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npj | heritage science Article https://doi.org/10.1038/s40494-025-01825-5 Spatial pattern and correlation of archaeological sites and cultural tourism resources in Shanxi Province, China Check for updates 1 1,2 1234567890():,; 1234567890():,; Tianjiao Zhang , Jiangsu Li 1 1 3 , Junxin Song , Jikun Huang & Ioannis Liritzis Archaeological sites serve as unique spatial carriers that preserve historical memories and cultural heritage, making their protection and the integrated development of cultural tourism critical issues in the ﬁeld of cultural heritage studies. This study investigates the spatial distribution and correlation between archaeological sites (ASs) and cultural tourism resources (CTRs) in Shanxi Province, using a spatial database of ASs. The ﬁndings show that: (1) Shanxi has abundant ASs and CTRs, with ASs concentrated in the southern region. The distribution of CTRs largely mirrors that of ASs. (2) A strong positive correlation exists between ASs and CTRs, particularly with museums. In terms of spatial combinations, ASs show a more pronounced synergy with national key cultural relics protection units. (3) Integration of ASs and CTRs in Shanxi is classiﬁed into four types, with the southern region showing the highest integration, designated as the AS-CTR advantage area. Tailored strategies for further integration could be developed based on regional resource advantages, spatial correlations, and integration types. Chronically, archaeological sites have remained enigmatic and largely inaccessible to the public. A key challenge in integrating archaeological sites with cultural tourism resources (CTRs) amidst the “archaeological fever” is bridging the gap between these sites and the public. This would allow people to experience the signiﬁcance and warmth of these places, fostering a deeper appreciation for their historical importance1. Achieving this requires not only the effective utilization of the cultural value of archaeological sites and a full exploration of their tourism potential, but also the integration of these sites with existing CTRs to maximize the value of both2. As global attention to cultural heritage preservation and cultural tourism continues to intensify, studying the spatial and temporal distribution characteristics of archaeological sites and CTRs, and exploring their spatial correlations, provides valuable insights into the current status and interrelationships of these resources within the region. This approach not only provides a scientiﬁc foundation for regional industry planning, but also helps strike a balance between conservation and development. Furthermore, it enables the transformation of the cultural value of archaeological sites into economic value, while facilitating the integration of regional resources to strengthen the competitiveness of cultural tourism. In recent decades, interdisciplinary integration has gained considerable attention, with the advent of technologies such as GIS fostering the convergence of geography and archaeology3,4. This has shifted research on archaeological sites and CTRs from isolated studies to spatially oriented explorations, driving interdisciplinary development, and enhancing theoretical and methodological frameworks. Archaeological sites are locations where material evidence of human activity is buried and preserved5. As particular cultural resources, they embody invaluable historical memories, cultural signiﬁcance, and social identity, offering considerable potential for tourism development6. These sites are a central focus in disciplines such as archaeology and history, with related research primarily concentrated in four key areas: a) Site investigation and discovery7,8; b) The application of scientiﬁc tools in archaeology9–11; c) Research on the cultural complexity of archaeological sites12–14; and d) The protection and sustainable development of ancient sites15,16. These research categories are closely interconnected, collectively advancing the ﬁelds of archaeology and cultural heritage. They contribute to a deeper academic understanding of human history and provide a scientiﬁc foundation for the protection and preservation of archaeological sites. However, among these areas, the integration of archaeological sites with CTRs is primarily involved in the context of research on the protection and sustainability of ancient sites17,18. CTRs are integral to the development of regional tourism19, encompassing A-level tourist attractions (A-TAs)20, national key cultural relics protection units (NKRPs), and museums21,22. CTRs are a focal point for disciplines such as geography, economics, and resource science, with extensive research dedicated to 1 Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng, China. 2Collaborative Innovation Center on Yellow River Civilization jointly built by Henan Province and Ministry of Education, Henan University, Kaifeng, China. 3Alma Mater Europaea University, Maribor, Slovenia. e-mail:

[email protected]

npj Heritage Science | (2025)13:290 1 Article https://doi.org/10.1038/s40494-025-01825-5 areas including resource evaluation and classiﬁcation23,24, protection and sustainable development25,26, development and utilization models27, tourist experience and needs28,29, digitalization and smart applications30, and the excavation and inheritance of cultural connotations31,32. In summary, research on archaeological sites and CTRs has made signiﬁcant progress, providing a solid foundation for further exploration. However, studies speciﬁcally focusing on the integration of archaeological sites with CTRs remain limited. As regional cultural heritage preservation and cultural tourism development accelerate, understanding the spatial integration of archaeological sites and CTRs and establishing a scientiﬁc basis for their coordinated development have become pressing challenges in cultural tourism integration. Building on this foundation, this study selects museums, A-TAs, and NKRPs as representative CTRs, focusing on Shanxi Province. It examines the spatial distribution patterns and correlations between archaeological sites and CTRs within the region. Based on these correlations, the study identiﬁes and categorizes different types of integrated development zones. The ﬁndings provide valuable insights for advancing the coordinated development of archaeological sites and CTRs. The research ﬁndings can serve as a reference for the practical integration of regional archaeological sites and CTRs. The innovations of this study are as follows: 1) This study develops a spatial database that includes comprehensive details about archaeological sites, such as their names, addresses, coordinates, types, dynasties, excavation years, and executors. This database is unprecedented, combining research value with exhibition functionality. It holds signiﬁcant potential for future applications in archaeology and cultural heritage studies. 2) Building on previous work, this study utilizes the DBSCAN spatial clustering technique to analyze the spatial distribution of archaeological sites and CTRs33–35. This extends the use of DBSCAN, highlighting its versatility and potential for broader applications in research on cultural heritage and tourism. 