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Review

Global Research Trends and Future Directions in Urban Historical Heritage Area Conservation and Development: A 25-Year Bibliometric Analysis

by
Jun Xia
1,
Jing Kang
2 and
Xiaolin Xu
1,*
1
Department of Environmental Design, Dongseo University, Busan 47011, Republic of Korea
2
Department of Landscape and Construction Engineering, Woosuk University, Jeonju 55338, Republic of Korea
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(10), 3096; https://doi.org/10.3390/buildings14103096
Submission received: 26 August 2024 / Revised: 12 September 2024 / Accepted: 24 September 2024 / Published: 27 September 2024
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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Abstract

:
Urban historical heritage areas serve as vital repositories of urban culture and history, playing a crucial role in cultural inheritance and the promotion of urban development. The protection and development of these heritage areas are essential for preserving the cultural characteristics and architectural styles of cities. Despite the growing body of research, a comprehensive review of the dynamic evolution, research frontiers, and future trajectories in this field remains absent. To bridge this gap, this study draws on the Web of Science Core Collection database, selecting 828 papers published between 2000 and 2024 that focus on urban historical heritage conservation and development. By employing Python programming and network analysis tools, this study conducted a systematic analysis of research structures and trends over the past 25 years. The results indicate that countries such as China and Italy, along with their respective research institutions, are at the forefront of global research in this area. Furthermore, this study identified research hotspots, including historic districts, sustainable urban development, urban regeneration, risk assessment, 3D modeling, digital documentation, and cultural tourism. This research not only discusses the challenges faced in the field but also explores future development trends, providing new theoretical perspectives and practical guidance for subsequent studies.

1. Introduction

Urban historical heritage areas are invaluable, non-renewable resources within cities, embodying rich historical, cultural, scientific, and artistic significance. These areas include historical districts, environments, and buildings [1,2]. UNESCO defines heritage as “the legacy we inherit from the past, with which we coexist today, and which we pass on to future generations [3]”. Compared to ordinary urban areas, urban historical heritage areas face more complex and diverse challenges related to efficiency and equity [4]. Often located in the city’s core, these areas, despite experiencing pressures of decline, demonstrate a high demand and potential for functional renewal and regeneration [5]. As regions characterized by a high concentration of land resources and various influencing factors, urban historical heritage areas impose particularly stringent requirements for land use efficiency [6,7].
In recent years, the concept of old city renovation has evolved from a focus on mere physical improvements to a comprehensive development approach that encompasses physical, economic, cultural, and social dimensions [4,8,9]. However, reconciling the tension between urban regeneration and heritage conservation often presents significant challenges [10,11]. Existing regeneration plans frequently overlook the unique characteristics of the land and the functions within historical areas, which can lead to the isolation and homogenization of heritage [12]. Historic city centers face the dual challenges of functional improvement and heritage preservation. This presents a significant dilemma in maintaining historical and cultural characteristics while addressing the demands for modernization during the redevelopment of aging urban areas [13,14].
The protection and development of urban historical heritage areas encompass multiple research dimensions, including the restoration and adaptive reuse of historical buildings [15], rational spatial planning [16], community participation, and sustainable economic development [4]. These factors are interrelated and collectively influence the effectiveness of heritage conservation and the development models in these areas [17]. Existing research primarily concentrates on conflict resolution, the coexistence of cultural and natural heritage, the role of local identity, economic impacts within heritage areas, and the sustainable development of tourism and urban planning frameworks [18,19,20]. Studies examine how to coordinate development amidst urbanization, balance the needs of various stakeholders, preserve the unique cultural and historical characteristics of the area, and achieve economic and social sustainability of heritage areas through tourism, community engagement, and legal policy instruments [21,22].
Bibliometric analysis, a statistical method employed to elucidate research outcomes and assess the impact of literature [23], has been extensively applied across various fields, including sustainable development, urban landscapes, green infrastructure, and low-carbon city design [24]. However, there is a significant lack of bibliometric studies that concentrate on the protection and development of global urban historical heritage areas. This gap underscores the necessity for a systematic analysis from various perspectives to gain a comprehensive understanding of the opportunities and challenges within this field [25]. To address this gap, 828 relevant documents were sourced from the Web of Science Core Collection database. A bibliometric analysis, employing Python programming and network analysis tools, was conducted to provide in-depth insights. This paper concentrates on the following key areas:
  • Identification of the primary research contributors in this field, including individuals, institutions, and countries, along with an analysis of their collaborative relationships.
  • Revelation of the core journals and significant literature in this field, accompanied by an evaluation of their academic impact.
  • Analysis of research hotspots and frontiers in this field, while also identifying the inherent limitations of existing research and potential directions for future inquiry.

2. Theoretical Background

Globally, the decline of urban historical heritage areas is particularly pronounced, especially in developing countries [26]. Many historic city centers face challenges such as the deterioration of social structures, inadequate public services, and infrastructure deficiencies, all of which contribute to a gradual erosion of cultural significance and identity [27]. Local government planning departments play a crucial role in addressing these issues [28]; however, they often lack sufficient resources and effective conservation strategies [29]. To maintain the historical integrity of urban heritage areas, it is essential to preserve their physical characteristics, including streets, building clusters, and overall appearance [30].
The protection and development of urban historical heritage areas are essential for preserving the cultural and architectural characteristics of cities [4,31]. Riegl’s (1903) theory on the value of historical heritage established a foundational framework for heritage conservation [32]. Since then, international cultural heritage organizations such as UNESCO and ICOMOS have continuously refined relevant conservation principles and guidelines [33]. Venida (2002), using Manila’s Quiapo district as a case study, investigated conflicts in heritage conservation, underscoring the importance of balancing the needs of various stakeholders during urban renewal and community development [34]. Ginting et al. (2016) emphasized the significance of preserving the unique cultural and historical characteristics of urban heritage areas, which are intricately linked to local identity. They underscored the crucial role that local identity plays in shaping and protecting urban heritage [35]. Aboukhater (2020) analyzed a culture-led urban development plan in Damascus’s old city, exploring the transformation of cultural districts and their socio-economic impacts, thus providing valuable insights for the post-conflict protection and regeneration of urban historical heritage areas [36]. Fouda et al. (2021) focused on new architectural design guidelines for the heritage area in Mansoura, Egypt, highlighting strategies for safeguarding cultural and historical integrity amid new developments in heritage areas [37].
Additionally, the economic value of historical sites is increasingly recognized, particularly in the context of urban development pressures that threaten their existence [38]. Striking a balance between preservation and redevelopment is a critical issue; cities, while pursuing modernization, should prioritize the protection of their cultural heritage [39]. This dilemma is especially pronounced in cities like Xi’an, where urban heritage serves not only as a symbol of identity but also as an integral component of the urban spatial structure [40]. Consequently, incorporating historical textures and landscapes into urban planning is essential for preserving cultural heritage, as these elements contribute significantly to the city’s unique character and identity [41].
The significance of urban heritage in tourism development is substantial, as heritage sites frequently serve as major attractions that drive local economic growth [42]; however, they require careful management to prevent overdevelopment and degradation [43]. The interaction between urban commerce and protected cultural landscapes highlights the necessity of balancing economic growth with heritage conservation [44]. Urban historical heritage areas are vital to the cultural and economic identity of cities [4]. Effective management strategies should incorporate community engagement, recognize the economic value of heritage, and strive to balance modernization with preservation [45]. As urbanization poses threats to cultural heritage, it is imperative for all stakeholders to prioritize the protection and enhancement of urban historical landscapes [46].

