Abstract
Historic district (HD) resilience has attracted increasing attention, yet integrated reviews that jointly address spatial, hazard, social, and evaluation dimensions remain limited. A PRISMA guided bibliometric analysis of 209 publications was conducted using TF-IDF, CiteSpace, and VOSviewer. It proposes a Resilience Assessment Framework, a heritage-hazard coupling model, and a digital resilience evaluation. This study establishes a basis for resilience optimization, risk management, and digital technology conservation in HDs.
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Introduction
As spatial and temporal carriers of urban civilization development, Historic districts (HDs) encompass dual genetic components comprising material civilization and intangible culture. Characterized by non-renewable and non-replicable properties, HDs integrate three-dimensional (3D) architectural fabric with cultural memory’s temporal dimension. This configuration exhibits dual attributes, namely the materialization of time and the temporalization of material1,2. The identification of HDs involves four criteria: integrity of cultural clusters, authenticity of spatial patterns, legibility of historical information, and sustainability of economic development3. The 1933 Athens Charter first established the concept of HDs, laying foundational principles for conservation4. The 1964 Venice Charter extended the conservation scope to include historic buildings and associated environments5. The 1987 Washington Charter specified conservation targets encompassing spatial configurations (SCs) of the urban fabric, architectural characteristics, contextual relationships, and historical functions6. In 2005, the International Council on Monuments and Sites (ICOMOS) identified disaster resilience as an essential element of heritage management frameworks7. The 2013 100 Resilient Cities (100RC) initiative promoted the development of resilience metrics8. The 2015 Sustainable Development Goal 11 (SDG 11) advocates building inclusive, safe, resilient, and sustainable cities9. This reflects an ongoing transition in HD conservation from isolated protective measures to systematic governance, with a focus on preserving the continuity of cultural context and ensuring holistic protection at the spatial scale in response to contemporary urban challenges.
Research on the resilience of HDs has experienced both theoretical paradigm shifts. Building on Holling’s10 ecological resilience theory, the conceptualization of resilience has evolved from engineering resilience to ecological resilience, and further to evolutionary resilience. This evolution has driven a transition in conservation strategies from individual building restoration toward integrated governance across social, cultural, and economic systems. Recent studies highlight standardization challenges within resilience assessment frameworks (RAFs)11. Through a systematic review of 36 hazard-related models, Tariq et al.12 identify limited applicability due to overreliance on quantitative metrics, advocating for community-based participatory approaches to enhance contextual sensitivity. This aligns with cultural heritage (CH) research: Tavares et al.13 find that the living transmission of intangible heritage strengthens community resilience, reflecting a post-2017 shift toward the interplay of heritage, sustainability, and place identity. Liu14 identifies a cognitive triad comprising material preservation, cultural reproduction, and spatial value reconstitution, based on bibliometric analysis. Wu15 uncovers a synergistic pattern of policy evolution in the governance of Chinese HD, characterized by policy impetus, technological adaptation, and community empowerment. Huang’s16 review marks a shift from singular hazard response to integrated resilience building. It can be seen that HD conservation research primarily focuses on four aspects: the continuity of street and alleyway patterns, the maintenance of historical appearance, the determination of evaluation criteria, and the delineation of conservation boundaries. However, with the emergence of new contradictions between disaster resilience and CH preservation in HDs undergoing urban renewal, the resilience of HD is being constrained.
This study adopts bibliometric analysis to systematically review the current state of research on the resilience of HDs. It addresses existing research gaps and focuses on four key areas: (1) the spatial specificity of HD scales; (2) prevalent hazard typologies with associated impact mechanisms; (3) core dimensions of resilience research in historic contexts; and (4) the application of digital technologies (DT) in the conservation and adaptive management of HDs. Based on the above analyses, the study constructs a RAF tailored to HDs, aiming to comprehensively reveal current research patterns, developmental trends, and persistent challenges, and to provide methodological guidance for future scholarship and practice.
Method
PRISMA
Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this study integrates standardized criteria into the text mining process to ensure transparency, rigor, and validity in the evaluation of existing interventions17.
TF-IDF
This study employs the Term Frequency–Inverse Document Frequency (TF-IDF) algorithm to extract keywords from abstracts on HD resilience18. The algorithm quantifies lexical salience by multiplying term frequency (TF) with inverse document frequency (IDF)19, highlighting terms with high local but low global frequency. This process identifies dominant themes and research directions, generating data-driven bibliometric insights to support resilience studies in hazard-prone HDs. The computational formula is as follows20:
Where TF(t, d) denotes the frequency of occurrence of word t in document d, DF(t, D) denotes the number of documents in which word t occurs in document collection D, and N is the total number of document collections.
Data collection
During the data collection phase, Scopus, Web of Science, and Google Scholar databases were utilized with a search strategy combining multiple descriptors: resilience, elasticity, assessment, HDs, and related synonyms. The search query was structured as: TS = (“Resilience” OR “Elasticity” OR “Flexibility” OR “Adaptability” OR “Robustness”) AND TS = (“Assessment” OR “Evaluation”) AND TS = (“Historic district” OR “Heritage district” OR “Neighborhood” OR “Cultural heritage” OR “Preservation area” OR “Conservation area”) AND TS = (“Adaptive reuse”).
