Big geodata mining: Objective, connotations and research issues

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The objective, connotations and research issues of big geodata mining were discussed to address its significance to geographical research in this paper. Big geodata may be categorized into two domains: big earth observation data and big human behavior data. A description of big geodata includes, in addition to the “5Vs” (volume, velocity, value, variety and veracity), a further five features, that is, granularity, scope, density, skewness and precision. Based on this approach, the essence of mining big geodata includes four aspects. First, flow space, where flow replaces points in traditional space, will become the new presentation form for big human behavior data. Second, the objectives for mining big geodata are the spatial patterns and the spatial relationships. Third, the spatiotemporal distributions of big geodata can be viewed as overlays of multiple geographic patterns and the characteristics of the data, namely heterogeneity and homogeneity, may change with scale. Fourth, data mining can be seen as a tool for discovery of geographic patterns and the patterns revealed may be attributed to human-land relationships. The big geodata mining methods may be categorized into two types in view of the mining objective, i.e., classification mining and relationship mining. Future research will be faced by a number of issues, including the aggregation and connection of big geodata, the effective evaluation of the mining results and the challenge for mining to reveal “non-trivial” knowledge.

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Correspondence to Tao Pei.

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Foundation: National Natural Science Foundation of China, No.41525004, No.41421001

Author: Pei Tao (1972-), Professor, specialized in big geodata mining.

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Pei, T., Song, C., Guo, S. et al. Big geodata mining: Objective, connotations and research issues. J. Geogr. Sci. 30, 251–266 (2020) doi:10.1007/s11442-020-1726-7

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  • big earth observation data
  • big human behavior data
  • geographical spatiotemporal pattern
  • spatio-temporal heterogeneity
  • knowledge discovery