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Identifying Smart City Leaders and Followers with Machine Learning

Author

Listed:
  • Fangyao Liu

    (College of Electronic and Information, Southwest Minzu University, Chengdu 610093, China)

  • Nicole Damen

    (School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, NE 68182, USA)

  • Zhengxin Chen

    (College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA)

  • Yong Shi

    (College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA)

  • Sihai Guan

    (College of Electronic and Information, Southwest Minzu University, Chengdu 610093, China)

  • Daji Ergu

    (College of Electronic and Information, Southwest Minzu University, Chengdu 610093, China)

Abstract

Smart cities have been a popular topic for the city stakeholders. A smart city is the next urban lifestyle that citizens expect. Due to the hypercompetitive and globalized economy, many cities have already started or are about to start their smart city projects. There is no uniform benchmark to evaluate the smart cities’ performance. Several organizations use their own indicators to evaluate smart cities worldwide or nationwide. This research paper leverages fuzzy logic to label smart city leaders and followers based on various organization’s evaluation meta results and then uses machine learning techniques to identify the key characteristics of leaders and followers. Based on the training data performance, the Support Vector Machine (SVM) is used to predict who will be the next smart city leader or follower. According to the proposed prediction framework, we have successfully predicted 30 smart city leaders and 20 followers.

Suggested Citation

  • Fangyao Liu & Nicole Damen & Zhengxin Chen & Yong Shi & Sihai Guan & Daji Ergu, 2023. "Identifying Smart City Leaders and Followers with Machine Learning," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9671-:d:1172769
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    References listed on IDEAS

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    1. Marsal-Llacuna, Maria-Lluïsa & Colomer-Llinàs, Joan & Meléndez-Frigola, Joaquim, 2015. "Lessons in urban monitoring taken from sustainable and livable cities to better address the Smart Cities initiative," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 611-622.
    2. Robert G. Hollands, 2008. "Will the real smart city please stand up?," City, Taylor & Francis Journals, vol. 12(3), pages 303-320, December.
    3. Luca Mora & Roberto Bolici & Mark Deakin, 2017. "The First Two Decades of Smart-City Research: A Bibliometric Analysis," Journal of Urban Technology, Taylor & Francis Journals, vol. 24(1), pages 3-27, January.
    4. Vito Albino & Umberto Berardi & Rosa Maria Dangelico, 2015. "Smart Cities: Definitions, Dimensions, Performance, and Initiatives," Journal of Urban Technology, Taylor & Francis Journals, vol. 22(1), pages 3-21, January.
    5. Lazaroiu, George Cristian & Roscia, Mariacristina, 2012. "Definition methodology for the smart cities model," Energy, Elsevier, vol. 47(1), pages 326-332.
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    Cited by:

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    2. Ibrahim Mutambik & John Lee & Abdullah Almuqrin & Abdulrhman Alkhanifer & Mohammed Baihan, 2023. "Gulf Cooperation Council Countries and Urbanisation: Are Open Government Data Portals Helping?," Sustainability, MDPI, vol. 15(17), pages 1-22, August.

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