Geographic big-data: Anew opportunity for geography complexity study

被引:0
|
作者
地理大数据为地理复杂性研究提供新机遇
机构
[1] [1,2,Cheng, Changxiu
[2] 1,2,Shi, Peijun
[3] 1,Song, Changqing
[4] 1,Gao, Jianbo
来源
| 2018年 / Science Press卷 / 73期
关键词
D O I
10.11821/dlxb201808001
中图分类号
学科分类号
摘要
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. © 2018, Science Press. All right reserved.
引用
收藏
相关论文
共 50 条
  • [1] Wireless Big-Data: Opportunity and the Design Challenging
    Ma, Jianguo
    Fu, Haipeng
    [J]. 2016 IEEE MTT-S INTERNATIONAL CONFERENCE ON NUMERICAL ELECTROMAGNETIC AND MULTIPHYSICS MODELING AND OPTIMIZATION (NEMO), 2016,
  • [2] Big data: A new opportunity for transport geography?
    Tranos, Emmanouil
    Mack, Elizabeth
    [J]. JOURNAL OF TRANSPORT GEOGRAPHY, 2019, 76 : 232 - 234
  • [3] Challenge or opportunity? Navigating change in the era of exascale and big-data
    Harrison, Robert
    [J]. ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2019, 257
  • [4] A Study on Construction CALS Big-Data Service
    Kim, Jinuk
    Kim, Namgon
    [J]. 2018 FIFTH INTERNATIONAL CONFERENCE ON SOCIAL NETWORKS ANALYSIS, MANAGEMENT AND SECURITY (SNAMS), 2018, : 309 - 314
  • [5] Big-Data Visualization
    Keim, Daniel
    Qu, Huamin
    Ma, Kwan-Liu
    [J]. IEEE COMPUTER GRAPHICS AND APPLICATIONS, 2013, 33 (04) : 20 - 21
  • [6] Big-Data analysis used NGS and study of evolution
    Ikeo, Kazuho
    [J]. GENES & GENETIC SYSTEMS, 2015, 90 (06) : 360 - 360
  • [7] A Study for Big-Data (Hadoop) Application in Semiconductor Manufacturing
    Kang, Sheng
    Chien, Wei-Ting Kary
    Yang, Jun Gang
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM), 2016, : 1893 - 1897
  • [8] A measurement-based study of big-data movement
    Addanki, Ranjana
    Maji, Sourav
    Veeraraghavan, Malathi
    Tracy, Chris
    [J]. 2015 EUROPEAN CONFERENCE ON NETWORKS AND COMMUNICATIONS (EUCNC), 2015, : 445 - 449
  • [9] Neurotrauma as a big-data problem
    Huie, J. Russell
    Almeida, Carlos A.
    Ferguson, Adam R.
    [J]. CURRENT OPINION IN NEUROLOGY, 2018, 31 (06) : 702 - 708
  • [10] BigCache for Big-data Systems
    Roger, Michel Angelo
    Xu, Yiqi
    Zhao, Ming
    [J]. 2014 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2014, : 189 - 194