3. Materials and Methods

3.1. Data Source

The Web of Science Core Collection (WOS) database is one of the primary academic resources utilized globally by researchers, librarians, and other information users [47]. It encompasses multiple academic disciplines and provides high-quality, widely cited journal articles, conference papers, books, and other resources [48]. This database is extensively employed in bibliometric research. For the purpose of this study, data were meticulously collected from six prominent databases available on the Web of Science (WOS) platform. These include the Science Citation Index Expanded (SCI-EXPANDED), which focuses on the natural sciences; the Social Sciences Citation Index (SSCI), which encompasses a variety of disciplines within the social sciences; and the Arts & Humanities Citation Index (A&HCI), which addresses research in the arts and humanities. Additionally, this research incorporates data from the Conference Proceedings Citation Index-Science (CPCI-S) and the Conference Proceedings Citation Index-Social Science and Humanities (CPCI-SSH), both of which provide insights into research trends within their respective fields. Lastly, the Emerging Sources Citation Index (ESCI) is included to cover fresh and innovative research areas [49].

3.2. Data Search Strategy

Given that the database is subject to daily updates, a comprehensive online search was conducted within a single day to ensure data accuracy [50]. This research focuses on the topic of “urban historical heritage conservation and development”. Accordingly, our search strategy encompassed three key areas: “urban”, “historical heritage areas”, and “conservation and development”. To thoroughly cover the relevant literature in these domains, we expanded the keyword expressions of keywords for these three thematic areas, as detailed in Table 1.
The search strategy employed boolean operators, utilizing AND to connect the three thematic areas, while OR was used to link the keywords within each area. The search was conducted in the Web of Science Core Collection (WOSCC), which encompasses the databases SCI-E, SSCI, A&HCI, CPCI-S, CPCI-SSH, and ESCI. The search was conducted in English, and the time range was established from 1 January 2000, to 1 August 2024. This selection was based on the observation that prior to 2000, articles were sparse and relatively scattered. The year 2000 marked a critical turning point for this field, characterized by a global increase in related policies and research, signifying the onset of systematic and rapid development. Consequently, this 25-year time span was chosen to comprehensively capture the research dynamics of the field, ensuring both the timeliness and representativeness of the analyzed literature. The document types were limited to articles and proceeding papers. A total of 1522 documents were retrieved. After thoroughly cleaning the data, which included standardizing formats, removing gaps, irrelevant topics, and duplicate articles, 828 articles were selected for further data analysis. The detailed search process is illustrated in Figure 1.

3.3. Bibliometric Analysis

Bibliometrics is a discipline that integrates mathematics, statistics, and various quantitative methods to analyze the structural distribution, quantitative relationships, evolving patterns, and management of literature and information resources [51]. Its primary objective is to uncover the development trends of scientific research, identify research hotspots, and assess academic impact by examining characteristics such as the quantity, distribution, and citation of publications [52]. Currently, a variety of analytical tools are widely utilized in the field of bibliometrics, including VOSviewer, CiteSpace, and Bibliometrix [53].
In this study, we chose to utilize Python for bibliometric analysis. Compared to traditional bibliometric tools such as VOSviewer and CiteSpace, Python offers robust scientific computing libraries, including NumPy, Pandas, and SciPy, which efficiently process and analyze substantial volumes of bibliographic data [54]. Python provides greater flexibility and enhanced capabilities in data processing than dedicated tools like VOSviewer and CiteSpace, enabling it to manage more complex and diverse data formats [55]. Furthermore, Python supports a variety of data visualization libraries, such as Matplotlib, Seaborn, and Plotly, which not only generate high-quality charts and visual outputs but are also highly customizable to meet specific research needs [56]. In summary, Python’s extensive library and tool support make it ideal for cross-domain data integration and analysis. By leveraging Python’s powerful data processing and analysis capabilities, along with its efficient visualization tools, we can effectively present analysis results and provide substantial support for scientific research [57].

4. Research Results

4.1. Analysis of Global Research Collaboration Networks

Co-authorship in academic papers represents one of the most direct and traceable forms of scientific collaboration, encompassing cooperation at individual, institutional, and national levels [58]. Analyzing co-authored papers offers valuable insights into collaboration networks among various authors from micro, meso, and macro perspectives [59]. This analysis also aids in identifying leading researchers in the global field of urban historical heritage protection and development [60].

