The Spatial Relationship Characteristics and Differentiation Causes Between Traditional Villages and Intangible Cultural Heritage in China

Abstract

Traditional villages (TVs) and intangible cultural heritage (ICH) serve as dual carriers for the living transmission of agrarian civilization, with their spatial compatibility being crucial for the sustainable development of cultural ecosystems. Existing research shows deficiencies in quantitative analysis, multidimensional driving mechanism interpretation, and spatial heterogeneity identification. This study establishes a three-phase framework (“spatial pattern identification–spatial relationship analysis–impact mechanism assessment”) using nationwide data encompassing 8155 TVs and 3587 ICH elements. Through the comprehensive application of the spatial mismatch index, Optimal-Parameter Geographic Detector (OPGD), and multiscale geographically weighted regression (MGWR) model, we systematically reveal their spatial differentiation patterns and driving mechanisms. Key findings: First, TVs exhibit a “three-primary-core and two-secondary-core” strong agglomeration pattern, while ICH shows multi-center balanced distribution. Significant positive spatial correlation coexists with prevalent mismatch: 65% of China’s territory displays positive mismatch (ICH dominance) and 35% displays negative mismatch (TV dominance). Second, the spatial mismatch mechanism follows a “weakened natural foundation with dual drivers of socio-economic dynamics and cultural policy momentum”, where the GDP, tertiary industry ratio, general public budget expenditure, number of ICH inheritors, museums, and key cultural relic protection units emerge as dominant factors. Third, core drivers demonstrate significant spatial heterogeneity, with economic factors showing differentiated regulation while cultural policy elements exhibit distinct regional dependency. The proposed “economy–culture” dual governance approach, featuring cross-scale analysis methods and three-dimensional indicator system innovation, holds practical value for optimizing cultural heritage spatial governance paradigms.

1. Introduction

1.1. Overview of Traditional Villages and Intangible Cultural Heritage in China

Traditional villages, a concept emerging during China’s rapid social transformation in the 21st century, refer to those “villages formed earlier, possessing relatively rich traditional resources, and holding certain historical, cultural, scientific, artistic, social, and economic value that warrant protection” [1]. According to the “2012 Statistical Yearbook of Urban–Rural Development” issued by the Ministry of Housing and Urban–Rural Development, China’s village count plummeted from 3.4 million in 2002 to 2.67 million in 2012, with an average daily loss of 80 to 100 villages. Between 2004 and 2010 alone, 3998 TVs disappeared in the Yangtze and Yellow River basins, underscoring the extreme urgency of preservation [2]. In 2012, the Ministry of Housing and Urban–Rural Development, the Ministry of Culture, the National Cultural Heritage Administration, and the Ministry of Finance jointly issued the “Circular on Conducting Surveys of TVs”, initiating a nationwide survey. Over the following decade, six national surveys were organized (2012, 2013, 2014, 2015, 2017, and 2022), culminating in the inclusion of 8155 TVs of significant conservation value into the National Conservation Registry (

Table 1. Number of TVs announced in different batches in China (Source: compiled by author).

	Batch 1	Batch 2	Batch 3	Batch 4	Batch 5	Batch 6	Total
East China	197	229	270	637	1361	398	3092
Southwest China	186	478	411	384	416	219	2094
Central–South China	130	142	150	188	408	237	1255
North China	93	38	96	265	336	163	991
Northwest China	38	25	53	111	122	280	629
Northeast China	2	3	14	13	23	39	94
Total	646	915	994	1598	2666	1336	8155

and

Table 2. Typical TVs in different regions. (Source: https://www.dmctv.cn/ (accessed on 29 May 2025)).

EC: Xidi Village, Anhui.	SWC: Dengnuo Village, Yunnan.	CSC: Zhangguying Town, Hunan.
NC: Cuandixia Village, Beijing.	NWC: Dangjia Village, Shaanxi.	NEC: Wooden Houses Village, Jilin.

). China has since established a comprehensive conservation registry system for TVs, marking their preservation as a national strategic priority.

On 26 March 2005, the State Council of China promulgated the “Interim Measures for the Application and Assessment of National Representative Works of Intangible Cultural Heritage”, defining ICH as follows:

“various traditional cultural expressions (such as folk customs, performing arts, traditional knowledge and skills, along with related instruments, objects, and handicrafts) and cultural spaces that have been passed down through generations and are closely related to people’s lives.”

Liu Zhuang et al. conducted a comparative analysis between this definition and UNESCO’s ICH concept, concluding that the former aligns better with China’s practical context and Chinese linguistic nuances [3]. From 2006 to 2021, the State Council released five batches of national representative ICH projects in 2006, 2008, 2011, 2014, and 2021, totaling 1557 items. When counted by declared regions or institutions, these comprise 3610 sub-items. Due to data limitations, the research data do not include Hong Kong, Macao, and Taiwan Province, with only 3587 items (

Table 3. Number of ICH projects announced in different batches in China (Source: compiled by author).

	Batch 1	Batch 2	Batch 3	Batch 4	Batch 5	Total
Northeast China	36	65	24	20	28	173
Southwest China	116	202	71	56	65	510
Northwest China	117	200	75	68	84	544
Central–South China	124	198	83	74	70	549
North China	129	266	95	87	84	661
East China	237	420	212	152	129	1150
Total	759	1351	560	457	460	3587

). According to the Interim Measures, ICH is classified into ten major categories (

Table 4. The distribution of ten categories of ICH in different regions (Source: compiled by author).

	L1	L2	L3	L4	L5	L6	L7	L8	L9	L10	Total
Northeast China	16	22	8	16	16	7	26	9	28	25	173
Southwest China	100	50	11	71	45	22	71	40	83	17	510
Northwest China	87	58	17	76	46	32	79	46	77	26	544
Central–South China	62	65	20	66	88	23	74	41	84	26	549
North China	132	68	61	40	90	47	74	33	75	41	661
East China	228	153	49	86	185	51	104	82	135	77	1150
Total	625	416	166	355	470	182	428	251	482	212	3587

and

Table 5. Representative projects of ten categories of ICH. (Source: https://www.ihchina.cn/ (accessed on 29 May 2025)).

L1: Yixing Purple Clay Pottery Making.	L2: Zhangzhou Woodblock New Year Prints.	L3: Liaocheng Acrobatics.	L4: Yangge	L5: Kunqu Opera.
L6: Traditional Chinese Medicine Diagnostics.	L7: Mongolian Long Tune Folk Songs.	L8: The Butterfly Lovers.	L9: Spring Festival	L10: Yangzhou Storytelling.

): traditional craftsmanship (L1), traditional fine arts (L2), traditional sports, entertainment, and acrobatics (L3), traditional dance (L4), traditional opera (L5), traditional medicine (L6), traditional music (L7), folk literature (L8), folk customs (L9), and Quyi (folk performing arts, L10).

1.2. Symbiotic Relationship and Spatial Compatibility Challenges Between TVs and ICH

TVs, as historical witnesses of agrarian civilization, bear profound historical, cultural, and scientific value, serving as important spatial carriers for the continuity of regional culture [4,5]. ICH, as living cultural expressions passed down through generations, embodies national spirit and collective memory, functioning as a core bond for sustaining cultural diversity [6]. The two exhibit a symbiotic relationship in the “space–culture” dimension: TVs serve as physical carriers and community practice fields for the living transmission of ICH, while ICH endows villages with unique identity markers and vitality through cultural identity [7]. Therefore, the spatial compatibility between TVs and ICH ensures mutual reinforcement between the two, collectively sustaining cultural transmission. This compatibility refers to their coordinated interaction across three dimensions: (1) geographic overlap in distribution patterns, (2) functional synergy in cultural ecosystem services (e.g., identity construction and knowledge transmission), and (3) dynamic equilibrium in resource allocation (e.g., policy support and economic investment). Under China’s rural revitalization and cultural power strategies, the 14th Five-Year Plan for Intangible Cultural Heritage Protection explicitly proposes building a “heritage community” governance goal, emphasizing coordinated protection of both. However, the synergistic development of TVs and ICH currently faces severe challenges of spatial compatibility imbalance, urgently requiring scientific identification of their spatial mismatch mechanisms to achieve sustainable cultural heritage development.

