地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (6): 1294-1306.doi: 10.12082/dqxxkx.2020.190702

• 大数据与城市管理 • 上一篇    下一篇

基于多源数据的中国智慧城市发展状态评价

杜德林1,2, 黄洁1,*(), 王姣娥1,2   

  1. 1. 中国科学院地理科学与资源研究所 中国科学院区域可持续发展分析与模拟重点实验室,北京 100101
    2. 中国科学院大学资源与环境学院,北京 100049
  • 收稿日期:2019-11-20 修回日期:2020-01-19 出版日期:2020-06-25 发布日期:2020-08-25
  • 通讯作者: 黄洁 E-mail:huangjie@igsnrr.ac.cn
  • 作者简介:杜德林(1994— ),男,山西霍州人,博士生,主要从事交通地理与区域发展研究。E-mail: dudl.19b@igsnrr.ac.cn
  • 基金资助:
    中国科学院战略性先导科技专项(A类)资助(XDA19040402)

Assessment of Smart City Development Status in China based on Multi-source Data

DU Delin1,2, HUANG Jie1,*(), WANG Jiaoe1,2   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-11-20 Revised:2020-01-19 Online:2020-06-25 Published:2020-08-25
  • Contact: HUANG Jie E-mail:huangjie@igsnrr.ac.cn
  • Supported by:
    Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19040402)

摘要:

随着移动互联网、云计算、大数据等新一轮信息通信技术的发展,智慧城市逐渐成为城市建设的重要发展趋势。“十三五”期间,全国各城市纷纷制定智慧城市建设或发展规划,并将其定位为城市中长期发展战略的重要组成部分。由于智慧城市涉及范围广泛、内容体系庞杂,目前还在不断发展完善之中,尚未形成统一的评价标准。基于此,本文对比了国内外智慧城市评价重点与趋势,以提高城市可持续发展能力、实现高效、公平的城市管理、保障民生福祉为目标,构建了包括智慧经济、智慧交通、智慧医疗、智慧教育、智慧管理等多子系统的智慧城市评价指标体系;运用文本、网页、统计等多源数据,本文开展了全面的、统一的、多层次、模块化的全国智慧城市发展状态评估,并从子系统协调程度对智慧城市发展提出了建设性意见。研究发现:① 从整体评价结果分析,除北京、上海、广州、武汉、成都、杭州、天津和南京8个城市综合得分及各子系统得分都较高外,绝大部分城市的智慧建设水平不高;② 在空间分布上,沿海地区的智慧城市建设水平普遍高于内陆地区,直辖市和省会城市高于其他城市;③ 从协调关系分析,70%以上城市的5个子系统为中度甚至低度协调,80%以上城市以智慧教育或智慧医疗成为发展短板,这也是未来政府应关注的重点。本文通过构建指标体系,探讨了中国智慧城市的发展状态,为城市未来的发展和管理提供了借鉴与参考。

关键词: 智慧城市, 多层次指标体系, 大数据, 智慧经济, 智慧交通, 智慧教育, 智慧医疗, 智慧管理

Abstract:

With the development of information and communication technology, such as mobile internet, cloud computing, and big data, smart city has gradually become the important development tendency of urban construction. During the period of the 13 th five-year plan, cities have formulated their smart city construction (or development) plans and regarded these plans as key part of their medium- and long-term urban development strategies. Because smart city involves various context and massive indexes, the assessment of smart cities is undergoing continuous development and improvement. Thus, so far, an unified assessment standard is still lacking. Based on this, we compare the domestic and international index systems to assess smart city development. With the objectives including improving cities' capability of sustainable development, implementing efficient and fair management, and ensuring urban residents' wellbeing, this paper proposes an assessment index system with multiple layers and five sub-systems including smart economy, smart transport, smart healthcare, smart education, and smart management. This paper employs text, webpage, and statistic data and conducts a comprehensive, uniform, and multi-layer assessment to evaluate smart city development status for Chinese cities. finally, we offer constructive suggestion on smart city development from the perspective of sub-system coordination. Main findings are shown here. First, except 8 cities, namely Beijing, Shanghai, Guangzhou, Wuhan, Chengdu, Hangzhou, Tianjin, and Nanjing, most cities are at a relatively low level of smart city development. Second, based on the spatial pattern, cities in the coastland are at a relatively higher level of smart city development than those in the inland. Municipalities and provincial capitals are at an obviously higher smart development level than the other cities. Third, from the coordination perspective, five sub-systems are not coordinated well with more than 70% cities showing moderate- to low-level coordination. Overall, the average development level of smart education and smart healthcare are the lowest among all sub-systems. More than 80% of cities have disadvantages in the two sub-systems. Based on these findings, this paper provides some policy implications for the future development of cities. The government should pay more attention to the coordination of sub-systems, such as smart education and smart healthcare. For smart transport, most cities have already had a high level of development, and the efficiency and fairness of transport development will be more important in the future. Innovation and global development have become the key factors restricting the development of smart economy and should be considered in future policy-making. In addition, government and the relevant departments should strengthen the top-level design, module construction, and index statistics of the smart management.

Key words: Smart city, multiple-layer index system, big data, smart economy, smart transport, smart education, smart healthcare, smart management