Many recent scientific studies have been conducted based on the relationship between cultural heritage and tourism. Cultural heritage is one of the most attractive segments of tourism and includes various forms of heritage, such as historical monuments, museums, archaeological sites, and religious and national ceremonies (Geçikli et al., 2024)(Qiu et al., 2022). On the other hand, tourism allows these objects to be introduced to the general public and maintains them economically sustainable (Barnes & J., 2022). Therefore, cultural heritage and tourism are complementary concepts.

Cultural heritage is an essential resource for tourism, attracting visitors and contributing to the socio-economic development of a region (Cerisola & Panzera, 2024). According to data provided by the (Commission, 2025), cultural tourism accounts for approximately 40% of Europe's tourism, which continues to grow yearly. In Uzbekistan, the cultural heritage of historical cities such as Bukhara, Samarkand, and Khiva has turned these locations into major tourist centers, leading to the development of service infrastructure and having a significant positive impact on the local economy (Juraturgunov et al., 2023). Moreover, cultural heritage tourism helps balance the regional distribution of tourists (Cerisola & Panzera, 2024). While tourism flows are typically concentrated in large cities and natural landscapes, destinations with cultural heritage also attract visitors to more remote regions expanding the geographical scope of tourism activity (Espino Hidalgo, 2020)(Huang et al., 2016). Heritage is significant for ensuring sustainable tourism development at the local level.

An additional approach to enhancing the role of cultural heritage in tourism development is the application of circular tourism principles. Circular tourism develops tourism activities through resource efficiency, minimizing waste, and regenerating the local economy. For heritage tourism, this includes extending the lifecycle of cultural attractions, promoting the reuse and revitalization of heritage sites, and fostering greater community participation (Rudan, 2023)(Axhami et al., 2023). In regions such as Bukhara, where there is an unequal distribution of resources, adopting circular tourism strategies can help redirect tourist flows to underdeveloped areas, stimulate local artisanal production, and preserve the authenticity of both major and minor heritage assets.

Tourism can serve as a strong economic incentive for preserving and developing cultural heritage (Plata et al., 2022). Revenues generated from visitor fees, tourism-related services, and investments from international organizations can be directed towards restoring, conserving, and maintaining cultural heritage sites (Chenavaz et al., 2022). Notably, the World Heritage List of UNESCO attracts global attention, ensuring economic sustainability through tourism (U.N.E.S.C.O., 2014).

However, tourism also has negative impacts on cultural heritage. Excessive tourist traffic can physically damage heritage sites, while overdevelopment of infrastructure may disrupt the historical and ecological balance of an area ((Cerisola & Panzera, 2024); (AlMasri & Ababneh, 2021)). For instance, in some notable cultural-tourism hubs, such as Venice and Barcelona, an overwhelming influx of tourists has adversely affected the daily lives of residents and raised challenges in preserving the authenticity of heritage sites ((Mazzamuto & Picone, 2022); (Bertocchi & Visentin, 2019)). As well, tourism can lead to the commercialization of cultural heritage. Local culture may be altered or misrepresented in such cases to cater to tourist expectations (Cerisola & Panzera, 2024). As a result, certain cultural elements will have a risk of losing their authenticity, with commercial interests of the tourism industry taking precedence over genuine cultural values (Tang et al., 2019).

Digital technologies are one of the key strategies for the balanced development of heritage and tourism, requiring significant research efforts (Maietti, 2023)(Lian & Xie, 2024). GIS technologies and spatial analysis methods have been widely applied in cultural heritage studies, expanding the possibilities of examining the spatial relationships between tourism and heritage sites (Huang, 2024)(Liu et al., 2024). Research by (Ciski et al., 2019) demonstrates that GIS serves as a crucial tool for mapping cultural heritage sites and studying their spatial distribution through spatial statistical methods.