A three-stage filtration protocol was implemented (Fig. 1): title relevance, inclusion criteria (paper source, time frame, paper type, and abstract match), and content relevance. A sample of highly relevant papers was obtained for further analysis.
PRISMA flow diagram of literature search and review.
Data acquisition
Historic district (HD) resilience has been examined for the period 2009–2024 using VOSviewer and CiteSpace for bibliometric analysis, focusing on resilience assessment (RA) in the context of hazard. Following the PRISMA protocol, 209 publications were identified (Fig. 2a). Of these, 104 address hazard-related assessments and 109 address RA in HDs. Annual output increased markedly after 2016, with local peaks in 2020 and 2023. A Gompertz growth model fitted to the yearly counts yielded R² = 0.891 (Fig. 2b). By 2023, the annual count reached 30, approximately 15 times the 2012 level, indicating sustained growth in scholarship on resilience in hazard-prone historic settings.
a Annual number of publications by research theme related to historic districts, disasters and resilience. b Total annual publications with Gompertz fitted trend line and 95% confidence and prediction bands.
Country analysis
To evaluate the global research landscape, collaboration, and influence of hazard RA systems for HDs, regional publication distribution was analyzed (Fig. 3). China contributed 20.5% of the total publications, followed by Italy, Portugal, the United Kingdom (UK), and the United States (US) at 6.8% each, and Australia at 3.7%. As urban resilience has gained international attention, related scholarly output has steadily increased. The Eni Enrico Mattei Foundation in Italy has played a central role in advancing hazard resilience strategies. In China, a historic city protection framework was introduced in 1982, and by 2024, several pilot communities had been developed to demonstrate integrated hazard mitigation21. In Portugal, seismic hazard resilience has become a national priority due to the structural vulnerability of its HDs. In the UK, the 2011 Managing Risks and Enhancing Resilience program led to the establishment of the London Climate Change Partnership (LCCP) and the UK Climate Impacts Program (UKCIP)22. In the US, the 100RC initiative launched by the Rockefeller Foundation in 2015 fostered cross-sector collaboration through a structured system of four dimensions, 12 goals, and 52 indicators23. Despite national variations in priorities and implementation mechanisms, shared challenges persist in advancing a comprehensive hazard-resilience framework for the protection of CH.
Regional distribution and Countries with the largest number of publications and citations.
Research trends
This study employs CiteSpace keyword burst analysis to trace the evolution of research on the resilience of HDs. Temporal slicing24 identifies three phases of shifting research focus from 2009 to 2023 (Fig. 4).
Top keywords with the most frequent occurrence.
Phase I (2009–2015) is defined by terms such as Spatial Morphology Conservation, Traditional Building Restoration, and Community Resilience, reflecting early theoretical attention to spatial preservation, structural rehabilitation, and vulnerability assessment. Martins25 reported elevated vulnerability indices in historic urban areas with aging infrastructure and dense populations. Phase II (2016–2020) introduces terms like Geographic Information System (GIS) Spatial Analysis, Urban Resilience, and Damage Assessment, signaling a transition toward geospatial technologies for quantitative hazard assessment. Merciu et al.26 employed GIS-based modeling and isochrone analysis to assess emergency accessibility in the historic center of Bucharest under earthquake scenarios, with the aim of improving transport resilience in HDs. Phase III (2021–2024) features keywords such as Digital Twin (D-Twin) CH, and AI Hazard Prediction, reflecting an intelligence-driven phase led by DT, Machine Learning (ML), and large language models (LLMs). Suárez27 proposed the Urban Resilience Index to evaluate integrated resilience across ecological, social, and governance domains in the Madrid region. Overall, the burst keyword analysis from 2021 to 2024 reveals a paradigmatic shift in HD resilience research. This shift moves from traditional conservation-oriented approaches toward integrated systems thinking, emphasizing the incorporation of D-Twin technology, big data analytics, and artificial intelligence (AI) into heritage conservation practices. The trend underscores the development of intelligent, context-sensitive, and sustainability-oriented frameworks for assessing resilience under complex multi-hazard conditions.
Keyword clustering
This study employs VOSviewer to analyze keywords from literature on the evaluation systems of HDs for hazard prevention from a resilience perspective, using modularity optimization clustering and intelligent local moving algorithms. From 729 keywords, 108 with a frequency of no less than 2 were selected, generating a keyword network comprising 60 nodes, 661 links, and a total link strength of 1027. Keyword co-occurrence frequency reveals interrelationships among terms. The applied algorithms classify keywords into thematic clusters while optimizing network layout. Node colors and sizes indicate keyword frequency, and line connections reflect inter-node distances and linkage strength, thus visualizing semantic associations and thematic evolution28. The clustering results define four thematic categories: spatial characteristics, hazard typologies, dimension selection, and assessment methods. Figure 5 presents the inter-cluster structure. Cluster 1 (red) pertains to spatial scales, representing resilience research across urban, national, community, and architectural levels. Cluster 2 (green) involves the classification of hazards, identifying fire, earthquakes, floods, and climate change as primary hazard agents affecting HD. Cluster 3 (blue) focuses on dimension selection in RA, incorporating keywords such as resilience, GIS, evaluation, and modeling to build multidimensional evaluation frameworks. Cluster 4 (yellow) addresses assessment methodologies, including analytic hierarchy process (AHP), DT, and laser scanning (LS), highlighting the emerging role of DT in RA for HD. Based on clustering analysis, resilience research in HD can be categorized into four core domains: spatial scale, hazard classification, dimension selection, and methodology application.
