Recent advance and challenge in geospatial big data mining

doi:10.11947/j.AGCS.2022.20220068

Abstract

Abstract: In the era of big data, geospatial big data provide new opportunities for understanding complex human-land relationships. Data mining is essential for revealing valuable spatio-temporal patterns (e.g., clusters, outliers, association rules, etc.) hidden in geospatial big data. Geospatial big data has some unique characteristics, e.g., fine spatio-temporal granularity, wide spatio-temporal scope, rich information on human-land relationships, high spatio-temporal bias, and low spatio-temporal precision. Geospatial big data requires specially designed data mining methods given its unique characteristics. In this study, we first analyzed the relationships between spatial data mining and geospatial big data mining, then reviewed recent advances in geospatial big data mining, and finally summarized the challenges and further research directions of geospatial big data mining. This review is expected to provide some valuable reference for the improvement of theories and methods of geospatial big data mining.

Key words: geospatial big data, data mining, spatio-temporal pattern, scale

CLC Number:

P208

LIU Yaolin, LIU Qiliang, DENG Min, SHI Yan. Recent advance and challenge in geospatial big data mining[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1544-1560.

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