The Kernel Density Estimation (KDE) method has been widely used in research to assess the geographic density of cultural heritage sites and determine the development levels of tourist zones (Bonnier et al., 2019)(Yuan et al., 2022)(Wei et al., 2024). KDE has been employed to analyze the concentration of cultural heritage sites and tourism resources, leading to the development of recommendations for tourism planning. The Nearest Neighbor Analysis (NNA) method has been applied to determine the spatial clustering of cultural heritage sites and assess its impact on tourist flows (Yuan et al., 2022). Research by (E. et al., 2022) suggests that NNA is an effective tool for identifying the spatial relationships among cultural sites and their regional interactions.

The Gini Index and Lorenz Curve have been used to analyse inequalities in the spatial distribution of cultural heritage sites. (Martín Martín et al., 2018) applied the Gini coefficient to evaluate the spatial concentration of cultural heritage sites, demonstrating main tourism spots in Europe, the Balearic Islands in Spain. Additionally, (Zhang et al., 2022) showed that the Lorenz Curve can be applied to assess the distribution of cultural heritage sites.

Moran’s I is another spatial statistical method increasingly applied in cultural heritage research to measure spatial autocorrelation, whether cultural sites are randomly dispersed, clustered, or evenly distributed. This method evaluates the degree to which the presence of heritage sites in one location is similar to those in neighbouring areas, thereby uncovering underlying spatial structures (Heydari Dastenaei & Niknami, 2024). Moran’s I is particularly effective in identifying spatial dependencies across large geographic regions, contributing to more precise heritage management and tourism planning. Studies have demonstrated its utility in diagnosing spatial imbalances and guiding policies for spatial equity in tourism development (Rodríguez Rangel & Sánchez Rivero, 2020)

A relevant study by (Valjarević et al., 2017) applied GIS-based geostatistical modelling to evaluate the tourism potential of Lukovska Spa, Serbia. Their work demonstrates how spatial interpolation, natural attribute indexing, and geosite assessment can be integrated to identify tourism development zones and manage underutilized areas, which aligns closely with the methodology and objectives of this research. The overall conclusion from these studies is that spatial analysis methods play a vital role in effectively managing the cultural heritage sites, analysing their connection to tourism infrastructure, and developing sustainable tourism strategies. Therefore, when combined with GIS technologies, KDE, NNA, the Gini Index, Moran’s I, and the Lorenz Curve are recognized as effective methods for examining spatial inequalities, the distribution of tourist flows, and the spatial characteristics of heritage sites in cultural heritage research.

However, these analyses have primarily been applied at a global scale or to tourist regions in some countries, while GIS-based spatial analyses of Uzbekistan's cultural heritage and tourism infrastructure, particularly in Bukhara region, remain insufficiently explored. Therefore, this study is not only one of the first to examine the spatial relationships between cultural heritage and tourism in the Bukhara region but also serves as a crucial scientific foundation for developing cultural tourism strategies using GIS-based spatial approaches in other regions of Uzbekistan in the future.

1. Brief overview of the Bukhara Region

Bukhara region is located in the southwestern part of Uzbekistan Figure 1, along the Kyzylkum Desert and the banks of the Amu Darya River. The geographical coordinates of the region range from 38°30' to 40°00' N latitude and 63°45' to 65°30' E longitude.

Administratively, Bukhara is divided into 13 districts, including Bukhara city (regional center), Kagan city (center of Kagan district), and the districts of Olot, Koravulbazar, Karakul, Peshku, Romitan, Shafirkan, Gijduvan, Vobkent, Kagan, Jondor, and Bukhara. Bukhara region has a continental climate, characterized by hot and dry summers and cold, relatively dry winters. In July, the average temperature can reach +37°C, with occasional spikes above this level.

During winter, temperatures range between -2°C and +3°C, with sporadic short-term snowfall. The annual precipitation is quite low, averaging between 100–200 mm, mostly occurring in spring and autumn. Dry desert winds are frequently observed during spring and summer. The climate of Bukhara is favorable for tourism, particularly during spring and autumn, making these seasons the most suitable periods for visiting the region.

