Spatial Characteristics and Influencing Factors of Traditional Villages Distribution in the Yellow River Basin

Abstract

Traditional villages in the Yellow River Basin of China are vital carriers of cultural heritage, ecological sustainability, and socio-economic development. This study employs spatial econometric analysis to examine the distribution patterns and influencing factors of 888 traditional villages in the region. The findings reveal a clustering pattern, with high-density areas in Shanxi and medium-density clusters in Shaanxi and Qinghai, while northern and southern regions remain sparse. Over time, the spatial center of village distribution has shifted along a north–south–north–east–west trajectory. The spatial distribution of traditional villages exhibits distinct regional characteristics and differences, shaped by several key influencing factors. These include elevation, precipitation, river proximity, road density, and the presence of cultural heritage units. Results show that in the upper reaches of the Yellow River Basin, natural factors primarily determine village locations. In contrast, economic development and infrastructure factors play a larger role in shaping village transformation in the middle and lower reaches. Policy interventions, such as cultural heritage protection, have a greater impact on remote upper areas. The influence of these factors varies spatially, highlighting the importance of region-specific conservation strategies. Based on these findings, this study proposes targeted strategies for the conservation and development of traditional villages, including multi-type protection systems, cultural corridor construction, watershed-based governance, and enhanced infrastructure and policy support. These strategies aim to support the sustainable development and long-term preservation of traditional villages in the Yellow River Basin. By integrating geographic, economic, and cultural perspectives, this research provides valuable insights into the spatial evolution of traditional villages and informs policy recommendations for achieving balanced rural development.

1. Introduction

Traditional villages are settlements that have emerged organically and evolved over long periods through the continuous habitation and agricultural activities of rural populations within specific natural and geographical environments [1]. These villages are marked by strong regional characteristics, historic depth, and continuity [2,3]. Their spatial layout, architectural styles, and cultural institutions have taken shape through prolonged interactions between natural and social systems, exhibiting both ecological adaptability and cultural persistence [4,5]. As vital carriers of agrarian civilization, traditional villages preserve local knowledge, historical memory, and cultural identity. They also represent the historical process of human–environment co-evolution [6,7], giving them significant value in both historical–cultural inheritance and socio-economic development [8,9]. In recent years, the concept of traditional villages has expanded beyond static settlement forms to encompass dynamic systems that integrate ecological, social, and cultural values [10,11,12]. In China, traditional villages play a vital role in regional prosperity. Recognizing their importance, China’s 2024 No. 1 Central Document emphasizes the need for the comprehensive protection and sustainable utilization of traditional villages. Between 2012 and 2024, China’s Ministry of Housing and Urban-Rural Development designated 8155 national traditional villages across six batches [13]. This has established a comprehensive and systematic framework for their protection to ensure their continued legacy.

The Yellow River Basin, the cradle of Chinese civilization, is home to numerous well-preserved traditional villages. Nine provinces and autonomous regions within the Yellow River Basin are home to 2016 traditional villages, representing 24.7% of the national total. Additionally, 888 villages, or 10.9% of the total, are situated along the mainstream of the Yellow River and its surrounding regions [14]. These settlements are widely distributed, rich in historical and cultural heritage, and deeply shaped by regional characteristics. Various geographical areas have nurtured distinct natural landscapes, ecological environments, historical and cultural resources, architectural styles, and folk traditions. As significant cultural heritage sites in the historical evolution of settlements along the Yellow River, these traditional villages serve as anchors of historical memory, cultural identity, and ecological balance. Their protection and sustainable use not only contribute to the long-term development of the region [15] but are also essential for cultural heritage conservation, ecological protection, and regional revitalization.

In recent years, rapid urbanization and industrialization have accelerated rural population outflows [16], and rural decline has become a global challenge. This decline results in the deterioration of traditional architecture, the erosion of traditional lifestyles, and the increasing homogenization of cultural landscapes [17]. Traditional villages are increasingly threatened by depopulation, aging populations, and the erosion of cultural identity. These trends have further disrupted cultural heritage transmission, exacerbating the challenges faced by traditional villages [18,19]. Consequently, the progressive decline and vanishing of traditional villages has attracted global attention, highlighting the need for comprehensive research and policy interventions to ensure their long-term sustainability [20,21].

