大数据驱动下智慧城市建设的若干思考

马新强; 刘勇; 范婧; 黄羿; 吴茂念; 张明义

doi:10.3981/j.issn.1000-7857.2017.21.016

科技导报 >

2017 , Vol. 35 >Issue 21: 131 - 137

DOI: https://doi.org/10.3981/j.issn.1000-7857.2017.21.016

学术聚焦

大数据驱动下智慧城市建设的若干思考

马新强 ,
刘勇 ,
范婧 ,
黄羿 ,
吴茂念 ,
张明义

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1. 贵州大学计算机科学与技术学院, 贵阳 550025;
2. 浙江大学智能系统与控制研究所, 杭州 310007;
3. 重庆文理学院机器视觉与智能信息系统重点实验室, 重庆 412060;
4. 湖州师范学院信息工程学院, 湖州 313000

马新强,副教授,研究方向为城市计算、健康医疗大数据,电子信箱:xinqma@163.com

收稿日期: 2017-03-23

修回日期: 2017-04-20

网络出版日期: 2017-11-16

基金资助

国家自然科学基金项目（61562011）；浙江大学工业控制技术国家重点实验室开放课题（ICT170330）；浙江省自然科学基金项目（LY16F020015）；重庆市基础科学与前沿技术研究项目（cstc2016jcyjA0568，cstc2015jcyjA40026）

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Big data-driven smart city:Some thoughts

MA Xinqiang ,
LIU Yong ,
FAN Jing ,
HUANG Yi ,
WU Maonian ,
ZHANG Mingyi

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1. College of Computer Science and Technology, Guizhou University, Guiyang 550025, China;
2. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310007, China;
3. Key Laboratory of Machine Vision & Intelligent Information System, Chongqing University of Arts and Sciences, Chongqing 412060, China;
4. School of Information Engineering, Huzhou University, Huzhou 313000, China

Received date: 2017-03-23

Revised date: 2017-04-20

Online published: 2017-11-16

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摘要

在当前的智慧城市建设中，随着互联网、物联网、通信网及大规模智能化应用服务的深入发展，产生了海量、异构、多源的城市时空大数据。在大数据时代，智慧城市的建设值得人们更深层次的思考。如何应对这些大规模、复杂的城市数据，尤其是对数据的感知、采集、存储、管理、分析、挖掘、计算、可视化及高效的服务应用都面临着巨大挑战。本文主要从技术、立法、管理3个视角重点阐述了大数据对智慧城市发展驱动作用。在技术上从纵向和横向两个视角分析城市时空大数据的特征及应用；从数据资产的角度强调了立法在智慧城市中的作用；从政策管理、奖惩与市场机制等方面分析了管理在智慧城市建设中的重要性；从综合视角思考大数据驱动下智慧城市建设中的若干问题。

关键词： 智慧城市; 大数据; 城市计算; 数据挖掘; 可视化分析

本文引用格式

马新强 , 刘勇 , 范婧 , 黄羿 , 吴茂念 , 张明义 . 大数据驱动下智慧城市建设的若干思考[J]. 科技导报, 2017 , 35(21) : 131 -137 . DOI: 10.3981/j.issn.1000-7857.2017.21.016

Abstract

In the construction of smart city, the depth development of Internet, Internet of things, communication networks and large-scale intelligent services will result in massive, heterogeneous, multi-source big spatio-temporal urban data. In the era of big data, we should think deeply about the construction of smart city. How to deal with these large-scale and complicated urban data is facing enormous challenges, including data collection, sensing, storage, management, mining, visualization and efficient services. In this paper we mainly discuss the driving effects of big data on smart city in the views of technology, legislation and management. We analyze the features and applications of big spatio-temporal urban data in two directions, point out the importance of legislation from the perspective of data assets, and summarize the management of smart city in terms of policy management, rewards and punishments, and market mechanisms. Finally, we put forward some thoughts on the construction of smart city driven by big data.

