A Bibliometric Analysis of Research on Historical Buildings and Digitization

Abstract

The wealth of published data are valuable because, in addition to contributing to the advancement of scientific, technical, and policy knowledge, they can also provide critical information and guidance regarding published content, subject changes, and trends that demand greater attention. In the 21st century, digital technologies play an increasingly important role in “data capture”, “building management”, “virtual reconstruction”, and “building restoration”. The indispensable role of digital technology in addressing “data capture”, “building management”, “virtual reconstruction”, and “building restoration” has resulted in the publication of several high-quality publications. In this study, we retrieve textual data from Web of Science and mine the content of the documentary data using COOC, VOSviewer, CiteNetwork, and academic influence evaluation (AIE) software to gain insights into the prospects and opportunities for historic architecture and digitization research. The results indicate that greater progress has been made in research on the use of digital technologies for the conservation of historic buildings from 2019 to 2023, but cross-disciplinary, cross-institutional, and cross-border collaboration should be enhanced. The research frontiers identified indicate that photogrammetry, 3D modeling, point cloud, and deep learning will require sustained attention in the near future. Additionally, computational analyses of academic influence reveal that Italian institutions and authors have dominated research in this field in recent years. A new strategy and framework for data-driven bibliometric analysis involving historical architecture and digitization techniques are presented in this study. Based on general bibliometric methods, this study innovatively explores the scientific knowledge base and knowledge flow of highly cited articles, provides comprehensive evaluation indicators such as H-index, G-index, P-index, and Z-index for high-impact journals, institutions, and authors, and proposes a COOC-based idea to address the consistency of data sources among multiple software.