Keyword clustering.
Results
Bibliometric analysis defines the resilience of HDs as a dynamic evolutionary process in which these areas gradually recover from an initial state of vulnerability and achieve functional enhancement following exposure to natural disasters, anthropogenic disturbances, and public health emergencies. This process typically includes the stages of collapse, low resilience, recovery, antifragility, and full resilience (Fig. 6). Based on the results of CiteSpace keyword burst detection and VOSviewer clustering analysis, the research on HD resilience demonstrates a temporal evolution from theoretical foundations to technological integration and the development of intelligent systems. Current research increasingly concentrates on four major areas: spatial characteristic analysis, hazard type identification, selection of evaluation dimensions, and the application of DT.
Dynamic Framework of Resilience.
Spatial characteristics of resilience research in historic districts
Current research on the relationship between urban morphology and resilience remains at an exploratory stage. From a morphological perspective, Feliciotti29 proposed a five-dimensional RAF, incorporating spatial elements such as plot units, street interfaces, and block structures, with dimensions including adaptability, accessibility, diversity, modularity, and redundancy. Zhao30, using quantitative methods, examined how overall spatial form, street network topology, and building cluster configurations influence the resilience performance of HDs. In international practice, multi-scalar governance approaches are evident in heritage conservation strategies. France adopts a three-tier planning and control system that operates at the macro (urban), meso (district), and micro (building) levels31. The United States promotes heritage renewal through historic overlay zoning and functional activation at the community level32. The United Kingdom applies thematic planning to coordinate interventions across buildings, public spaces, and transport infrastructure, emphasizing collaboration between building and district scales33. In China, a gradient “three-zone” regulatory system supports street-level morphological control through texture restoration in core protection zones, spatial coordination in construction control zones, and landscape guidance in environmental coordination zones34. Drawing on the hierarchical structure of urban heritage spaces and the scale-based logic reflected in these international practices, this study classifies its analytical framework into four spatial levels: urban35, community36,37, district38,39, and building40,41,42,43, in order to systematically identify resilience mechanisms across scales.
At the urban scale, historic cities are defined as conservation control zones with clearly delineated historical boundaries and relatively intact spatial structures and landscapes. Urban resilience indicators at this level reflect the ability to sustain essential urban functions in the face of natural hazards, social disturbances, and economic shocks. This capacity is supported by diversified infrastructure systems, adaptive planning mechanisms, and robust social networks44,45. Chelleri46 conceptualizes cities as interconnected systems, underscoring the need for resilience frameworks that integrate social, economic, and infrastructural dimensions. Meerow47 further highlights the interdependence of urban systems within broader regional and global networks. Drawing on the “triangle” model of public safety, Liu et al.11 assess urban resilience through three core components: hazard resilience, emergency response capacity, and infrastructure robustness.
At the community scale, historic resilience refers to the capacity of neighborhoods to sustain and recover core functions during disruptions, with increasing emphasis on cultural identity and self-organization. Key challenges include natural hazards, socioeconomic inequality, and resource scarcity. Proposed strategies to enhance resilience include strengthening social cohesion, improving resource accessibility, and enhancing emergency response capacity48. In a study of Italian historic communities, Giudice49 found that greater public participation is often associated with higher resilience. Sharifi50 identified five dimensions of social resilience: social structure, social capital, safety and well-being, equity and diversity, and local culture. Appler51 and Rumbach52 further underscored the roles of community cohesion, economic organization, and cultural identity in shaping resilience.
At the HD scale, resilience involves the integrated consideration of cultural continuity, architectural preservation, SC, community cohesion, and restorative capacity53. Cantatore54 developed GIS-based vulnerability maps incorporating indicators such as built fabric, heritage attributes, population density gradients, and tourism flows. Hu55 conducted a quantitative assessment of architectural density, public space distribution, and heritage assets to examine the balance between conservation goals and sustainable development. Yan et al.56 applied the Pressure–State–Response model to assess resilience to natural hazards in HDs, identifying weaknesses in hazard management, critical infrastructure, and emergency evacuation systems.
At the architectural scale, RA focuses on ensuring structural integrity and functional continuity of individual historic buildings under natural hazards and social change. Prieto57 employed a fuzzy logic approach to evaluate resilience based on SC, construction materials, and cultural value. Rich58 proposed a forward-looking framework that enhances resilience through hazard-mitigation strategies, including the use of anti-corrosive materials, routine maintenance, and structural reinforcement.