1.3. Current Research Progress and Limitations

Existing research has conducted multidimensional explorations of TVs and ICH. Regarding research themes, studies on TVs primarily focus on spatial distribution characteristics [8,9,10], influencing factors [11,12,13], conservation and renewal strategies [14,15], and tourism development models [16,17,18]. ICH research emphasizes spatial differentiation patterns [19,20], tourism revitalization pathways [21,22], inheritance and innovation mechanisms [23,24], digital dissemination technologies [25], and living conservation practices [26], with particular attention to its spatial distribution modes and influencing factors [27,28]. Notably, recent years have seen scholars exploring their interactions; for instance, Nie et al. revealed the agglomeration characteristics of ICH and TVs in the Yellow River Basin through spatial analysis [29]; Wu et al. analyzed their spatial correlations in Guizhou from a heterogeneity perspective [30]; and Li et al. applied symbiosis theory to examine their synergistic effects in rural tourism [31]. These studies provide important foundations for understanding their relationship, but a systematic analytical framework has yet to be established.

With the deepening concept of holistic cultural ecosystem conservation, academia has gradually shifted its focus to the co-evolution mechanisms between ICH and TVs. The current research mainly follows two pathways: First, case-based governance model studies, such as the work of Wang et al. [9], who compared three villages to reveal how hybrid governance models enhance village resilience under different cultural heritage contexts. Second, the exploration of spatial differentiation laws, exemplified by Li et al. [9], who incorporated ICH as an influencing factor when analyzing TVs’ spatial distribution, and Zhang et al. [32], who systematically investigated the spatial distribution and driving mechanisms of China’s ICH resources.

However, the existing research still exhibits certain limitations: First, most studies analyze TVs and ICH as independent subjects, resulting in contradictory conclusions regarding spatial aggregation and mismatch [33,34,35]. The few studies combining both concepts remain confined to specific river basins or provincial cases [29,36], lacking systematic investigation across China’s culturally diverse regions at a national scale. Second, the research methods require deeper and broader dimensions. Most studies employ qualitative descriptive analyses, failing to quantitatively examine the spatial interaction mechanisms between these two heritage types. In driving mechanism research, current findings predominantly adopt one-dimensional perspectives, such as Chen et al. [8] focusing on natural and socio-cultural factors influencing TVs in Zhejiang Province, or Li et al. [12] revealing the spatial differentiation patterns of Guangdong’s villages from a cultural landscape perspective. An integrated analytical framework combining natural, social, and cultural multidimensional driving mechanisms has yet to be established. Third, while scholars have extensively explored the global correlations between TVs and ICH, the identification of spatial heterogeneity remains insufficient. Mainstream analytical methods predominantly rely on traditional global regression models (e.g., OLS) or correlation coefficient analyses [30], which assume spatial homogeneity and struggle to capture the localized dynamic characteristics of driving factors. Although some researchers have introduced geographically weighted regression (GWR) [37] to reveal the spatial autocorrelation characteristics of factors influencing TVs’ distribution through spatial kernel functions, existing studies lack explanations for both the macro-level driving mechanisms and micro-level operational pathways of TV–ICH spatial mismatch.

1.4. Innovations of This Study

To address the above research gaps, this study implements the following improvements: First, based on nationwide data, this study innovatively constructs a three-dimensional indicator system (“natural environment–social economy–cultural policy”) covering 16 indicators (including number of museums and ICH inheritors), breaking through traditional one-dimensional explanatory models. Second, this study comprehensively applies GIS spatial analysis, bivariate spatial autocorrelation, and the spatial mismatch index to quantify the spatial relationship characteristics between TVs and ICH. Additionally, the Optimal-Parameter Geographic Detector (OPGD) is employed to investigate the impact mechanisms of their spatial mismatch, identifying core driving factors with significant explanatory power (q-value > 0.2). Third, this study introduces the multiscale geographically weighted regression (MGWR) model to analyze the spatial heterogeneity of core driving factors through adaptive bandwidth algorithms.

The theoretical exploration of this study focuses on three scientific questions: (1) the spatial patterns and spatial relationship characteristics of TVs and ICH at the national scale, (2) the influence mechanisms of 16 “natural environment–social economy–cultural policy” factors on the spatial mismatch between TVs and ICH, and (3) the nonlinear impact pathways and spatial scale effects of core driving factors on their mismatch.

Through a progressive analytical framework of “spatial pattern identification–spatial relationship analysis–impact mechanism assessment”, this study provides a theoretical foundation and practical pathways for the coordinated governance of large-scale cultural ecosystems.

2. Materials and Methods

2.1. Study Area

As a nation with a long history of agrarian civilization, China has developed the world’s largest and most diverse system of TVs (Figure 1), shaped by its complex natural geographical environments and multi-ethnic humanistic patterns. These villages not only serve as the physical embodiment of agrarian ecological wisdom but also act as core transmission fields for the intangible cultural heritage (ICH) created by China’s 56 ethnic groups. This study employs a macro-level dataset of 8155 nationally recognized TVs and 3587 associated national representative ICH items (excluding areas without data), in order to systematically reveal the spatial relationships and formation mechanisms between TVs and ICH, holding significant implications for global cultural diversity development.

2.2. Data Sources

The data used in this study and their sources are shown in

Table 6. Summary of data sources (Source: compiled by author).

Type	Name	Data Source	Remarks
Basic Data	National Provincial and Municipal Administrative Boundaries	Standard Map Service (http://bzdt.ch.mnr.gov.cn/ (accessed on 3 February 2025))	China Standard Administrative Division Data GS(2024)0650
	National Traditional Villages	8155 in total from six batches jointly announced by the Ministry of Housing and Urban–Rural Development, the Ministry of Culture, and the Ministry of Finance	Data for Hong Kong, Macao, and Taiwan Province are not included
	National Intangible Cultural Heritage	3610 items from five batches publicly disclosed by the State Council and the national cultural and tourism administrative department (https://www.ihchina.cn/ (accessed on 3 February 2025))	Due to data limitations, the research data does not include Hong Kong, Macao, and Taiwan Province, with only 3587 items
Natural Geographic Environment	Mean Elevation	Geospatial data cloud (https://www.gscloud.cn/ (accessed on 3 February 2025))	2021, 250 m precision
	Mean Slope	GIS slope processing
	Mean Temperature	National Qinghai–Tibet Plateau Data Center (https://data.tpdc.ac.cn/ (accessed on 3 February 2025))	2021, 1000 m precision
	Mean Precipitation
	Mean River Density	OpenStreetMap (http://www.openstreetmap.org/ (accessed on 18 November 2021))	/
Social and Economic	Resident Population	Data from the Seventh National Population Census	/
	Proportion of Ethnic Minority Population
	Proportion of Population Aged 65 and Above
	Urbanization Rate
	GDP	2022 National Urban Statistical Yearbook and provincial statistical yearbooks	The 2022 yearbook statistics for 2021
	Proportion of the Tertiary industry
	General Public Budget Expenditure
	Road Network Density	OpenStreetMap (http://www.openstreetmap.org/ (accessed on 18 November 2021))	/
Cultural Policy	National Museum POI	AutoNavi Maps (https://ditu.amap.com/(accessed on 18 November 2021))	/
	National Key Cultural Relics Protection Units	5058 items announced in eight batches by the State Council (http://www.ncha.gov.cn/ (accessed on 3 February 2025))	/
	Intangible Cultural Heritage Inheritors	3057 people publicly announced by the State Council and the national cultural and tourism administrative department (https://www.ihchina.cn/ (accessed on 3 February 2025))	/

.