4.1.1. Author Collaboration Networks and Their Core Research Forces

The analysis of author collaboration serves as an effective method for evaluating the research output of key authors within a specific field and assessing the intensity of their collaborations [61]. The collaboration network among authors is illustrated in Figure 2. This figure was generated using the selected analysis unit, applying an appropriate threshold, and excluding isolated nodes. The size of the nodes corresponds to the publication count for each author, with larger nodes indicating a greater volume of publications. Furthermore, the thickness of the lines connecting the nodes represents the frequency of collaboration; thicker lines signify a higher number of co-authored works.
Based on data from the Web of Science Core Collection, there are a total of 2441 researchers worldwide dedicated to the preservation and advancement of urban historical heritage sites, resulting in 4166 collaborative connections. The overall density of the network is 0.0014, while the average clustering coefficient is 0.7801. To maintain high cohesion and minimize coupling among author groups, the Louvain algorithm, which is based on principles of modularity maximization, was employed to identify and analyze nodes within the author collaboration network, thereby classifying authors into distinct groups. Figure 2 illustrates that authors with two or more publications are prominently featured. Furthermore, the network contains several isolated sub-networks, indicating that researchers in this field often collaborate in small teams with limited interactions outside their groups.
Among these, the subnetwork led by Chen Fulong, which comprises 28 nodes, represents the largest research team. Key members of this team include Wang Xinyuan, Guo Huadong, Fang Chaoyang, Zhou Wei, Liu Jie, and Tang Panpan, among others. Additionally, the second-largest subnetwork consists of 22 researchers, including Fang Wang, Dong Ying, and Zhu Xiaohua. The remaining collaborative teams are smaller in scale and have relatively fewer publications.
Overall, the author collaboration network displays a low connection density, with nodes appearing relatively isolated. This indicates that academic collaboration in this field is still in its early stages, marked by a lack of cooperative relationships and limited collaborative strength. The field as a whole exhibits a pattern of “overall dispersion with small areas of concentration”, and a large-scale collaborative model has yet to materialize.
Table 2 illustrates the ten most prolific authors, representing five distinct countries, thereby emphasizing the global distribution of research within this field. These countries comprise China, Italy, Germany, Malaysia, and Greece. Wang Fang and Chen Fulong emerge as the most productive authors, each having published six papers. Following closely is Rosa Lasaponara, who has contributed five papers. Furthermore, Rosa Lasaponara, Abdelaziz Elfadaly, and Heike Oevermann have made notable academic contributions, with each author publishing five, four, and four papers, respectively.
An analysis of author collaboration networks has revealed trends in international cooperation and the distribution of academic groups in the field of urban historical heritage conservation and development. Although the overall author collaboration network exhibits low density and a degree of isolation, core research teams have emerged, particularly from countries such as China, Italy, and Germany. Notable authors, including Fang Wang and Chen Fulong, have focused their research on urban studies, architectural technology, and remote sensing.
Looking ahead, as international collaboration deepens, we anticipate an increase in the diversity and complexity of research. This is particularly evident at the intersection of critical issues such as global climate change, sustainable urban development, and cultural heritage conservation, where international cooperation is poised to yield groundbreaking advancements [62]. By leveraging emerging technologies such as artificial intelligence, blockchain, remote sensing, and big data analysis, interdisciplinary research collaborations are expected to strengthen, providing innovative solutions for the intelligent management and precise conservation of urban historical heritage areas [63]. This trend suggests that future research will prioritize a more integrated approach to theory and practice, thereby enhancing the effective application of heritage conservation within the context of global urbanization.

4.1.2. Analysis of Research Institution Collaboration

The analysis of institutional collaboration networks effectively illustrates the collaborative publication activities among various research institutions [61]. This analysis facilitates an examination of the collaboration network within the research field and enables the prediction of future trends in institutional collaboration. Utilizing Python’s NetworkX library and the Louvain algorithm, this study conducted the detection and identification of the institutional collaboration network, resulting in the generation of an institutional collaboration map (see Figure 3). The academic collaboration network among research institutions in this area is depicted in Figure 3. Each node’s size corresponds to the quantity of publications generated by the institution; nodes that are larger signify a higher output of publications. The thickness of the lines that connect the nodes represents the degree of collaboration among institutions, where thicker lines denote a more significant number of co-authored papers.
In this research field, data indicates that a total of 796 institutions worldwide have engaged in collaborative efforts, resulting in 996 cooperative links. The overall network density is 0.0031, and the average clustering coefficient of the nodes is 0.4146. Compared to author collaborations, institutional collaborations are significantly stronger. Figure 3 highlights institutions with more than four publications. The analysis of the institutional collaboration network reveals that the most prominent collaborative groups are centered around the Egyptian Knowledge Bank (EKB), the Chinese Academy of Sciences, and Politecnico di Milano. The largest collaborative group is led by the Egyptian Knowledge Bank (EKB), which comprises 39 institutions and accounts for 16.18% of the total publications. Within this group, 29 institutions collaborate directly with the Egyptian Knowledge Bank (EKB), and mutual collaboration also exists among these 29 institutions.
Overall, the number of links in the network accounts for only 0.26% of the total possible connections that could be established between the institutional nodes. This suggests a substantial opportunity for enhancing collaboration, both among various institutional clusters and within their internal sub-networks, highlighting the need to further strengthen collaborative relationships [64].
Furthermore, regarding the types of institutions involved, universities dominate the collaborative landscape, comprising 640 out of the 796 participating institutions, which accounts for 80.40% of the total. Following universities, research institutes contribute 63 institutions, representing 7.91%, while companies account for 93 institutions, or 11.68%. This distribution of institutional types indicates that global research on the preservation and development of urban historical heritage areas has predominantly adopted a ‘university-centered’ model, with research institutes and companies playing supportive roles. This model underscores the pivotal role that universities play in advancing research in this field, while research institutes and companies provide essential complementary support [65].
Table 3 provides a comprehensive overview of the ten most prolific institutions in terms of the number of published articles, highlighting their contributions to the academic community. In addition to the raw number of publications, the table also takes into account the concept of betweenness centrality. This metric serves to gauge the significance of a node within a network, indicating that a higher betweenness centrality value suggests a more pivotal intermediary role for that node in connecting various other nodes within the framework of academic collaboration and influence [66]. Among the institutions listed in Table 3, the Egyptian Knowledge Bank (EKB) emerges prominently, securing the top position with an impressive total of 29 published articles. Following closely is the Chinese Academy of Sciences, which has contributed 25 articles. Other noteworthy institutions include Southeast University with 16 articles, along with the University of London, the Italian National Research Council (known as Consiglio Nazionale delle Ricerche, CNR), and Tongji University, each of which has also made significant contributions by publishing 13 articles. University College London, Peking University, and the Polytechnic University of Milan each published 11 articles, while Xi’an University of Architecture and Technology contributed 10 articles. Among these institutions, seven are universities, one is a research institute, and two are companies. Collectively, these institutions contributed a total of 152 articles, with the seven universities accounting for 85 articles, which represents 10.27% of the total research articles in this field (828 articles). This data further underscores the central role that universities play in research related to the preservation and development of urban historical heritage areas, while also highlighting the significant contributions made by research institutes and companies in advancing academic progress in this domain.
The analysis of inter-institutional collaboration reveals that, despite the involvement of numerous research institutions worldwide in the study of urban historical heritage conservation and development, there remains significant potential for enhancing collaboration. Institutions such as the Chinese Academy of Sciences and the Egyptian Knowledge Bank occupy central positions within the collaboration network. Their research encompasses various dimensions, ranging from the digital preservation of historical heritage sites to regional economic development. As global attention toward the sustainable development of urban historical heritage areas intensifies, future institutional collaborations are expected to expand into more diverse research directions, particularly in fields such as ecological protection, community engagement, and policy innovation [67]. Joint projects among institutions will increasingly emphasize multidisciplinary integration [68].

