paper highlights that the combination of geographic big-data and complexity science would be the mainstream scientific method of geography in the 21st century.

Key words: geographic big-data, the fourth paradigm, nonlinearity, geography complexity