Big spatial vector data management: a review. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Yao, Xiaochuang; Li, Guoqing
• Is Part Of: Big earth data. Volume 2:Issue 1(2018)
• Page Start: 108
• Page End: 129
• Publication Date: 2018-01-02
• Subjects: Big data -- vector data -- big spatial vector data (BSVD) -- big data management -- review; Earth sciences -- Periodicals; Earth sciences -- Research -- Periodicals; Geographic information systems Periodicals; 550
• DOI: 10.1080/20964471.2018.1432115
• Languages: English
• ISSNs: 2096-4471

Abstract

Abstract: Spatial vector data with high-precision and wide-coverage has exploded globally, such as land cover, social media, and other data-sets, which provides a good opportunity to enhance the national macroscopic decision-making, social supervision, public services, and emergency capabilities. Simultaneously, it also brings great challenges in management technology for big spatial vector data (BSVD). In recent years, a large number of new concepts, parallel algorithms, processing tools, platforms, and applications have been proposed and developed to improve the value of BSVD from both academia and industry. To better understand BSVD and take advantage of its value effectively, this paper presents a review that surveys recent studies and research work in the data management field for BSVD. In this paper, we discuss and itemize this topic from three aspects according to different information technical levels of big spatial vector data management. It aims to help interested readers to learn about the latest research advances and choose the most suitable big data technologies and approaches depending on their system architectures. To support them more fully, firstly, we identify new concepts and ideas from numerous scholars about geographic information system to focus on BSVD scope in the big data era. Then, we conclude systematically not only the most recent published literatures but also a global view of main spatial technologies of BSVD, including data storage and organization, spatial index, processing methods, and spatial analysis. Finally, based on the above commentary and related work, several opportunities and challenges are listed as the future research interests and directions for reference.