The preservation, sustainable use, and adaptive development of traditional villages have become key topics in academic research and policy discussions. Scholars from different fields have examined different aspects. In the field of architecture and urban planning, scholars focus on the compatibility between spatial structure and architectural form [22], landscape patterns [23], clan systems, and traditional lifestyles [24]. Recent studies also emphasize the disturbances caused by urban expansion and infrastructure development on the spatial structure of traditional villages [25,26], highlighting the role of public participation in spatial planning and advocating a shift toward socially sustainable village development [27,28]. Cultural studies explore the role of traditional villages in fostering emotional attachment, collective memory, and institutional evolution [29,30]. These studies call for a shift from “static conservation” to “living utilization” to enhance cultural continuity and community identity [31,32]. Research on land use patterns, industrial development, and the optimization of human settlements stresses the need to align cultural experience, industrial organization, and spatial adaptability in achieving sustainability [33,34]. Quantitative sustainability assessment frameworks have been developed to support village classification and the formulation of differentiated conservation strategies [35,36]. Geographical studies analyze the spatial distribution, pattern characteristics, and evolutionary mechanisms of villages [37,38], providing technical support for spatial governance and regional coordination [39]. Meanwhile, tourism-focused studies explore the development, use, and sustainable management of tourism resources within traditional villages, emphasizing the role of community engagement [40,41], spatial evolution pathways [42], and the importance of coordinated mechanisms to ensure sustainable tourism [43,44]. Additionally, some studies have identified significant spatial coupling between traditional villages and intangible cultural heritage, particularly in areas with deep cultural sedimentation, geomorphological transitions, and economic underdevelopment, highlighting the spatial potential for integrated cultural preservation and tourism development [7].

Considering the spatial variation in traditional village distribution, scholars have explored the adaptability of traditional village conservation and development by examining village morphology [45], cultural landscapes [46], human–environment interactions [47], and livelihood diversity [48]. Findings suggest that natural geographic factors, such as location, topographical features, and land use patterns, are the key factors influencing the spatial layout of traditional villages [49,50]. In contrast, regional economic development and policy environments further influence their preservation and transformation [51,52]. Additionally, at different spatial scales, the spatial patterns and driving forces of traditional villages respond differently across regions [53]. Therefore, it is essential to analyze the spatial heterogeneity of traditional villages across different regions and explore their mechanisms of decline and revitalization based on regional conditions [54]. Regional empirical studies have deepened the understanding of spatial clustering and multi-factor coupling mechanisms in traditional village distribution. In northwest China, village patterns are largely shaped by terrain and water resources, resulting in an “environment-dependent culturally conservative” pattern [55]. In contrast, ethnic villages in southeast China are primarily influenced by topography, forming a “single-core agglomeration locally dispersed” structure [56]. In the Yellow River Basin, the distribution of traditional villages is mainly driven by natural factors, with social factors playing a supplementary role, together exhibiting a notable nonlinear enhancement effect [57]. Studies in the Central and Jiangnan regions emphasize the spatial synergy among natural, institutional, and cultural elements [58,59]. At the national level, research reveals a strong spatial coupling between traditional villages and impoverished areas, which are often located in ecologically fragile zones. In these regions, the natural environment simultaneously serves as a protector of cultural heritage and a constraint on economic development [48]. With the implementation of the “centralized contiguous protection and utilization” policy, the relational structure of the villages is shifting from a focus on cultural linkage to industrial coordination, fostering a more regionalized and networked conservation model [60]. Some studies have proposed constructing heritage corridors as spatial linkages to restructure village networks and promote the integration of cultural conservation with tourism development [61].

Regarding research methods, the study of traditional villages has evolved from qualitative description to multidimensional quantitative analysis and integrated modeling approaches. Widely adopted methods include the AHP-Entropy method [54], SolVES model [62], TOPSIS [63], and random forest algorithms [64], which support the evaluation of village development levels, the construction of cultural inheritance mechanisms, the quantification of ecological and social values, and the assessment of governance performance. Spatial statistics and GIS technologies are also widely applied. Methods such as Kernel Density Estimation, standard deviation ellipses, and GeoDetector models are frequently used to reveal spatial clustering patterns and multi-factor driving mechanisms [65,66].

Existing research has explored the conservation, inheritance, and sustainable development of traditional villages, revealing the complexity and multidimensional nature of their spatial–temporal distribution patterns and the challenges associated with their protection and development. Given these complexities, it is essential to establish a multi-scale, multi-factor analytical framework that integrates regional economic and social development models to explore the spatial distribution and sustainable development mechanisms of traditional villages. Currently, significant gaps remain in accurately identifying the spatial and temporal characteristics of villages in the Yellow River Basin, distinguishing their core cultural values, and fostering regionally coordinated development.

