PROGRESS IN GEOGRAPHY ›› 2018, Vol. 37 ›› Issue (10): 1314-1327.doi: 10.18306/dlkxjz.2018.10.002

Special Issue: 地理大数据

• Special Column: Young Geographer Forum • Previous Articles     Next Articles

Research progress and trends of parallel processing, analysis, and mining of big spatiotemporal data

Xuefeng GUAN(), Yumei ZENG*()   

  1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • Received:2018-08-31 Revised:2018-10-13 Online:2018-10-28 Published:2018-10-28
  • Contact: Yumei ZENG;
  • Supported by:
    National Natural Science Foundation of China, No.41301411


With the rapid development of the Internet, Internet of things, and cloud computing technology, data with geographical location and time tag are accumulated in an explosive way, and this indicates that we are in the era of big spatiotemporal data. In addition to the typical "4V" characteristics, big spatiotemporal data also contain rich semantic information and dynamic spatiotemporal patterns. Although massive spatiotemporal data have promoted the evolvement of various cross-disciplinary studies, traditional methods of data processing and analysis would no longer meet the requirements of efficient storage and real-time analysis of such data. Therefore, it is of great importance to integrate big spatiotemporal data with high-performance computing/cloud computing. To address this problem, this article begins with the concept and origin of big spatiotemporal data, and introduces its unique characteristics. Then, the performance requirements generated by current big data applications are analyzed, and the status quo of the underlying hardware and software is summarized. Furthermore, the article comprehensively reviews parallel processing, analysis, and mining methods for big spatiotemporal data. Finally, we conclude with the challenges and opportunities of storage, management, and parallel processing analysis of big spatiotemporal data.

Key words: big spatiotemporal data, high-performance computing, parallel spatial analysis, data mining, progress and trends