Page 22 - JCAU-5-3

Journal of Chinese
Architecture and Urbanism Development of industrial heritage reuse modes

University, Qingdao University of Technology, Tianjin It is evident that research scholars in the field of
University, Southeast University, Beijing University of Civil industrial heritage in China predominantly come from
Engineering and Architecture, School of Architecture, backgrounds in architecture and urban planning. In
South China University of Technology, School of Design, addition, scholars from fields such as human geography,
Jiangnan University, Jianghe School of Architecture, art and design, and archaeology have also contributed
Northeastern University, and Tsinghua University, are among to the research on the preservation and revitalization of
the prominent institutions. It is worth mentioning that industrial heritage.
Tianjin, Chongqing, Shanghai, and Qingdao have emerged 4. Research hotspot and research path
as significant industrial development cities in modern times.
These cities possess a rich foundation in terms of industrial 4.1. Analysis of research hotspots
development, making them important contributors to the Keywords extracted from academic papers serve as an
field of industrial heritage research and reuse (Zhao, 2020).
important tool for literature retrieval, as they reflect
the core content and ideas of the articles (Su, 2020). By
analyzing the co-occurrence frequency of keywords in
the literature database, the relationship between different
topics can be determined, and hot research topics can
be identified through the analysis of keyword content
and network density. In this study, CiteSpace was used
to analyze the literature database, covering from 2003 to
2023 with a time slice of 1 year. The analysis focused on
keywords, with the top 100 keywords in each time slice,
using “Cosine” as the intensity of the connection. The “Find
cluster” function was applied to cluster the keywords, with
a maximum clustering value set to K = 9. The resulting
keyword clustering graph is shown in Figure 3.
The keyword clustering view, represented by different
clusters, provides insights into the structural features
between clusters, highlighting key nodes and important
Figure 3. Industrial heritage activation utilization mode keyword connections (Chen et al., 2015). In the graph, the size of
co-occurrence map. Source: Drawing by the authors the circles corresponds to the frequency of the keywords,

Figure 4. Keywords timeline graph depicting the industrial heritage activation utilization mode. Source: Drawing by the authors

Volume 5 Issue 3 (2023) 4 https://doi.org/10.36922/jcau.1034