Exploring spatial heterogeneity and influencing factors of cultural inheritance level in mountain traditional villages: a case of Leishan County

Abstract

In Southwest China’s multi-ethnic mountainous regions, fragmented terrain has preserved numerous traditional villages. Yet the progression of urbanization and tourism has eroded cultural heritage in these villages, rendering the preservation-development equilibrium an urgent challenge. Crucially, the specific factors affecting cultural inheritance in local villages require further investigation. To address this gap, this study applied Cultural Ecosystem Theory to evaluate the Cultural Inheritance Level (CIL) of 43 villages in Leishan County, Guizhou. The Multiscale Geographically Weighted Regression (MGWR) model was employed to identify driving factors and quantify their spatially varying impacts. The findings revealed significant regional spatial differentiation in the CIL. Notably, areas with rugged terrain were more affected by positive factors—cultural heritage protection policies. The pressures of mass tourism are negatively correlated with CIL, with amplified effects in tourism-developed regions. This study delivers a CIL assessment framework and targeted policy recommendations for cultural heritage protection within this regional context.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-35275-1.

Introduction

Geographical isolation and ecological diversity in multi-ethnic mountainous areas promote the convergence of distinct ethnic cultures, forming unique village heritage that preserves cultural memory and identity^1,2. In this context, Guizhou’s mountainous regions in China serve as both an ethnic convergence zone and an area with one of the nation’s highest densities of traditional villages³. Traditional villages in China are defined as historically continuous vernacular settlements that maintain distinct socio-cultural practices while acting as contemporary communities and critical heritage sites⁴. As a cornerstone of identity and collective memory, cultural heritage supports social cohesion and sustainable development. However, accelerated modernization and the global expansion of tourism are transforming historically isolated mountain communities. Although development offers livelihood opportunities for mountain villages worldwide^5,6, it simultaneously erodes local cultural authenticity^7,8. Consequently, achieving a balance between cultural heritage preservation and regional development remains a critical challenge⁹.

This challenge is particularly acute in Guizhou Province, Southwest China, which hosts significant ethnic diversity and possesses one of China’s highest densities of traditional village clusters¹⁰. Despite their rich cultural heritage, these villages face multiple challenges: fragmented topography worsens infrastructure gaps¹¹, and although poverty has been reduced, the province’s per capita GDP remains low by global standards¹². This economic condition drives substantial population outmigration¹³, which depletes young adults and weakens the social foundations for cultural inheritance—a risk intensified by high-altitude constraints^14,15. Additionally, tourism-driven commercialization is progressively eroding the cultural integrity of these villages. Given this multifaceted crisis, there is an urgent need for a heritage-transmission assessment to safeguard cultural heritage in multi-ethnic mountain regions (Fig. 1).

Fig. 1.

Image of the traditional village of Leishan County, Guizhou.

Source: Figure was taken by research team.

Such an assessment can be framed through the concept of Cultural Inheritance Level (CIL), which measures the breadth, validity, and depth of cultural transmission across generations or groups, emphasizing sustainability and vitality¹⁶. However, assessing CIL is challenging due to its interdisciplinary nature—spanning sociology, geography, and heritage conservation—and because China’s official statistics seldom cover village-level cultural data, limiting systematic archiving¹⁷. The theoretical underpinnings of CIL have evolved over time. Early research concentrated on material cultural elements^18,19, later shifting to a material-intangible dichotomy that risks fragmenting culture itself^20,21. While “Cultural Evolution Theory” explains broad change patterns, it may overlook non-evolutionary determinants such as social structures²². Conversely, “Social Learning Theory” addresses micro-level cognition but fails to incorporate macro-social dynamics²³. Huxley’s “Cultural Pyramid” model provides an analytical framework but is static and ignores cross-system interactions²⁴. A more integrative perspective comes from Steward, who defines cultural ecosystems as interdependent systems where human culture and the natural environment co-evolve²⁵. This concept constitutes the theoretical foundation of cultural ecology. Although Steward’s framework includes social, economic, and ecological dimensions, it omits cultural ontology transmission mechanisms²⁶. Therefore, integrating the “cultural pyramid” theory with cultural ecology offers a viable approach for investigating CIL and its determinants within traditional villages of multi-ethnic mountainous regions.

Applying this integrated framework is relevant in Guizhou’s multi-ethnic mountain villages, which exhibit significant socio-economic disparities. These disparities—manifested reflected in differential infrastructure investment, tourism financing allocations, and agricultural resource development—create divergent contexts for cultural transmission^27,28. Empirical evidence confirms that natural and ethnic variations fundamentally shape cultural diversity patterns¹², illustrating a distinct culture-ecology linkage in the area. A notable example is Leishan County, where varied terrain hosts intermixed ethnic settlements. Although ethnic intermixing is extensive at regional scales, distinct intra-ethnic clustering persists at the village level, as captured by the local saying: “Miao villages clinging to mountains, Dong villages nestling by waters²⁹”. This spatial heterogeneity influences both village distribution and cultural preservation resources. For instance, Miao timber structures cost more to maintain than masonry, making them more vulnerable to funding shortages. Such spatial factors potentially drive variations in CIL, highlighting the need for spatial approaches to: (1) identify cultural transmission mechanisms embedded in landscape contexts; and (2) optimize geographically targeted conservation strategies. Thus, a systematic analysis of the spatial determinants influencing CIL gradients is imperative.