Key words： smart city; big data; urban computing; data mining; visual analysis

参考文献

[1] Hall R E, Bowerman B, Braverman J, et al. The vision of a smart city[C/OL].[2017-04-10]. https://ntl.bts.gov/lib/14000/14800/14834/DE2001-773961.pdf.
[2] Batty M. Big data, smart cities and city planning[J]. Dialogues in Hu-man Geography, 2013, 3(3):274-279.
[3] Martinez-Balleste A, Perez-Martinez P A, Solanas A. The pursuit of cit-izens' privacy:A privacy-aware smart city is possible[J]. IEEE Commu-nications Magazine, 2013, 51(6):136-141.
[4] Petrolo R, Loscrì V, Mitton N. Towards a smart city based on cloud of things, a survey on the smart city vision and paradigms[J]. Transactions on Emerging Telecommunications Technologies, 2015, 28(1), doi:10. 1002/ett.
[5] Nam T, Pardo T A. Smart city as urban innovation:focusing on manage-ment, policy, and context[C]//Proceedings of the International Confer-ence on Theory and Practice of Electronic Governance. New York:ACM, 2011:185-194.
[6] Li D R, Cao J J, Yao Y. Big data in smart cities[J]. Science China Infor-mation Sciences, 2015, 58(10):1-12.
[7] Li D R, Yao Y, Shao Z F, et al. From digital Earth to smart Earth[J]. Science Bulletin, 2014, 59(8):722-733.
[8] Li D R, Shao Z F, Zhou X R, et al. Geomatics for smart cities-concept, key techniques, and applications[J]. Geospatial Information Science, 2013, 16(1):13-24.
[9] 王家耀, 邓国臣. 大数据时代的智慧城市[J]. 测绘科学, 2014, 39(5):3-7. Wang Jiayao, Deng Guochen. Smart city in the era of big data[J]. Science of Surveying and Mapping, 2014, 39(5):3-7.
[10] Zheng Y, Capra L, Wolfson O, et al. Urban computing:Concepts, methodologies, and applications[J]. ACM Transactions on Intelligent Systems & Technology, 2014, 5(3), doi:10.1145/2629592.
[11] Zheng Y. Introduction to urban computing[J]. Geomatics & Informa-tion Science of Wuhan University, 2015, 40(1):1-13.
[12] 徐静, 陈秀万. 基于信息空间理论的智慧城市模型构建[J]. 科技导报, 2013, 31(22):56-59. Xu Jing, Chen Xiuwai. Construction of smart city model based on information space theory[J]. Science & Technology Review, 2013, 31(22):56-59.
[13] Song C M, Qu Zehui, Blumm N, et al. Limits of predictability in hu-man mobility[J]. Science, 2010, 327(5968):1018-1021.
[14] 潘纲, 李石坚, 齐观德, 等. 移动轨迹数据分析与智慧城市[J]. 中国计算机学会通讯, 2012, 8(5):31-37. Pan Gang, Li Shijian, Qi Guande, et al. Moving trajectory data analysis and smart city[J]. Communications of the CCF, 2012, 8(5):31-37.
[15] 孙未未, 毛江云. 轨迹预测技术及其应用——从上海外滩踩踏事件说起[J]. 科技导报, 2016, 36(9):48-54. Sun Weiwei, Mao Jiangyun. Trajectory prediction technology and its application from the the bund stampede in Shangha[J]. Science & Technology Review, 2016, 36(9):48-54.
[16] Zheng X H, Chen W, Wang P, et al. Big Data for social transportation[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 17(3):620-630.
[17] Chen W, Guo F Z, Wang F Y. A survey of traffic data visualization[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(6):1-15.
[18] Zheng Y X, Wu W C, Chen Y Z, et al. Visual analytics in urban com-puting:An overview[J]. IEEE Transactions on Big Data, 2016, 2(3):276-296.
[19] 眉间尺. 智慧城市建设不能目中无"人"[N]. 科技日报, 2016-04-15. Mei Jianchi. Construction of smart city unable to people[N]. Science and Technology Daily, 2016-04-15.