2.3. Methods

2.3.1. Kernel Density Analysis

Kernel density analysis is a non-parametric spatial analysis method that is used to calculate the density of observed targets in adjacent areas. This method is commonly applied to visualize the spatial distribution and variation patterns of point data, reflecting the clustering or dispersion of elements; it intuitively illustrates the spatial agglomeration or dispersion of TVs and ICH [27,38].

$$g_n\left(x\right)={\displaystyle \frac{1}{nb}}\sum _{i=1}^n k\left({\displaystyle \frac{x-x_i}{b}}\right)$$

(1)

where $x_1$, $x_2$,…, $x_i$ are independent and identically distributed samples in the overall density function, $g_n\left(x\right)$ is the estimated value at point $x$, $k\left({\displaystyle \frac{x-x_i}{b}}\right)$ is the kernel function, b (b > 0) is the bandwidth, and $x-x_i$ is the distance from the estimation point $x$ to the sample $x_i$.

2.3.2. Geographic Concentration and Spatial Distribution Equilibrium Analysis

The geographic concentration index (G) measures the concentration degree of spatial distribution for TVs and ICH across regions [39,40]:

$$G={\displaystyle \frac{100}{M}}\sqrt{\sum _{i=1}^n X_i^2}$$

(2)

where $X_{i }$ is the number of TVs (or ICH) in province $i$, $M$ is the total number of TVs (or ICH) nationwide, and $n$ is the number of provinces. A higher $G$ indicates a greater concentration. When ${G=G}_0$ (where ${G=G}_0$ represents uniform distribution), the distribution is balanced. If ${G>G}_0$, the TVs (or ICH) are clustered; otherwise, they are dispersed.

The imbalance index $S$ evaluates the equilibrium of spatial distribution for TVs and ICH [41]:

$$S={\displaystyle \frac{\sum _{i=1}^n Z_i-50\left(n+1\right)}{50\left(2n-\left(n+1\right)\right)}}$$

(3)

where $n$ is the number of provinces, and $Z_i$ is the cumulative percentage of TVs (or ICH) in the $i$-th province after sorting by descending proportion. $S$ ranges from 0 to 1: $S=0$ indicates uniform distribution across provinces; $S=1$ indicates complete concentration in one province.

2.3.3. Spatial Quantity Relationship Model

The spatial quantity relationship model measures the correlation between two elements [42], quantitatively reflecting the spatial relationship between the quantities of TVs and ICH.

$$R={\displaystyle \frac{ps-qr}{\sqrt{\left(p+q\right)\left(r+s\right)\left(p+r\right)\left(q+s\right)}}}$$

(4)

where $R$∈[−1,1], $R>0$ indicates positive correlation, and $R<0$ indicates negative correlation, while $p$ is the number of grids containing both TVs and ICH, $q$ is the number of grids with only TVs, $r$ is the number of grids with only ICH, and $s$ is the number of grids with neither. Significance is tested using

$$X^2={\displaystyle \frac{n(ps-qr{)}^2}{\left(p+q\right)\left(r+s\right)\left(p+r\right)\left(q+s\right)}}$$

(5)

If $\left|X^2\right|>X_a^2\left(1\right)$, the quantitative spatial correlation between TVs and ICH is significant. If $\left|X^2\right|<X_a^2\left(1\right)$, the correlation is insignificant ($n$ is the total number of grids).

2.3.4. Bivariate Spatial Autocorrelation Analysis

This analysis identifies aggregation patterns in the spatial distribution of TVs and ICH [43], including positive correlations (“high–high” or “low–low”) and negative correlations (“low–high” or “high–low”) within adjacent regions.

$$I_{ab}^i={\displaystyle \frac{x_a^i-{\overline{x}}_a}{s_a}}\ast \left(\sum _{j=1}^n W_{ij}\cdot {\displaystyle \frac{x_b^j-{\overline{x}}_b}{s_b}}\right)$$

(6)

where $x_a^i$ and $x_b^j$ are the density values of TVs and ICH in spatial units $i$ and $j$, respectively; ${\overline{x}}_a$ and ${\overline{x}}_b$ are the mean densities of TVs and ICH, respectively; $s_a$ and $s_b$ are the variances of attributes $a$ and $b$, respectively; and $W_{ij }$ is the spatial weight matrix.

2.3.5. Gravity Center Model

The gravity center model identifies the spatial equilibrium point of geographic elements within a region, serving as a key analytical tool for exploring their spatial patterns [44,45]. This model was applied to analyze the spatial dynamics of TVs and ICH. Calculating the gravity center coordinates further determines the spatial deviation distance and deviation index between their respective gravity centers.

$$\left\{\begin{array}{c}A={\displaystyle \sum _{i=1}^n} W_i{u}_i\\ B={\displaystyle \sum _{i=1}^n} W_i{v}_i\end{array}\right.$$

(7)

$$D_{t-i}=R\sqrt{(A_t-A_i{)}^2+(B_t-B_i{)}^2}; \mathrm{r}={\displaystyle \frac{D}{\sqrt{S}}}\times 100$$

(8)

where $\left(A,B\right)$ are the latitude and longitude coordinates of the gravity center for a geographic element, $\left(u_i,v_i\right)$ are the coordinates of the $i$-th sub-region, $W_i$ is the attribute value of the geographic element in the $i$-th sub-region, $\left(A_t,B_t\right)$ and $\left(A_i,B_i\right)$ denote the gravity center coordinates of TVs and ICH, respectively, and $R$ is the conversion coefficient, typically set to 111.11.

2.3.6. Spatial Mismatch Index

Based on spatial mismatch theory, the spatial mismatch index (SMI) model [46] quantifies the equilibrium of TVs’ and ICH’s distribution across provinces, measuring their spatial coupling relationship and mismatch intensity [47,48]:

$$SM{I}_i=\left({\displaystyle \frac{IC{H}_i}{ICH}}-{\displaystyle \frac{T{V}_i}{TV}}\right)\times 100$$

(9)

$$W_i={\displaystyle \frac{\mid {SMI}_i\mid }{\sum _{i=1}^n \mid {SMI}_i\mid }}$$

(10)

where ${SMI}_i$ is the spatial mismatch index for province $i$, $TV$ is the total number of TVs nationwide, ${TV}_i$ is the number of TVs in province $i$, $ICH$ is the total number of ICH items nationwide, and ${ICH}_i$ is the number of ICH items in province $i$.

The magnitude of $SMI$ reflects the similarity in spatial distribution between TVs and ICH. A lower similarity (i.e., higher absolute $SMI$) indicates greater spatial mismatch, while a smaller absolute value suggests weaker mismatch [46]. $W_i$ quantifies province $i$’s contribution to the national spatial mismatch, with larger values indicating higher contributions.