3) For the ﬁrst time, this research incorporates social network analysis to explore the cooperation network among archaeological excavation executors36–38. This approach introduces a new perspective on understanding relationships and collaborations within archaeological projects. 4) This study adapts and applies a spatial correlation model developed by Chinese scholars to analyze the spatial relationship between archaeological sites and CTRs39–41. By doing so, it broadens the scope of this model’s application. The marginal contributions of this study are mainly reﬂected in the following aspects: 1) At the content level, this study breaks through the limitations of isolated research by focusing on the spatial evolution and correlation between archaeological sites and various CTRs. It integrates archaeological site resources into the development of regional cultural tourism, providing a foundation for the deep integration of these resources. 2) In terms of research perspective, this study transcends the limitations of traditional disciplinary perspectives by integrating spatial location, regional connections, and other geographical elements. 3) In terms of research scale, it changes the previous focus only on speciﬁc archaeological sites42, examining the spatial distribution and correlation of archaeological sites and CTRs within a provincial region at the prefecture level, providing a macro reference for the integrated planning of regional CTRs. Methods Study areas and data sources Shanxi Province is located on the eastern edge of the Loess Plateau, covering an area of 156,700 km² and consisting of 11 prefecture-level cities (Fig. 1). According to the most recent census data, its population reached 34,659,900 in 2023. Shanxi has been instrumental in shaping the early development and governance of China. It was the heart of the Yellow River culture, the birthplace of Chinese civilization, and a cradle for the emergence of early states43. With a written history spanning over 3000 years, Shanxi is often referred to as the “Museum of Ancient Chinese Culture” and the “Cradle of Chinese Civilization”. The data utilized in this study include: (1) Archaeological site data sourced from “The Chinese Archaeological Yearbook”, which documents new archaeological discoveries from 1984 to 2018, npj Heritage Science | (2025)13:290 encompassing a total of 500 archaeological sites. It is important to note that this data reﬂects the status as of 2018, as archaeological excavations in Shanxi ceased in 2019 due to the COVID-19 pandemic. Key information on the archaeological sites, including name, location, type, dynastic attributes, excavation executors, and excavation year, was extracted through text mining techniques from the Yearbook. Detailed site addresses were then geocoded, and a spatial database of archaeological sites in Shanxi Province was constructed using ArcGIS software. (2) A-level tourist attractions (A-TAs) data was obtained from “the List of A-Level Tourist Attractions in Shanxi Province”, published by the Shanxi Provincial Department of Culture and Tourism. As of the end of 2023, Shanxi Province has 311 A-TAs, including three World Heritage Sites. (3) Museum Data was sourced from “the 2019 National Museum Directory”, published by the State Administration of Cultural Heritage. As of 2019, Shanxi Province hosted 219 museums, comprising 6 ﬁrst-class museums, 17 second-class museums, and 17 thirdclass museums. (4) National Key Cultural Relics Protection Units (NKRPs) data was retrieved from “the List of National Key Cultural Relics Protection Units in Shanxi Province”, issued by the Shanxi Provincial Cultural Relics Bureau. This list, which includes eight batches, contains 531 NKRPs. (5) Geospatial Data for Shanxi Province and its prefecture-level cities were obtained from the National Basic Geographic Information System 1:4 million dataset. The study used the vectorized standard map of Shanxi Province as the base map, aligning the geospatial coordinates of archaeological sites, A-TAs, NKRPs and museums. These data were then geocoded, and a comprehensive database of archaeological sites and tourism resources in Shanxi Province was built using ArcGIS software, providing a robust foundation for spatial analysis. DBSCAN algorithm The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is a representative density-based spatial clustering method proposed by Ester et al.44. DBSCAN is primarily governed by two key parameters: Eps and Min-points. Eps represent the radius of the study area, which is typically determined based on the Euclidean distance between objects and the distance in descending order of K. Min-points indicate the minimum number of neighboring points required to form a dense region, as deﬁned by the user. The Euclidean distance between two points, A(X 1 , X 2 ) and B(Y 1 , Y 2 ), in space is given by the following Eq. (1): DðA;BÞ ¼ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ðX 2 X 1 Þ2 þ ðY 2 Y 1 Þ2 ð1Þ where X 1 , X 2 , Y 1 , Y 2 represent the respective horizontal and vertical coordinates of points A and B. DBSCAN deﬁnes a cluster as the largest set of densely connected points, capable of partitioning regions with sufﬁcient density into distinct clusters. A key advantage of DBSCAN is its ability to form clusters of arbitrary shapes. In this study, the DBSCAN algorithm is implemented using Python and ArcGIS Pro software, leveraging an improved adaptive parameter DBSCAN algorithm45. The optimal values for Eps and Min-points are automatically determined through kernel density estimation and local density analysis. Silhouette scores are employed as evaluation metrics to select the most appropriate parameter combinations. The clustering results are then visualized using ArcGIS Pro. This approach allows for the identiﬁcation of areas with varying densities of archaeological sites and CTRs. Ultimately, this analysis aims to reveal concentration areas and distribution patterns of archaeological sites and CTRs in Shanxi Province, providing a spatial basis for further exploration of the correlations between these resources. Social network analysis Social network analysis is a quantitative method based on graph theory and mathematical principles, which has become increasingly prevalent in 2 Article https://doi.org/10.1038/s40494-025-01825-5 Fig. 1 | Extent of the study area. a National-scale contextual location, b Study area extent and location of archaeological site, national key cultural relics protection unit, A-level tourist attraction, and museum. Note: The abbreviations used in Fig. 1 are archaeological site (AS), national key cultural relics protection unit (NKRP), A-level tourist attraction (A-TA). geographical research in recent years46. This study focuses on the linkage network of archaeological excavation executors in Shanxi Province, using a network analysis approach to evaluate modularity. Modularity is deﬁned as the difference between the sum of the weights of edges connecting nodes within a community and the sum of the weights of edges in a random network. Mathematically, modularity is expressed as follows in Eq. (2): the