2. Formation of Research Sources

The study and data collection of cultural heritage sites in the Bukhara region began in 2021 and is still ongoing. The research on heritage sites was conducted based on the list approved by the Cabinet of Ministers of the Republic of Uzbekistan under Resolution No. 846, which confirms the National Register of Immovable Tangible Cultural Heritage Objects related to the Bukhara region, 2019. The geographical locations (coordinates) of the heritage sites were collected through Google Maps, Yandex Maps, and field trips mainly. All heritage sites were categorized based on their original function (purpose of construction), based on the data provided by the Bukhara Regional Department of Cultural Heritage Table 1.

The heritage register includes mosques, madrasas, khanaqahs, palaces, fortresses, mounds, mausoleums, chillakhonas, minarets, reservoirs, water canals, hammams, caravanserais, fortress walls, fortress gates, prisons, covered bazaars (tim) and arches (toki), ancient houses and courtyards, theaters, statues, busts, squares, sardobas (water resevour), bridges, synagogues, churches, ghuslkhonas (bathing - ablution houses), ayvons (verandas), sheds, bas-reliefs, cemeteries, banks, schools, hospitals, shrines, and memorial complexes. Furthermore, all 829 heritage sites were chronologically classified into five historical periods, providing a diachronic understanding of heritage accumulation in the region Table 2.

The largest share of heritage sites belongs to the Islamic period (8th century–1917), comprising 559 sites, followed by the pre-Islamic period (110 sites), the Soviet period (36 sites), and the independence period (28 sites). For the remaining 96 heritage sites, the historical period of origin could not be precisely determined, and thus they were categorized as “unspecified.” This chronological classification complements the functional one and reveals how Bukhara’s heritage developed across historical periods, showing that religious and educational architecture dominated during the Islamic era, while administrative and social infrastructure emerged in later stages.

All 829 heritage sites approved by Cabinet of Ministers Resolution No. 846 were studied Figure 2. To substantiate the analyses mathematically and statistically, Gini Index, Average Nearest Neighbor (NNA), and Kernel Density Estimation (KDE), Moran’s I analyses were conducted

This research aims to support the implementation of circular tourism strategies in the Bukhara region by enhancing spatial productivity and increasing the concentration of cultural heritage assets (Ottaviani et al., 2023). Spatial statistical methods such as the Gini Index, Nearest Neighbor Analysis (NNA), and Kernel Density Estimation (KDE), Moran’s I are applied to explore spatial disparities and clustering patterns, while also uncovering practical opportunities to promote more balanced and regenerative tourism ((Puttanapong et al., 2022); (Qi et al., 2022)). These tools facilitate the identification of underutilized heritage areas and enable the effective application of circular tourism practices. Spatial analysis in this context provides a strong scientific foundation for maximizing existing tourism potential, reducing environmental pressure, and advancing sustainable regional development.

The Gini index is a statistical tool used to measure equality and inequality in the distribution of resources or indicators ((Ultsch & Lötsch, 2017);(Biró & Néda, 2020)). This index ranges between 0 and 1, where 0 represents perfect equality, and 1 indicates maximum inequality. The primary purpose of the Gini index is to analyze fairness in the distribution and accessibility of resources. It is widely used in economics to assess income inequality, in resource management to evaluate the distribution of natural resources, and in cultural tourism to examine how cultural heritage sites are spatially distributed across different regions. In this study, the Gini index was applied to identify disparities in the distribution of cultural heritage sites across different regions of Bukhara and to evaluate how this level of inequality affects the development of cultural tourism. The analysis revealed challenges, such as overconcentration of heritage sites in certain areas and scarcity in some areas of Bukhara, which limits limit equal access to tourism opportunities.

This insight helps determine which regions require resource redistribution to ensure a more balanced and equitable tourism sector. Moreover, the Gini index provides an opportunity to assess the role of cultural heritage sites in tourism development. If sites are excessively concentrated in one region, other areas might remain underdeveloped regarding tourism potential. By revealing such inequalities, the index assists in strategic tourism planning. Within the framework of this study, analyzing the Gini index across regions serves as a key indicator for strategic decision-making in tourism development. The levels of inequality identified through the index will help maximize regional tourism potential and pinpoint underdeveloped areas. This approach establishes the foundation for regional planning that supports the broad-scale development of cultural tourism.

