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Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018

Author

Listed:
  • Jovani Taveira de Souza

    (Department of Production Engineering, Federal University Technology, Av. Monteiro Lobato, 84016-210, Ponta Grossa, Paraná, Brazil)

  • Antonio Carlos de Francisco

    (Department of Production Engineering, Federal University Technology, Av. Monteiro Lobato, 84016-210, Ponta Grossa, Paraná, Brazil)

  • Cassiano Moro Piekarski

    (Department of Production Engineering, Federal University Technology, Av. Monteiro Lobato, 84016-210, Ponta Grossa, Paraná, Brazil)

  • Guilherme Francisco do Prado

    (Department of Production Engineering, Federal University Technology, Av. Monteiro Lobato, 84016-210, Ponta Grossa, Paraná, Brazil)

Abstract

Smart cities (SC) promote economic development, improve the welfare of their citizens, and help in the ability of people to use technologies to build sustainable services. However, computational methods are necessary to assist in the process of creating smart cities because they are fundamental to the decision-making process, assist in policy making, and offer improved services to citizens. As such, the aim of this research is to present a systematic review regarding data mining (DM) and machine learning (ML) approaches adopted in the promotion of smart cities. The Methodi Ordinatio was used to find relevant articles and the VOSviewer software was performed for a network analysis. Thirty-nine significant articles were identified for analysis from the Web of Science and Scopus databases, in which we analyzed the DM and ML techniques used, as well as the areas that are most engaged in promoting smart cities. Predictive analytics was the most common technique and the studies focused primarily on the areas of smart mobility and smart environment. This study seeks to encourage approaches that can be used by governmental agencies and companies to develop smart cities, being essential to assist in the Sustainable Development Goals.

Suggested Citation

  • Jovani Taveira de Souza & Antonio Carlos de Francisco & Cassiano Moro Piekarski & Guilherme Francisco do Prado, 2019. "Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018," Sustainability, MDPI, vol. 11(4), pages 1-14, February.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:4:p:1077-:d:207097
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    References listed on IDEAS

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    Cited by:

    1. Marcela Marçal Alves Pinto & João Luiz Kovaleski & Rui Tadashi Yoshino & Regina Negri Pagani, 2019. "Knowledge and Technology Transfer Influencing the Process of Innovation in Green Supply Chain Management: A Multicriteria Model Based on the DEMATEL Method," Sustainability, MDPI, vol. 11(12), pages 1-33, June.
    2. Jun Houng Kim & Seok-Jin Eom, 2019. "The Managerial Dimension of Open Data Success: Focusing on the Open Data Initiatives in Korean Local Governments," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    3. Ana De Las Heras & Amalia Luque-Sendra & Francisco Zamora-Polo, 2020. "Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era," Sustainability, MDPI, vol. 12(22), pages 1-25, November.
    4. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    5. Nammi Kim & Seungwoo Yang, 2021. "Characteristics of Conceptually Related Smart Cities (CRSCs) Services from the Perspective of Sustainability," Sustainability, MDPI, vol. 13(6), pages 1-48, March.
    6. Murillo Vetroni Barros & Rômulo Henrique Gomes Jesus & Bruno Silva Ribeiro & Cassiano Moro Piekarski, 2023. "Going in Circles: Key Aspects for Circular Economy Contributions to Agro-industrial Cooperatives," Circular Economy and Sustainability, Springer, vol. 3(2), pages 861-880, June.

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