cooperative network relationships among participants involved in various stages of archaeological excavations in Shanxi Province, providing a foundation for exploring collaborative models in archaeological excavation efforts within the region. Q¼ 1 X Aij 2m ij ki kj δðCi ; Cj Þ 2m ð2Þ where m represents the total number of edges in the initial graph, and Aij denotes the weight of the edge between nodes i and j. If an edge exists between nodes i and j, thenAij = 1; otherwise, Aij = 0. K i represents the sum of the weights of all edges connected to node i, known as the degree of node i. The function δ(Ci ; Cj ) indicates whether nodes i and j belong to the same community: if they do, δ(Ci ; C j ) = 1; otherwise, δ(C i ; C j ) = 0. The modularity value, denoted as Q, ranges from [−1, 1], with values closer to 1 indicating a more accurate delineation of the network’s community structure. Generally, if Q > 0.3, the network is considered to exhibit a signiﬁcant community structure. In this study, the Modularity module in Gephi software is employed to partition the network into communities based on node characteristics. The modularity value is then calculated to assess the accuracy of the community division. Distinct colors are used to visually represent different communities. This approach enables an analysis of npj Heritage Science | (2025)13:290 Spatial correlation models and spatial analysis methods Building upon existing research47, a 10 km × 10 km grid was overlaid on the administrative map of Shanxi Province, designed to encompass the general distribution of archaeological sites and CTRs. This grid, divided along longitudinal and latitudinal lines, consists of 2676 individual grids, each covering an area of 100 km². However, minor variations in grid area were observed near the provincial borders. Subsequently, the grid layer, archaeological site point layer, and CTRs layer were combined to create a composite layer. The topological database of the composite layer then calculates the binary data for archaeological site points and CTRs points within each grid. Finally, Eq. (3) is applied to determine the spatial correlation index R between the two variables. The R value ranges from −1 to 1, with positive values indicating a positive correlation and negative values indicating a negative correlation. The larger the value of R, the stronger the correlation between archaeological sites and CTRs. To determine the statistical signiﬁcance of the R value, an analysis is performed using Eq. (4). If X 2 > X 2α ð1Þ, it can be concluded that the spatial correlation between the representative items of archaeological sites and CTRs is signiﬁcant. Conversely, if X 2 < X 2α ð1Þ, the spatial association relationship is not considered signiﬁcant. According to the chi-square 3 Article https://doi.org/10.1038/s40494-025-01825-5 distribution table, X 2α ð1Þ ¼ 3.841 at a signiﬁcance level of α= 0.0547. ad bc R ¼ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ða þ bÞðc þ dÞða þ cÞðb þ dÞ X2 ¼ nðad bcÞ2 ða þ bÞðc þ dÞða þ cÞðb þ dÞ ð3Þ ð4Þ where a is the number of grids that contain both representative items of archaeological sites and CTRs; b and c are the number of grids that contain only archaeological sites and only CTRs, respectively; d is the number of grids that do not contain both archaeological sites and CTRs, and n is the total number of grids. We applied a spatial analysis method to quantify the spatial relationship between archaeological sites and CTRs. Speciﬁcally, we generated buffer zone maps centered on CTRs, each with a 10 km radius, roughly representing a half-hour travel distance. These buffer zones were then overlaid onto a layer depicting archaeological sites. We calculated the ratio of archaeological sites within each buffer zone to the total number of archaeological sites. This index reﬂects the combined advantages of archaeological sites and each CTR. The paper also utilizes a spatial correlation model to explore the spatial relationship between archaeological sites and CTRs, with the aim of identifying which CTRs have the strongest correlation with archaeological sites. This analysis establishes the foundation for identifying integration and development zones where archaeological sites and CTRs with complementary advantages can be combined. k-means clustering The k-means clustering method is an automated data partitioning technique that groups data points based on shared characteristics. The method begins by selecting an initial cluster center and then iteratively adjusts the clusters according to a distance metric until a ﬁnal grouping is achieved48. In this study, the method is used to identify archaeological sites with similar advantages and potential areas for tourism integration and development. The process is illustrated in Eqs. (5) and (6): 2 F¼ ðnR 1Þ c ð1n R2 ¼ ðSST R2 nc Þ SSEÞ=SST ð5Þ ð6Þ where n indicates the number of elements, nc indicates the number of classes (groups), SST is a statistic reﬂecting differences between groups, and SSE is a statistic reﬂecting similarities within groups. This paper primarily applies the k-means clustering method to identify areas with similar advantages between archaeological sites and CTRs, classifying them into distinct integration development zones. For each type of integration development zone, tailored strategies for the integration of archaeological tourism development can be proposed. The goal is to promote the protection and sustainable utilization of archaeological sites in Shanxi Province while fostering the high-quality development of cultural tourism integration. Results Trends in the temporal evolution of archaeological sites From 1984 to 2018, 500 archaeological sites were identiﬁed in Shanxi Province. This study divides the period into four phases: 1984–1990, 1991–2000, 2001–2010, and 2011–2018. During this time, 106 archaeological excavation executors participated in the excavations, as detailed in Supplementary Table 1. These excavations form the basis for research, site protection, and development. To analyze the excavation patterns of archaeological sites in Shanxi Province, social network analysis was used to construct a linkage network of excavation executors, as illustrated in Fig. 2. The modularity values for each period exceeded 0.30 (Table 1), indicating a high degree of npj Heritage Science | (2025)13:290 accuracy in the community structure of the network divisions. Over this period, the number of excavation executors increased signiﬁcantly, from 22 to 106. This trend highlights the growing involvement of executors in archaeological excavations and the strengthening of collaborative relationships among them. To further explore these trends, a modular algorithm was applied to divide the network into distinct communities or sub-networks49. As shown in the sub-network directory (Supplementary Table 2), the Shanxi Provincial Archaeological Research Institute serves as the central hub of the linkage network, coordinating several archaeological excavation teams. Key teams within the sub-network are interconnected, facilitating the exchange and sharing of archaeological information. Collaboration among excavation executors is primarily driven