Despite this imperative, spatial determinants of CIL in traditional villages remain underexplored in multi-ethnic mountainous contexts³⁰. Accordingly, this study adopts an analytical framework that investigates CIL through the synergistic interplay of human activities, natural ecology, and socioeconomic conditions, with the aim of delineating the spatially varying intensities of these drivers within the cultural ecosystem. The findings are expected to provide actionable insights for policymaking and a theoretical basis for governmental authorities tasked with protecting cultural heritage in traditional villages located in multi-ethnic mountainous regions.

Materials and methods

Study area and data sources

This study focuses on traditional villages in Leishan County, Guizhou Province. Located between 26°02′N to 26°34′N and 107°55′E to 108°22′E, the county contains 68 nationally protected “Traditional Chinese Villages,” representing one of the highest concentrations in Guizhou. The population is 92.7% ethnic minorities, including Miao, Shui, and Dong groups, who have preserved distinctive cultural heritage such as Miao stilted houses, embroidery, Lusheng dance, and pristine landscapes^31,32. Despite this cultural richness, the region faces socio-economic challenges. In 2024, the rural per capita disposable income reached 15,300 RMB, equivalent to only 44% of the national average for rural areas, and the population outflow rate reached 25%³³. In response, the Provincial Government plans to invest 544 million yuan in tourism development for ethnic-characteristic villages^34,35. As the starting point of “China’s Rural Tourism No. 1 Highway,” Leishan’s villages face the pressing challenge of balancing tourism growth with cultural preservation. Studying the spatial differentiation and drivers of CIL here is therefore of direct practical relevance.

A random sampling method was employed to select 43 out of 68 national-level traditional villages in Leishan County for detailed investigation (Fig. 2). Data were compiled from village committees and local governments, these included the “Traditional Village Protection Plan”³⁶, “Traditional Village Protection Archives”³⁷, and “Traditional Village Construction Project List”³⁸ of the sample villages, as well as the “Village Socioeconomic Statistical Bulletin”³⁹ from the eight towns under the jurisdiction of Leishan County and the “County Transportation Fourteenth Five-Year Plan”⁴⁰.

Fig. 2.

Geographical distribution of the case traditional villages in Leishan County, Guizhou. Maps were created using ArcGIS Pro 3.0.0 (Environmental Systems Research Institute, USA. https://www.esri.com/).

Theoretical framework

The theoretical framework of this study is grounded in the “Cultural Pyramid” model. Culture is understood as a unified whole comprising both material and intangible dimensions, since rigid binary classifications are often criticized for fragmenting holistic cultural understanding⁴¹. This study adapts the “Cultural Pyramid” (Fig. 3a) into a “Cultural Heritage Pyramid” (Fig. 3b) to better capture the logic of cultural ecosystems^42,43. This pyramid represents an organically nested system where each layer supports the ones above. Specifically, the material culture layer at the base includes human-made artefacts—such as traditional architecture, roads and tools—alongside their ambient natural environments, agricultural landscapes and spatial patterns. Within this ecosystem, material heritage acts as quantifiable “nodes” formed through long-term community-environment interaction²⁴. Building upon this foundation, the behavioral culture layer serves as an intermediary between the material base and the conceptual realm, translating spiritual values (e.g., clan ethics, feng shui) into physical space (e.g., ancestral halls, village layout). This layer encompasses norms, social organizations and governance models that regulate the relationships among people and between people and the material world. Through generational practice, these norms evolve into intangible cultural heritage, with quantifiable indicators encompassing village governance, social practice, rituals activities, inheritance mechanisms and community participation⁴⁴. At the apex, the spiritual-culture layer is the most profound and stable. It permeates the other two layers, guiding community behavior and material creation (Fig. 3c). For example, ancestor worship is expressed through ancestral halls (material) and rituals (behavioral), measurable by residents’ participation and commitment to transmission⁴⁵. By conducting surveys and quantification across three levels in various villages, the CIL is comprehensively evaluated through the dimensions: diversity, integrity, and continuity (Fig. 3d).

Fig. 3.

Theoretical analysis framework for the CIL in traditional villages.

Source: (a,b)²⁴; (c–e) prepared by authors.

