A bibliometric and scientometric review of research on crowdsourcing in smart cities. Issue 1 (25th November 2022)

• Record Type: Journal Article
• Authors: Jiang, Youtao; Guo, Bowen; Zhang, Xunda; Tian, Hongxin; Wang, Yue; Cheng, Ming
• Is Part Of: IET smart cities. Volume 5:Issue 1(2023)
• Page Start: 1
• Page End: 18
• Publication Date: 2022-11-25
• Subjects: bibliometrics -- crowdsourcing -- review -- scientometrics -- smart city; Smart cities -- Periodicals; City planning -- Technological innovations -- Periodicals; Cities and towns -- Growth -- Periodicals; Sustainable urban development -- Periodicals; Sustainable development; City planning -- Technological innovations; Cities and towns -- Growth; Periodicals; 307.76
• DOI: 10.1049/smc2.12048
• Languages: English
• ISSNs: 2631-7680

Abstract

Abstract: The smart city model for operation and governance of modern cities requires huge quantities of data about people and the environment to support related applications, and crowdsourcing is effective for collecting and processing such data. Numerous reviews of the literature on crowdsourcing and smart cities as separate research fields have been published. Research on the intersection of the two is relatively new and lacks a systematic literature review. This study applies bibliometric and scientometric research methods and the research tools to study 367 related publications retrieved from the Web of Science database. Keyword co‐occurrence analysis was conducted to find the distribution of publications, research cooperation, and major research areas. Document co‐citation analysis was used to examine the domain knowledge structure, and citation burst detection was used to identify trends in research in this topic area. The analysis results clarify the research content and evolutionary context and reveal emerging research trends. Abstract : This study applies bibliometric and scientometric research methods and the research tools to study 367 related publications retrieved from the Web of Science database. Keyword co‐occurrence analysis was conducted to find the distribution of publications, research cooperation, and major research areas. Document co‐citation analysis was used to examine the domain knowledge structure, and citation burst detection was used to identify trends in research in this topic area.