by administrative linkages, reﬂecting the substantial inﬂuence of administrative structures. This has led to the formation of a collaborative system involving the Provincial Archaeological Research Institute, the Municipal Culture and Tourism Bureau, the Culture and Tourism Bureaus at the county and district levels, and local museums. This system aligns closely with China’s national conditions and administrative management framework. The archaeological management model in China, which primarily relies on administrative-level coordination, reﬂects the country’s national conditions and governance structure. Under this framework, following archaeological excavations, movable cultural heritage is generally preserved in ofﬁcial institutions, such as local museums and cultural centers, after undergoing restoration, research, and archiving. However, the value of immovable above-ground architectural sites is often insufﬁciently explored and inadequately showcased. The effective protection, management, and utilization of archaeological sites have thus become critical challenges in archaeology and cultural heritage conservation25,50. This issue is not only vital for preserving human history but also plays a direct role in ensuring the sustainable use of cultural heritage in contemporary society and enhancing the broader dissemination of its cultural signiﬁcance. Across the globe, numerous countries and regions have recognized the urgency of this challenge and are actively exploring and implementing effective management models51. For instance, the establishment of the Mediterranean Archaeological Network has facilitated the sharing of archaeological data across societies and advanced the digitization of management, offering both researchers and the public convenient access to a vast array of data and userfriendly interfaces52,53. This model not only provides a solid foundation for archaeological research but also signiﬁcantly boosts public participation and engagement. Features such as interactive 3D displays in virtual museums enhance public interest and foster meaningful discussions surrounding archaeology. As a province replete with archaeological resources, Shanxi should adopt advanced international archaeological management models. By strengthening data sharing and digitization, such as the creation of an open archaeological database to break down information barriers, Shanxi can facilitate the wider dissemination and sharing of archaeological ﬁndings. This approach will not only improve the protection and utilization of archaeological sites but also contribute to the sustainable development of cultural heritage. From the perspective of the temporal distribution of archaeological excavations, the overall trend indicates a ﬂuctuating increase in the number of excavations (Fig. 3a, b). In terms of historical dynasties, the Neolithic, Eastern Zhou, and Han dynasties exhibit the highest number of excavations, while there have been no archaeological excavations conducted for the Three Kingdoms, the Sixteen Kingdoms, and the Western Xia dynasties (Fig. 3c). This discrepancy is primarily linked to the duration of the historical dynasties and their cultural prosperity. There is a direct correlation between the length of a dynasty’s existence and the number of archaeological sites that have been excavated. In terms of the typology of archaeological sites, the majority of excavations conducted in Shanxi Province are related to burial sites. The number of burial sites far exceeds other categories, followed by artifacts, ancient city ruins, and settlements. Excavations in palaces, temples, gardens are limited, with none exceeding 10 excavations (Fig. 3d). Archaeological research in the region has predominantly focused on the study of burial remains. Shanxi 4 Article https://doi.org/10.1038/s40494-025-01825-5 Fig. 2 | Network linkage of archaeological excavation executors in Shanxi Province at different phases. a 1984–2018, b 1984–1990, c 1991–2000, d 2001–2010, e 2011–2018. Note: The names of the excavators of the archaeological deposits represented by the numbers are given in Supplementary information 1. Table 1 | Parameters related to social network analysis Period Modularity Edges Nodes Communities 1984–2018 0.37 22 28 5 1984–1990 0.31 26 35 5 1991–2000 0.34 44 61 11 2001–2010 0.35 68 121 7 2011–2018 0.38 106 192 10 contains a signiﬁcant number of burial sites, many of which are wellpreserved and yield abundant remains. For example, the excavation of the “Jin Hou tomb in Tianma-Qucun” has provided invaluable insights into the funeral system and social development during the Western Zhou period. Spatial evolution of archaeological sites and cultural tourism resources The spatial clustering results for archaeological sites in Shanxi Province were derived using the DBSCAN clustering algorithm, based on the archaeological site data (Fig. 4). The silhouette scores for all stages exceeded 0.70 (Table 2), indicating strong clustering performance. Between 1984 and 2018, ﬁve clusters of archaeological sites were identiﬁed in Shanxi Province (Fig. 4a). The southern region of Shanxi emerged as a key area for archaeological excavations. Cluster 1 located in the southwestern part of Shanxi, is the largest, comprising 196 archaeological npj Heritage Science | (2025)13:290 sites (clustered points), which accounts for 39.20% of the total. These points are primarily centered around the junction of Linfen and Yuncheng, extending north and south. The remaining four clusters are of similar size, each containing fewer than 12% of the total clustered points. The DBSCAN clustering method also identiﬁed 163 noise points, representing 32.60% of the total archaeological sites in Shanxi Province. These noise points are scattered and relatively isolated, but local clustering patterns persist, particularly in areas such as the southern region of Yuncheng City, Shuozhou City, and the border between Changzhi and Jinzhong cities. These clustered noise points may have the potential to evolve into new clusters in the future. The distribution of archaeological clusters in Shanxi Province exhibits a distinct pattern across different phases. Between 1984 and 1990, two clusters are formed (Fig. 4b), a trend that continues into 1991–2000 (Fig. 4c). In the following period, 2001–2010, four clusters emerge (Fig. 4d), while three clusters are identiﬁed in 2011–2018 (Fig. 4e). Notably, Cluster 1 is the largest during 1984–1990, concentrated primarily in the southeastern part of Linfen City and the northern area of Yuncheng City. Cluster 2 is predominantly located in the central region of Xinzhou City. During this phase, 49 noise points are observed, mostly scattered across the cities of Jinzhong and Changzhi in eastern Shanxi. The clustering scale in the southern part of Shanxi Province further expanded between 1991 and 2000, extending into the southern city of Yuncheng. This cluster alone accounts for 64.56% of the total clusters during this phase. Cluster 2 is relatively small and primarily concentrated at the junction of Taiyuan, Lvliang, and Jinzhong in central Shanxi Province. During this period, only 20 noise points were identiﬁed, accounting for 25.32% of the total clusters. The clustering patterns in 5 https://doi.org/10.1038/s40494-025-01825-5 Article Fig. 3 | Statistical map of the number, age and type of archaeological sites, 1984–2018. a Number of archaeological sites by phase, b Number of archaeological sites by year, c Number of archaeological sites by dynasty, d Number of archaeological sites by type. Note: The codes used in Fig. 1c are Paleolithic (I), Neolithic (II), Xia (III), Shang (IV), Western Zhou (V), Eastern Zhou (VI), Qin (VII), Han (VIII), Jin (IX), Southern and Northern dynasties (X), Sui (XI), Tang (XII), Five dynasties and ten kingdoms (XIII), Song (XIV), Liao (XV), Jinn (XVI), Yuan (XVII), Ming (XVIII), Qing (XIX);The codes used in Fig. 1d are ancient city ruins (I), settlement (II), palace (III), burial (IV), temple (V), dwelling (VI), ancient site (VII), handicraft site (VIII), grottoes (IX), mural (X), engineering site (XI), geological survey (XII), paleontological (XIII), integrated archaeological survey (XIV), artifact (XV). 