To examine the factors shaping CIL, this study adopts a cultural ecology perspective, which posits that CIL evolves through the interaction of four spatial systems: natural, social, economic, and residential⁴⁶. These systems function as follows: The natural system denotes the physical substrate that sustains human settlements and constitutes a fundamental driver of cultural development, encompassing objective indicators such as topography, slope, and biotic communities. The social system comprises the collective social relations forged during village evolution, operationalized through indicators such as population mobility, ageing, and institutional frameworks. The economic system encompasses villagers’ material production modes and wealth accumulation, proxied by indicators of tourism, rural specialty industries, and household income. Collectively, the social and economic systems act as exogenous cultural drivers, mediating the influence of industrial development and governmental policy on heritage transmission. In contrast, the residential system—comprising localized, ecologically adapted elements like infrastructure, buildings, and transport—functions as an endogenous driver by improving living standards and reinforcing cultural continuity. Accordingly, this study integrates region-specific representative indicators (Table 2) to construct a spatial determinants model of CIL based on the four abovementioned systems (Fig. 3e).

Table 2.

Indicators of factors influencing the CIL (C) in Leishan County.

Perspectives	Designator	Indicators	Evaluation criteria
Nature	X1	Slope	The ratio of the vertical height to the horizontal width of the slope at the center of the village
	X2	Elevation	Elevation of the village center point
	X3	Landscape diversity	The number of ecological landscapes within the village area
	X4	Forest coverage rate	The ratio of forest area to total land area within the village area
	X5	Soil sand content	The proportion of sand particles in the soil within the village area
	X6	Average annual precipitation	The average multi-year rainfall within the village area, calculated by dividing the total accumulated rainfall over the period by the number of years
	X7	Biodiversity index	The number of biological species within the village area.
Society	X8	Hollowing rate	The percentage of vacant households out of the total number of households within the village area.
	X9	Aging population rate	The ratio of the population aged 65 and above to the total population within the village area.
	X10	Population density	The ratio of the total population to the area of the village
	X11	Intangible cultural heritage influence	The number of intangible cultural heritages recognized by the nation as having significance and influence
	X12	Cultural heritage policy intensity	The number of policy measures Being implemented for the protection and inheritance of cultural heritage in the local area
Economy	X13	Urbanization rate of the town	The ratio of the urban household registered population to the total population within the town area.
	X14	Tourism employment population	The number of people engaged in tourism-related occupations within the village area
	X15	Annual revenue from village collective tourism	The average annual income obtained by the village’s collective tourism industry over the past three years.
	X16	Industrial diversity index	The number of types of industries within the village area.
	X17	Number of enterprises in the village	The number of enterprises within the village area.
	X18	Annual household income per capita	The average annual income per capita across all households within the village area, calculated by dividing the total annual income of all members in each household by the number of people in the household, and then dividing by the total number of households
	X19	Average annual tourist reception volume	The average annual number of tourists received over the past three years
	X20	Village development and construction fund	The total amount of funds for the development and construction of the village
	X21	Investment in cultural heritage	The total amount of funds invested in cultural heritage within the village area.
	X22	Number of agritourism operations	–
	X23	Number of local specialty sales operations	–
	X24	Number of specialty food operators	–
Habitat	X25	Public facilities completeness	The number of public infrastructure facilities within the village area
	X26	Availability of inter-village transportation	Whether there is inter-village transportation (0 for no, 1 for yes).
	X27	Distance to central township	The driving distance to the central town in kilometers (KM).
	X28	Distance to county town	The driving distance to the County Town in kilometers (KM).
	X29	Utilization rate of public activity spaces	The proportion of villagers who use public activity spaces for traditional activities daily, relative to the total village population

The evaluation system of CIL

This study synthesized theoretical frameworks to construct a CIL evaluation system across three dimensions: cultural diversity, cultural integrity, and cultural continuity. Cultural diversity measures the richness of the “Cultural Heritage Pyramid” across its three layers. At the material–ecological level, this includes the variety of cultural landscapes and architectural typologies (D1–D3); at the institutional–practical and spiritual levels, it encompasses the diversity of social organizations, festivals and rituals, folk beliefs, and traditional crafts (D4–D5). Cultural integrity assesses the strength and authenticity of the linkages within the cultural ecosystem. Indicators examine whether the material spatial morphology still embodies traditional values (D6), whether institutional practices continue to be enacted in their original physical settings (D7), and whether traditional social functions remain operative (D8–D9). Cultural continuity gauges the vitality of the cultural ecosystem through the persistence of inherited conceptions. Core indicators include the stability of bearer communities, the frequency of transmission activities, and villagers’ participation (D10–D14). Institutional continuity is proxied by the level of governmental support for local traditional culture (D15). All evaluation criteria were derived from the “Evaluation and Identification Index System for Traditional Villages (Trial)” and extant research (Table 1).

Table 1.

Comprehensive evaluation index system for CIL.