2.3.7. Optimal-Parameter Geographic Detector

The effectiveness of geographic detectors in revealing spatial differentiation and influencing factors has been widely validated by scholars [49]. Traditional geographic detectors require manual categorization of variables, which introduces subjectivity and computational inaccuracies. The Optimal-Parameter Geographic Detector (OPGD) [50] optimizes the discretization of continuous variables using multiple statistical methods (equal intervals, natural breaks, quantile breaks, geometric intervals, and standard deviation intervals) to achieve the highest q-value. Therefore, this study employs the OPGD to analyze the influencing factors contributing to the spatial mismatch between TVs and ICH.

$$q=1-{\displaystyle \frac{\sum _{i=1}^K N_i{\sigma }_i^2}{N{\sigma }^2}}$$

(11)

$$q=1-{\displaystyle \frac{SSW}{SST}}$$

(12)

where $i$ is the stratification of influencing factors, $N_i$ is the number of units in stratum $i$, $N$ is the total number of units in the entire region, ${\sigma }_i^2$ and ${ \sigma }^2$ are the variances of the spatial association index in stratum $i$ and the entire region, respectively, and SSW and SST are the sum of intra-stratum variances and the total variance of the entire region, respectively.

2.3.8. Multiscale Geographically Weighted Regression

The multiscale geographically weighted regression (MGWR) model, proposed by Yu et al. in 2017 [51], addresses the limitation of traditional GWR methods that operate at a single spatial scale. MGWR significantly enhances the ability to capture spatial heterogeneity by selecting optimal bandwidths for variables at different spatial scales, thereby enabling robust and adaptive exploration of local dynamic variations [52]. The formula is expressed as follows:

$$y_i={\beta }_{b{w}_0}\left(u_i,{\nu }_i\right)+\sum _{k=1}^m {\beta }_{b{w}_k}\left(u_i,{\nu }_i\right)X_{ik}+{\epsilon }_i$$

(13)

where $b{w}_k$ is the bandwidth adopted for the regression coefficient of variable $k$, $y_i$ is the dependent variable at location $\left(u_i,{\nu }_i\right)$, $X_{ik}$ is the explanatory variable $k$ at location $\left(u_i,{\nu }_i\right)$, ${\beta }_{b{w}_0}\left(u_i,{\nu }_i\right)$ is the intercept term of the regression equation, ${\beta }_{b{w}_k}\left(u_i,{\nu }_i\right)$ is a continuous function of spatial location, and ${\epsilon }_i$ is an independent random error term.

This study combines the OPGD with MGWR to enhance research efficiency through the following technical paths: (1) Utilizing the OPGD model to detect the intensity of the driving factors and the interaction effects of the spatial mismatch between TVs and ICH. (2) Constructing OLS, GWR, and MGWR regression models and comparing them to determine MGWR as the optimal model. (3) Further revealing the spatial heterogeneity of the core driving factors through the spatial distribution of MGWR regression coefficients, thereby significantly improving the accuracy and reliability of the analysis results.

2.4. Research Framework

By comprehensively applying the above methods, the research framework was determined, as shown in Figure 2.

3. Results

3.1. Analysis of the Spatial Layout of TVs and ICH

3.1.1. Spatial Characteristics of TVs

According to the regional statistical results (Figure 3c), the spatial distribution of TVs in China exhibits significant regional clustering. East China (EC) and Southwest China (SWC) are home to 37.92% and 25.68% of the national total TVs, respectively, while Central–South China (CSC), North China (NC), and Northwest China (NWC) show moderate proportions, and Northeast China (NEC) has an extremely low proportion. The reasons for this uneven distribution may include mountainous terrain barriers in the southwest preserving TVs, while the dense river network in the east led to the continuation of the farming tradition, and rapid industrialization in the northeast eroded TVs. At the provincial level, Yunnan, Guizhou, and Hunan in Southwest China dominate the top three rankings, collectively accounting for nearly 30% of the national total, highlighting the natural advantages of mountainous geographical environments and multi-ethnic cultures in preserving TVs. Among eastern coastal provinces, Zhejiang and Fujian perform prominently, while economically strong provinces such as Guangdong and Jiangsu have significantly fewer TVs, reflecting the erosive effect of urbanization on traditional settlements. Central and western provinces such as Shanxi and Anhui maintain moderate numbers due to their historical heritage, whereas highly urbanized regions in the northwest, northeast, and municipalities (e.g., Xinjiang, Shanghai, Tianjin) exhibit minimal preservation of TVs, influenced by geographic conditions or modernization.

The kernel density analysis results of TVs (Figure 3a) reveal a spatial pattern of “three high-density cores and two secondary-density cores” nationwide. The high-density core areas are concentrated at the junction of Shanxi–Hebei–Henan, the adjacent regions of Zhejiang–Fujian–Anhui–Jiangxi, and the bordering areas of Hunan–Guizhou–Chongqing–Guangxi, forming a tripartite dominance. Secondary-density zones are located in northwestern Yunnan and eastern Gansu, with the three high-density cores and two secondary-density zones spatially interconnected through spread.

Additionally, based on the Lorenz curve, geographic concentration index, and spatial distribution equilibrium results, the spatial distribution of TVs across China exhibits a strong imbalance. The Lorenz curve (Figure 3d) shows that the top 5 provinces account for over 40% of the total, while the bottom 13 provinces collectively contribute less than 10%. The geographic concentration index (G = 24.49), significantly higher than the uniform distribution hypothesis value (G₀ = 17.96), indicates a concentrated distribution of TVs in a few provinces. The imbalance index (S = 0.52) further confirms the pronounced spatial unevenness of TVs.

3.1.2. Spatial Characteristics of ICH

According to regional statistical results (Figure 3c), the distribution of ICH in China exhibits a polarized yet moderately balanced pattern. East China (EC) and Northeast China (NEC) account for 32.06% and 4.82% of the national ICH total, respectively, while Southwest China (SWC), Central–South China (CSC), North China (NC), and Northwest China (NWC) show relatively uniform distributions, each contributing approximately 15%. The uneven distribution of ICH may stem from East China’s historical role as a cultural and economic core, while Northeast China’s lower proportion could reflect industrial transformation, with other regions maintaining balanced preservation due to distinct regional characteristics. At the provincial level, Zhejiang Province leads significantly, with 257 ICH items, ranking as the most resource-rich region, while the Ningxia Hui Autonomous Region (28 items) occupies the lowest position, reflecting a nearly tenfold gap. Eastern coastal provinces with strong economies (e.g., Zhejiang, Shandong, Jiangsu, Guangdong) and historically significant provinces (e.g., Shanxi, Hebei) dominate the first tier, benefiting from the synergy among ICH conservation efforts, economic support, and historical accumulation. In contrast, central–western provinces (e.g., Qinghai, Gansu, Xizang) and certain municipalities (e.g., Shanghai, Tianjin) lag due to accelerated modernization or limited resource allocation. Border regions such as Xinjiang, Guizhou, and Yunnan, leveraging their multi-ethnic cultural diversity, form the second tier, highlighting the positive role of ethnic characteristics in ICH transmission. However, shortcomings in declaration and conservation persist in southwestern provinces such as Guangxi and Chongqing.

The kernel density analysis results of ICH (Figure 3b) reveal a spatially clustered yet relatively balanced distribution pattern across China, characterized by “two high-density cores, three secondary-density cores, and moderate uniformity”. The high-density core areas are concentrated in the Beijing–Tianjin–Hebei coordinated development zone and the Shanghai–Nanjing–Hangzhou coastal region, while the three secondary high-density zones are located in southeastern Guizhou, coastal Guangdong, and southern Xizang. Additionally, significant ICH distributions can be observed in Qinghai, Sichuan, Yunnan, Hubei, and Fujian.

Additionally, based on the Lorenz curve, geographic concentration index, and spatial equilibrium analysis, the distribution of ICH exhibits regional clustering but demonstrates better equilibrium compared to TVs. The Lorenz curve (Figure 3e) shows that the top six provinces account for nearly 30% of the national total, while the bottom six provinces collectively contribute less than 10%. The geographic concentration index (G = 19.72), slightly higher than the uniform distribution hypothesis value (G₀ = 17.96), and the imbalance index (S = 0.26), far lower than the 0.52 value for TVs, confirm the coexistence of “multi-core clustering” and overall dispersion in ICH distribution, reflecting its superior spatial equilibrium compared to TVs.