Resilience research encompasses multiple spatial scales, ranging from the urban to the architectural level, and presents a layered perspective from macro to micro dimensions, as illustrated in Fig. 7. At the urban scale, the focus is on strategic planning and coordination across interconnected systems. The community scale emphasizes the stability of social structures, equitable resource distribution, and self-organizing capacity. The HD scale requires balancing the continuity of overall spatial form with the heterogeneity of individual building units, addressing the interplay between heritage conservation and contemporary functional needs through meso-scale governance. At the architectural scale, attention is directed toward maintaining functional continuity and protecting cultural significance, typically through structural reinforcement, façade repair, and digital documentation. Comparative analysis suggests that HDs are generally characterized by high-density development, compact spatial organization, and strong continuity in traditional street patterns. Under disaster conditions, localized spatial failures may lead to broader spatial disruptions, potentially affecting functional operations and the continuity of cultural expression. Accordingly, enhancing the resilience of HDs requires more than physical intervention. A comprehensive approach that considers SC, environmental integration, cultural continuity, community participation, and economic support is needed to develop a multidimensional, multi-tiered resilience framework aimed at improving overall adaptive and recovery capacity.
Spatial scale characteristics of resilience research.
Types of disasters in the study of historic district resilience
By clarifying the research objects, further identification of the impacts of different hazard types on HDs enables the precise localization of critical vulnerabilities and the determination of the hazard types to which each district is most susceptible, thereby providing a scientific basis for developing context-specific resilience enhancement strategies. Based on TF-IDF term frequency statistics and word cloud analysis (Fig. 8), the hazards faced by HDs are categorized into natural hazards, human-induced hazards, and public health hazards. In terms of natural hazards, earthquakes, floods, and fires may damage building structures, resulting in the loss of CH and the disruption of economic functions. Human-induced hazards primarily stem from infrastructure degradation, narrow road networks, and insufficient emergency facilities, which easily lead to traffic bottlenecks and hinder emergency response. Public health hazards, on the other hand, are exacerbated by population aging, high residential vacancy rates, and large tourist crowds, which increase the complexity of emergency management. Systematic identification of hazard types and their mechanisms allows the formulation of targeted resilience enhancement measures tailored to different risk scenarios, thereby improving the overall resilience of HDs.
Word cloud of resilience research in historic districts.
With regard to natural disasters, HDs are particularly sensitive to events such as earthquakes, floods, and typhoons, due to the aging of buildings and the presence of dense spatial layouts. For instance, Boukri59 developed a rapid seismic vulnerability assessment model to estimate the collapse probability of historic buildings in Blida, Algeria, and proposed corresponding reinforcement and contingency planning strategies in response to the area’s high population density and prevalence of unreinforced masonry. Inzulza60 conducted a post-hazard timeline analysis following the 2010 Chilean earthquake, examining environmental degradation and the erosion of community networks, and recommended resilience enhancement through heritage building preservation and participatory governance. Sevieri61 employed Monte Carlo simulations to assess typhoon vulnerability in heritage roofing systems in the Philippines, advocating wind-resistant retrofitting materials. To address the increasing flood frequency driven by climate change, Kittipongvunes62 analyzed the 2011 flood in the Ayutthaya heritage zone, proposing integrated urban drainage systems, multi-hazard zoning, and community flood preparedness programs. Yang63 optimized blue-green infrastructure in Gulangyu’s HD using drone-based remote sensing (RS) and GIS classification to mitigate urban heat island effects. Villanueva64 designed comprehensive hazard management systems for European heritage areas, integrating 3D GIS decision-support tools with risk assessment modules, structural reinforcement databases, and multi-hazard simulation platforms.
From a human-induced hazard perspective, fire hazards65, urbanization66, and tourism development67,68 pose potential risks to HDs, potentially leading to the loss of CH and damage to built structures. Li69, using the case of Ciqikou HD in Chongqing, identified multiple fire risk factors associated with commercialization, proposed a fire RAF, and suggested strategies such as optimizing commercial layouts, improving fire protection coverage, and enhancing visitor safety awareness to reduce risk. Ge70 based on empirical studies of HDs in China, argued that urban renewal impacts cultural resilience and heritage conservation, and proposed a cultural sustainability evaluation system integrating heritage protection, cultural vitality, and ecological-cultural resilience using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Liu71, through a case study on Fujian Tulou, examined the social and economic changes brought about by tourism development and their implications for community livelihoods, emphasizing the need to balance economic interests and cultural values in heritage conservation practices, and emphasizing the collaborative role of communities and governments in cultural protection and management.
Public health risks in HDs are amplified by high building density, leading to elevated transmission rates and governance challenges. Jewett72 conducted a rapid scoping review on the role of social cohesion in community resilience during Coronavirus Disease 2019 (COVID-19), providing evidence for the UN Research Roadmap for Recovery.