The following Equation1 was used to calculate the Gini index, where : Gini index, : Number of regions, X_i :i : -Cumulative share of regions (in order), Y_i i : -Cumulative share of Heritage sites.

(1)

The calculation steps are as follows

Nearest Neighbor Analysis (NNA) is a key statistical method used to analyze the spatial patterns of objects within a region, such as cultural heritage sites. This method helps determine whether heritage sites are clustered, randomly distributed, or dispersed ((Zhang et al., 2024);(Wei et al., 2024);(E. et al., 2022)).

In cultural heritage research, NNA plays a crucial role in identifying spatial patterns, aiding in the development of heritage conservation and tourism strategies. Identifying clusters is particularly important for optimizing resource management and prioritizing the development of specific areas. Additionally, NNA results serve as a foundation for other geostatistical analyses, such as density and accessibility assessments.

The Nearest Neighbor Analysis (NNA) calculation is based on the nearest neighbor index (R), which is determined using the following Equation 2. :

(2)

here, d0 : the observed average nearest neighbor distance between points, d_e : the expected average distance in a random distribution, calculated as Equation 3:

(3)

Where, N : the total number of points (heritage sites), A: -the area of the studied region.

If R <1 the objects are clustered (concentrated in specific areas).

If R =1 the objects are randomly distributed.

If R >1 the objects are dispersed (spread out over the region).

The statistical significance of the spatial pattern is evaluated using the z-score -score, calculated as Equation 4:,

(4)

Where where σ represents the standard error of the observed distances. A negative z-scoreZ-score indicates a cluster, a positive z-score indicates dispersion. The further the z-scoreZ-score deviates from zero, the more significant the spatial pattern.

Kernel Density Estimation (KDE) is a statistical method used in spatial analysis to examine the geographic distribution of objects, allowing for the identification of density levels within a Bukhara region. This method serves as an essential tool for visualizing spatial patterns and analyzing distribution mathematically based on geographic data. KDE-generated density maps play a strategic role in the effective management and development of cultural heritage sites and tourism resources. By applying KDE, it is possible to determine where objects are most concentrated and conceptualize their distribution as a continuous surface. This method helps in managing tourist flows, optimizing infrastructure, and identifying underdeveloped areas. In particular, assessing the spatial density of cultural heritage sites provides valuable insights for allocating resources and making informed decisions on tourism development strategies.

KDE mathematically expresses density as follows Equation 5. :

(5)

Where f_n(x): the density function, representing the density value for a given area, n: the total number of objects (829 sites in this study), h: the bandwidth, defining the radius of influence in the calculation, x: the analyzed point, xi: the coordinates of the heritage sites, k: the Kernel function, which estimates the distribution based on mathematical formulas. For this research, the Gaussian Kernel function was applied, expressed in Equation 6.

(6)

where , represents the distance of the object from the analyzed point.

The density maps produced through KDE provide a scientific basis for identifying geographic disparities between regions, studying underdeveloped areas, and strategically managing tourist flows. KDE is recognized as an effective tool in scientific research for optimizing resource allocation and developing tourism infrastructure. In this study, KDE played a crucial role in analyzing the distribution of cultural heritage sites in Bukhara region, forming a key part of the research. The findings from this method will be employed to develop recommendations for enhancing tourism at heritage sites.

Bivariate Moran's I is a statistical method designed to identify spatial autocorrelation between two variables, allowing for the determination of their interregional spatial association. In this study, the method was applied to analyze the spatial relationship between the number of tangible cultural heritage sites and population density.

