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Visualization and Analysis of Mapping Knowledge Domain of Urban Vitality Research

Author

Listed:
  • Jian-gang Shi

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Wei Miao

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Hongyun Si

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

Abstract

Mapping knowledge domain (MKD) is an important research technology in bibliometrics. It provides a visual perspective for researchers and helps researchers to clearly understand the general situations of specific research fields. Presently, MKD has been widely used in fields such as ecology and transportation safety. In this paper, we conduct a systematically bibliometric analysis of the development trend on urban vitality research by using VOSviewer software, and this work is based on the related articles published in the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) between 1998 and 2018. In the implementation, we firstly introduce the annual numbers of articles, source journals, highly cited papers, and production countries relating to urban vitality research. Then, we adopt a keywords co-occurrence analysis to explore the topic distribution, research frontiers, and research trends of urban vitality research intuitively. Overall, the Mapped Knowledge Domain method has proved to be an effective way to assess research trends in specific disciplines and to contribute to researchers and governments with respect to management and decision-making on issues of science.

Suggested Citation

  • Jian-gang Shi & Wei Miao & Hongyun Si, 2019. "Visualization and Analysis of Mapping Knowledge Domain of Urban Vitality Research," Sustainability, MDPI, vol. 11(4), pages 1-17, February.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:4:p:988-:d:205951
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    Cited by:

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    2. Jiangang Shi & Wei Miao & Hongyun Si & Ting Liu, 2021. "Urban Vitality Evaluation and Spatial Correlation Research: A Case Study from Shanghai, China," Land, MDPI, vol. 10(11), pages 1-15, November.
    3. Yihao Jiang & Zhaojin Chen & Pingjun Sun, 2022. "Urban Shrinkage and Urban Vitality Correlation Research in the Three Northeastern Provinces of China," IJERPH, MDPI, vol. 19(17), pages 1-22, August.
    4. Yi Xue & Zhonggen Yu, 2023. "Bibliometric Analysis of Ambiguity Tolerance: Unearthing Its Role in Sustainable Language Education," Sustainability, MDPI, vol. 15(15), pages 1-21, August.
    5. Hongyun Si & Jian-gang Shi & Daizhong Tang & Shiping Wen & Wei Miao & Kaifeng Duan, 2019. "Application of the Theory of Planned Behavior in Environmental Science: A Comprehensive Bibliometric Analysis," IJERPH, MDPI, vol. 16(15), pages 1-26, August.
    6. Anne Parlina & Kalamullah Ramli & Hendri Murfi, 2021. "Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means," Sustainability, MDPI, vol. 13(5), pages 1-28, March.
    7. Piotr Michalak & Krzysztof Szczotka & Jakub Szymiczek, 2023. "Audit-Based Energy Performance Analysis of Multifamily Buildings in South-East Poland," Energies, MDPI, vol. 16(12), pages 1-21, June.
    8. Yin, Xicheng & Wang, Hongwei & Wang, Wei & Zhu, Kevin, 2020. "Task recommendation in crowdsourcing systems: A bibliometric analysis," Technology in Society, Elsevier, vol. 63(C).
    9. Wuliyasu Bai & Liang Yan & Jingbo Liang & Long Zhang, 2022. "Mapping Knowledge Domain on Economic Growth and Water Sustainability: A Scientometric Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4137-4159, September.

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