Criterion	Sub-criteria	Indicator	Source
Cultural diversity value(B1)	Richness of material culture Heritage (C1)	Richness of historical environment elements (D1)	I47
		Richness of traditional buildings (D2)	II
		Richness of major historical and cultural sites (D3)	II
	Richness of intangible cultural heritage (C2)	Richness of intangible culture (D4)	II
		Diversity of ethnic minority populations (D5)	I32
Cultural integrity value (B2)	Degree of preservation of material culture heritage (C3)	Degree of preservation of traditional buildings(D6)	I47
		Degree of preservation of historical environmental elements(D7)	I48
		Degree of preservation of Major historical and cultural sites(D8)	I49
	Degree of preservation of intangible cultural heritage (C4)	Scale of intangible culture (D9)	I50, II
Cultural continuity value (B3)	Residents’ participation (C5)	Richness of traditional folk activities (D10)	I51
		Annual frequency of traditional skills training (D11)	I52
		Numbers of inheritor (D12)	I52
		Degree of villagers participation in folk activities (D13)	II
	Longevity (C6)	Scarcity of intangible culture (D14)	II
	Government support (C7)	Special protection fund for major historical and cultural sites (D15)	I53

Note: I: Literature; II: The Chinese document: Evaluation and Recognition Index System for Traditional Villages (Trial)⁵⁴.

Calculation of research methods

Entropy weight-TOPSIS method

To deal with the varying data units, this study employed the “Max-Min Normalization” method to standardize the original data, where all indicators were positive. Subsequently, the “Entropy Weight-TOPSIS” method was applied to evaluate CIL, eliminating subjective weight assignments. The entropy weight method calculates the information entropy of each indicator and determines their weights based on relative impact⁵⁵, thereby determining the criteria layer weights. The formulas are as follows:

1

2

3

4

5

In Eq. (1) to (5) of the entropy weight method, denotes the original data matrix, where represents the value of the -th indicator for the -th evaluation sample. denotes the standardized value of . To avoid the occurrence of in calculations, a correction of 0.00001 was added to the data. represents the proportion of the -th indicator for the -th evaluation sample; represents the information entropy of the -th indicator; represents the entropy weight of the -th indicator.

The TOPSIS method evaluates the quality of each sample by approximating the ideal solution⁵⁶. In this study, it was applied to derive the scores for the three criteria in CIL and the comprehensive evaluation score. The relevant formulas are as follows:

6

7

Let represent the score (weighted standardized value) of the -th indicator for the -th sample. denotes the optimal solution, defined as the maximum value in the -th column of .Conversely, denotes the worst solution, defined as the minimum value in the -th column of . The Euclidean distances from the -th evaluation object to its positive ideal solution and negative ideal solution are denoted as and , respectively. Finally, the relative closeness coefficient , which quantifies CIL and its three dimensions is calculated. The coefficient , and values close to 1 mean higher proximity to the optimal solution.

Spatial autocorrelation

Global spatial autocorrelation is commonly used to describe the spatial distribution patterns of data across an entire study area⁵⁷. In this study, Moran’s I statistic was employed to analyze the global spatial autocorrelation characteristics of CIL and to test the standardized residuals of the Multiscale Geographically Weighted Regression (MGWR). The formula is expressed as follows:

8

where , is the total number of spatial units; and represent the attribute values of the -th and -th spatial units, respectively; denotes the mean of all observed values of this attribute variable. is the element of the spatial weight matrix, which quantifies the spatial association between the -th and -th spatial units.

Multi-scale geographically weighted regression models

Geographically weighted regression (GWR) is a local spatial regression model⁵⁸. Compared to Ordinary Least Squares (OLS), GWR offers enhanced flexibility and accuracy in identifying and analyzing influencing factors while explicitly accounting for the spatial heterogeneity of each variable. Additionally, due to its single global bandwidth, GWR may forcibly homogenize variables with significant local-scale variations. A bandwidth that is too large masks local heterogeneity, while one that is too small causes parameter instability. In contrast, Multiscale Geographically Weighted Regression’s (MGWR) strength lies in assigning independent bandwidths to each explanatory variable, allowing variables to operate at distinct spatial scales—thereby enabling the model to simultaneously capture local details and global trends. This flexibility better aligns with the complexity of multiscale processes in real-world contexts⁵⁹. The core formula is as follows:

9

Let denote the -th predictor variable in the -th grid, represents CIL of the -th grid; indicate the centroid coordinates of the -th grid; is the total number of predictor variables; denotes the local regression coefficient of the -th variable at the spatial position of the -th grid; is the random error term. When applying the MGWR model, spatial autocorrelation tests must be conducted on both the dependent variable and its standardized residuals⁶⁰.