3.2. Analysis of the Spatial Relationship Between TVs and ICH

3.2.1. Quantitative Spatial Relationship

Based on the GIS platform, a 50 km × 50 km grid was overlaid on the study area, integrating data from 8155 TVs and 3587 ICH sites. Spatial overlay analysis generated their distribution patterns and grid decomposition results (Figure 4a). Statistical analysis revealed that among the 4261 grids nationwide, only 650 grids contain both TVs and ICH, while 643 grids include only TVs, 389 grids include only ICH, and 2579 grids lack both. The quantitative spatial relationship model index was calculated as follows:

$$R={\displaystyle \frac{650\times 2579-643\times 389}{\sqrt{\left(650+643\right)\times \left(389+2579\right)\times \left(650+389\right)\times \left(643+2579\right)}}}=0.398$$

(14)

$$X^2={\displaystyle \frac{293\times (650\times 2579-643\times 389{)}^2}{\left(650+643\right)\times \left(389+2579\right)\times \left(650+389\right)\times \left(643+2579\right)}}=674.657$$

(15)

$R$ = 0.398, and the chi-squared test value $X^2$ = 674.657, which is significantly higher than the critical value $X_a^2\left(1\right)$= 3.84 at the α = 0.05 level, indicating that there is a strong positive correlation between the spatial distribution of TVs and ICH, and the statistical significance is remarkable.

3.2.2. Spatial Correlation Relationship

As shown in the LISA cluster results (Figure 4b) “high–high” clusters are concentrated in three major corridors: southern Shanxi and its border areas with Shaanxi and Henan, the Hunan–Guizhou boundary zone, and the Shanghai–Zhejiang–Fujian–Guangdong southeastern coastal belt. These regions, leveraging natural barriers formed by mountain ranges and water systems (e.g., the Tai-hang Mountains, Wuling Mountain Range, and southeastern hills), effectively buffer the rapid penetration of modernization, fostering a “mutually reinforcing mechanism” between the spatial integrity of TVs and the cultural–ecological conservation of ICH; villages provide living practice carriers for ICH, while ICH injects cultural continuity into villages. No “low–low” clusters were observed in the study area, suggesting that the overall decline of cultural heritage and villages has not yet formed spatial scale effects. “Low–high” clusters are distributed contiguously in the Beijing–Tianjin–Shandong urban agglomeration and provinces such as Hubei, Xizang, and Xinjiang, reflecting the combined effects of rural hollowing-out and policy biases toward ICH under rapid urbanization, manifesting as prominent ICH resource advantages but lagging in terms of village conservation. “High–low” clusters are sporadically scattered in the western provinces (Xinjiang, Xizang, Sichuan, Gansu, Yunnan, etc.), where village entities are well preserved but ICH revitalization remains weak, potentially linked to latent crises such as inheritor attrition and insufficient cultural excavation.

3.2.3. Spatial Mismatch Relationship

(1). Deviation of the Center of Gravity: As shown in Figure 5a,b, the spatial deviation between the gravity centers of TVs and ICH in China exhibits significant regional variations. High-deviation zones are concentrated in eastern coastal and central economically active provinces, medium-deviation zones are mostly located in transitional areas of North China and Southwest China, and low-deviation zones are primarily clustered in ethnic minority regions of Southwest and Northwest China, as well as geographically enclosed provinces. At the provincial scale, high-deviation-index provinces (e.g., Tianjin: 0.803, Ningxia: 0.706) are often influenced by rapid urbanization, characterized by ICH clustering in urban areas and marginalization of TVs. In contrast, low-deviation-index provinces (e.g., Sichuan: 0.024, Yunnan: 0.049) achieve spatial coupling through the preservation of ethnic cultural authenticity and geographic isolation. Notably, the deviation distance and index are not strictly positively correlated—Xinjiang shows the largest deviation distance (238.19 km) but a diluted index (0.186) due to its vast territory, while Shanghai has the smallest deviation distance (11.51 km) but a moderate index (0.136), reflecting the high reliance of ICH on artificial carriers in megacities and weak linkage with TVs.

(2). Mismatch in Scale: According to the SMI-based mismatch classification results (Figure 5c), spatial mismatch is significant nationwide. Positive mismatch zones (ICH-dominant) account for 65% of the country, primarily located north of the Yangtze River and dominated by moderate mismatch types. Negative mismatch zones (TV-dominant) constitute 35% of the country, concentrated south of the Yangtze River, with medium–high mismatch types prevailing. At the provincial level (Figure 5d), positive mismatch zones include Xizang, Shandong, and Jiangsu, exhibiting pronounced ICH advantages, while negative mismatch zones, such as Yunnan, Guizhou, and Hunan, show distinct dominance of TVs. Regarding mismatch contributions, Yunnan (8.91%), Guizhou (7.88%), Hunan (7.82%), and Beijing (7.00%) exhibit extremely high values, serving as the primary drivers of national mismatch; Jiangsu, Jiangxi, and Shandong follow moderately, whereas central provinces such as Henan and Qinghai contribute minimally.

3.3. Analysis of the Impact Mechanism of Spatial Mismatch Between TVs and ICH

3.3.1. Selection of Driving Factors

To investigate the influence mechanisms of spatial mismatch between TVs and ICH, we selected 16 driving factors from three dimensions—natural geographical environment, socio-economic, and cultural policy—based on relevant research [9,35,53,54,55,56,57,58,59,60] and expert opinions (

Table 7. Mismatch contribution degree driving factors (Source: compiled by author).

Types	Driving Factors
Natural geographical environment	Mean Elevation (X1), Mean Slope (X2), Mean Temperature (X3), Mean Precipitation (X4), Mean River Density (X5)
Society and economy	Resident Population (X6), Ethnic Minority Population Proportion (X7), Population Proportion Aged 65 and Above (X8), Urbanization Rate (X9), Road Network Density (X10), GDP (X11), Proportion of Tertiary Industry in GDP (X12), General Public Budget Expenditure (X13)
Cultural policy	Number of Museums (X14), Number of Key Cultural Relic Protection Units (X15), Number of Intangible Cultural Heritage Inheritors (X16)

). To expand the sample size and ensure the accuracy of the results, the spatial mismatch index (SMI) and mismatch contribution degree (W) of cities nationwide were further calculated (Figure 6). The W values of 347 prefecture-level cities with valid data were used as the dependent variables in the geographic detector, while the 16 driving factors served as independent variables for the subsequent analysis.

3.3.2. Results of the Optimal-Parameter Geographic Detector

(1). Optimal Discretization Results for Continuous Variables: Using R language and the Optimal-Parameter Geographic Detector (OPGD), four discretization methods (equal, natural, quantile, and geometric) with 3–15 classification intervals were applied to calculate the q-values of 16 driving factors (Figure 7). The optimal discretization scheme was selected based on the highest q-values to assess the influence of each driving factor on the W.

(2). Single Factor Detection: The core driving factors are predominantly concentrated in the socio-economic and cultural policy dimensions (

Table 8. The fitting effect of the MGWR (Source: compiled by author).

