SCIENTIA GEOGRAPHICA SINICA ›› 2017, Vol. 37 ›› Issue (3): 321-330.doi: 10.13249/j.cnki.sgs.2017.03.001

Special Issue: 地理大数据 人口与城市研究

• Orginal Article •     Next Articles

Combination Between Big Data And Small Data: New Methods of Urban Studies in The Information Era

Xiao Qin(), Feng Zhen   

  1. School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, Jiangsu, China
  • Received:2016-04-28 Revised:2016-08-18 Online:2017-03-20 Published:2017-03-20
  • Supported by:
    National Nature Sciences Foundation of China (41571146, 41301166, 41371149), National Science and Technology Supporting Program of China (2015BAJ08B01)


Appearance of Information and communication technology has set off a new wave of big data to promote a transformation of the traditional methods in urban studies. However, types of limitations of big data also make scholars rethink the role of small data in specific applications for research. We believe that the small data will not lose its value, instead, it can be combined with big data in urban study, which is needed to focus on relationship between urban and resident activity in the information era. Therefore, we should discuss a new framework for such combination on complicated urban problems and diversified resident demands. Firstly, we put forward to three methodologies including combination between physical space and activity space, combination between correlativity and causality, and combination between macro-scale analysis and micro-scale analysis. Secondly, based on above methodologies, we build three method frameworks for urban studies in the information era, namely ‘Spatial development evaluations for big samples+Spatial difference and connection discovery+Factors discussions for small samples’, ‘Model building for small samples+Factors discussions+Verifications and explorations for big samples’, and ‘Micro-analysis of activities+Delineations of activity space+Factors discussions’. Finally, we discuss applications of above three method frameworks.

Key words: big data, small data, EDCF, MFVE, AADSF, urban studies, information era

CLC Number: 

  • F219