The use of the bivariate form enabled the integration of demographic and spatial factors into the analysis, thereby implementing a socioeconomic integration approach. The spatial interaction between the number of cultural heritage sites (x) and population density ( y) across 13 districts of the Bukhara region was calculated using the GeoDa software. This approach served to analyze how the distribution of the population and demographic pressure influence access to and utilization of cultural heritage.

The calculation was carried out based on the following Equation 7.:

Where n: number of districts, x_i: number of cultural heritage sites in each district, y_j: population density in neighboring areas, x, y: mean values, w_ij: spatial weights matrix (based on queen contiguity), s0: sum of weights.

Through this analysis, the distribution of cultural heritage resources in densely populated areas, as well as the level and accessibility of their utilization, were identified. Method plays an important role in studying spatial inequality and the principles of equity in resource utilization. Although indicators such as income levels, economic dependency on tourism, and employment have not been included at this stage due to current statistical limitations, the analysis is planned to expand in the future based on the availability of relevant data.

The calculations and visualization were performed using Python (version 3.13.1). The Gini index was computed following the given method. The results of this calculation of the cumulative share of the variable to calculate the Gini Index are presented in Table 3. Based on the Gini index formula, data arrangement, and the calculation of each summation were using the paired values of X_i, X_i+1, Y_i, and Y_i+1. The results obtained for each pair are presented in Table 4. As a result, the Gini index value was calculated as = 0.569. The Gini index indicates a significant disparity in the distribution of cultural heritage sites across the Bukhara region. With a value of = 0.569, the results confirm that a large proportion of heritage sites are concentrated on a few areas, particularly in Bukhara city. Since the Gini index approaching 1 signifies increasing inequality, this result highlights the uneven spatial distribution of heritage sites.

These findings provide critical insights for regional planning and resource management, emphasizing the need for strategic redistribution of tourism and heritage conservation efforts. The Lorenz curve, presented in Figure 3, further illustrates the unequal distribution of cultural heritage resources. The analysis shows that in excess of 50% of heritage sites are concentrated in Bukhara city and Bukhara district, accounting for a total of 54%. Meanwhile, six districts—Shafirkan, Romitan, Peshku, Karakul, Koravulbazar, and Olot—collectively contain only around 10% of all heritage sites. This distribution is significantly influenced by economic, social, and geographical factors, which must be considered when analyzing spatial disparities. Moreover, the results justified the necessity of considering the spatial distribution of heritage sites while planning heritage utilization and tourism development.

Proved to be highly useful in determining the geographic distribution of 829 cultural heritage sites in Bukhara region and assessing their spatial patterns. This analysis provided insights into heritage site utilization and the potential for regional development. Using NNA, the spatial arrangement of heritage sites was evaluated at both the regional level and within individual districts, categorizing them into three main distribution patterns: dispersed, random, or clustered. The analysis was conducted using QGIS 3.40. The NNA results provided scientifically grounded insights into the spatial distribution of cultural heritage sites in Bukhara Table 5, contributing to an understanding development patterns of cities and districts. Based on the findings, the following results were drawn.

NNA analysis of the spatial distribution of cultural heritage sites in Bukhara Region. The analysis was conducted for 12 districts of the region, as Olot district was excluded due to the absence of cultural heritage sites. Using this method, the spatial distribution patterns of heritage sites—clustered, random, or dispersed—were identified, providing valuable insights for the management of tourism and cultural heritage.

According to the NNA results, cultural heritage sites in Bukhara city and its surrounding districts exhibit strong clustering. Specifically, in Bukhara city, the nearest neighbor index ( R) = 0.336, indicating a high concentration of sites in historical centers. This confirms the city's cultural and historical importance. Similarly, Bukhara district ( R = 0.579) and Jondor district ( R = 0.566) also show the evidence of clustering, reflecting the concentration of heritages in these areas. Meanwhile, Gijduvan district (R = 0.663) and Kagan city (R = 0.509) demonstrate moderate clustering, with a relatively lower density of cultural heritage sites.