Statistical Data	GWR	MGWR
R-Squared	0.6888	0.6793
Adjusted R-Squared	0.6272	0.6359
AICC	684.9744	686.6123
σ²	0.3726	0.364
Sigma-Squared MLE	0.3112	0.3207
Effective Degrees of Freedom	289.8374	305.7647

). In the cultural policy dimension, the number of ICH inheritors (X16) (q = 0.465, p < 0.001) demonstrates the strongest explanatory power for spatial mismatch, indicating that the living transmission of ICH plays a decisive role in the “TVs–ICH” spatial mismatch. The number of key cultural relic protection units (X15) (q = 0.348, p < 0.001) and the number of museums (X14) (q = 0.319, p < 0.001) follow, reflecting the significant impact of cultural infrastructure allocation on spatial mismatch. In the socio-economic dimension, GDP (X11, q = 0.282, p < 0.001), the proportion of tertiary industry in GDP (X12, q = 0.281, p < 0.001), and general public budget expenditure (X13, q = 0.291, p < 0.001) also exhibit strong explanatory power, suggesting that economic development levels influence spatial mismatch patterns through resource allocation mechanisms. Moderate explanatory power can be observed for the resident population (X6, q = 0.181, p < 0.001), urbanization rate (X9, q = 0.089, p < 0.05), and road network density (X10, q = 0.081, p < 0.05), implying potential conflicts between ICH conservation and TV preservation in areas with high population density and urbanization pressure.

The secondary influencing factors are mainly natural geographical variables (Figure 8): the mean slope (X2, q = 0.114), mean temperature (X3, q = 0.100), and mean precipitation (X4, q = 0.091) show weak explanatory power (q < 0.15) but pass significance tests (p < 0.05), indicating systematic associations with spatial mismatch through indirect pathways or synergistic interactions with other factors. Overall, socio-economic and cultural policy factors exert a higher intensity of influence on spatial mismatch, while natural geographical factors exhibit relatively weaker impacts.

(3). Interaction Detection: As shown in Figure 9, the two-factor interaction detection results indicate that the synergistic effects of driving factors significantly influence the spatial distribution differences between TVs and ICH. Most factor interactions exhibit positive superposition effects, including nonlinear enhancement and two-factor enhancement. Among these, the interaction between road network density (X10) and the number of ICH inheritors (X16) is the strongest, with a q-value of 0.64. This suggests that areas with convenient transportation may experience accelerated urbanization, leading to rural hollowing-out, while ICH inheritors, supported by policies, concentrate in urban or cultural hubs, forming a “transportation-driven polarization of cultural resources”. Additionally, interactions among factors such as the number of key cultural relic protection units (X15), urbanization rate (X9), GDP (X11), and general public budget expenditure (X13) also demonstrate strong explanatory power, indicating that their combined effects exacerbate the spatial mismatch between TVs and ICH. This synergy between socio-economic and cultural policy factors reflects the inherent conflict between development efficiency orientation and heritage conservation ethics during modernization, ultimately leading to the ecological separation of cultural heritage carriers (TVs) and cultural practice subjects (ICH).

Notably, natural environmental factors such as mean temperature (X3) and mean precipitation (X4) exhibit single-factor nonlinear weakening effects when interacting with certain socio-economic or cultural policy factors, highlighting their foundational constraints on the spatial mismatch of TVs and ICH. However, such effects may gradually diminish during modernization, overshadowed by socio-economic activities and cultural policy interventions, forming a pattern of “natural foundations receding in favor of anthropogenic drivers”. These results quantitatively identify nonlinear relationships among factors, enhancing the explanatory power of combined factor interactions.

3.3.3. Results of Multiscale Geographically Weighted Regression

(1) Comparative Analysis of Model Performance: Based on the factor explanatory power and significance derived from the geographic detector, we selected six core explanatory variables from two dimensions—socio-economic (i.e., GDP, proportion of tertiary industry in GDP, general public budget expenditure) and cultural policy (i.e., number of museums, key cultural relic protection units, and ICH inheritors)—to construct a multiscale geographically weighted regression (MGWR) model addressing spatial non-stationarity. The overall model performance evaluation (Table 8) shows that while the original R2 of GWR (0.6888) is slightly higher than that of MGWR (0.6793), the adjusted R2 indicates that MGWR (0.6359) outperforms GWR (0.6272), demonstrating that MGWR mitigates model inflation caused by local overfitting in GWR by controlling variable-scale heterogeneity. The model explains approximately 63.5% of the spatial variation in the mismatch index, indicating good fitting performance.

MGWR exhibits significantly higher effective degrees of freedom compared to GWR, confirming its ability to identify spatial scale differences in driving factors (e.g., economic factors may have global effects, while cultural factors exhibit local characteristics). Furthermore, MGWR’s sigma-squared MLE (0.3112) is smaller than GWR’s (0.3207), indicating improved spatial stationarity of error terms through hierarchical bandwidth optimization.

(2) Estimation of Variable Coefficient β: The spatial heterogeneity effects of various explanatory variables are shown in

Table 9. Estimation of MGWR coefficient β (Source: compiled by author).

Explanatory Variables	Mean	Standard Deviation	Minimum	Median	Maximum
GDP	−0.7491	0.378	−1.4264	−0.7193	−0.0784
Proportion of Tertiary Industry in GDP	0.0185	0.0096	0.0021	0.0184	0.0435
General Public Budget Expenditure	0.2948	0.3208	−0.2198	0.2838	0.849
Number of Museums	0.2744	0.144	0.0769	0.2378	0.5718
Number of Key Cultural Relic Protection Units	0.2431	0.3181	−0.2286	0.1275	1.1819
Number of Intangible Cultural Heritage Inheritors	0.8483	0.2879	0.3892	0.8473	1.3918

. GDP exhibits a national negative correlation with significant spatial heterogeneity, indicating that economic scale expansion significantly reduces the spatial mismatch between TVs and ICH. The proportion of the tertiary industry in GDP shows a weak positive correlation and relatively balanced spatial distribution, reflecting the limited role of service sector development in balancing ICH’s spatial distribution. General public budget expenditure demonstrates an overall positive correlation with marked spatial heterogeneity, although some regions exhibit negative correlations with mismatch contribution. The number of ICH inheritors displays a strong positive synergistic effect, suggesting that concentrated inheritors may trigger a “talent siphoning effect”, leading to ICH’s detachment from its original context and urban concentration. Museums exhibit moderate positive associations, reflecting that cultural facilities, while enhancing heritage identity through knowledge dissemination, may risk “siphoning-style conservation” by clustering ICH in urban areas away from TVs. Key cultural relic protection shows spatial heterogeneity, revealing that administrative measures strengthen TV–ICH symbiosis in cultural core zones but may exacerbate mismatch in urbanizing frontiers through “freeze-style conservation”, emphasizing the need for policy tools to align with local cultural ecosystem resilience. Overall, economic factors act as exogenous drivers shaping the macro-pattern of spatial mismatch, while cultural policy factors influence system stability through endogenous resilience mechanisms.

4. Discussion

As dual carriers for the living transmission of agrarian civilization, the spatial compatibility between traditional villages (TVs) and intangible cultural heritage (ICH) is critical to the sustainable development of cultural ecosystems. Based on nationwide data encompassing 8155 TVs and 3587 ICH items, this study systematically reveals their spatial differentiation patterns and driving mechanisms through the integrated application of the spatial mismatch index (SMI), Optimal-Parameter Geographic Detector (OPGD), and multiscale geographically weighted regression (MGWR) models. The findings provide strategic references for the holistic and dynamic conservation of TVs and ICH in China.

