Journal of Geo-information Science ›› 2016, Vol. 18 ›› Issue (6): 719-726.doi: 10.3724/SP.J.1047.2016.00719

Special Issue: 地理大数据

• Orginal Article •     Next Articles

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;


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