In summary, the disaster characteristics of HDs at the neighborhood scale primarily arise from the combined effects of spatial, functional, and social factors. Spatially, high-density built environments, narrow passageways, and heritage conservation constraints reduce evacuation efficiency and increase the risk of secondary hazards, such as fires following earthquakes and structural damage during floods. Functionally, the coexistence of residential, commercial, and tourism activities leads to differentiated levels of risk exposure, with areas of high tourist concentration being more susceptible to public health emergencies and fire hazards. Socially, declining community cohesion, population aging, and a high proportion of transient populations further weaken the capacity for emergency response and self-rescue. Compared with the building scale, which focuses on individual structural damage, and the urban scale, which emphasizes systemic infrastructure failures, the core challenge at the neighborhood scale lies in the structural tension between the rigid constraints of heritage protection and the dynamic requirements for enhancing disaster resilience. Hazard profiles also vary significantly across HDs, yet existing evaluation frameworks fail to fully capture vulnerability characteristics and resilience performance under multi-hazard conditions. Moreover, research on the relationship between disasters and heritage conservation remains limited, particularly regarding the systematic classification of district types and their predominant hazards. It is therefore necessary to establish targeted RAFs based on explicit risk-profile identification to accurately locate critical weaknesses and propose optimization measures, thereby strengthening the overall resilience of HDs.
Dimension selection for historic district resilience
In recent years, empirical studies on HDs have supported the progressive development of multi-dimensional RAFs. Cantatore54 developed a tripartite framework comprising environmental, architectural, and socio-spatial dimensions through a case study of Apulia’s HDs in Italy, using community participation metrics to evaluate the hazard resilience of heritage assets. Briz73 examined seismic and climatic hazard impacts on district resilience using a four-dimensional framework incorporating historical value, economic variables, technological parameters, and environmental conditions. Tlemsani74 quantified commercial heritage resilience in Bursa’s historic market district using a three-axis assessment of cultural, economic, and spatial dimensions. Huang75 proposed a four-part framework integrating environmental, commercial, social, and spatial indicators to address urban decline and environment-related hazards in Kaifeng’s Shuanglongxiang district. Zhang76 constructed a cultural value assessment model centered on identity, incorporating SC, cultural identity, and community engagement through case studies in Beijing. Rahimi77 demonstrated the influence of community inclusivity and interaction dynamics on district resilience through functional analysis in Tabriz’s Sorkhab district. Moghadas et al.78 assessed flood hazard resilience in Tehran’s HDs using a six-dimensional framework encompassing social, economic, institutional, infrastructural, community capital, and environmental parameters. Despite these contributions, limitations remain, including fragmented indicator selection, absence of a unified conceptual structure, reliance on expert judgment, and difficulties in operationalizing cultural dimensions. To address these issues, this study applies TF-IDF analysis to systematically identify dominant themes in the literature (Fig. 9).
TF-IDF heatmap of resilience dimensions in historic district studies.
The results highlight six high-frequency dimensions: culture, history, society, architecture, economy, and environment. Among these, culture and history are most prominent, social participation and architectural conservation constitute key components, and economic and environmental dimensions demonstrate interdependent associations. Compared with expert-based classification methods, this approach offers a systematic and replicable foundation for identifying relevant dimensions and informing the development of a logically coherent and structurally integrated framework for assessing the resilience of HDs.
Evaluation methods in historic district resilience research
HDs exhibit dual attributes, namely the materialization of time and the temporalization of matter. Resilience evaluation requires both the identification of cultural continuity and the quantification of progressive physical deterioration. Accordingly, assessment methods are commonly categorized into three types: qualitative, quantitative, and mixed approaches (Fig. 10).
Analytical methods for assessing resilience in historic districts.
Qualitative methodologies are widely applied to examine the historical and cultural contexts of HDs, primarily through surveys, in-depth interviews, and comparative case studies. These approaches interpret cultural identity, disaster preparedness practices, and community-based support systems. Durrant79 uses questionnaires to prioritize historic building restoration, facilitating phased interventions based on urgency. Lawangen and Roberts80 and Salehi81 adopt interview-based methods: Lawangen and Roberts integrate storytelling to emphasize traditional knowledge in early warning and social networks; Salehi investigates how community engagement and tactical urbanism contribute to revitalization. Additionally, Li82 conducted case studies to examine the relationship between CH and disaster resilience.
Quantitative approaches to RA in HDs commonly rely on multi-dimensional indicators for risk evaluation. Frequently used methods include semantic differential (SD) analysis, the AHP, and fuzzy logic models. Gao et al.83 apply SD analysis to examine the relationship between landscape resilience and human perception. Harbiankova84 combines AHP with GIS data and a sustainability assessment model to evaluate the resilience of historic neighborhoods. Cacciotti85 develops the Multi-Criteria Integrated Value Model for Sustainability Evaluation (MIVES), grounded in AHP, to assess CH resilience within a sustainability framework. Moaddab86 adopts a fuzzy logic approach to evaluate resilience across cultural, historical, physical, and socio-economic dimensions. Giovinazzi87 proposes a GIS and indicator-based framework for assessing seismic impacts and monitoring resilience in the Camerino–San Severino historic area in Italy, which is implemented through the WebGIS platform ARCH DSS to support spatial risk identification.