4.1.3. Analysis of National Collaboration Networks

Based on the address information from various publications, a total of 82 countries worldwide have engaged in research focused on the conservation and development of urban historical heritage areas. Figure 4 depicts the web of global collaborations, emphasizing the partnerships formed between these nations. In this illustration, each node signifies a country, and the size of the node corresponds to the quantity of publications emanating from that nation; larger nodes indicate a higher number of publications. The thickness of the lines that connect these nodes denotes the intensity of collaboration among the countries; thicker lines represent stronger cooperation.
Figure 4 contains a total of 82 nodes and 166 links, leading to a network density of 0.0500. The 82 nations are spread over six continents: 29 in Asia, 5 in North America, 7 in South America, 30 in Europe, 2 in Oceania, and 9 in Africa. This collaboration network includes all 82 countries, where cooperative relationships are relatively concentrated, indicating that these nations maintain strong collaborative ties in the field of research focused on the preservation and development of urban historical heritage areas.
Table 4 presents the top ten countries ranked by the number of published articles. China leads with 230 articles, followed by Italy with 74 articles and the United Kingdom with 55 articles. Spain occupies the fourth position with 52 articles, while the United States ranks fifth with 50 articles. Further analysis indicates that China’s prominent position in the preservation and development of urban historical heritage can be significantly attributed to two main factors: substantial government funding and the establishment of specialized laboratories dedicated to this field. This comprehensive support encompasses multiple tiers of investment, including contributions from national, provincial, municipal, and institutional sources, as well as backing from corporate entities. The collaborative efforts across these various levels of funding not only strengthen initiatives aimed at heritage preservation but also facilitate research and development in specialized facilities designed to enhance urban historical conservation efforts [69].
In contrast, although Indonesia has a relatively high number of publications, its low betweenness centrality within the international collaboration network indicates that its research activities are somewhat isolated, exhibiting limited international influence. Conversely, Sweden displays the opposite characteristics; despite having a lower publication count, it occupies a vital bridging role in international collaborations, significantly enhancing cross-national cooperation and knowledge sharing.
Through the analysis of national collaborations, it is evident that the trend of globalization in this research field is becoming increasingly prominent. In particular, China, the United States, Spain, Italy, and the United Kingdom have demonstrated strong academic productivity in this area. This phenomenon indicates that as research hotspots evolve across different regions globally, the field exhibits distinct regional characteristics. For instance, China has achieved notable success in integrating heritage conservation with urban development, while European countries tend to focus more on the digital preservation of cultural heritage and the promotion of international cooperation mechanisms [70].
Future research is expected to prioritize transnational collaboration, especially in tackling global challenges associated with heritage conservation [71]. These challenges encompass threats to heritage sites posed by climate change and the sustainable management of historical heritage tourism [72]. Strengthening national cooperation, particularly with the support of international organizations such as UNESCO, will enable the sharing of experiences and technologies, ultimately enhancing the overall effectiveness of global heritage conservation initiatives [73].

4.2. Analysis of Co-Citation

When two or more journals, documents, or authors are cited together in the reference list of a third document, a co-citation relationship is established among them [74]. Co-citation analysis serves as a valuable tool for mapping the structure of a discipline, tracking the developmental dynamics of a scientific field, and assessing the interrelationships between various disciplines [75]. Consequently, the three primary forms of co-citation analysis elucidate the structure and interconnectedness among journals, documents, and authors [76].

4.2.1. Analysis of Journal Co-Citation Relationships

In academic research, when two journals appear together in the reference list of the same paper, they are referred to as “co-cited”. A higher frequency of co-citation typically indicates a strong correlation between the two journals within a specific academic field [77]. Through journal co-citation analysis, researchers can identify both core and peripheral journals in a given field, as well as the network of relationships among them [78]. This analysis aids in understanding the pathways of knowledge dissemination and the distribution of academic influence in that area. This study involved 4135 journals, with the related research articles forming 10,879 directed connections, resulting in a network density of 0.0006. Across all journals, the total citation count reached 13,409, which included 359 self-citations and 13,050 citations from different sources.
As shown in Figure 5, a co-citation network of journals was generated by utilizing the NetworkX library in Python. In this representation, each node corresponds to a specific journal, with the size of the node indicating how many citations that journal has garnered. To emphasize significant journals, those receiving 70 or more citations were chosen and marked. The lines that link the nodes represent co-citation associations, where the thickness of the lines illustrates the frequency of these co-citations, and the arrow directions display the flow of citations. Within this network, larger nodes are indicative of higher citation totals, highlighting the greater relevance of those journals within the discipline. Additionally, thicker lines reflect more frequent co-citations, emphasizing the stronger academic connections among journals.
The analysis indicates that the journal Sustainability-Basel holds significant influence and recognition within this research field, having accumulated a total of 231 citations, which includes 52 self-citations and 179 external citations. This underscores the journal’s prominent role in disseminating academic knowledge in the domain. As a citing journal, Sustainability-Basel has referenced 925 other journals, with Cities being the most frequently cited, appearing 25 times. As a cited journal, Sustainability-Basel has been referenced by 93 other journals, with Land-Basel citing it the most frequently, at 20 times.
Additionally, in the network graph, the color of each node represents the clustering results, illustrating the categorization of different journals based on research themes and publication focus. Journals sharing the same color belong to the same category, while different colors signify distinct research topics. By calculating the feature vector of journal keywords using the log-likelihood ratio (LLR) algorithm and assessing the similarity between journals through a cosine similarity matrix, a clustering analysis was conducted using the K-means method. The optimal number of clusters was determined to be 15, with a silhouette coefficient of 0.8204, indicating strong clustering performance.
The figure presents four categories of journals. Among these, journals such as Sustainability-Basel and Cities are classified under Category #8, which emphasizes research areas including historic districts, adaptive reuse, urban planning, heritage conservation, heritage values, social impact, space syntax, and heritage management. In contrast, journals like City, categorized as Category #3, focus on publishing articles related to urbanization, heritage conservation, tourism, urban heritage, and sustainability. Additionally, journals such as Antipode, International Journal of Cultural Property, and Geographical Analysis, classified under Category #7, concentrate on sustainability, cultural tourism, and sustainable development. Lastly, journals like Geographical Annals, Series B, which fall into Category #11, are dedicated to research areas encompassing urbanization, sustainable development, industrial heritage, conservation, and cultural heritage. These classifications highlight the thematic relationships and academic influence among various journals in the study of the conservation and development of urban historical heritage districts.
The co-citation analysis of academic journals underscores the influence of various publications in the field of urban historical heritage conservation and development. Journals such as Sustainability, Cities, and Land are frequently cited, indicating that articles published in these journals form the foundational literature of the discipline. Current research hotspots focus on sustainable development, the conservation and utilization of urban heritage, and the revitalization of historical buildings.

