地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (6): 1307-1319.doi: 10.12082/dqxxkx.2020.190524

• 大数据与城市管理 • 上一篇    下一篇

时空大数据在城市群建设与管理中的应用研究进展

陈芳淼1, 黄慧萍1,2,*(), 贾坤3   

  1. 1. 中国科学院空天信息创新研究院,北京 100094
    2. 中国科学院大学资源与环境学院,北京 100049
    3. 北京师范大学地理科学学部,北京 100875
  • 收稿日期:2019-09-16 修回日期:2019-12-24 出版日期:2020-06-25 发布日期:2020-08-25
  • 通讯作者: 黄慧萍 E-mail:huanghp@aircas.ac.cn
  • 作者简介:陈芳淼(1983— ),女,北京人,博士,助理研究员,研究方向为城市规划与遥感监测。E-mail: chenfm@aircas.ac.cn
  • 基金资助:
    国家重点研发计划重点专项(2017YFB0503800)

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)

摘要:

伴随新型城镇化进程的不断推进,城市群已经成为地区社会经济发展的重要核心。大数据时代的到来促使新兴时空大数据在城市/城市群建设与管理中发挥着重要作用,并成为当前学术界的研究热点。大数据挖掘技术与融合分析技术将成为未来研究城市群的重要方法。本研究总结归纳了时空大数据在城市群建设与管理中的应用研究进展,对常见城市群时空大数据类型、获取方法和分析技术进行分类整理,并对基于资源调查和多源时空数据分析的城市/城市群研究进展进行分析,特别是对时空大数据及其技术在城市群建设与管理中的主要研究展开归类分析,认为目前时空大数据在城市群建设与管理应用领域主要涉及5大方向:城市群空间界定与发展监测、交通网络监测、关联性分析与功能布局评价、产业协同分析和环境监测与评估。最后,本文分析了现阶段时空大数据在城市群建设与管理应用中的发展瓶颈,提出了相关对策建议,并对未来研究发展趋势提出了展望。

关键词: 城市群建设与管理, 时空大数据, 数据类型, 数据挖掘, 大数据技术与应用, 融合分析, 研究进展

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