Results

CIL and its Spatial heterogeneity characteristics

This study applied the Entropy Weight-TOPSIS method to calculate scores for the three CIL sub-dimensions (diversity, integrity, continuity) and the comprehensive level. Scores were classified into five tiers (very low to very high) using the Jenks natural breaks method. The spatial weights can be found in Supplementary Information Figure S1. Maliao Village achieved the highest comprehensive score (0.52) and the top scores across all sub-dimensions (0.85, 0.87, 0.47). In contrast, Tixiang and Maoping Village recorded the lowest comprehensive score (0.02) with low sub-dimension values (Fig. 4). Overall, CIL scores were concentrated between 0.0 and 0.3, with only a few villages achieving higher values. The mean score was 0.1656, indicating a relatively low level of CIL. While comprehensive scores generally aligned with sub-dimension trends, some villages showed variability. Only seven of the 43 villages had high diversity scores, whereas the other 36 scored low, suggesting that most villages possess limited material and intangible heritage and that preservation efforts should focus on existing assets. Although diversity and integrity indices followed similar patterns, the continuity index displayed a distinct trend. For instance, villages such as Nanmeng, Yemeng, and Pingxiang saw their comprehensive scores lowered by poor continuity despite high diversity and integrity. Conversely, Xiaojia and Getou Villages achieved higher overall scores through strong continuity indices, even with lower diversity and integrity performance (Fig. 5).

Fig. 4.

Evaluation levels of 43 traditional villages. (A) Diversity, (B) Integrity, (C) Continuity, (D) Comprehensive level.

Fig. 5.

Comparison of CIL and its three sub-dimensions for 43 traditional villages.

This study mapped the spatial distribution of CIL in Leishan County (Fig. 6). Villages with high cultural diversity scores were scarce and spatially fragmented, forming isolated cultural “islands.” This pattern indicates a limited capacity to systematically preserve multifaceted cultural elements—such as diverse handicrafts, festivals, and traditional architecture—across the county, highlighting challenges in maintaining cultural diversity. Notably, villages near the main ridge of Leigong Mountain exhibited lower diversity scores despite potentially retaining pristine cultural forms (Fig. 6A). Cultural integrity was the weakest of the three sub-dimensions. Only six villages attained high scores, all located in peripheral areas distant from the county’s geographic center. This result reflected widespread difficulties in preserving heritage integrity throughout Leishan County, where both material heritage conservation and intangible heritage continuity faced severe challenges. The inverse spatial gradient—higher integrity in the peripheral and lower in the core—suggested that central zones were more exposed to modernization pressures, leading to the erosion of practiced cultural behaviors and authentic material culture (Fig. 6B). In contrast, cultural continuity performed relatively better. A larger number of villages attained high scores, with western and central regions forming two distinct “cultural continuity clusters”. These clusters were situated near major transportation routes, which facilitated the flow of cultural information and supported the implementation of transmission activities (Fig. 6C). Overall, villages with high CIL scores were rare and spatially dispersed across Leishan County. High-scoring villages failed to form contiguous advantageous zones, instead clustering locally within specific townships. Geographically, high-value areas were concentrated primarily in the northwestern, western, and northern sectors of themountain range, along with its southern foothills. These zones constituted core conservation areas where cultural heritage transmission had been sustained with varying degrees of success. Conversely, extensive intermediate and selected peripheral areas showed suboptimal performance, collectively producing a fragmented spatial pattern dominated by low values. These distinct spatial heterogeneities collectively shaped the ultimate distribution of CIL indices (Fig. 6D).

Fig. 6.

Spatial distribution of CIL in Leishan County, (A) Diversity, (B) Integrity, (C) Continuity, (D) Comprehensive level. Maps were created using ArcGIS Pro 3.0.0 (environmental systems research institute, USA. https://www.esri.com/).

The spatial influencing factors of CIL

The spatial influencing factors of CIL

The process of cultural heritage transmission in traditional villages is influenced by a variety of factors, yet the exact impact of these factors remains unclear. In this study, 29 indicators were selected within the cultural ecosystem of traditional villages (Table 2). The spatial weights can be found in Supplementary Information Figure S2. The stepwise regression method was employed to identify significant influencing variables from a large pool of candidate independent variables and to construct a regression model with predictive accuracy. Variables were retained only if their F-test entry probability was and removed if , leading to the exclusion of Natural Systems (X1, X2, X5–X7), Social Systems (X9, X10), Economic Systems (X15–X18, X23), and Habitat Systems (X25–X29) for non-compliance. The parsimonious model retained 12 explanatory variables (Table 3), with all variance inflation factor (VIF) values below 7.5. The F-value was 61.396, and the P-value was less than 0.001. The Durbin-Watson (D-W) statistic was 1.946, indicating that the model was statistically significant and robust. Second, the spatial autocorrelation test results for the CIL index in Leishan County showed a Moran’s I index of -0.22, a Z-value of -1.87, and met the significance threshold at the 0.02 level. The spatial distribution exhibited a dispersed pattern. Finally, seven variables that met the stringent p-value criterion () were selected as the final explanatory variables for subsequent analysis (X4, X8, X13, X20, and X22 were also excluded for failing to meet the criteria).

