测绘学报 ›› 2022, Vol. 51 ›› Issue (7): 1544-1560.doi: 10.11947/j.AGCS.2022.20220068

• 地图学与地理信息 • 上一篇    下一篇

地理大数据挖掘研究进展与挑战

刘耀林1, 刘启亮2, 邓敏2, 石岩2   

  1. 1. 武汉大学资源与环境学院, 湖北 武汉 430079;
    2. 中南大学地球科学与信息物理学院, 湖南 长沙 410083
  • 收稿日期:2022-02-28 修回日期:2022-06-17 发布日期:2022-08-13
  • 作者简介:刘耀林(1960-),男,博士,教授,国际欧亚科学院院士,主要从事地理数据挖掘、空间分析等研究工作。E-mail:yaolin610@163.com
  • 基金资助:
    国家重点研发计划(2017YFB0503601);国家自然科学基金(41771432;41730105;41971353)

Recent advance and challenge in geospatial big data mining

LIU Yaolin1, LIU Qiliang2, DENG Min2, SHI Yan2   

  1. 1. School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China;
    2. School of Geosciences and Info-physics, Central South University, Changsha 410083, China
  • Received:2022-02-28 Revised:2022-06-17 Published:2022-08-13
  • Supported by:
    The National Key Research and Development Foundation of China (No. 2017YFB0503601)|The National Natural Science Foundation of China (NSFC) (Nos. 41771432|41730105|41971353)

摘要: 大数据时代,全面涵盖人类活动与地理环境信息的地理大数据为更全面认识“人-地”关系提供了新的机遇。数据挖掘是地理大数据产生“大价值”的关键。与传统目的性采样数据(或“小数据”)相比,地理大数据具有更细的时空粒度、更广的时空范围、更丰富的人地关系信息、更高的时空有偏性及更低的时空精度。地理大数据的独特性使得地理大数据挖掘面临新的挑战。本文首先对地理大数据挖掘与空间数据挖掘的区别与联系进行分析;然后,对当前地理大数据挖掘方法、应用及软件的研究进展进行回顾和总结;最后,对地理大数据挖掘面临的挑战和发展趋势进行了展望。通过对地理大数据挖掘研究进展进行系统的分析,有望为地理大数据挖掘理论与方法的完善提供一定的参考和借鉴。

关键词: 地理大数据, 数据挖掘, 时空模式, 尺度

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

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