4.1. Spatial Pattern and Relationship Characteristics of TVs and ICH

TVs exhibit strong clustering due to natural geographical barriers and ethnic cultural diversity, concentrated primarily in Southwest China and East China, while ICH forms a “multi-core coexistence” pattern supported by economic strength and policy empowerment, with high-density areas focused on economically developed urban agglomerations and ethnic border regions. The spatial distributions of TVs and ICH show a significant positive correlation (R = 0.398). In natural barrier zones (e.g., the Taihang Mountains and Wuling Mountain Range), the obstruction of modernization fosters a bidirectional empowerment mechanism where “villages provide carriers” and “ICH perpetuates culture”. However, urbanization and policy interventions intensify spatial mismatch: high-deviation zones in eastern regions (e.g., Tianjin, Shanghai) witness ICH clustering in urban areas, while low-deviation zones in Southwest China (e.g., Yunnan, Sichuan) achieve geographic coupling through ethnic cultural authenticity. Scale mismatch further reveals contradictions in economically advanced provinces (e.g., Zhejiang), where ICH conservation advantages conflict with TVs’ decline, while ethnic regions (e.g., Yunnan, Guizhou) face imbalances between TVs’ preservation strengths and insufficient ICH revitalization. Notably, Yunnan appears as a low-deviation-index zone in gravity center mismatch but a negative high-mismatch zone in scale mismatch. This paradox likely stems from its mountainous and plateau terrain hindering rapid urbanization, enabling TVs and ICH to coexist within closed geographic units with spatially concentrated locations (low deviation), while insufficient ICH recognition due to inheritor attrition and weak declaration awareness results in high scale-based mismatch.

4.2. Driving Mechanism of Spatial Mismatch Relationship Between TVs and ICH

The OPGD model’s results indicate that socio-economic factors and cultural policy elements exert higher influence intensities on spatial mismatch effects compared to natural geographical environmental factors (q < 0.12). Dominant factors influencing the spatial mismatch between TVs and ICH include the GDP, proportion of tertiary industry in GDP, general public budget expenditure, number of ICH inheritors, number of museums, and number of cultural relic protection units, with the highest q-value reaching 0.465. Combined with the two-factor interaction results, we can conclude that the spatial mismatch between TVs and ICH in China stems from the cumulative effects of multiple factors, with its core mechanism being a three-dimensional interaction of “weakened natural foundation driven by dual cores of socio-economic dynamics and cultural policy momentum” (Figure 10). Here, natural geographical environments play a weak objective constraining role, socio-economic factors act as the primary synergistic driver, and cultural policy serves regulatory and responsive roles in TV selection, ICH recognition, cultural infrastructure allocation, and rural revitalization. Amid population mobility, cultural inheritance, and economic development, TVs face unavoidable dilemmas of abandonment versus revitalization, while ICH confronts the dual challenges of in situ preservation and de-territorialized transmission, ultimately forming two distinct spatial mismatch types (positive and negative) across provinces.

4.3. Spatial Heterogeneity of Core Driving Factors

From the perspective of economic development, regional economic levels exert differentiated regulatory effects on the “TVs–ICH” spatial mismatch. The GDP indicator reveals a gradient-decreasing negative spatial correlation from Southeastern to Northwestern China. In economically advanced coastal regions such as the Pearl River Delta and Southern Fujian, GDP (Figure 11a) shows a strong negative correlation with spatial mismatch contribution (−1.426 < β < −1.193), suggesting that economic growth in these areas enhances the linkage between TVs and ICH. This may stem from improved cultural resource integration driven by economic development, fostering market transformations such as folk culture tourism and ICH craft exhibitions. However, vigilance is needed against over-commercialization and marginalization of TVs and ICH, which could weaken their intrinsic connections. In the western border regions, the regulatory effect of economic factors weakens significantly (−0.296 < β < −0.078), reflecting underdeveloped areas still in the scale-expansion phase, where heritage conservation has not yet been effectively integrated into development systems. The proportion of tertiary industry in GDP (Figure 11b) exhibits a nationwide positive correlation, gradually weakening from north to south. The strongest positive correlations (0.031 < β < 0.043) are observed in Heilongjiang, Jilin, and Liaoning, although the overall correlations remain weak, indicating limited balancing effects of service sector development on spatial mismatch. Notably, general public budget expenditure (Figure 11c) displays pronounced spatial heterogeneity. Significant positive drivers (0.647 < β < 0.849) appear in the southeastern coastal areas and Beijing’s periphery: southeastern regions leverage fiscal advantages to cluster cultural facilities in urban areas, while Beijing capitalizes on political resources to develop ICH, forming urban-oriented resource allocation patterns that accelerate ICH’s migration from rural to urban areas, intensifying spatial heterogeneity. Conversely, negative drivers (−0.220 < β < −0.056) are observed in Sichuan, Chongqing, and Xizang, where concentrated cultural resources and TVs enable local governments to strengthen TV–ICH spatial linkages through context-specific conservation strategies. This implies that the “urban-biased” allocation of cultural budgets in different regions may influence ICH’s spatial displacement.

Cultural policy elements exhibit distinct region-dependent regulatory effects on spatial mismatch. The number of museums (Figure 11d) shows a global positive correlation, decreasing gradually from south to north. In the southeastern coastal areas, strong positive spatial lock-in effects (0.485 < β < 0.57) suggest that museum clusters enhance ICH’s visibility and dissemination but also accelerate rural cultural hollowing-out by siphoning resources, detaching ICH from TV contexts, and reflecting the incompatibility between “museumification” conservation models and village cultural ecosystems. Meanwhile, the number of key cultural relic protection units (Figure 11e) displays marked spatial heterogeneity: Negative correlations (−0.229 < β < −0.082) appear in Chongqing, Sichuan, and Xinjiang, where concentrated TVs, deep historical heritage, and the role of protection units as cultural landmarks and ICH practice sites strengthen regional cultural value superposition effects, forming aggregated cultural networks that drive cultural tourism economies and enable sustainable conservation. Conversely, positive correlations (0.632 < β < 1.182) in the Pearl River Delta, southern Fujian, and Anhui imply that administrative-led conservation in some regions may sever organic ties between heritage and TVs, underscoring the need for context-specific cultural governance. The number of ICH inheritors (Figure 11f) exhibits a global positive correlation, weakening northward, with ultra-strong spatial synergy (1.187 < β < 1.392) in the multi-ethnic southwest regions (Guizhou, Guangxi, Chongqing, Yunnan, Sichuan). This likely stems from urban- or scenic-area-clustered inheritor studios under administrative “embedded aggregation”, which disrupt the spatial ties between inheritors and native villages, while tourism-driven “stage-like” transformations of ICH cultural spaces exacerbate TVs–ICH spatial mismatch.

5. Conclusions

In this study, we constructed a three-phase analytical framework—”spatial pattern identification, spatial relationship analysis, and impact mechanism assessment”—integrating the spatial mismatch index, Optimal-Parameter Geographic Detector (OPGD), and multiscale geographically weighted regression (MGWR) model to systematically reveal, for the first time, the spatial differentiation patterns of traditional villages (TVs) and intangible cultural heritage (ICH) in China, as well as their driving mechanism of “weakened natural foundation with dual cores of socio-economic and cultural policy drivers”. This innovative methodological framework overcomes the limitations of qualitative approaches in cultural heritage research, establishing a three-dimensional indicator system and cross-scale analysis that provide scientific decision-making tools for national cultural ecosystem conservation planning and spatial heritage governance under rural revitalization strategies. This advances cultural heritage conservation from empirical judgment to spatially quantified governance, demonstrating strong interdisciplinary value and practical application potential. The key conclusions are as follows:

(1) The spatial distribution patterns of TVs and ICH in China exhibit distinct differences. TVs are highly clustered in East China and Southwest China (over 60% of the national total), forming three high-density core areas, the “Shanxi–Hebei–Henan border”, “Zhejiang–Fujian–Anhui–Jiangxi adjacent region”, and “Hunan–Guizhou–Chongqing–Guangxi bordering zone”, demonstrating strong spatial imbalance (G = 24.49, S = 0.52). In contrast, ICH displays a “multi-center equilibrium” pattern, with high-density clusters in the “Beijing–Tianjin–Hebei” and “Yangtze River Delta” regions, characterized by a significantly lower geographic concentration (G = 19.72) and imbalance index (S = 0.26) compared to TVs. This spatial divergence reflects the rigid influence of natural geographical constraints on physical carriers (TVs) and the flexible regulatory role of socio-economic–cultural factors in non-material cultural forms (ICH), highlighting the coexistence of TVs’ strong agglomeration and ICH’s relative dispersion.