Mixed methods combine qualitative and quantitative approaches to improve the comprehensiveness and accuracy of RAs in HDs. These methods are primarily applied in three domains. In terms of spatial data acquisition, Choi88 constructed a digital cube model using satellite, drone, and terrestrial sensors to collect disaster information, providing spatial and temporal data. Liu89 combined RS from drones, GIS, and Convolutional Neural Networks (CNNs) to identify historical architectural styles, heritage appearance, and spatial patterns. Benkhedda90 analyzed color changes in HD by applying hyperspectral RS, support vector machines (SVMs), and CNNs. Laohaviraphap et al.91 used AI and Internet of Things (IoT) technologies for real-time monitoring and prediction of structural deterioration. Liu et al.92 adopted semantic segmentation (U-Net, Structure from Motion (SfM), CNNs) for automated façade monitoring. At the modeling and analysis level, Fan et al.93 used You Only Look Once (YOLO) and CNNs to detect building damage features, improving maintenance precision. Zu et al.94 combined SVM, random forests, Light Detection and Ranging (LiDAR), Building Information Modeling (BIM), semantic segmentation, and point clouds to automate diagnostics. Sun et al.95 applied generative adversarial networks (GANs) and computer vision for intelligent retrofitting, while Xu et al.96 used the Stable Diffusion model and ControlNet to extract façade features for flexible design. Xie et al.97 proposed an automated, high-precision method for structural identification, deterioration monitoring, and D-Twin construction based on multimodal point cloud semantic segmentation technology. For decision support, Zhang et al.98 integrated Generative Pre-trained Transformer 4 (GPT-4) and knowledge graphs to optimize renovation strategies. Lupu et al.99 combined K-means and joint-choice models to guide tourism economy optimization. Kimura et al.100 utilized smart devices and photogrammetry to develop visitor management systems. Sufi and Khalil101 utilized AI-based location intelligence and sentiment analysis on social media to detect disaster zones and inform risk-aware decision-making. Erdurmuş and Yavuz102 used genetic algorithms and non-dominated sorting genetic algorithms to support adaptive design for sustainable development in HDs.
The conservation of HDs requires the integrated application of qualitative and quantitative research methods. Qualitative research based on historical documents and spatial morphological analysis identifies the cultural context and evolutionary characteristics of architectural heritage, while quantitative research employing spatial information models and statistical methods measures physical spatial attributes and their changes. Advances in DT have introduced new approaches for data collection, information processing, and decision-making. however, HDs possess both significant cultural value and structural vulnerability, necessitating careful application of these technologies to maintain the authenticity and integrity of heritage assets. Existing studies have explored various digital tools, but systematic research defining the scope and priorities of technologies at different conservation stages remains limited. Establishing a clear protection pathway is required to coordinate cultural conservation, spatial restoration, and resilience enhancement.
Discussion
This study systematically reviews existing research on the resilience of HDs from four key perspectives: spatial characteristics, hazard types, evaluation dimensions, and assessment methods. On this basis, a preliminary analytical framework for HD resilience is established. Building upon this framework, future research may further focus on three directions (Fig. 11).
From left to right, the figure integrates bibliometric results of the literature review, the current state of research, and emerging research trends. The grey column on the far right summarises a four-step technical pathway for resilience assessment, progressing from identification and classification of hazard scenarios to assessment and intervention.
(1) Guided by the core principles of the Convention Concerning the Protection of the World Cultural and Natural Heritage and informed by an examination of the coupling between the intrinsic value attributes of HDs and disaster impacts, this study advances a two-dimensional assessment model (Fig. 12) with heritage value and disaster risk frequency as its axes. The model departs from uniform conservation paradigms by calibrating protection strategies to contextual variability in HDs, and its four quadrants delineate distinct value–risk configurations, each aligned with specific applications of DT.
Four-quadrant plot of disaster frequency versus historic district value.
For Quadrant I (high value, high hazard), preventive monitoring and dynamic response are prioritized. The HD of Venice, characterized by distinctive architectural heritage and aquatic cultural identity, is exposed to tidal flooding and sea-level rise. Technologies such as DT, GIS-based monitoring, and hydrological simulation have been applied to enhance hazard preparedness103,104,105. In Lisbon’s HD, RS data fusion, structural vulnerability assessment models, and intelligent drainage optimization algorithms have been adopted to address multi-hazard scenarios, balancing CH conservation with urban resilience enhancement106. In Camerino-San Severino, Italy, following consecutive earthquakes in 2016–2017, GIS-based tools and systematic vulnerability assessments were developed to monitor resilience and strengthen seismic hazard adaptability87.
For Quadrant II (high value, low hazard), modernized management systems support routine maintenance and mitigate tourism pressures. The Forbidden City, despite a relatively low-hazard context, employs DT, drone inspections, and intelligent security to protect traditional wooden architecture amid urbanization107. In Popoli, Italy, GIS and Historic Building Information Modeling (HBIM), combined with semantic modeling and parametric analysis, facilitate preventive conservation and support long-term digital monitoring108. Sanfang Qixiang in Fuzhou, China, an architectural complex from the Ming and Qing dynasties, applies parametric shape grammar, algorithmic generation, and parametric modeling to assess spatial form and inform future planning109.