In contrast, Vobkent district (R = 0.933) and Shafirkan district ( R = 1.009) indicate that the heritage sites are randomly distributed. These areas do not exhibit a clear systematic pattern. Random distribution may create unique opportunities for tourism development, while at the same time, it can complicate tourism infrastructure planning. Similarly, the Koravulbazar district (R = 3.997) and Karakul district ( R = 1.340) show a dispersed distribution of heritage sites, which are related to the geographical or economic characteristics of the region.

Based on the NNA results, the following conclusions were drawn for tourism development in the Bukhara region. Clustered areas, especially Bukhara city and its surrounding districts, should be developed as tourism centers. In these areas, the creation of tourist routes and the expansion of service networks should be a priority. Randomly and dispersedly distributed areas can be developed by improving infrastructure and creating routes that connect heritage sites. This approach helps balance tourism flows across the region and ensures the efficient use of all heritage sites. This analysis also provides an important foundation for drawing conclusions regardingconcluding the cultural heritage of the Bukhara region and for developing strategic regional management plans.

Analyses were conducted using QGIS 3.40 and Python 3.13.1 to examine the spatial distribution of heritage sites in each region separately. The analysis of each region yielded the following results. The geographical area of Bukhara city (Figure 4) contains 354 heritage sites, with almost all located near the western part of the city, showing strong clustering. This area includes the Historic Center of Bukhara, which was inscribed on the (U.N.E.S.C.O., 2025) World Heritage List in 1993. 2000 A 2000-year-old region served as a social, economic, and political center historically, and many heritage sites have been preserved. In the remaining areas, heritage sites are either scarce or absent, concentrating tourism development in a single area and showing a highly unequal distribution across the city. While this situation facilitates heritage management, it also results in excessive tourist density in one location and the centralization of infrastructure facilities. The result indicates the necessity of reasonably expanding the number of heritage sites around the area.

KDE analysis conducted for the Bukhara district (Figure 5) examined the distribution of 94 heritage sites. The results indicate that the heritage sites are concentrated in the western part of the district, demonstrating a one-sided distribution pattern. The density of heritage sites in this region suggests that, as Bukhara city, most of the heritage concentration is clustered in a single area, particularly in the district's western part. Regarding tourism development through heritage, the areas enclosed within the red contour highlight the necessity of expanding infrastructure to support tourism activities. Additionally, identifying new heritage sites in the remaining areas should be considered to enhance tourist attraction potential and promote balanced tourism development across the district.

The Jondor district (Figure 6) is one of the largest districts in the Bukhara region, extending from east to west. It contains 76 heritage sites. In the KDE analysis, the heritage sites have formed two distinct clusters. The majority of these sites are located in the eastern part, indicating that the main heritage concentration is limited to a single area. A significant portion of the district lacks heritage sites, highlighting an unequal distribution. However, the broad contour areas facilitate the development and management of tourism infrastructure by making planning and implementation more efficient.

The KDE analysis of Kagan district Figure 7 shows that heritage sites are distributed over a wider area than the previously analyzed districts, and the KDE contour lines indicate a broader clustering and development potential relative to the district’s territory. Kagan district contains a total of 63 heritage sites. The analysis identified that this district has significant potential due to its closeness to Bukhara International Airport and the presence of the railway station within the area. However, based on the existing analysis, expanding heritage preservation and utilizing tourism potential should be accompanied by infrastructure development to ensure effective management and sustainable growth.

Vobkent district Figure 8 contains 61 heritage sites evenly distributed across the area. As a result, the NNA analysis produced an R-value close to 1. While equal distribution increases the development potential of the district, it may also present challenges in cluster-based development and regional management. However, considering that the denser areas represent the district center, the KDE contour lines suggest the possibility of gradual expansion toward larger areas as a phased approach to the development.

Gijduvan district (Figure 9) contains a total of 50 heritage sites. These sites are concentrated in the southern part of the district, indicating a high degree of clustering in a single area. This situation supports the potential for developing cultural tourism in the southern part. In contrast, the absence of resources in the northern part suggests that there is a need for exploring opportunities for developing other types of tourism or increasing the number of heritage sites strategically and integrating them into tourism activities.

