Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities

被引:144
|
作者
Matheus, Ricardo [1 ]
Janssen, Marijn [1 ]
Maheshwari, Devender [1 ]
机构
[1] Delft Univ Technol, Fac Technol Policy & Management, Jaffalaan 5, NL-2628 BX Delft, Netherlands
基金
欧盟地平线“2020”;
关键词
Data science; Dashboards; E-government; Open government; Open data; Big data; Smart City; Design principles; Transparency; Accountability; Trust; Policy-making; Decision-making; LINKED DATA BOLD; BIG DATA; GOVERNMENT; IMPACT; INFORMATION; STRATEGY;
D O I
10.1016/j.giq.2018.01.006
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
Dashboards visualize a consolidated set data for a certain purpose which enables users to see what is happening and to initiate actions. Dashboards can be used by governments to support their decision-making and policy processes or to communicate and interact with the public. The objective of this paper is to understand and to support the design of dashboards for creating transparency and accountability. Two smart city cases are investigated showing that dashboards can improve transparency and accountability, however, realizing these benefits was cumbersome and encountered various risks and challenges. Challenges include insufficient data quality, lack of understanding of data, poor analysis, wrong interpretation, confusion about the outcomes, and imposing a pre-defined view. These challenges can easily result in misconceptions, wrong decision-making, creating a blurred picture resulting in less transparency and accountability, and ultimately in even less trust in the government. Principles guiding the design of dashboards are presented. Dashboards need to be complemented by mechanisms supporting citizens' engagement, data interpretation, governance and institutional arrangements.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Data-driven decision-making for wastewater treatment process
    Han, Hong-Gui
    Zhang, Hui-Juan
    Liu, Zheng
    Qiao, Jun-Fei
    [J]. CONTROL ENGINEERING PRACTICE, 2020, 96
  • [22] A Data-Driven Simulator for Assessing Decision-Making in Soccer
    Mendes-Neves, Tiago
    Mendes-Moreira, Joao
    Rossetti, Rosaldo J. F.
    [J]. PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2021), 2021, 12981 : 687 - 698
  • [23] Data-driven decision-making in emergency remote teaching
    Maya Botvin
    Arnon Hershkovitz
    Alona Forkosh-Baruch
    [J]. Education and Information Technologies, 2023, 28 : 489 - 506
  • [24] Elementary teachers’ perceptions of data-driven decision-making
    Natalie Schelling
    Lisa DaVia Rubenstein
    [J]. Educational Assessment, Evaluation and Accountability, 2021, 33 : 317 - 344
  • [25] Scalable intelligent data-driven decision making for cognitive cities
    Akshi Kumar
    Arunima Jaiswal
    [J]. Energy Systems, 2022, 13 : 581 - 599
  • [26] Data-driven decision-making in emergency remote teaching
    Botvin, Maya
    Hershkovitz, Arnon
    Forkosh-Baruch, Alona
    [J]. EDUCATION AND INFORMATION TECHNOLOGIES, 2023, 28 (01) : 489 - 506
  • [27] Data-driven decision-making in classification algorithm selection
    Oreski, Dijana
    Redep, Nina Begicevic
    [J]. JOURNAL OF DECISION SYSTEMS, 2018, 27 (27) : 248 - 255
  • [28] Scalable intelligent data-driven decision making for cognitive cities
    Kumar, Akshi
    Jaiswal, Arunima
    [J]. ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS, 2022, 13 (03): : 581 - 599
  • [30] DATA-DRIVEN DECISION-MAKING FOR MALARIA ELIMINATION IN NAMIBIA: DESIGN AND IMPLEMENTATION OF CUSTOMIZED DASHBOARDS IN DHIS2
    Nghipumbwa, Mwalenga
    Didier, Bradley
    Pindolia, Deepa
    Balachandran, Lakshmi
    Gast, Laura
    Matavire, Rangarirai
    Uusiku, Petrina
    Nitschke, Anne-Marie
    [J]. AMERICAN JOURNAL OF TROPICAL MEDICINE AND HYGIENE, 2017, 97 (05): : 280 - 281