For Quadrant III (low value, high hazard), integrating safety assessment with value transformation is necessary to balance protection and vitality. In Kitanomachi, Japan, limitations of regulation-based conservation under seismic threat have prompted digital place-making, including augmented and virtual reality and metaverse platforms, to enhance preparedness and promote cultural value renewal and heritage-led regeneration110. In Tainan, Taiwan, augmented-reality technologies are used in response to typhoons and earthquakes to strengthen CH transmission and support urban regeneration111.
For Quadrant IV (low value, low hazard), community-based participatory conservation, functional activation, and social-capital-driven strategies can enable sustainable heritage use. In the redevelopment of the Wuhan Tiandi HD, BIM and laser-scanning technologies supported digital archiving and precise spatial analysis, providing a digital foundation for resilience development112.
In summary, the model classifies HDs based on the dimensions of heritage value and disaster risk, and aligns each quadrant with corresponding DT application strategies. By incorporating objectives such as emergency response, routine management, and cultural value into the design of technical pathways, the model provides a structured analytical framework for the development of resilience-oriented digital conservation systems.
(2) Through a systematic review of current resilience studies in HDs, it has been identified that existing frameworks often lack adaptability when applied to the specific context of HDs. This is primarily reflected in the difficulty of quantifying cultural value, the mismatch between spatial scales and conservation needs, insufficient representation of social participation mechanisms, and the absence of established assessment pathways for evaluating the resilience and adaptability of historic buildings. To address these limitations, a resilience evaluation framework for HDs has been developed by synthesizing existing research findings. First, the framework draws on the Leadership in Energy and Environmental Design for Neighborhood Development system proposed by the U.S. Green Building Council in 2007. This system focuses on four core dimensions: environment, buildings, society, and economy, and introduces the concept of sustainable communities, emphasizing the coordinated advancement of ecological conservation and overall resilience. Second, the framework incorporates the Sustainability Tools for Assessing and Rating (STAR) Communities model, introduced in 2012. It is structured around seven thematic goals: built environment, climate and energy, economy and jobs, education and community, equity and empowerment, health and safety, and natural systems. This model underscores the integration of social equity, economic vitality, and environmental adaptability, and was incorporated into the LEED certification system in 2017, thereby enhancing the coherence and practical applicability of sustainability evaluations113. Furthermore, scholars such as Tahoon114, Ragheb115, and Zhao116 have proposed resource allocation evaluation frameworks tailored to HDs, emphasizing the importance of cultural preservation, social vulnerability, and sustainable regeneration. These studies provide a valuable foundation for the development of a context-specific assessment system. Building upon these foundations, this study applies the TF-IDF method to extract key terminology from recent literature on HD resilience. Based on the results, five core dimensions are identified: economic, environmental, cultural, architectural, and social, which together constitute the criteria layer of the proposed evaluation framework. To enhance the framework’s operationalization and quantifiability, an indicator system comprising 25 measurable variables was constructed through comprehensive literature analysis (Fig. 13)117.
Conceptual framework for the RAF of historic districts.
These indicators encompass critical factors such as tourist volume, business composition, housing vacancy rate, green visibility, street connectivity, heritage authenticity, building materials, façade characteristics, community composition, and emergency preparedness. This framework provides a holistic depiction of the adaptive capacity of HDs in response to economic disruptions118,119,120, environmental disturbances121,122,123, public crises124, physical degradation125, and cultural erosion126,127. Finally, five key resilience attributes are distilled from the framework: robustness, accessibility, diversity, uniqueness, and self-organization. These attributes offer a structured analytical basis and quantitative support for RA, post-disaster recovery planning, and sustainable development in HD contexts.
(3) Although the integration of DT has facilitated RA and disaster prevention in HDs, systematic incorporation into resilience enhancement research remains limited128. To address this gap, a DT-driven assessment framework is proposed, comprising four stages: spatial analysis, disaster identification, assessment dimension development, and protective technology application, corresponding to the phases of identification, classification, assessment, and intervention. The framework provides a data-driven approach to strengthening resilience and management capacity in HDs across the disaster cycle (Fig. 14).
Digital framework for historic district resilience assessment.
In the spatial feature identification phase, technologies such as GIS, HBIM, D-Twin, deep learning (DL) models, and eye-tracking can be employed to conduct integrated analyses of urban morphology, building types, and environmental conditions, contributing to improved efficiency and accuracy in data extraction within historic spatial environments129,130. However, certain high-precision data acquisition methods, such as LS, are associated with challenges including high acquisition costs, complex data processing procedures, and platform compatibility issues131,132. In addition, environmental factors such as lighting conditions, weather, and material reflectivity may affect data quality and therefore require careful consideration prior to on-site data collection133.
In the disaster and value classification phase, tools such as Multi-Criteria Decision Analysis (MCDA) and risk mapping can categorize HDs into distinct value–risk scenarios to inform differentiated conservation strategies. In high-risk, high-value areas, disaster monitoring and early warning can be supported through remote sensing (RS), IoT sensors, object detection algorithms such as YOLO and CNN, and time-series models including LSTM. In high-value, low-risk areas, GIS-HBIM integration, D-Twins, and BIM technologies can facilitate lifecycle management and preventive maintenance. In low-value, high-risk areas, spatial reuse and reinterpretation of heritage significance can be aided by AR, VR, metaverse platforms, and cultural modeling tools. For low-value, low-risk areas, gradual, community-led conservation can be supported by low-intervention approaches such as 3D modeling, 3D printing, and GANs, together with crowdsourcing and participatory visualization. The generalizability of applied models remains constrained by variability in cultural, geographic, and institutional contexts, requiring localized adaptation through integrated qualitative and quantitative methods134. Although UAV photogrammetry and LiDAR provide high precision, noise interference and the absence of standardized workflows for complex historic structures continue to limit implementation135,136.