To address these challenges, it is essential to analyze spatial patterns and evolutionary processes, establish a robust cultural protection system, strengthen regional collaboration and tailored development strategies, and enhance the utilization of cultural resources. These efforts will create effective pathways for the sustainable revitalization and high-quality development of traditional villages in the Yellow River Basin. This study examines 888 nationally designated traditional villages located along the mainstream of the Yellow River and its surrounding regions. By employing advanced spatial analysis techniques, including Kernel Density Estimation, hotspot analysis, and Geographically Weighted Regression, this study systematically assesses the spatial distribution patterns and driving mechanisms of these traditional villages. This study aims to: (1) adopt a watershed-scale perspective to systematically identify the macro-level spatial distribution patterns and regional heterogeneity of traditional villages; (2) incorporate multi-dimensional spatial analysis tools to improve the precision and explanatory power in identifying driving mechanisms; (3) propose differentiated conservation strategies based on spatial heterogeneity and typological evolution logic. The ultimate goal is to provide a robust theoretical foundation and actionable policy recommendations to support the systematic protection and dynamic development of traditional villages in the Yellow River Basin, in alignment with the national strategies for ecological civilization and the sustainable utilization of cultural resources.

2. Materials and Methods

2.1. Study Area

The Yellow River, the second longest river in China, is honored as the “Mother River” for its historical and cultural significance. It flows through nine provinces and autonomous regions—Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shanxi, Shaanxi, Henan, and Shandong—before emptying into the Bohai Sea at Kenli District, Shandong Province. Spanning 5464 km in length, the river has an elevation drop of 4.48 km and a total basin area of 795,000 km².

The research area of this study includes the mainstream region and its surrounding regions of the Yellow River Basin, covering 75 prefecture-level administrative divisions (including cities, prefectures, and leagues) across 9 provinces and autonomous regions, from the river’s source to its estuary [14]. Defined by the interrelated dynamics of resource utilization, socio-economic development, and ecological conservation, this integrated geographic scope reflects both hydrological and administrative considerations. The total study area covers 1.006 million km², which accounts for 28.21% of the total land area of the nine provinces and autonomous regions. This includes 412,000 km² in the upper basin, 359,000 km² in the middle basin, and 235,000 km² in the lower basin.

In most provinces and autonomous regions, the basin area is smaller than the administrative area. This study employs an integrated spatial approach to precisely define both the hydrological and administrative boundaries. The Yellow River Basin features diverse geomorphological landscapes, including plateaus, plains, and hills. It also serves as a population center and a vital economic corridor within the nine provinces and autonomous regions. Figure 1 illustrates the study area’s location and the distribution of traditional villages across the upper, middle, and lower reaches of the Yellow River Basin.

The Yellow River Basin is home to diverse regional cultures from its upper to lower reaches and has preserved a wealth of historical sites and traditional villages. Based on six batches of China’s National Traditional Villages List, released by the Ministry of Housing and Urban-Rural Development and other relevant departments, a total of 888 nationally designated traditional villages are located in 45 of the 75 prefecture-level divisions, spanning eight of the nine provinces and autonomous regions within the Yellow River Basin study area. Notably, Guyuan, Wuzhong, and Hohhot lie in transitional zones where administrative boundaries intersect both the upper and middle reaches. The spatial distribution of these traditional villages is highly uneven across the basin’s three sections: 221 villages (24.9%) are located in the upper reaches, 648 villages (73.0%) in the middle reaches, and only 19 villages (2.1%) in the lower reaches. These disparities highlight the uneven distribution of nationally designated traditional villages within the Yellow River Basin, as shown in

Table 1. Distribution statistics of national traditional villages in the Yellow River Basin.