2001–2010 and 2011–2018 are similar, with noise points representing 23.78% and 39.64% of the total, respectively. In both periods, the largest clusters are found in southern Shanxi, with two clusters forming at the junctions of Changzhi and Jincheng, as well as Taiyuan and Jinzhong. From 2001 to 2010, archaeological sites in the western part of Datong City form a smaller cluster. However, by 2011–2018, the distribution of archaeological sites in this area becomes more dispersed, and no clusters are formed. In conclusion, the primary areas for archaeological excavations in Shanxi Province are Linfen, Yuncheng and Changzhi, in the southern region, and Taiyuan, in the central region. A closer examination of the archaeological sites in relation to their dynastic periods and types shows that excavations in Linfen, Yuncheng and Changzhi are predominantly associated with the Neolithic, Eastern Zhou, and Han dynasties. Additionally, a notable number of sites from the Jin, Ming, and Qing dynasties have been uncovered in Changzhi. Excavations in Taiyuan are primarily linked to the Northern and Southern Dynasties, as well as the Sui and Tang Dynasties. In terms of site typology, the majority of excavations are focused on burials, ancient city ruins, and artifact sites. To further explore the spatial and temporal distribution characteristics of archaeological sites in Shanxi Province, we conducted cluster analysis based on two dimensions: dynasty and site type. Dynasties and site types with fewer than 15 archaeological sites were excluded from the analysis. In terms of dynasties (Supplementary Fig. 1), the southern part of Shanxi Province emerges as a key area for archaeological excavations across most periods, except during the Northern and Southern Dynasties, when the number of sites in the southern region is relatively small. Particularly during the Neolithic, Eastern Zhou, and Han dynasties, large-scale agglomeration centers form at the junction of Yuncheng City and Linfen City. This highlights the southern region of Shanxi Province as a signiﬁcant area during these historical periods, characterized by more intensive human activity. Additionally, the central part of Shanxi Province also stands out as a major region for archaeological sites, with clusters forming during the Neolithic, Xia, Shang, Eastern Zhou, and Qing dynasties. Regarding site types (Supplementary Fig. 2), burial and artifact sites are more widely distributed, as they are the most numerous. However, they are still predominantly concentrated at the border of Yuncheng and Linfen cities, as well as in the southern part of Changzhi City. Ancient city ruins and dwelling sites exhibit a more dispersed spatial distribution, while ancient sites, handicraft sites and integrated archaeological surveys are more concentrated in speciﬁc areas. Overall, the southern part of Shanxi Province emerges as the primary region for the distribution of archaeological sites, with all site types clustered around the junction of Yuncheng and Linfen cities. npj Heritage Science | (2025)13:290 6 Article https://doi.org/10.1038/s40494-025-01825-5 Fig. 4 | DBSCAN clustering results of archaeological sites in Shanxi Province at different phases. a 1984–2018, b 1984–1990, c 1991–2000, d 2001–2010, e 2011–2018. Table 2 | Parameters related to DBSCAN clustering of archaeological sites Period Number of Ass (pcs) Eps (km) Minpoints Number of clusters (pcs) Silhouette scores 1984–2018 500 35 19 5 0.72 1984–1990 109 60 20 2 0.87 1991–2000 79 67 8 2 0.71 2001–2010 143 52 9 4 0.71 2011–2018 169 45 17 3 0.71 The spatial clustering of various types of Cultural Tourism Resources (CTRs) was also analyzed using the DBSCAN clustering algorithm, based on the spatial data of A-level tourist attractions (A-TAs), National key cultural relics protection units (NKRPs) and museums (Fig. 5). The npj Heritage Science | (2025)13:290 silhouette scores for all stages exceeded 0.65 (Table 3), indicating strong clustering performance. Four clusters of A-TAs were identiﬁed between 2001 and 2022 (Fig. 5a). These clusters are primarily concentrated in the central and southern parts of Shanxi, with a prominent agglomeration center at the junction of Taiyuan and Jincheng cities. Clusters 1 and 2 are larger, while Clusters 3 and 4 are smaller. Speciﬁcally, Cluster 1 is located in Jincheng City, encompassing 36 points, or 11.58% of the total. Cluster 2 is situated in central Shanxi, with 55 points (17.68% of the total), primarily centered around the junction of Taiyuan and Jinzhong. Clusters 3 is distributed at the junction of Linfen City and Yuncheng City, including 28 cluster points, accounting for 9.00% of the total. Cluster 4 is the smallest and contains only 18 points, mostly in the southern part of Yangquan City. Following the clustering process, 174 noise points were identiﬁed, accounting for 55.95% of the total. This suggests that while A-TAs are generally scattered, there are several potential agglomeration centers, particularly in the southwest and southeast of Jinzhong City, as well as in the southern part of Yuncheng City. 7 Article https://doi.org/10.1038/s40494-025-01825-5 Fig. 5 | DBSCAN clustering results of cultural tourism resources in Shanxi Province. a A-level tourist attraction, b National key cultural relics protection unit, c Museum. Table 3 | Parameters related to DBSCAN clustering of cultural tourism resources Period Number of CTRs (pcs) Eps (km) Minpoints (pcs) Number of clusters (pcs) Silhouette scores A-TA 311 32 15 4 0.65 NKRP 531 28 15 3 0.66 Museum 219 35 10 4 0.76 For NKRPs, three large clusters emerged between 1961 and 2019 (Fig. 5b), each containing more than 110 sites. These larger clusters are concentrated in central, southern, and southeastern Shanxi. Cluster 1 is primarily located in the southern regions of Yuncheng and Linfen cities, forming a distinct agglomeration center at their junction. Cluster 2 is situated in central Shanxi, extending north-south, with the junction of Taiyuan, Jinzhong and Lvliang as its core. Cluster 3 is located in southeastern Shanxi and extends north-south, with the junction of Changzhi and Jincheng as its core. A total of 161 noise points were identiﬁed in