4.2.2. Co-Citation Analysis of Core Literature

When two or more publications are cited together in the reference list of a paper, they are referred to as “co-cited documents”. A higher frequency of co-citation indicates a stronger academic relevance between the documents [79]. Figure 6 illustrates the findings from the co-citation analysis performed in this research. In this figure, each node corresponds to a cited document, with the top 70 most frequently cited documents identified by the name of the first author and the year of publication. Lines connecting the nodes symbolize relationships of co-citation. The dimensions of each node indicate the number of citations the document has garnered; thus, larger nodes represent more significant documents within the discipline. The thickness of the connecting lines indicates the co-citation frequency, where thicker lines reflect more robust academic ties between the documents. Arrows denote the citation direction; for instance, an arrow leading from A to B signifies that Author A’s paper made reference to Author B’s work.
We conducted a thematic clustering analysis of the literature concerning the preservation and development of urban historic heritage areas. This study encompassed 15,638 documents and their references. From the documents with co-citation frequencies greater than one, 2366 were selected for analysis, which involved 34,693 keywords. These documents were categorized into 10 groups, yielding a silhouette coefficient of 0.6797 for the clustering. As illustrated in Figure 6, different colors represent distinct document clusters, and the document nodes reveal three major categories, each corresponding to a unique research theme.
The top three thematic clusters, ranked by the number of citations for the most significant publications within each category, are as follows: Thematic Cluster #9 is defined by the keywords heritage site, the representative publication for this cluster is titled “Remote Sensing Archaeology: Tracking and Mapping Evolution in European Scientific Literature from 1999 to 2015”, authored by Athos Agapiou and published in 2015 in the Journal of Archaeological Science: Reports. Thematic Cluster #2 is characterized by the keywords with the key publication being “Testing the Dimensionality of Place Attachment and Its Relationships with Place Satisfaction and Pro-environmental Behaviours: A Structural Equation Modelling Approach”, authored by Haywantee Ramkissoon and published in 2012 in Tourism Management. Lastly, Thematic Cluster #1 is associated with the keywords represented by the work titled “Landslide Hazard, Monitoring and Conservation Strategy for the Safeguard of Vardzia Byzantine Monastery Complex, Georgia”, authored by C. Margottini and published in 2015 in Landslides.

4.2.3. Co-Citation Analysis of High-Impact Authors

By analyzing the co-citation relationships among authors in a substantial body of literature, researchers can construct an academic author network within a specific field. This network not only helps identify the leading scholars in the field but also uncovers their scholarly connections and collaborative relationships. Furthermore, it enables the assessment of each author’s influence within the domain [80]. In this study, an author co-citation analysis graph was generated using Python’s NetworkX library (version 3.2.1) (see Figure 7). The statistical data indicate that a total of 12,624 authors from the selected documents and their citations were included, resulting in 17,926 co-citation relationships and generating 18,159 citations, with an overall network density of 0.0001.
Based on the co-citation of authors, a clustering analysis was conducted on all authors and their referenced counterparts within the theme of conservation and development of urban historical heritage areas in the Web of Science (WOS) database. An in-depth analysis was performed on 2520 authors who had been co-cited more than once. These authors were associated with a total of 43,945 keywords, which were classified into 15 categories, resulting in a silhouette coefficient of 0.7636. A silhouette coefficient greater than 0.5 indicates that partitioning all authors into 15 clusters is justifiable.
Figure 7 presents an analysis of author co-citation after focusing on the 85 most prominent nodes. In this visualization, every node corresponds to an individual author, and the dimensions of the node reflect how frequently they have been cited—the larger the node, the more significant the author’s impact within the discipline. The arrows along the lines connecting nodes represent the relationships established through co-citation, with the direction of the arrow indicating which author is being cited. Different colors are used to differentiate among clusters, with authors within the same cluster shown in identical hues. This indicates robust academic connections among these authors, suggesting they have comparable research paths.
In Figure 7, the nodes are associated with three clusters: #2, #5, and #14. These clusters are ranked based on the authors with the highest citation counts in each category, and the top three clusters have been selected for detailed analysis. Cluster #14 ranks first, with Yung Ehk identified as the most cited author. The authors within this cluster are co-cited and collectively focus on research areas such as cultural heritage, conservation, urban heritage, sustainable development, and world heritage sites, all within the context of preserving and developing urban historical heritage areas. Cluster #5 ranks second, with Nunkoo R as the most cited author. This cluster is characterized by co-citation relationships among authors who contribute to research on sustainability, heritage conservation, tourism, urban regeneration, and world heritage, specifically concerning urban historical heritage areas. Cluster #2 ranks third, with Chhabra D as the most cited author. The authors in this cluster are co-cited for their work on topics such as China, urban tourism, heritage, cultural tourism, and historic districts, focusing on the conservation and development of urban historical heritage areas.
Additionally, in terms of citation counts, Yung Ehk’s papers are ranked first, having been cited 28 times between 2014 and 2024 in journals such as *Sustainable Development*, with an average annual citation rate of 2.5. The citing authors belong to clusters #1, #2, #3, #4, #5, #7, #9, #11, #12, #13, and #14, with the majority of citations originating from authors in cluster #14. Poria Y’s papers rank second, having received 27 citations between 2010 and 2024 in journals such as *Proceedings of the Social and Behavioral Sciences*, which corresponds to an average annual citation rate of 1.8. The citing authors are from clusters #1, #3, #4, #7, and #11, with the highest number of citations coming from authors in cluster #4. Pendlebury J’s papers are ranked third, with 25 citations accumulated between 2009 and 2024 in journals such as *Land Use Policy*, yielding an average annual citation rate of 1.6. The citing authors are from clusters #0, #2, #3, #4, #5, #7, #9, #11, #13, and #14, with authors in cluster #2 citing Pendlebury J’s work most frequently. This citation pattern has significantly advanced research in the field of urban historical heritage conservation and development.
Co-citation analysis uncovers connections between journals, publications, and authors, thereby assisting readers in grasping the knowledge framework and evolving trends within the domain of urban historical heritage conservation and development [81]. Such analyses lay the groundwork for pinpointing research hotspots, emphasizing the journals and literature that have notably impacted research trajectories, thus offering direction for upcoming investigations [82].