Table 3.

Stepwise regression analysis results.

Indicators	Unstandardized coefficients		Standardized coefficients	t	Sig. ()	95.0% confidence Interval for B		Collinearity statistics
	B	Std. Error	Beta			Lower Bound	Upper Bound	Tolerance	VIF
X12	31.444	1.897	0.649	16.578		27.570	35.317	0.851	1.175
X11	0.217	0.018	0.489	12.166		0.181	0.254	0.808	1.238
X14	0.001	0.000	0.434	9.173		0.000	0.001	0.583	1.716
X21	0.000	0.000	0.362	9.370		0.000	0.001	0.875	1.143
X24	0.014	0.003	0.252	5.363		0.009	0.019	0.593	1.688
X19	0.000	0.000	− 0.228	− 4.535		0.000	0.000	0.517	1.935
X3	− 0.044	0.011	− 0.164	− 3.936		− 0.067	− 0.021	0.753	1.328
X4	0.075	0.028	0.110	2.642		0.017	0.133	0.755	1.325
X8	0.082	0.034	0.112	2.433		0.013	0.151	0.613	1.632
X22	0.002	0.001	0.097	1.954	0.060	0.000	0.004	0.531	1.883
X20	− 0.026	0.008	− 0.142	− 3.159		− 0.042	− 0.009	0.648	1.544
X13	0.677	0.328	0.105	2.064		0.007	1.347	0.505	1.979

a. F (Entry: 0.5, Removal:0.1): 61.396 .

b. D-W (1.7 ~ 2.3): 1.946.

c. Dependent Variable: The CIL index.

d.

Comparison and testing of regression models

In the OLS model, X3 was excluded due to statistical insignificance (Table 4). The remaining six explanatory variables served as independent variables in both the GWR and MGWR analyses. Adjusted R² values closely matched R² values across all three models (OLS, GWR, MGWR). Spatial autocorrelation tests on standardized residuals revealed spatially random distributions, suggesting no overfitting and supporting the explanatory capabilities of each model. However, the OLS model’s significant Koenker statistic ( indicated statistically significant non-stationarity (Table 4). Regression models exhibiting such significance are generally well-suited for GWR or MGWR analyses. Furthermore, the primary study objective is to reveal and map the spatially varying processes of the explanatory variables. Therefore, although the OLS model yields the lowest AICc among the three, selecting the MGWR model as the optimal choice is more appropriate for obtaining coefficient surfaces that capture these local variations (Table 5).

Table 4.

Covariance test.

Indicators	Unstandardized coefficients	()	VIF
X12	31.554		1.150
X11	0.232		0.100
X14	0.001		1.253
X21	0.000		1.126
X24	0.015		1.494
X19	-0.000		1.648
X3	-0.031		1.103

a. R² = 0.917, Adjust R² = 0.900.

b. F = 54.932;

c. Chi-Square Statistic: 620.12;

d. Koenker’s Statistic = 14.50;

e. Jarque-Bera (BP) Statistic = 0.184;

f. Dependent Variable: The CIL index.

g.

Table 5.

Comparison table of the results of the models.

Model indications	OLS	GWR	MGWR
AICc	− 3.447	39.461	35.140
R2	0.917	0.936	0.944
Adjusted R2	0.900	0.913	0.923
Bandwidth	–	16.950
		X12	Cultural heritage policy intensity	11.710
		X11	Intangible cultural heritage influence	9.870
		X14	Tourism employment population	12.760
		X21	Investment in cultural heritage	108.740
		X24	Number of specialty food operators	108.740
		X19	Average annual tourist reception volume	108.740
Moran’s I	− 0.061	− 0.058	0.044
Z Score	− 0.330	− 0.304	0.607
	0.741	0.761	0.544

Moreover, in the local R² analysis by using GWR, this factor reveals regional variations in the performance authenticity of the regression model. The model exhibits stronger explanatory power in the eastern township areas compared to the western townships. While western Leishan County is relatively more economically developed and features flatter terrain than the east, this has led to discrepancies in GWR outcomes for the western region. Therefore, MGWR was required to analyze and interpret the results by accounting for the distinct spatial scales of action across different explanatory variables (Fig. S3).

The spatial differentiation of the influencing factors

1. Cultural heritage policy intensity.

The intensity of cultural heritage policies is strongly and positively correlated with CIL distribution in Leishan County, highlighting the role of local government commitment. Among the six factors examined, policy intensity was the most influential driver. Its bandwidth of 11.71 indicated pronounced spatial non-stationarity, with effects most evident at the township scale. This pattern likely stemmed from the rugged terrain, which shortened the effective policy radius and accelerated the attenuation of impact compared to the west. Accordingly, the influence of this factor was stronger in eastern townships and remained significant in southern villages. In summary, while policy intensity dominates CIL distribution county‑wide, township units in topographically complex areas require more policy focus ​(Fig. 7A).

