地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (6): 719-726.doi: 10.3724/SP.J.1047.2016.00719

所属专题: 地理大数据

• 地球信息科学理论与方法 •    下一篇

基于互联网大数据的区域多层次空间结构分析研究

牛方曲(), 刘卫东*()   

  1. 1. 中国科学院区域可持续发展分析与模拟重点实验室,北京 100101
    2. 中国科学院地理科学与资源研究所,北京 100101
  • 收稿日期:2016-01-03 修回日期:2016-03-28 出版日期:2016-06-10 发布日期:2016-06-10
  • 通讯作者: 刘卫东 E-mail:niufq@lreis.ac.cn;liuwd@igsnrr.ac.cn
  • 作者简介:

    作者简介:牛方曲(1979-),男,安徽淮南人,博士,助理研究员,研究方向为可持续发展。E-mail: niufq@lreis.ac.cn

  • 基金资助:
    国家自然科学基金项目(41530751、41101119);国家科技支撑计划项目(2012BAJ15B02)

Identifying the Hierarchical Regional Spatial Structure Using Internet Big Data

NIU Fangqu(), LIU Weidong*()   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2016-01-03 Revised:2016-03-28 Online:2016-06-10 Published:2016-06-10
  • Contact: LIU Weidong E-mail:niufq@lreis.ac.cn;liuwd@igsnrr.ac.cn

摘要:

大数据逐渐成为各领域学者开展研究的重要途径,目前在人文-经济地理学界逐渐得到重视,并进行了初步应用,相关研究依据尺度不同可以分为居民出行和消费、城市空间结构、区域社会经济联系等。但目前大数据在人文-经济学的应用研究还属起步阶段,少有研究基于大数据对区域多层级空间结构进行系统甄别分析。本文在采集互联网大数据的基础上,结合统计数据、交通路网等传统数据评价城市综合实力、城际联系强度,并基于此构建区域空间结构计算机算法分析区域多层级空间结构。京津冀案例应用揭示了京津冀多层级体系结构,确定了各城市辐射范围、城际相互作用关系。本文初步探索使用互联网大数据甄别区域空间结构,希望能为人文-经济地理领域开展大数据应用研究提供参考。

关键词: 大数据, 城际联系, 城市综合实力, 区域多层级空间结构, 京津冀

Abstract:

With the development of Information and Communication Technology (ICT), big data is now becoming an important tool to carry on researches in many fields. In the domain of human geography, big data technology has gained more and more attention, and there are extensive studies carried out which could be categorized into three groups of research hotspots: the residential behavioral spatial pattern, the urban space and the regional structure. But few researches have explored the regional hierarchical spatial structure systematically based on the big data, and the relevant researches are primarily based on the survey data, which has a bottleneck towards the volume and the detailed micro-data acquisition. This study combined the internet big data mined from internet with the GDP and the spatial traffic network data, etc. to identify the regional spatial structure. The gathered data was categorized into three categories: the point data, the line data and the polygon data. They are used to specify the urban overall capacity and the intensity of interactions between city pairs and the serving area respectively. Based on the obtained data, an algorithm was developed to identify the hierarchical regional structure. This method was applied to the Beijing-Tianjin-Hebei region. And a hierarchical regional structure was demonstrated by a multi-way tree created with this algorithm. The established multi-way tree identifies a regional urban ranking system in detail and can be of great help to decision makers in delineating the regional spatial policies. The results shows that Beijing which has the highest overall capacity becomes the core city (root node) and has the largest serving area, but the matured secondary cities around Beijing are still expected to share Beijing's serving functions, and there is a development inequality existing between the north and south part of this region. This study provides a good reference to researches in the domain of human geography with the application of big data.

Key words: big data, inter-city relation, centrality, hierarchical regional structure, Beijing-Tianjin-Hebei region