4.3. Keyword Co-Occurrence Analysis

Keywords serve as essential markers for the inclusion and indexing of literature, providing a concise summary of the core content [83]. Keyword co-occurrence analysis, which identifies frequently co-occurring terms, helps uncover research hotspots, key themes, and emerging trends within a specific field [84]. In this study, Figure 8 illustrates the keyword co-occurrence analysis, where a threshold of 5 was set for the examination. A total of 2517 keywords were discovered, leading to 8396 connections and a network density of 0.00. Each node in the figure signifies a keyword, and the size of the node corresponds to the frequency of its occurrence; consequently, a larger node indicates a more frequent appearance of the keyword. Furthermore, the betweenness centrality of each node reflects its potential role as a mediator in the network; thus, nodes with greater connections demonstrate higher betweenness centrality.
As illustrated in Figure 8, the keyword “cultural heritage” is represented by the largest node, exhibiting a betweenness centrality of 0.23. This indicates that “cultural heritage” has the highest co-occurrence weight with other keywords and serves as a pivotal support point for the entire keyword co-occurrence network. The keywords “conservation” (betweenness centrality of 0.11) and “heritage” (betweenness centrality of 0.07) rank second and third, respectively. These keywords span the entire research timeline and maintain close connections with other keywords and research directions. The analysis underscores the central role of “cultural heritage” in research concerning the protection and development of urban historical heritage areas, as well as the significance of keywords such as “conservation” and “heritage”. This further reflects the research hotspots and development trends within this field.
As shown in Table 5, the keywords are organized according to their co-occurrence weights. The critical frequency value is determined using Price’s law, with the calculation formula, where M = 0.749 N max 1 / 2 , where M represents the lower limit for assessing the frequency of prominent keywords, and Nmax denotes the highest frequency of occurrence for any keyword. According to Price’s law, if Nmax is 70, then M calculates to 6.27, indicating that keywords with a frequency exceeding 6.27 can be identified as research hotspots in this field. Based on this criterion, the data presented in the table reveal that the research hotspots concerning the protection and development of urban historical heritage areas include the following keywords: cultural heritage, urban heritage, sustainability, heritage conservation, GIS, historical district, cultural tourism, urbanization, urban planning, and cultural landscape. The frequencies of these keywords all exceed 50, underscoring their significant research value in this domain and representing current research hotspots and trends.

4.4. Keyword Emergence and Research Trend Analysis

Keyword emergence analysis is a technique in text mining that identifies keywords exhibiting a significant increase in frequency within texts, thereby revealing dynamic shifts, trends over time, and new focal points within a research area [85,86]. In this article, the Kleinberg algorithm is employed for the analysis of keyword emergence. This algorithm, based on the hidden Markov model, interprets fluctuations in keyword rankings over various time intervals as a process of state transition. This framework facilitates the identification of the most probable emerging keywords, along with their corresponding time frames and intensities of emergence. The Kleinberg algorithm comprises three critical parameters: the minimum duration (t), the multiplicative distance (s) that separates states, and the challenge of transitioning between states (γ). These parameters play a crucial role in affecting the outcomes and sensitivity of the keyword emergence analysis [87].
Utilizing the Kleinberg algorithm, Figure 9 presents the findings of a keyword emergence analysis focused on urban historical heritage protection and development from the year 2000 to 2024. In this study, several parameters have been clearly established to define the criteria for analyzing keyword emergence. First, a minimum emergence duration, denoted as t, has been set at 2 years. This means that only those keywords that demonstrate an emergence period longer than 2 years are taken into account in the analysis. Secondly, a multiplicative distance, represented as s, is assigned a value of 1.2, which signifies notable fluctuations in keyword rankings when observed over different timeframes. Lastly, the state transition difficulty, indicated by γ, is quantified at 0.4, implying a tendency for keywords to have an increased likelihood of progressing to a higher state in their emergence profile. These carefully defined parameters contribute to a more nuanced understanding of the dynamics of keyword emergence within the specified field.
In Figure 9, the regions highlighted in red represent periods during which keywords exhibited significant bursts, each lasting more than two years. This indicates a substantial increase in their frequency for at least two consecutive years. The areas marked in blue depict the intervals when these keywords were included in the comprehensive analysis period. The right side of the figure illustrates the intensity of these bursts, demonstrating that higher values correspond to an increased research focus on these keywords within the respective field. The histogram depicting keyword bursts reveals that investigations into the protection and development of urban historical heritage sites can be categorized into three distinct phases, each characterized by different research emphases and the growing prominence of various hotspot keywords.
The first phase, spanning from 2001 to 2016, represents the initial stage of research focused on the protection and development of urban historic heritage areas. During this period, studies primarily concentrated on key concepts such as ‘heritage’, ‘tourism’, and ‘conservation’. Scholars began to investigate the definitions and values associated with urban historical heritage, as well as its relationship to economic development. In the context of a burgeoning global tourism industry, researchers aimed to explore ways to leverage historical heritage to stimulate local economic growth while mitigating the adverse effects of over-commercialization on these heritage areas. Conservation emerged as the central theme of this phase, with scholars striving to effectively safeguard urban historical heritage amid rapid urbanization, ensuring it was not lost due to development or neglect [88]. As research advanced, scholars increasingly acknowledged the need to balance the preservation of historical heritage with the functional demands of modern cities, thereby laying the groundwork for more complex studies in subsequent years [89].
The second stage spans from 2016 to 2020, during which research on the protection and development of urban historical heritage areas has experienced stable growth. The focus of this research has expanded to include urban heritage management, geographic information systems (GIS), urban conservation, urban tourism, and urban development. Key terms such as residents’ perceptions, cultural heritage, politics, and remote sensing have gained prominence. The scope of research has shifted from a singular emphasis on heritage protection to a more multifaceted approach that incorporates various perspectives, methodologies, and applications. The academic community has engaged in extensive discussions regarding effective management strategies for these heritages within the context of modern urban development, particularly highlighting the role of GIS and remote sensing technologies in enhancing the efficiency of heritage protection and management. Furthermore, the challenge of balancing the preservation of historical heritage with the functional requirements of contemporary cities, along with the influences of urban tourism, residents’ perceptions, and political factors on heritage protection, has emerged as a significant research theme during this stage [90].
The third phase, spanning from 2020 to 2024, marks the maturation of research focused on the protection and development of urban historical heritage areas. During this period, research hotspots expanded to include keywords such as “risk assessment”, “3D modeling”, “historic preservation”, “revitalization”, “sustainable urban development”, “cultural landscape”, “urban heat island”, “industrial heritage”, “urban regeneration”, “architecture”, “city center”, “policy”, “cultural heritage preservation”, “urban landscape”, “historic district”, “architectural heritage”, “market”, “digital documentation”, and “UNESCO”. This diversification of research interests reflects the increasing depth and complexity of the field.
As urbanization accelerated, the challenges confronting urban historical heritage areas became more intricate. Risk assessment emerged as a vital tool for addressing threats posed by natural disasters, climate change, and human activities [91]. The implementation of 3D modeling and digital documentation technologies has introduced new opportunities for the accurate recording and safeguarding of historical heritage [92]. Researchers have concentrated on achieving a balance between preservation and revitalization to foster sustainable urban development while upholding the cultural value and vibrancy of heritage areas [93]. The focus on subjects such as cultural landscapes, industrial heritage, and urban heat islands underscores a growing emphasis on the overall environment and ecosystem of heritage sites [94]. Additionally, the integration of urban regeneration initiatives and policy frameworks, alongside the involvement of UNESCO, further highlights the significance of these heritage sites in the context of global cultural preservation [1].
These developments illustrate the evolution of research on the protection and development of urban historical heritage areas, highlighting the transition from its inception to a more mature state. This progression indicates that the field has not only been enriched and expanded theoretically but has also yielded more comprehensive practical approaches. Consequently, this advancement has fostered the harmonious coexistence of cultural heritage and modern urban development.