the cluster analysis, representing 30.32% of the total. This suggests a more concentrated distribution of NKRPs. Museums in Shanxi formed four distinct clusters between 1919 and 2023 (Fig. 5c). These clusters are predominantly concentrated in central, southern, and southeastern Shanxi. However, the distribution of museums is more scattered compared to that of A-TAs and NKRPs. Cluster 1 is located in the western part of Datong, comprising 20 cluster points, where several specialized museums have been established around the Yungang Grottoes. Cluster 2 is the largest, includes 63 points and extends southward from the junction of Taiyuan and Jinzhong, forming a linear distribution along the western boundary of Jinzhong. Cluster 3, with 26 points, is concentrated in Linfen and Yuncheng, creating a clear agglomeration at their junction. Cluster 4, containing 22 points, is located in the southern part of Changzhi, with signs of further expansion southward. A total of 88 museum noise points were identiﬁed, accounting for 40% of the total. This suggests that while the distribution of museums is relatively dispersed, there is considerable potential for the formation of new clusters in the future. In conclusion, from the perspective of CTR clustering, A-TAs, NKRPs, and museums have formed substantial clusters in Shanxi Province, with a predominant concentration in the central and southern regions. The spatial npj Heritage Science | (2025)13:290 distribution of archaeological sites shows a high degree of consistency in their characteristics. Spatial relevance of archaeological sites and cultural tourism resources As a pivotal element of the tourism industry, A-TAs play a crucial role in enhancing the appeal of tourist destinations. A spatial association model was employed to evaluate the degree of spatial association between archaeological sites and A-TAs, identifying 64 grids containing both archaeological sites and A-TAs. The spatial association index (R) was calculated as 0.10, and the chi-square statistic (jX 2 j) yielded a value of 28.65, which exceeded the critical threshold of 3.84, thereby passing the signiﬁcance test at the 5% level. This indicates a signiﬁcant positive correlation between the spatial distributions of archaeological sites and A-TAs at the provincial level. To further examine the resource combination advantages of A-TAs and archaeological sites, a buffer zone with a 10 km radius was created around each A-TA (Fig. 6a). The number of archaeological sites within this buffer zone was then quantiﬁed. The proportion of archaeological sites within the buffer zone relative to the total number of archaeological sites was used to assess the resource combination advantages between A-TAs and archaeological sites. The results show that the buffer zone encompasses 311 archaeological sites, representing 62.20% of the total. Notably, the combined resource advantages are most pronounced in Taiyuan, Yangquan and Linfen, with all archaeological sites in Taiyuan and Yangquan located within the designated buffer zone. The combined advantages are relatively stronger in Jinzhong, Shuozhou and Datong, while they are weaker in Xinzhou, Yuncheng and Jincheng. Lvliang and Changzhi exhibit the most limited combined advantages (Fig. 7a). The integration of NKRPs with archaeological sites has emerged as a key means of illustrating the evolution of human civilization. A spatial correlation model was applied to examine the relationship between archaeological sites and NKRPs to assess the potential for further integration. The analysis identiﬁed 104 grids containing both archaeological sites and NKRPs. The calculated R value was 0.15, and the jX 2 j value was 60.24, surpassing the threshold of 3.84. These ﬁndings indicate a signiﬁcant positive correlation between the distribution of archaeological sites and NKRPs at the provincial level. To further explore the beneﬁts of integrating NKRPs and archaeological sites, a buffer zone was established with the NKRPs at its center, extending over a 10 km radius (Fig. 6b). The ﬁndings reveal that a total of 416 archaeological sites is located within the designated 8 https://doi.org/10.1038/s40494-025-01825-5 Article Fig. 6 | Analysis map of buffer zone of A-level tourist attractions, national key cultural relics protection units and museums. a The buffer zone of A-level tourist attractions, b The buffer zone of national key cultural relics protection units, c The buffer zone of museums. Note: The abbreviations in Fig. 6 are A-level tourist attraction (A-TA), national key cultural relics protection unit (NKRP). Fig. 7 | Association of A-level tourist attractions, national key cultural relics protection units and museums with archaeological sites. a Association of A-level tourist attractions with archaeological sites, b Association of national key cultural relics protection units with archaeological sites, c Association of museums with archaeological sites. Note: The abbreviations in Fig. 7 are A-level tourist attraction (A-TA), national key cultural relics protection unit (NKRP). buffer zone, representing 83.20% of the total number of archaeological sites. The combined resource advantages are most pronounced in Taiyuan, Yangquan, Linfen, and Yuncheng, with all archaeological sites in Taiyuan and Yangquan located within the buffer zone. In contrast, the resource advantages are more moderate in Jinzhong and Jincheng, and weaker in Lvliang, Changzhi, and Datong. Shuozhou and Xinzhou exhibit the most limited combined advantages (Fig. 7b). The relationship between archaeological sites and museums is based on a shared goal, the preservation and dissemination of the invaluable heritage of human civilization. The spatial correlation model revealed that 66 grids contain archaeological sites and museums. The calculated R value was 0.25, and the jX 2 j value was 172.07, exceeding the threshold of 3.84. These results suggest a signiﬁcant positive correlation between the spatial distribution of archaeological sites and museums at the provincial level, with a stronger degree of spatial correlation than that observed between A-TAs and NKRPs. To further investigate the integration of museum and archaeological site resources, a buffer zone was established with museums at its center, extending over a 10 km radius (Fig. 6c). The analysis found that 327 archaeological sites, representing 65.40% of the total, are located within this buffer zone. Notably, the combined resource advantages are most pronounced in Taiyuan, Changzhi, and Linfen. In contrast, Yuncheng, Shuozhou and Datong exhibit more signiﬁcant combined advantages, while Jinzhong, Xinzhou, Lvliang and Jincheng show weaker advantages. Yangquan displays the most limited combined advantages (Fig. 7c). npj Heritage Science | (2025)13:290 Classiﬁcation of type zones for integrated development of archaeological sites and cultural tourism resources This study aimed to analyze the correlation between archaeological sites and CTRs, to enhance our understanding of their spatial heterogeneity across the 11 prefecture-level cities in Shanxi Province. To achieve this, we counted the number of archaeological sites within grids that contained both archaeological