[20] 王静远, 李超, 熊璋, 等. 以数据为中心的智慧城市研究综述[J]. 计算机研究与发展, 2014, 51(2):239-259. Wang Jingyuan, Li Chao, Xiong Zhang, et al. Survey of data-centric smart city[J]. Journal of Computer Research and Development, 2014, 51(2):239-259.
[21] Paulos E, Goodman E. The familiar stranger:Anxiety, comfort, and play in public places[C]//Conference on Human Factors in Computing Systems, CHI 2004. New York:ACM, 2004:223-230.
[22] Zhang J B, Zheng Y, Qi D K. Deep spatio-temporal residual networks for citywide crowd flows prediction[C/OL].[2017-04-10]. https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14501/13964
[23] Zheng Y. Trajectory data mining:An overview[J]. ACM Transactions on Intelligent Systems & Technology, 2015, 6(3):1-29.
[24] Zheng Y. Methodologies for cross-domain data fusion:An overview[J]. IEEE Transactions on Big Data, 2015, 1(1):16-34.
[25] 徐宗本. 用好大数据须有大智慧[N]. 人民日报, 2016-03-15. Xu Zongben. Good use of big data must have great wisdom[N]. People's Daily, 2016-03-15.
[26] 程学旗, 靳小龙, 杨婧, 等. 大数据技术进展与发展趋势[J]. 科技导报, 2016, 34(14):49-59. Cheng Xueqi, Ji Xiaolong, Yang Jing, et al. Technological progress and trends of big data[J]. Science & Technology Review, 2016, 34(14):49-59.
[27] Chen C L P, Zhang C Y. Data-intensive applications, challenges, tech-niques and technologies:A survey on big data[J]. Information Scienc-es, 2014, 275(11):314-347.
[28] Lake B M, Salakhutdinov R, Tenenbaum J B. Human-level concept learning through probabilistic program induction[J]. Science, 2015, 350(6266):1332-1338.
[29] 刘勇, 廖依伊. 类人概念学习:机器学习下一个飞跃?[J]. 科技导报, 2016, 34(7):80-81. Liu Yong, Liao Yiyi. Human-level concept learning:the next leap in machine learning[J]. Science & Technology Review, 2016, 34(7):80-81.
[30] 陈孝良. 小样本的类人概念学习与大数据的深度强化学习[J]. 科技导报, 2016, 34(7):82-84. Chen Xiaoliang. Small sample of human-level concept learning and deep reinforcement learning of big data[J]. Science & Technology Review, 2016, 34(7):82-84.
[31] Itoh M, Yokoyama D, Toyoda M, et al. Visual exploration of changes in passenger flows and tweets on Mega-City Metro Network[J]. IEEE Transactions on Big Data, 2016, 2(1):85-99.
[32] Wu W C, Xu J Y, Zeng H P, et al. TelCoVis:Visual exploration of co-occurrence in urban human mobility based on telco data[J]. IEEE Transactions on Visualization & Computer Graphics, 2016, 22(1):935-944.
[33] 王忠. 个人数据的大数据应用需要构建溯源机制[J]. 科技导报, 2014, 32(4):12. Wang Zhong. Construct of traceability mechanism for big data application of personal data[J]. Science & Technology Review, 2014, 32(4):12.
[34] 王融. 关于大数据交易核心法律问题——数据所有权的探讨[J]. 大数据, 2015, 1(2):49-55. Wang Rong. Discussion on the legal core question of the data ownership in big data trade[J]. Big Data Research, 2015, 1(2):49-55.
[35] 王黎洲. 医疗大健康及大数据的应用及其隐私保护分析[J]. 中国卫生产业, 2016, 13(13):120-122. Wang Lizhou. Analysis of the application of medical one health and big data and their privacy protection[J]. China Health Industry, 2016, 13(13):120-122.
[36] 冯登国, 张敏, 李昊. 大数据安全与隐私保护[J]. 计算机学报, 2014, 37(1):246-258. Feng Dengguo, Zhang Min, Li Hao. Big data security and privacy protection[J]. Chinese Journal of Computers, 2014, 37(1):246-258.
[37] Yang K, Jia X H. Expressive, efficient, and revocable data access con-trol for multi-authority cloud storage[J]. IEEE Transactions on Paral-lel & Distributed Systems, 2014, 25(7):1735-1744.

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