Fig. 7.

Spatial distribution of MGWR coefficients for influencing factors of CIL. Maps were created using ArcGIS Pro 3.0.0 (Environmental Systems Research Institute, USA. https://www.esri.com/).

• (2).Intangible cultural heritage influence.

The influence of intangible cultural heritage—reflecting a village’s socio‑cultural recognition and perceived value of its intangible assets—positively correlates with CIL in Leishan County. Among all factors, it demonstrated the strongest spatial variation, with a bandwidth of only 9.87. Its effects manifested at the village scale and varied markedly, correlations were particularly strong in the rugged core of Leigong Mountain but weakened sharply in the relatively flat western terrain. Ethnic cultural diversity in the county thus produced differential impacts even at micro‑scales, creating a clear spatial gradient where influence intensified from foothill settlements toward higher‑elevation zones (Fig. 7B).

• (3).Tourism employment population.

The results demonstrated that this factor exhibited a bandwidth of 12.76, indicating similarly pronounced spatial heterogeneity in its correlation coefficients. Within the context of tourism industry development in Leishan County, this factor exerted a county-wide positive correlation effect on CIL, yet its impact coefficients varied across township units. The highest correlation coefficients occurred in the rugged northeastern region, particularly in Fang Township and Xijiang Town. As the county’s most developed tourism zone, the concentration of tourism employment in this area drove local cultural tourism development while promoting the preservation of traditional cultural practices (Fig. 7C).

• (4).Investment in cultural heritage.

Investment in Cultural heritage denotes sociopolitical investments in village-level cultural heritage preservation, serving as a proxy indicator for developmental utilization intensity of cultural assets. The analytical results revealed that this factor exhibited restricted spatial variability, with a bandwidth of 108.74 indicating its influence nearly blanketed the entire Leishan County, thus resulting in limited spatial divergence of correlation coefficients. However, compared with developed regions, underdeveloped areas maintained sensitivity to this factor, attributable to prioritized policy funding mechanisms. In contrast, economically advanced regions exhibited higher local engagement in tourism services, which to some extent diminished the relative impact of policy-driven capital (Fig. 7D).

• (5).Number of specialty food operators.

The number of specialty food operators serves as an indicator of tourism development and the preservation of unique rural culinary culture. Unlike general tourism employment, these operators remain primarily engaged in agricultural activities while also providing catering services. The analysis revealed a weak positive correlation between this factor and CIL at the county level, with its influence declining progressively from the economically developed northern townships toward the less-developed south. This suggests that in areas with stronger economies or tourism sectors, such operators can support the transmission of local food traditions. Furthermore, despite pronounced ethnic diversity, the extensive bandwidth suggests that intermixed ethnic distribution tends to dilute culinary distinctiveness. Consequently, preservation culinary heritage largely depends on tourism-oriented promotion to attract outside interest, which limits its substantive impact (Fig. 7E).

• (6).Average annual tourist reception volume.

The annual average tourist volume indicates the intensity of tourism development and local economic vitality in villages. Its negative correlation with CIL, together with a county-scale bandwidth, signaled latent threats across Leishan County. This pattern appeared in both tourism-underdeveloped southern townships and tourism-intensive northern areas. Although tourist volume typically correlates with tourism economic growth, excessive expansion disrupted local culture. The pressure of mass tourism in mountainous terrain has begun to hinder intergenerational cultural transmission county-wide, underscoring the imperative to coordinate tourism pressure governance with cultural preservation initiatives (Fig. 7F).

Discussion

Traditional villages, as vital carriers of rural culture, are frequently situated in ethnically diverse settlements with complex terrain which are traditionally seen as a natural barrier to cultural preservation⁶¹. This study, however, reveals a nuanced relationship: while rugged topography can help protect indigenous cultures, it may also hinder their communicative reach, thereby constraining local cultural transmission. This insight differs from prior research that often relies on quantitative preservation metrics, overlooking qualitative aspects like integrity and continuity. Transportation infrastructure emerges as a key facilitator of cultural dissemination⁶². It is consistent with this study’s results, as villages with better transport accessibility demonstrated higher cultural continuity compared to remote areas. Beyond physical accessibility, the safeguarding of intangible cultural heritage itself significantly enhances local cultural continuity—a causal linkage substantiated by both prior scholarship and the present analysis⁶³. Furthermore, empirical contexts strongly corroborate with this study, showing that government-led initiatives for rural cultural heritage conservation have achieved notable progress⁶⁴. Yet a critical gap remains: given the vast number of traditional villages, sole reliance on state stewardship remains inadequate. Research from China and Europe demonstrates that stakeholder-driven conservation significantly boosts cultural transmission potential⁶⁵, a finding consistent with the results presented here. Policymakers should therefore prioritize: cultivating grassroots preservation awareness and providing tailored assistance to heritage practitioners and communities to advance safeguarding efforts in multi-ethnic mountainous regions.