5. Discussion

5.1. Current Status and Challenges of Global Research Collaboration

Although research collaboration focused on the protection and development of urban historical heritage areas is gradually increasing on a global scale, the overall collaboration network remains relatively loose, lacking sufficient depth and breadth [95]. An analysis of author collaboration reveals that while a few prolific authors and their teams exert significant academic influence in the field, interactions among researchers are generally restricted to small-scale partnerships within the same region or discipline [95]. Cross-regional and interdisciplinary collaborations have yet to gain momentum [96]. This limited collaborative model not only constrains academic innovation but also hampers the broad dissemination and practical application of research findings. Similarly, the analysis of institutional collaboration highlights comparable deficiencies [97]. While universities dominate research in this area and certain institutions excel in international cooperation, the overall network exhibits low cooperation density [98]. This suggests that collaboration among universities, research institutions, and enterprises has not yet reached its full potential. Such circumstances restrict the translation of academic achievements into practical applications and diminish the effectiveness of research in advancing the protection and development of urban historical heritage [99].
In the analysis of international collaboration, several countries, including China, Italy, and the United Kingdom, are at the forefront of research in this field and play a crucial role in promoting international cooperation [100]. Conversely, other nations with substantial research output, such as Indonesia, despite their high volume of publications, exhibit low centrality in international collaboration, indicating that their research activities are relatively isolated [101]. This regional disparity may arise from differences in research resources, policy support, and levels of international academic exchange among countries [102]. Therefore, it is essential to enhance international collaborative projects, academic exchanges, and joint research initiatives in the future, particularly concerning resource allocation and policy support [61]. Such efforts would help bridge these gaps and facilitate global knowledge sharing and innovation [103].

5.2. Evolution of Research Hotspots and Future Directions

Keywords such as co-occurrence analysis and burst detection have illuminated the research hotspots and trends in the protection and development of urban historical heritage areas [104]. Over the past two decades, research in this field has evolved from its initial focus on conservation and tourism to encompass more intricate topics, including sustainable development, management strategies, and technological applications [105]. Notably, recent years have witnessed the integration of emerging technologies such as geographic information systems (GIS), remote sensing, and 3D modeling, which have enriched the research landscape and diversified methodologies [106]. This trend signifies a transition in the study of urban historical heritage areas from theoretical exploration to practical application, characterized by increasing complexity and depth [107].
However, as the pace of urbanization accelerates and the impacts of global climate change intensify, the challenges faced by urban historical heritage areas are becoming increasingly severe [108]. Future research must prioritize strategies for achieving sustainable urban development while preserving heritage values [45]. In particular, areas such as cultural tourism, risk assessment, digital preservation technologies, community engagement, and policy innovation are anticipated to emerge as key research directions moving forward [109,110,111].

6. Conclusions

This study employs bibliometric methods to systematically analyze global research outcomes related to the conservation and development of urban historical heritage areas from 2000 to 2024. Utilizing data from the Web of Science database, along with Python programming and network analysis tools, a total of 828 relevant articles were meticulously examined, resulting in the following key conclusions:
In the global research field of urban historical heritage conservation and development, countries such as China, Italy, the United Kingdom, Spain, and the United States are the primary contributors. China leads in the number of published papers, showcasing its academic leadership in this domain. Institutions such as the Egyptian Knowledge Bank (EKB), the Chinese Academy of Sciences, Southeast University, and the University of London play a significant role in fostering international collaborations. However, the global academic cooperation network remains largely fragmented, indicating a need for enhanced transnational collaboration.
The analysis of the author collaboration network identifies scholars such as Chen Fulong and Wang Fang as key contributors in this field, whose research has significantly advanced the discipline. However, collaboration among researchers remains relatively weak, with most partnerships occurring within small-scale research teams. This situation underscores the need for improved scientific cooperation in this area.
Co-citation analysis reveals the knowledge base and academic influence within this field. Highly cited papers predominantly focus on themes such as “cultural heritage conservation and sustainable development” and “management and reuse of historical heritage areas”, underscoring the central academic significance of these topics. Journals such as Sustainability, Land, and Cities occupy prominent positions in the co-citation network, facilitating scholarly discussions on the conservation and development of urban historical heritage areas.
Keyword analysis indicates that terms such as “Historic District”, “Sustainable Urban Development”, “Urban Regeneration”, “Risk Assessment”, “3D Modeling”, “Digital Documentation”, and “Cultural Tourism” are prominent research hotspots in this field. Notably, emerging research directions, including “digital conservation”, “heritage area regeneration”, “risk assessment”, and “sustainable development”, are gaining traction as frontier topics. This trend suggests that, with technological advancements, digital technologies and risk management strategies are becoming increasingly vital in the realm of heritage conservation.
Although this study systematically reveals the research status and trends in the conservation and development of urban historical heritage areas through bibliometric methods, it has certain limitations. The analysis relies exclusively on the Web of Science database, omitting other significant academic databases such as Scopus, RISS, and Google Scholar, which may impact the comprehensiveness of the findings. Therefore, future research should incorporate multiple databases to provide a more thorough analysis and further investigate the dynamics and development trends in this field.
Future studies should not only deepen the investigation of existing research hotspots but also focus on expanding into innovative areas. Promoting the integration of heritage conservation with emerging fields, such as smart cities and the green economy, could lead to more forward-looking and holistic solutions. Additionally, comparative studies across different regions and cultural contexts should be strengthened, particularly in analyzing the unique experiences and challenges faced by high-contribution countries in urban heritage conservation. This approach would foster broader international consensus and collaboration.

