Smart City Gnosys
Smart city article details
| Title | Machine Learning For Resilient And Sustainable Cities: A Bibliometric Analysis Of Smart Urban Technologies |
|---|---|
| ID_Doc | 35974 |
| Authors | Luan B.; Feng X. |
| Year | 2025 |
| Published | Buildings, 15, 7 |
| DOI | http://dx.doi.org/10.3390/buildings15071007 |
| Abstract | With the acceleration of urbanization, the construction of smart cities has become a global focal point, with machine learning technology playing a crucial role in this process. This study aims to conduct a bibliometric analysis of the published research in the fields of smart cities and machine learning, using visualization techniques to reveal the spatiotemporal distribution patterns, research hotspots, and collaborative network structures. The goal is to provide systematic references for academic research and technological innovation in related fields. The results indicate that the development of this field exhibits distinct phases and regional characteristics. From a temporal perspective, research has undergone three stages: initial development, rapid growth, and stable consolidation, with the period from 2017 to 2021 marking a critical phase of rapid expansion. In terms of spatial distribution, countries such as China and the United States are at the forefront of this field, whereas regions like Africa and South America have a relatively low research output due to constraints in research resources and technological infrastructure. A hotspot analysis revealed that research topics are increasingly diverse and dynamically evolving. Issues such as data privacy, cybersecurity, sustainable development, and intelligent transportation have gradually become focal points, reflecting the dual demand of smart city development for technological innovation and green growth. Furthermore, collaboration network analysis indicates that international academic cooperation is becoming increasingly close, with research institutions in China, the United States, and Europe playing a central role in the global collaboration system, thereby promoting technology sharing and interdisciplinary integration. Through a systematic bibliometric analysis, this study identifies key application directions and future development trends in the research on smart cities and machine learning, providing valuable insights for academic research and technological advancements in related fields. © 2025 by the authors. |
| Author Keywords | bibliometrics; machine learning; Scimago; smart cities; VOSviewer |
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