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Citizen-centered big data analysis-driven governance intelligence framework for smart cities

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  • Ju, Jingrui
  • Liu, Luning
  • Feng, Yuqiang

Abstract

Sensors and systems within rapidly expanding smart cities produce citizen-centered big data which have potential value to support citizen-centered urban governance decision-making. There exists a wealth of extant conceptual studies, however, further operational studies are needed to establish a specific path towards implementation of such data to governance decision-making with analytical algorithms that are appropriate for each step of the path. This paper proposes a framework for the use of citizen-centered big data analysis to drive governance intelligence in smart cities from two perspectives: urban governance issues and data-analysis algorithms. The framework consists of three layers: 1) A data-merging layer, which builds a citizen-centered panoramic data set for each citizen by merging citizen-related big data from multiple sources in collaborative urban governance via similarity calculation and conflict resolution; 2) a knowledge-discovery layer, which plots the citizen profile and citizen persona at both individual and group levels in terms of urban public service delivery and citizen participation via simple statistical analysis techniques, machine learning, and econometrics methods; and 3) a decision-making layer, which uses ontology models to standardize urban governance-related attributes, personas, and associations to support governance decision-making via data mining and Bayesian Net techniques. Finally, the proposed framework is validated in a case study on blood donation governance in China. This research highlights the value of citizen-centered big data, pushes data-to-decision research from conceptual to operational, synthesizes previously published frameworks for citizen-centered big data analysis in smart cities, and enhances the mutual supplement cross multiple disciplinaries.

Suggested Citation

  • Ju, Jingrui & Liu, Luning & Feng, Yuqiang, 2018. "Citizen-centered big data analysis-driven governance intelligence framework for smart cities," Telecommunications Policy, Elsevier, vol. 42(10), pages 881-896.
  • Handle: RePEc:eee:telpol:v:42:y:2018:i:10:p:881-896
    DOI: 10.1016/j.telpol.2018.01.003
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Jingrui Ju & Luning Liu & Yuqiang Feng, 2019. "Design of an O2O Citizen Participation Ecosystem for Sustainable Governance," Information Systems Frontiers, Springer, vol. 21(3), pages 605-620, June.
    2. Saeed Nosratabadi & Amir Mosavi & Ramin Keivani & Sina Ardabili & Farshid Aram, 2020. "State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability," Papers 2010.02670, arXiv.org.
    3. Si Ying Tan & Araz Taeihagh, 2020. "Smart City Governance in Developing Countries: A Systematic Literature Review," Sustainability, MDPI, Open Access Journal, vol. 12(3), pages 1-29, January.
    4. Mathias Eggert & Jens Alberts, 2020. "Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda," Business Research, Springer;German Academic Association for Business Research, vol. 13(2), pages 685-739, July.
    5. Chae, Bongsug (Kevin), 2019. "The evolution of the Internet of Things (IoT): A computational text analysis," Telecommunications Policy, Elsevier, vol. 43(10).
    6. Mimica R. Milošević & Dušan M. Milošević & Dragan M. Stević & Ana D. Stanojević, 2019. "Smart City: Modeling Key Indicators in Serbia Using IT2FS," Sustainability, MDPI, Open Access Journal, vol. 11(13), pages 1-28, June.
    7. Lin, Yanliu, 2018. "A comparison of selected Western and Chinese smart governance: The application of ICT in governmental management, participation and collaboration," Telecommunications Policy, Elsevier, vol. 42(10), pages 800-809.

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