Author Contributions

Conceptualization, X.X.; methodology, J.X. and X.X.; software, J.X.; validation, J.X. and J.K.; investigation, J.X. and J.K.; data curation, J.X.; writing—original draft, J.X.; writing—review and editing, J.X.; visualization, J.X. and J.K.; Resources, J.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data has already been analyzed and utilized in the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Data filtering flow chart.
Figure 1. Data filtering flow chart.
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Figure 2. Author Collaboration Network Analysis Diagram.
Figure 2. Author Collaboration Network Analysis Diagram.
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Figure 3. Institutional Collaboration Network Analysis Diagram.
Figure 3. Institutional Collaboration Network Analysis Diagram.
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Figure 4. National Collaboration Network Analysis Diagram.
Figure 4. National Collaboration Network Analysis Diagram.
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Figure 5. Journal Co-citation Analysis Diagram.
Figure 5. Journal Co-citation Analysis Diagram.
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Figure 6. Document Co-citation Analysis Diagram.
Figure 6. Document Co-citation Analysis Diagram.
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Figure 7. Author Co-citation Analysis Diagram.
Figure 7. Author Co-citation Analysis Diagram.
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Figure 8. Keyword Co-occurrence Analysis Diagram.
Figure 8. Keyword Co-occurrence Analysis Diagram.
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Figure 9. Keyword Burst Analysis Diagram.
Figure 9. Keyword Burst Analysis Diagram.
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Table 1. Search Keywords.
Table 1. Search Keywords.
TSSearch Terms
urban (Metropolis OR Urban OR city*)
Historical Heritage Areas(“Historic District*” OR “Historical Area*” OR “Heritage Area*” OR “Heritage Site*” OR “Historic Site*” OR “Cultural Heritage Area*” OR “Historic Zone*” OR “Historic Landmark Area*” OR “Historical Place*” OR “Historical Landmark*”)
Protection and Development(Conservation* OR Preservation* OR Protection* OR Development* OR Advancement* OR Progress* OR Expansion* OR protect* OR develop*)
The symbol ‘*’ is known as a wildcard. It is used to replace one or more characters, thereby expanding the search scope and helping to retrieve variations of related terms.
Table 2. Table of Top 10 Contributing Authors.
Table 2. Table of Top 10 Contributing Authors.
RankAuthorPublicationsBetweenness CentralityStart YearCountryAuthor’s Homepage
URL (accessed on 8 September 2024)
1Wang Fang60.00012016Chinahttps://orcid.org/0000-0001-6038-2002
2Chen Fulong60.00012016Chinahttps://orcid.org/0000-0003-1144-0004
3Rosa Lasaponara50.00002016Italyhttps://sciprofiles.com/profile/91844
4Abdelaziz Elfadaly40.00002018Italyhttps://sciprofiles.com/profile/718412?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name
5Heike Oevermann40.00002017Germanyhttps://www.researchgate.net/profile/Heike-Oevermann
6S. Mostafa Rasoolimanesh40.00002017Malaysiahttps://www.researchgate.net/profile/S-Mostafa-Rasoolimanesh
7Hui Shi30.00002018Chinahttps://sciprofiles.com/profile/436137?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name
8Harald A. Mieg30.00002017Germanyhttps://hu-berlin.academia.edu/HaraldMieg
9Apostolos Sarris30.00002013Greecehttps://orcid.org/0000-0001-6071-4767
10Elena Cantatore30.00002021Italyhttps://orcid.org/0000-0003-2294-6561
Table 3. Table of Top 10 Contributing Institutions.
Table 3. Table of Top 10 Contributing Institutions.
RankInstitutionsCountsBetweenness CentralityStart Year
1Egyptian Knowledge Bank (EKB)290.03412009
2Chinese Academy of Sciences250.03852011
3Southeast University—China160.03172013
4University of London130.01802017
5Consiglio Nazionale delle Ricerche (CNR)130.03062004
6Tongji University130.03202014
7University College London110.00942017
8Peking University110.02552006
9Polytechnic University of Milan110.02002017
10Xi’an University of Architecture & Technology100.00882012
Table 4. Top ten countries in terms of publication volume.
Table 4. Top ten countries in terms of publication volume.
RankCountriesCountsBetweenness CentralityStart Year
1Peoples R China2300.18512006
2Italy740.12182004
3England550.13222007
4Spain520.15212012
5USA500.11712001
6Turkey470.03232006
7Australia350.03132003
8Malaysia350.03212012
9Egypt290.05392009
10Indonesia280.00042013
Table 5. Table of Top 10 Keyword Co-occurrences.
Table 5. Table of Top 10 Keyword Co-occurrences.
RankKeywordWeightCountWeight per CountStart_
Year		CentralityCluster Label
1cultural heritage333704.7620060.238
2urban heritage120245.0020160.0511
3sustainability105234.5720090.063
4heritage conservation83194.3720130.042
5gis78174.5920070.044
6historic district78194.1120170.037
7cultural tourism70145.0020170.0310
8urbanization67144.7920150.0313
9urban planning66135.0820090.0411
10cultural landscape55124.5820150.024
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Xia, J.; Kang, J.; Xu, X. Global Research Trends and Future Directions in Urban Historical Heritage Area Conservation and Development: A 25-Year Bibliometric Analysis. Buildings 2024, 14, 3096. https://doi.org/10.3390/buildings14103096

AMA Style

Xia J, Kang J, Xu X. Global Research Trends and Future Directions in Urban Historical Heritage Area Conservation and Development: A 25-Year Bibliometric Analysis. Buildings. 2024; 14(10):3096. https://doi.org/10.3390/buildings14103096

Chicago/Turabian Style

Xia, Jun, Jing Kang, and Xiaolin Xu. 2024. "Global Research Trends and Future Directions in Urban Historical Heritage Area Conservation and Development: A 25-Year Bibliometric Analysis" Buildings 14, no. 10: 3096. https://doi.org/10.3390/buildings14103096

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