In the assessment dimension phase, methods such as the AHP, entropy weight method, Delphi method, and fuzzy comprehensive evaluation are used to construct resilience indicators covering physical, social, economic, environmental, and cultural dimensions. Technologies including GIS, BIM, UAV photogrammetry, semantic segmentation, and ML support spatial data extraction and analysis, enhancing structural consistency and reproducibility. However, platform compatibility, data accessibility, and regional resource disparities also constrain the universality of these methods137,138.
During the DT intervention phase, appropriate digital tools are selected based on the current status of the heritage site, including remote sensing (RS), BIM, AR, VR, metaverse platforms, and LLMs, to implement conservation strategies focused on disaster monitoring, facility management, cultural revitalization, and community empowerment139,140. While digital tools are effective for documentation and modeling, the protection and transmission of historical and CH still require the integration of traditional craftsmanship and the active participation of stakeholders76,141. In summary, DT serves as a practical tool across multiple stages of resilience planning and implementation in HDs. Effective application requires alignment with local sociocultural and governance contexts, alongside careful evaluation of technological constraints.
This study is subject to some limitations concerning data sources, methodological applicability, analytical tools, and evaluation standards. First, the literature review primarily focused on English-language peer-reviewed journal articles and conference proceedings from selected databases, while excluding policy reports and grey literature, which may still result in a partial representation of the research landscape. Second, the proposed multi-scale analytical framework remains conceptual and lacks empirical validation across diverse HD contexts. Third, the use of bibliometric and text-mining tools such as VOSviewer, CiteSpace, and TF-IDF is subject to algorithmic constraints, potentially affecting the accuracy of thematic extraction. Fourth, the classification and quantification of historical and spatial values involve a degree of subjectivity, and standardized evaluation protocols for RAs remain underdeveloped. Despite these limitations, the study provides a structured foundation and methodological reference for future research on HD resilience. Subsequent studies should aim to incorporate a broader range of data sources, empirically test the proposed framework in various settings, and work towards the development of standardized, objective evaluation metrics to enhance the robustness and applicability of RAs in HD.
Collectively, this study adopts a problem-driven systematic review approach based on the PRISMA guidelines, incorporating bibliometric tools such as VOSviewer, CiteSpace, and TF-IDF to examine the literature on HD resilience published between 2009 and 2024. On this basis, four analytical dimensions are constructed: spatial scale characteristics, hazard-inducing factors, resilience-dimension diversity, and digital assessment trends. The results indicate that China and Italy are the primary contributing countries in this field. Thematic evolution is observed, shifting from single-hazard resilience to integrated risk management and multidimensional evaluation frameworks. Multi-scale comparative analysis further reveals that, in addition to focusing on disaster response and recovery, HD studies increasingly emphasize CH conservation, architectural continuity, and the revitalization of cultural vitality. Among these themes, fire resilience emerges as a prominent area of concern. Based on these findings, this study develops a RAF comprising five dimensions: economic, environmental, cultural, architectural, and social. The framework is further integrated with a heritage–hazard coupling model and differentiated evaluation pathways. It provides a structured basis for context-specific risk identification and RA in historic districts and supports the prioritization of conservation interventions, spatial optimization strategies, and resilience-oriented planning and management practices. Drawing from the literature review, future research may focus on three key directions: (1) integrating heritage value characteristics with potential hazard factors to more precisely identify value-risk types and develop targeted conservation strategies; (2) applying and validating the RAF through empirical studies and long-term monitoring across various HD types and scales to enhance its generalizability; (3) systematically exploring technical approaches to resilience-based conservation by incorporating advanced DT and hybrid research paradigms, thereby improving the operationalization, comparability, and policy relevance of HD resilience assessment, and offering digital support for safeguarding and sustaining heritage values under disaster conditions.
Data availability
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. (TF-IDF).
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Acknowledgements
This study was supported by grants from the National Natural Science Foundation of China (No.42171219, No.42571270).
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Conceptualization, Y.D. and M.Y.; Methodology, Y.D. and M.Y.; Formal analysis, Y.D. and M.Y.; writing-original draft preparation, Y.D.; supervision, Y.L.; funding acquisition, Y.L.; Writing- Reviewing and Editing, Y.W., Q.L., and R.H.; language and editing, R.H. All authors read and approved the final manuscript.
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Du, Y., Li, Y., Yang, M. et al. Bibliometric analysis of advances in heritage resilience at the district scale. npj Herit. Sci. 13, 606 (2025). https://doi.org/10.1038/s40494-025-02191-y
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DOI: https://doi.org/10.1038/s40494-025-02191-y