The role of cultural industries is equally critical. Evidence from China and Cyprus indicates these industries in mountainous regions predominantly follow tourism-centric models^66,67. As grassroots socioeconomic agents, specialty food operators act as catalysts of cultural transmission and tend to cluster in more developed areas—an observation aligning with the findings⁶⁸. Deepening this perspective, research from Serbia identifies these operators not just as economic actors but as key transmitters of intangible heritage, perpetuating local culinary and ritual knowledge⁶⁹. Collectively, this study’s regression results and existing evidence support the paradigm that developed zones in multi-ethnic mountainous areas can leverage tourism-driven social capital to activate a dual agency—serving as both economic engines and cultural custodians⁷⁰. To counterbalance regional disparities, governments should prioritize sustained investment in heritage conservation within underdeveloped areas to ensure localized cultural continuity and advance preservation goals⁷¹.

This leads to an examination of tourism’s complex dualism. While tourism is recognized as a driver of rural economic development and cultural continuity^72,73. Although the findings partially supported this optimistic view, the academic community maintains substantive reservations regarding tourism’s cultural ramifications. Specifically, rapid tourism expansion has induced cultural commodification and progressive erosion⁷⁴. For instance, several studies indicated that over-commercialization diminishes heritage value and can cause cultural homogenization⁷⁵. Concurrently, findings from this and other studies demonstrate that tourism-driven urbanization disrupts traditional lifestyles, exacerbating cultural discontinuity⁷⁶. This challenge is acute in developing nations, where policy may overlook the balance between tourism growth and heritage conservation, particularly when prioritizing fiscal revenue over cultural sustainability^77,78. This study reinforces prior work documenting the detrimental impacts of mass tourism pressures on cultural transmission in mountainous regions, with intensified erosion in urbanizing zones. Evidence from the Philippines and China underscores that government-led tourism requires evidence-based planning to protect traditional lifeways and mitigate negative impacts^79,80. Consequently, managing tourism pressure and carefully calibrating development intensity are essential.

Finally, several limitations must be acknowledged. First, regarding methodological, research in close-knit communities highlights an “interdependent self-construal” shaping residents’ cultural identity and adaptive behaviors⁸¹ a subjective dimension not captured by this indicator-based CIL model. Secondly, concerning analysis, while long Short-Term Memory (LSTM) models have been applied in heritage protection⁸², future research using quantitative time-series data in Geographically and Temporally Weighted Regression (GTWR) could offer deeper insights into CIL trends. Third, geographically, this study is focused solely on Leishan County; extending the research to other multi-ethnic mountainous areas in China and internationally would be valuable. Future studies should therefore encompass broader geographical regions and more diverse cultural contexts. Therefore, this work should be viewed as a preliminary exploration of the spatial factors influencing CIL, offering insights for similar regions rather than definitive claims.

Conclusion

This study reveals the spatial differentiation mechanisms and underlying determinants of CIL in traditional villages within multi-ethnic mountainous regions. First, government-led cultural heritage preservation in China’s rural mountainous areas produces measurable outcomes, especially in geographically isolated and ethnoculturally cohesive areas, where policy intensity and intangible cultural heritage synergistically enhance inheritance. Second, spatial disparities remain: steep terrain limits cultural diversity, while rapid urbanization erodes authenticity in developing areas. However, improved transportation infrastructure facilitates sustaining cultural continuity, signaling the urgent need for policy interventions. For instance, underdeveloped regions exhibit sensitivity to policy-driven investments, whereas economically advanced areas benefit from tourism-oriented social capital. Notably, tourism development presents a paradox. Although growth in cultural‑industry employment can support heritage sustainability, mass tourism and commercialization threaten authenticity and continuity. These findings underscore the necessity of differentiated policy approaches. Policymakers should prioritize sustained fiscal support for underdeveloped areas and implement evidence‑based tourism planning to mitigate cultural commodification. Strengthening grassroots awareness will be critical for achieving effective cultural preservation in multi‑ethnic mountainous regions.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

CIL

Cultural inheritance level

GWR

Geographically weighted regression

MGWR

Multi-scale geographically weighted regression

OLS

Ordinary least squares

Author contributions

H. W.: conceptualization, formal analysis, writing-original draft and editing; L.F.: methodology, supervision; C.W.: review and editing, data curation. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (52468005) and the Humanities and Social Sciences Research Project of Guizhou University, China (GDYB2025004, 2025).

Data availability

The original data for this study are included in [supplementary information files].Correspondence and requests for materials should be addressed to C.W.

Declarations

Competing interests

The authors declare no competing interests.