(2) The spatial relationship between TVs and ICH in China exhibits significant systemic mismatch, coexisting with localized synergy. The quantitative spatial relationship model reveals a notable positive correlation (R = 0.398) between the two. Bivariate spatial autocorrelation analysis identifies “low–high” imbalances (low TV density and high ICH density) in regions such as Beijing–Tianjin–Hebei and Hubei, driven by rural hollowing-out and policy biases favoring ICH, while “high–low” revitalization challenges (high TV density and low ICH vitality) persist in parts of western provinces due to intergenerational transmission disruptions. The SMI demonstrates a nationwide scale mismatch: 65% of areas exhibit positive mismatch (ICH-dominant) in the northern regions, and 35% exhibit negative mismatch (TV-dominant) in the southern regions. Notably, southwestern provinces such as Yunnan and Guizhou, grappling with contradictions between ethnic cultural authenticity and lagging protection, emerge as core drivers of global mismatch.

(3) The spatial mismatch between TVs and ICH is a complex systemic issue dominated by a three-dimensional interaction mechanism of “weakened natural foundation driven by dual cores of socio-economic dynamics and cultural policy momentum”. Core driving factors concentrate on socio-economic (i.e., GDP, proportion of tertiary industry in GDP, general public budget expenditure) and cultural policy (i.e., number of ICH inheritors, key cultural relic protection units, museums), revealing deep-seated contradictions between development efficiency orientation and heritage conservation ethics during modernization. Natural geographical factors (i.e., slope, temperature, precipitation), despite passing significance tests (p < 0.05), exhibit weak explanatory power (q < 0.15), with their foundational constraints potentially overridden by socio-economic and cultural policy interventions in modernization, forming an evolutionary pattern of “natural foundations yielding to anthropogenic drivers”. The spatial mismatch fundamentally stems from the dynamic interplay among population mobility, cultural inheritance, and economic transformation, ultimately leading TVs to face the dual dilemma of “abandonment versus revitalization” and ICH to struggle with “in situ preservation versus de-territorialized transmission”, urgently requiring differentiated policies to reconcile conflicting potentials within the three-dimensional mechanism.

(4) The analysis based on the MGWR model reveals that the spatial mismatch pattern between TVs and ICH is driven by significant heterogeneity in socio-economic and cultural policy factors, with the model effectively mitigating spatial non-stationarity through hierarchical bandwidth optimization (adjusted R² = 0.6359). Among economic factors, GDP exhibits a global negative correlation (mean β = −0.7491), particularly in economically advanced regions such as the Pearl River Delta and southern Fujian (β = −1.426 to −1.193), where economic development enhances “TVs–ICH” linkages through resource integration, although vigilance is required to prevent commercialization from eroding their original cultural ties. General public budget expenditure shows marked spatial heterogeneity: positive drivers in southeastern coastal areas and Beijing (β = 0.647–0.849) reflect fiscal investments accelerating the urban clustering of ICH, while negative effects in Sichuan, Chongqing, and Xizang (β = −0.220 to −0.056) indicate localized policies strengthening TVs–ICH symbiosis. In cultural policy, the numbers of ICH inheritors (β = 0.8483) and museum facilities (β = 0.2744) exhibit global positive correlations, with intensified mismatch in the multi-ethnic southwest regions (β = 1.187–1.392) due to “embedded aggregation” of inheritors and tourism-driven “staged” transformations. Key cultural relic protection units negatively regulate in Sichuan, Chongqing, and Xinjiang (β = −0.229 to −0.082), fostering cultural network aggregation, but positively drive in the Pearl River Delta and Anhui (β = 0.632–1.182), highlighting how administrative conservation may sever heritage–village ties. This study underscores how economic factors shape macro-mismatch through exogenous drivers, while cultural policy influences system stability via endogenous resilience mechanisms. Policy design must balance regional heterogeneity, reconciling “efficiency orientation” with “ecological adaptation” to prevent “siphoning-style” and “freeze-style” conservation from damaging cultural heritage.

(5) Based on the mechanistic analysis of the spatial relationship between TVs and ICH in China, this study proposes the following recommendations to enhance the living heritage conservation system: First, establish a context-specific cultural regeneration system to promote the synergistic development of TVs and ICH. In low-mismatch zones, prioritize authenticity preservation as the foundation, locality-driven innovation as the catalyst, and systemic development as the nexus, building cross-sector collaboration platforms (e.g., “culture + tourism + ecology”) to deepen the integration and value enhancement of tangible and intangible heritage. For negative high-mismatch zones, systematically consolidate archival records, oral histories, and folk practices, leveraging digital technologies to extract cultural DNA and innovate rural narrative frameworks. For positive high-mismatch zones, safeguard the authenticity of historical architecture and cultural landscapes while revitalizing traditional crafts through ICH study tours, creative industries, and daily-life applications. Second, construct a dynamic conservation network that integrates all regional elements, centering on cultural inheritance and socio-economic factors while grounding in geographical characteristics, to enhance the scientific rigor, long-term efficacy, and sustainability of TV preservation and ICH transmission through differentiated policies. Third, develop region-specific “economy–culture” dual governance pathways to transcend traditional homogenized approaches. Economically, the southeastern coastal regions must establish commercialization boundary warning mechanisms in cultural resource markets, regulate ICH industry layouts via tax leverage, and curb rural cultural hollowing caused by urban fiscal agglomeration. Meanwhile, the western border areas should integrate heritage conservation with rural revitalization metrics, leveraging the advantages of late development to explore “low-impact development” models and avoid “destruction before conservation” pitfalls. Culturally, multi-ethnic regions in Southwest China need to rebuild the “inheritor–village” spatial anchoring system by implementing resident certification for ICH inheritors and limiting the scale of scenic-area-embedded studios. The eastern developed regions should conduct cultural ecosystem assessments for heritage sites, bundle key conservation projects with living ICH transmission in TVs, and reconstruct villages’ cultural–spatial chains through digital twin technologies.

While this study provides a multidimensional analytical framework for the holistic regional conservation of TVs and ICH, it is essential to objectively acknowledge four limitations inherent in the current exploration: First, in terms of data usage, the current research simplifies TVs and ICH into point-based data for computational convenience, potentially overlooking their actual spatial characteristics, such as blurred boundaries and dynamic transmission processes. Future studies could integrate more granular data (e.g., 3D scanning, spatiotemporal trajectories) to enhance their analytical precision. Second, regarding influencing factors, this study primarily considers traditional elements such as culture, economy, and geography; future work should incorporate emerging factors such as cultural consumption habits and digital technology applications to better reflect real-world complexities. Third, in methodological terms, while MGWR optimizes spatial analysis, it remains challenging to precisely capture cross-regional interaction effects. Subsequent research could employ system dynamics models to further analyze the spatiotemporal patterns of multi-factor interactions. Fourth, while our MGWR model reveals regional heterogeneity in drivers, its city-level resolution cannot prescribe policies for individual TVs. Future studies should incorporate village-scale data (e.g., infrastructure, demographic dynamics) to build decision frameworks classifying settlements into (1) maintenance, (2) development, (3) liquidation, and (4) integration. This will require merging spatial econometrics with granular field surveys.