Acta Geographica Sinica ›› 2019, Vol. 74 ›› Issue (3): 586-598.doi: 10.11821/dlxb201903014

Special Issue: 地理大数据

• Big Geodata • Previous Articles     Next Articles

Principle of big geodata mining

Tao PEI1,2(), Yaxi LIU1,2, Sihui GUO1,2, Hua SHU1,2, Yunyan DU1,2, Ting MA1,2, Chenghu ZHOU1,2   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2018-10-08 Revised:2019-02-15 Online:2019-03-25 Published:2019-03-19


This paper reveals the principle of geographic big data mining and its significance to geographic research. In this paper, big geodata are first categorized into two domains: earth observation big data and human behavior big data. Then, another five attributes except for "5V", including granularity, scope, density, skewness and precision, are summarized regarding big geodata. Based on this, the essence and effect of big geodata mining are uncovered by the following four aspects. First, as the burst of human behavior big data, flow space, where the OD flow is the basic unit instead of the point in traditional space, will become a new presentation form for big geodata. Second, the target of big geodata mining is defined as revealing the spatial pattern and the spatial relationship. Third, spatio-temporal distributions of big geodata can be seen as the overlay of multiple geographic patterns and the patterns may be changed with scale. Fourth, big geodata mining can be viewed as a tool for discovering geographic patterns while the revealed patterns are finally attributed to the outcome of human-land relationship. Big geodata mining methods are categorized into two types in light of mining target, i.e. classification mining and relationship mining. The future research will be facing the following challenges, namely, the aggregation and connection of big geodata, the effective evaluation of mining result and mining "true and useful" knowledge.

Key words: spatial pattern, spatial relationship, spatial distribution, flow space, spatio-temporal heterogeneity, knowledge discovery