River Basin	Province (Region)	City	Number of Traditional Villages	Proportion (%)
Upper Reaches	Qinghai	Haidong, Huangnan, Xining, Hainan	153	17.23%
	Gansu	Baiyin, Gannan, Lanzhou, Linxia	35	3.94%
	Ningxia	Guyuan, Wuzhong, Zhongwei, Yinchuan	10	1.13%
	Inner Mongolia	Baotou, Hohhot, Bayannur, Ulanqab	23	2.59%
Middle Reaches	Ningxia	Guyuan, Wuzhong	16	1.80%
	Inner Mongolia	Hohhot, Ordos	6	0.68%
	Shaanxi	Yulin, Weinan, Xianyang, Yan’an, Xi’an, Tongchuan, Baoji	120	13.51%
	Gansu	Tianshui, Pingliang, Dingxi, Qingyang	18	2.03%
	Henan	Luoyang, Sanmenxia, Jiyuan, Jiaozuo, Zhengzhou	62	6.98%
	Shanxi	Jincheng, Luliang, Jincheng, Linfen, Yuncheng, Xinzhou, Taiyuan, Changzhi, Shuozhou	426	47.97%
Lower Reaches	Henan	Puyang	1	0.11%
	Shandong	Jinan, Taian	18	2.03%
Total Basin	-	-	888	100%

.

2.2. Data Source

The dataset of 888 nationally designated traditional villages was obtained from the first to sixth batches of the Chinese Traditional Villages List, published by the Ministry of Housing and Urban-Rural Development of China (https://www.mohurd.gov.cn, accessed on 5 October 2024). The geographical coordinates were obtained using a latitude and longitude conversion tool (https://maplocation.sjfkai.com/, accessed on 5 October 2024). Topographic data for the Yellow River Basin were sourced from the Digital Elevation Model (DEM) provided by the National Earth System Science Data Center, with a resolution of 30 m (https://www.geodata.cn, accessed on 5 October 2024).

Additional datasets, including GDP and population raster data, annual average temperature and precipitation, normalized difference vegetation index (NDVI), ecosystem service value, potential crop yield, and net primary productivity (NPP), were obtained from the Resource and Environment Science Data Center (https://www.resdc.cn, accessed on 15 October 2024) and the National Earth System Science Data Center (https://www.geodata.cn, accessed on 15 October 2024).

Data on administrative boundaries, road networks, topographic, and hydrological features were retrieved from the National Cryosphere Desert Data Center (http://www.ncdc.ac.cn, accessed on 15 October 2024). Cultural heritage data, including information on national- and provincial-level heritage protection units, were obtained from the Yellow River Data Center (https://www.huanghe.ac.cn, accessed on 15 October 2024). Furthermore, the spatial relationships between traditional villages and county-level administrative centers, river systems, and major roads were analyzed using the Near Analysis tool in ArcGIS 10.8, supplemented by spatial visualization and processing in QGIS 3.40.

2.3. Research Methods

This study adopts a comprehensive set of spatial analytical methods to systematically examine the distributional patterns and driving factors of traditional villages in the Yellow River Basin. The Nearest Neighbor Index (NNI) and Kernel Density Estimation (KDE) methods are employed to evaluate spatial patterns and identify areas of concentration. The standard deviational ellipse (SDE) method helps analyze spatial orientation and track temporal evolution. Spatial autocorrelation methods, including Moran’s I and Getis–Ord Gi*, are applied to detect global and local clustering patterns. Additionally, the geographic detector model assesses the influence of various factors, while Geographically Weighted Regression (GWR) captures spatial heterogeneity in the relationships. Collectively, these methods provide a robust framework for understanding the spatial differentiation and developmental dynamics of traditional villages. A detailed explanation of each method is provided in the following paragraphs.

The Nearest Neighbor Index (NNI) method is used to analyze the overall spatial distribution pattern of traditional villages in the Yellow River Basin and evaluate the spatial proximity between villages within the region. Its formula is expressed as follows:

$$R={\displaystyle \frac{r_a}{r_e}}, r_a={\displaystyle \frac{{\sum }_{i=1}^n{d}_i}{n}}, r_e={\displaystyle \frac{1}{2\sqrt{n/A}}}$$

(1)

where $R$ represents the NNI value, $r_a$ is the observed mean nearest neighbor distance of traditional villages, and $r_e$ is the expected mean nearest neighbor distance under a random distribution. $d_i$ is the distance from the $i$-th traditional village to its nearest neighboring village, A is the total area of the study region, and $\mathrm{n}$ is the total number of traditional villages within the study area. The NNI value reflects the distributional tendency: if $R>1$, the villages are dispersed; if $R=1$, the villages follow a random distribution; if $R<1$, the villages are clustered [56]. This classification helps in understanding the spatial arrangement of traditional villages within the Yellow River Basin and provides insights into their distribution characteristics.