sites and A-TAs, NKRPs, and museums. Additionally, the k-means 9 Article https://doi.org/10.1038/s40494-025-01825-5 Fig. 8 | Typical areas of integrated development of archaeological sites and cultural tourism resources in Shanxi Province. a The types of integrated development, b Number of archaeological sites and cultural tourism resources in each type area. Note: The abbreviations used in Fig. 8 are archaeological site (AS), A-level tourist attraction (A-TA), national key cultural relics protection unit (NKRP). clustering method was employed to classify areas with similar advantages in archaeological site and tourism integration development. Three clustering modes were tested with k values of 3, 4, and 5. When k= 4, the mean pseudo F-statistic was highest, and the discrepancy between the maximum and minimum values was minimal. Therefore, k= 4 was selected for the cluster analysis. The clustering was based on the differences in mean values of the number of grids containing both archaeological sites and A-TAs, NKRPs, and museums. In this context, A-TAs were considered tourism resources, while NKRPs and museums were categorized as cultural resources. The results show that the 11 cities in Shanxi Province can be classiﬁed into four distinct types, as illustrated in Fig. 8. Type I areas include Linfen and Yuncheng, which are abundant in archaeological sites, A-TAs, NKRPs, and museums, all exhibiting high average values. This combination positions these as archaeological sites-cultural and tourism resource advantage areas, with signiﬁcant potential for the development of both archaeological sites and tourism. The Type II area includes Jincheng and Changzhi, which feature a higher number of NKRPs. However, the average number of archaeological sites is slightly lower compared to Type I areas. This suggests that these cities have made notable progress in the protection of cultural relics and have considerable potential for further development. Therefore, these regions can be considered as cultural resources advantage area. Type III areas consist of Jinzhong and Taiyuan, which exhibit a signiﬁcantly higher mean number of A-TAs compared to other cities. While these cities have fewer archaeological sites, they offer a stronger foundation for tourism development and greater potential for integrating CTRs. As a result, they are classiﬁed as tourism resources advantage area. Type IV areas include Datong, Lvliang, Shuozhou, Xinzhou and Yangquan, which are characterized by a lack of archaeological sites, A-TAs, NKRPs, and museums. Consequently, these cities are considered low dominance area, with limited potential for the integration and development of archaeological sites and other spatial carriers. npj Heritage Science | (2025)13:290 Discussion Accelerated urbanization has created a dual challenge for the protection and utilization of archaeological sites, with cultural tourism emerging as a means to balance these competing demands54. As a form of cultural tourism, archaeological tourism integrates archaeological sites with cultural and tourism resources, offering tourists unique and immersive experiences55. For instance, Mexico has developed ecological archaeological routes, allowing visitors to explore the charm and mystery of the Calakmul Biosphere Reserve and the Mayan forest56. In Spain, the Ulaca Oppidum site has introduced virtual tourism, transforming traditional methods and promoting the protection and revitalization of the site57. Similarly, Egypt has utilized dark archaeological sites for ghost tours, creating immersive, mystical experiences through storytelling and situational interactions58. China’s archaeological tourism began relatively late but has rapidly developed due to policy guidance and market demand. This growth has led to the creation of a cultural and tourism integration model centered around national archaeological site parks and museums. For example, the Liangzhu Ancient City site has established a composite space system with “site museum + site + virtual experience”59. The Sanxingdui site has integrated local intangible 10 Article https://doi.org/10.1038/s40494-025-01825-5 heritage and ecological landscapes, helping to shape a regional tourism brand60. Additionally, Yinxu has leveraged digital collections and launched a cross-border game to enhance its appeal61. However, archaeological tourism in China faces several challenges. In some areas of Shanxi Province, the connection between archaeological sites and surrounding cultural and tourism resources is weak, resulting in clear isolation. For example, the Jinyang Ancient City National Archaeological Site Park focuses primarily on site protection and basic display, while peripheral resources, such as Jinyang Lake and Mengshan, lack dynamic integration, making it difﬁcult to establish a cohesive regional linkage model. Furthermore, many archaeological site parks in China prioritize landscaping, leading to signiﬁcant homogenization and damage to the originality of the archaeological sites62. Research on the spatial correlation between archaeological sites and cultural tourism resources (CTRs) holds signiﬁcant theoretical and practical value in promoting their synergistic development. Such research not only provides a scientiﬁc basis for the optimal integration and efﬁcient use of cultural and tourism resources but also optimizes the cultural dissemination and inﬂuence of archaeological sites. While extensive studies have been conducted on the integration of CTRs addressing aspects such as resource interactions, integration mechanisms and cultural tourism product development. However, there is still a relative paucity of research that deeply explores the spatial correlation between archaeological sites and CTRs. This study, based on an analysis of the spatial distribution patterns of archaeological sites and cultural tourism resources, offers an in-depth examination of the spatial integration between these two elements. The goal is to support decision-making for the integrated development of archaeological sites and CTRs. However, there are certain limitations. Although it has identiﬁed a signiﬁcant correlation between archaeological sites and CTRs, as well as pinpointed areas with potential for archaeological tourism development through spatial correlation analysis, archaeological tourism in Shanxi Province has not been systematically developed. Consequently, the interactions between factors such as economic development, population density and regional policies, and their impact on archaeological tourism development are not explored from an economic and social perspective. Future research will focus on ﬁeldwork, questionnaires and other methods, supplemented by socioeconomic data and ﬁeld research ﬁndings. This will allow for an indepth analysis of how socioeconomic factors inﬂuence the integration of culture and tourism, as well as the development of archaeological tourism. Such an approach will become a new direction for the cross-research of archaeology and geography, economics and other disciplines. Based on the results regarding the spatial distribution, spatial correlation, and integration characteristics of archaeological sites and