Data-driven urban management: Mapping the landscape

被引:74
|
作者
Engin, Zeynep [1 ]
van Dijk, Justin [2 ]
Lan, Tian [2 ]
Longley, Paul A. [2 ]
Treleaven, Philip [1 ]
Batty, Michael [3 ]
Penn, Alan [4 ]
机构
[1] UCL, Dept Comp Sci, Malet Pl Engn Bldg, London WC1E 6EA, England
[2] UCL, Dept Geog, Pearson Bldg,Gower St, London WC1E 6BT, England
[3] UCL, Bartlett Ctr Adv Spatial Anal, Gower St, London WC1E 6BT, England
[4] UCL, Bartlett Sch Architecture, 22 Gordon St, London WC1H 0QB, England
基金
英国工程与自然科学研究理事会;
关键词
Data-driven society; Urban management and applications; Evidence-based decision making; SMART CITIES; CITY; SYSTEM; MODEL;
D O I
10.1016/j.jum.2019.12.001
中图分类号
TU98 [区域规划、城乡规划];
学科分类号
0814 ; 082803 ; 0833 ;
摘要
Big data analytics and artificial intelligence, paired with blockchain technology, the Internet of Things, and other emerging technologies, are poised to revolutionise urban management. With massive amounts of data collected from citizens, devices, and traditional sources such as routine and well-established censuses, urban areas across the world have - for the first time in history the opportunity to monitor and manage their urban infrastructure in real-time. This simultaneously provides previously unimaginable opportunities to shape the future of cities, but also gives rise to new ethical challenges. This paper provides a transdisciplinary synthesis of the developments, opportunities, and challenges for urban management and planning under this ongoing 'digital revolution' to provide a reference point for the largely fragmented research efforts and policy practice in this area. We consider both top-down systems engineering approaches and the bottom-up emergent approaches to coordination of different systems and functions, their implications for the existing physical and institutional constraints on the built environment and various planning practices, as well as the social and ethical considerations associated with this transformation from non-digital urban management to data-driven urban management.
引用
收藏
页码:140 / 150
页数:11
相关论文
共 50 条
  • [1] Big Data-Driven Urban Management: Potential for Urban Sustainability
    Wu, Min
    Yan, Bingxin
    Huang, Ying
    Sarker, Md Nazirul Islam
    [J]. LAND, 2022, 11 (05)
  • [2] Large Landscape Urban Irrigation: A Data-Driven Approach to Evaluate Conservation Behavior
    Quesnel, Kimberly J.
    Ajami, Newsha K.
    [J]. WATER RESOURCES RESEARCH, 2019, 55 (01) : 771 - 786
  • [3] A data-driven paradigm for mapping problems
    Zhang, Peng
    Liu, Ling
    Deng, Yuefan
    [J]. PARALLEL COMPUTING, 2015, 48 : 108 - 124
  • [4] The Potential of Knowing More: A Review of Data-Driven Urban Water Management
    Eggimann, Sven
    Mutzner, Lena
    Wani, Omar
    Schneider, Mariane Yvonne
    Spuhler, Dorothee
    de Vitry, Matthew Moy
    Beutler, Philipp
    Maurer, Max
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2017, 51 (05) : 2538 - 2553
  • [5] A Data-Driven Urban Metro Management Approach for Crowd Density Control
    Zhou, Hui
    Zheng, Zhihao
    Cen, Xuekai
    Huang, Zhiren
    Wang, Pu
    [J]. JOURNAL OF ADVANCED TRANSPORTATION, 2021, 2021
  • [6] A Data-driven urban metro management approach for crowd density control
    Zhou, Hui
    Zheng, Zhihao
    Cen, Xuekai
    Huang, Zhiren
    Wang, Pu
    [J]. Journal of Advanced Transportation, 2021, 2021
  • [7] Data-driven optimization in management
    Consigli, Giorgio
    Kleywegt, Anton
    [J]. COMPUTATIONAL MANAGEMENT SCIENCE, 2019, 16 (03) : 371 - 374
  • [8] Data-driven optimization in management
    Giorgio Consigli
    Anton Kleywegt
    [J]. Computational Management Science, 2019, 16 : 371 - 374
  • [9] Fuzzy and Data-Driven Urban Crowds
    Toledo, Leonel
    Rivalcoba, Ivan
    Rudomin, Isaac
    [J]. COMPUTATIONAL SCIENCE - ICCS 2018, PT III, 2018, 10862 : 280 - 290
  • [10] Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany
    Seleem, Omar
    Ayzel, Georgy
    de Souza, Arthur Costa Tomaz
    Bronstert, Axel
    Heistermann, Maik
    [J]. GEOMATICS NATURAL HAZARDS & RISK, 2022, 13 (01) : 1640 - 1662