Kagan city (Figure 10), as the administrative center of Kagan district (Figure 7), is the smallest area in the Bukhara region, containing 40 cultural heritage sites. The heritage sites are primarily concentrated in the central part of the city, forming a clustered distribution. As mentioned in the analysis of the Kagan district, the railway station is located in the city, and most tourists arrive through rail transport. Given this, Kagan city can serve as a transit heritage center for visitors traveling to see Bukhara’s cultural heritage and other historical sites. The unequal distribution of heritage sites is also visible through KDE contours, highlighting the need for further heritage identification efforts in the eastern and western parts of the city.

Shafirkan district Figure 11 has its heritage sites located in the southern part of the district. A large portion of the area lacks registered heritage sites. Given its 50 km distance from Bukhara, it necessitates development as either an independent tourism destination or an integrated part of other districts. A total of 31 heritage sites have been registered in the district. NNA analysis also indicated a random distribution, with R = 1.009, confirming the absence of clustering. Developing the district together with other district territories, identifying new heritage sites, and fully adapting the infrastructure would be appropriate.

The Romitan district Figure 12 consists of three parts, with 29 registered heritage sites. All the sites are located in the eastern part of the district, while the other two parts lack heritage sites. According to the analysis, the distribution is partially clustered. This district has a well-developed domestic tourism sector, primarily attracting local visitors due to the presence of pilgrimage sites. The main issue is the deficiency of heritage sites in the other two areas, which suggests the necessity of conducting scientific research to explore potential heritage sites and expand the existing register.

The next three districts, Peshku, Karakul, and Koravulbazar Figure 13, have the fewest heritage resources (with 25, 5, and 2 heritage sites, respectively). According to the analysis, the heritage sites in Karakul and Koravulbazar districts are dispersed and not clustered. Their joint development and infrastructure improvement present significant challenges. However, considering thematic opportunities, these districts can be developed in collaboration with other regions as part of a specialized grouping of heritage sites.

The results of the spatial analyses confirm the uneven distribution of heritage sites and their use in tourism, while clearly revealing concrete opportunities for the implementation of circular tourism strategies. Considering that over 80% of heritage sites are not used for tourism purposes and that the majority of active sites are concentrated in Bukhara city, circular tourism provides a practical foundation for balancing visitor flows and developing underutilized heritage areas. Through KDE and NNA results, high-density clusters and zones of scarcity are clearly identified, making it possible to design circular routes that connect both well-known and lesserknown sites. In this regard, circular tourism supports heritage preservation, sustainable development, and the diversification of tourism products, particularly in peripheral regions with potential for cultural, eco-, or community-based tourism.

Analysis to identify the spatial correlation between the number of cultural heritage sites and population density, a Bivariate Moran’s I statistical analysis was conducted. This approach allowed for the exploration of the socio-spatial relationship between demographic factors and heritage resources. The spatial interaction between cultural heritage sites (first variable) and population density (second variable) across 13 districts of the Bukhara region was calculated using the GeoDa software Figure 14.

The calculated Moran’s I value is –0.0621, indicating a very weak negative spatial correlation. In other words, no statistically significant relationship exists between the number of cultural heritage sites and high population density areas. A z-value of –0.0598 and a p-value of 0.498 suggest that the result is not statistically significant. This implies that the spatial relationship is likely to be random.

In the Moran's I Moran's scatterplot, the points do not show a precise alignment along the diagonal but are dispersed around the center instead. The permutation histogram shows that the observed I value is close to the expected value (E[I]= -0.0833), indicating the weakness of the statistical signal.

The analysis shows no systematic or consistent pattern between the distribution of cultural heritage sites and the spatial distribution of the population in the Bukhara region. This suggests that there may be disparities in access to resources between districts. Furthermore, taking into account spatial inequality and demographic pressure, there arisesconsidering spatial inequality and demographic pressure, there is a need to adapt heritage utilization strategies to regional differences.