Journal of Geo-information Science ›› 2020, Vol. 22 ›› Issue (6): 1307-1319.doi: 10.12082/dqxxkx.2020.190524

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Study on the Administration and Construction of Urban Agglomeration with Spatiotemporal Big Data: A Progress Review

CHEN Fangmiao1, HUANG Huiping1,2,*(), JIA Kun3   

  1. 1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    3. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • Received:2019-09-16 Revised:2019-12-24 Online:2020-06-25 Published:2020-08-25
  • Contact: HUANG Huiping E-mail:huanghp@aircas.ac.cn
  • Supported by:
    National Key Research and Development Program of China(2017YFB0503800)

Abstract:

With the development of the new type of urbanization, urban agglomeration plays a key role in modern social-economical development. To date, big data has been considered as a technological breakthrough and applied in many fields in recent years. Spatiotemporal big data mining and data fusion analysis can improve the efficiency of the administration and construction of cities/urban agglomeration in the new era of smart city. In this study, we aimed to review the types, acquisition methods, and analysis techniques of spatiotemporal big data. We investigated researches on urban agglomeration using spatiotemporal big data in order to identify the application of big data. We collected conference and journal articles as well as academic dissertations published in big data and data mining areas between 2004 and 2019. In total, wesummarized ten types of big data which were classified into traditionaltype and newtype categories, five big data acquisition methods including downloading, crawling, purchasing, and data processing, as well as seven most common big data analysis techniques. Five application fields on administration and construction of city/urban agglomeration using big data were concluded through literature review, including demarcation and spatial development monitoring, traffic network monitoring, association and function analysis, industrial coordination analysis, and environment assessment. Moreover, we summarized the bottlenecks of future big data applications, including: (1) difficulties in data management; (2) low-level data sharing; (3) high complexity of data analysis; (4) limitations of research ideas and application fields. In response to the above issues, some suggestions are listed: (1) government should strengthen policy support for more extensive information sharing and efficient information security assurance to create a favorable environment for the development of big data application; (2) constructing adaptable modern network infrastructure to create an all-in-one system which integrates network coordination, simulation, calculation, and administration; (3) building a big data management standard for urban agglomeration to solve the problems triggered by its characteristic of variability and multiformity; (4) promoting the establishment of big data platform to enforce an integrated information sharing mode from regional to national level; (5) adopting the international advanced technologies and methods with some new ideas from the studies on smart cities to build a technical system that is suitable in China. This study finally put forward that the construction of network infrastructure and spatiotemporal big data resources sharing platform could make new patterns of integrated analysis of big data possible, leading to highly effective supervision and strategical development of the city/urban agglomerations in the future.

Key words: urban agglomeration administration and construction, spatiotemporal big data, types of big data, data mining, big data technology and application, integrated analysis, research progress