CTRs, targeted countermeasures and recommendations for their integration and development are proposed. (1) Linfen and Yuncheng, both rich in archaeological sites and CTRs, should focus on integrating both underground and aboveground cultural heritage. These cities need to establish effective connections between high-quality archaeological sites and surrounding CTRs, such as world cultural heritage sites, NKRPs, patriotic education bases, science education centers, scenic spots, and historical and cultural cities. This will help address the isolation and lack of support for archaeological sites. By adopting integration models such as “archaeological sites + scenic spots”, “archaeological sites + study bases”, and “archaeological sites + museums”, the cities can foster new forms of archaeological tourism. This strategy will contribute to the creation of a cohesive, interconnected cultural and tourism development model for the entire region. (2) Jincheng and Changzhi possess a competitive advantage in cultural resources, though they lack high-quality tourism attractions. Situated in the Taihang Mountains, these cities feature unique landforms and forest resources, creating ideal conditions for recreation and tourism. Therefore, these cities should capitalize on the opportunity to develop national demonstration zones for the integrated development of cultural tourism and wellness, along with national forest parks. By combining archaeological sites with cultural resources and park npj Heritage Science | (2025)13:290 development, they can foster the synergistic growth of both cultural tourism and recreational industries. Local resources should be maximized to develop diverse sectors, including cultural experiences, health and wellness, eco-tourism and leisure holidays. Additionally, the establishment of archaeological exploration bases, cultural science and technology centers, folk culture hubs and characteristic cultural and creative functional areas63 will further enhance the region’s appeal. Exploiting these regional resources will create a cultural tourism destination that harmoniously blends thriving cultural heritage with recreational experiences. (3) Jinzhong and Taiyuan are areas with a strong advantage in tourism resources, which are more abundant than other regions. These cities should fully invest their high-quality tourism assets to elevate their appeal. At the same time, it is essential to deeply explore local cultural characteristics and promote the development of key archaeological heritage resources, such as the Archaeological Museum of Jinyang Ancient City. By integrating archaeological resources with tourism and cultural creative industries, these cities can open new pathways for the development of regional cultural tourism. (4) Datong, Lvliang, Shuozhou, Xinzhou and Yangquan are disadvantaged regions with relatively few archaeological sites, cultural resources and tourism attractions. These cities should actively promote the digital economy by harnessing their unique archaeological and cultural elements, along with untapped tourism resources. Through the adoption of modern information technologies, such as virtual reality and augmented reality, the virtual exhibition and global dissemination of cultural heritage can be realized. Moreover, integrating cultural and creative industries with digital experience products can make cultural experiences more engaging and interactive, while also providing new opportunities for the digital transformation of cultural heritage64. This study constructed a comprehensive long-term database of regional archaeological sites, summarized excavation trends in Shanxi, and analyzed the spatial distribution of archaeological sites and cultural tourism resources (CTRs) using DBSCAN clustering. A spatial correlation model was applied to examine the relationship between these archaeological sites and CTRs, while K-means clustering was used to categorize the integration types of archaeological sites and CTRs in Shanxi Province. The ﬁndings offer valuable insights for decision-making in regions with similar contexts, both within Shanxi and globally. The key conclusions of this study are as follows: (1) Shanxi Province has wealth of archaeological sites and CTRs. From 1984 to 2018, archaeological sites were predominantly clustered at the junction of Linfen and Yuncheng, with additional smaller clusters in other areas. Isolated sites also show potential for developing into new cluster hubs. The distribution of CTRs mirrors this pattern, with A-level tourist attraction (ATA), national key cultural relics protection unit (NKRP), and museums predominantly concentrated in the Yuncheng-Linfen, Changzhi-Jincheng, and Taiyuan-Jinzhong regions. (2) A signiﬁcant positive correlation exists between the distribution of archaeological sites and CTRs, such as A-TAs, NKRP and museums. The correlation between archaeological sites and museums is the strongest, while the connection with A-TAs is relatively weaker. In terms of spatial integration, the combination of archaeological sites with NKRPs offers the most beneﬁts, contrastingly integration with A-TAs is less pronounced. (3) The integrated development of archaeological sites and CTRs in Shanxi can be classiﬁed into four types: a) archaeological sites-cultural and tourism resource advantage area, b) cultural resource advantage area, c) tourism resource advantage area, and d) low dominance area. The ﬁrst three types encompass six districts, while ﬁve districts are considered disadvantaged. Moving forward, Shanxi Province should implement a differentiated strategy for the integration of archaeological sites and cultural tourism resources, tailored to the unique advantages, relevance, and integration characteristics of each region. 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Participative co-creation of archaeological heritage: case insights on creative tourism in Alentejo, Portugal. Ann. Tour. Res. 79, 102790 (2019). Acknowledgements This work was supported by the National Natural Science Foundation of China (42271192). It is also supported by the Social Sciences Planning Special Project of Henan Province (2024ZT010), also supported by Think Tank Research Projects of Higher Education Institutions in Henan Province (2022ZKYJ05). The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. Author contributions T.Z.: Conceptualization, Formal analysis, Methodology, Investigation, Validation, Writing-original draft, Writing-review & editing. J.L.: Conceptualization, Methodology, Investigation, Validation, Writing-original draft, Writing-review & editing, Funding acquisition. J.S.: Data curation, Software. J.H.: Data curation, Formal analysis. Ioannis Liritzis: Validation, Writing review & editing. Competing interests The authors declare no competing interests. Additional information Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s40494-025-01825-5. Correspondence and requests for materials should be addressed to Jiangsu Li. Reprints and permissions information is available at http://www.nature.com/reprints Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modiﬁed the licensed material. 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