Kernel Density Estimation (KDE) is an effective method for mapping traditional village density in the Yellow River Basin and identifying high-density clusters [50]. The calculation formula is as follows:

$$f\left(x,y\right)={\displaystyle \frac{1}{n{h}^2}}\sum _{i=1}^{n}K\left({\displaystyle \frac{\sqrt{{\left(x-x_i\right)}^2+{\left(y-y_i\right)}^2}}{h}}\right)$$

(2)

where $f\left(x,y\right)$ represents the estimated density at location $\left(x,y\right)$, $K$ represents the Gaussian kernel function, $\left(x_i,y_i\right)$ denotes the actual coordinate locations of traditional village points, $h$ is the search radius, and $n$ is the number of traditional village points within the search radius. Higher KDE values reflect greater clustering, while lower values imply more dispersed settlements. By applying KDE, the spatial structure of village distribution can be visualized, allowing for the identification of densely populated areas and aiding in the formulation of conservation and development strategies.

Spatial autocorrelation is generally classified into global and local spatial autocorrelation. Global spatial autocorrelation is commonly measured using Moran’s I index, which quantifies the overall spatial correlation of traditional villages within the Yellow River Basin. The calculation formula is as follows:

$$I={\displaystyle \frac{n{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{ij}\left(z_i{z}_j\right)}{W{\sum }_{i=1}^n{z}_i^2}}$$

(3)

where $I$ is Moran’s $I$ index, $z_i$ and $z_j$ are the standardized observed values of spatial units $i$ and $j$, respectively, $w_{ij}$ is the spatial weight function between locations $i$ and $j$, $n$ is the total number of spatial units, and $W$ is the sum of all spatial weights. The value of Moran’s I indexes ranges from −1 to 1. A positive value indicates a positive spatial autocorrelation (clustered distribution), a negative value signifies a negative spatial autocorrelation (dispersed distribution), and a value close to zero suggests a random spatial distribution.

However, global spatial autocorrelation analysis provides only an overall assessment of spatial patterns and does not identify specific clustering regions. To address this limitation, local spatial autocorrelation analysis is employed. Hotspot analysis (Getis–Ord $G_i^{*}$) is used to explore local spatial autocorrelation, providing a more detailed evaluation of the distribution characteristics of clustered and dispersed areas [67]. The calculation formula is as follows:

$$G_i^{*}={\displaystyle \frac{{\sum }_{j=1}^n{w}_{ij}{x}_j-\overline{x}{\sum }_{j=1}^n{w}_{ij}}{s^2}}$$

(4)

where $G_i^{*}$ is the hotspot value at location $i$, $w_{ij}$ is the spatial weight between locations $i$ and $j$, and $x_j$ is the observed value at location $j$. The parameter $\overline{x}$ represents the global mean of the observed values, while s denotes the global variance of the observed values. The total number of traditional villages within the study area is n. A positive $G_{i }^{*}$ value ($G_i^{*}>0$) indicates a statistically significant clustering hotspot, signifying a high-density distribution of traditional villages. Conversely, a negative $G_i^{*}$ value ($G_i^{*}<0$) identifies a cold spot, indicating a low-density or sparse distribution of traditional villages. Spatial autocorrelation analysis helps determine whether traditional villages in the Yellow River Basin are spatially clustered or evenly distributed. It also identifies significant clustering patterns, providing a deeper understanding of spatial distribution trends.

The standard deviational ellipse (SDE) method is used to quantify the directional trends and spatial evolution of traditional villages across the six batches. This method is primarily used to detect shifts in spatial centers and track distributional changes over time. The corresponding formula is presented as follows:

$$\theta ={\displaystyle \frac{1}{2}}arctan\left({\displaystyle \frac{2{\sigma }_{xy}}{{\sigma }_x^2-{\sigma }_y^2}}\right)$$

(5)

$${\mathsf{\sigma }}_x^2={\displaystyle \frac{1}{n}}{\sum }_{i=1}^n{\left(\widetilde{{\mathrm{x}}_i}\right)}^2, {\mathsf{\sigma }}_y^2={\displaystyle \frac{1}{n}}{\sum }_{i=1}^n{\left(\widetilde{{\mathrm{y}}_i}\right)}^2,{\mathsf{\sigma }}_{xy}={\displaystyle \frac{1}{n}}{\sum }_{i=1}^n\left(\widetilde{{\mathrm{x}}_i}\widetilde{{\mathrm{y}}_i}\right)$$

where the azimuth angle $\left(\theta \right)$ represents the primary orientation of traditional village distribution. The major axis describes expansion along the dominant direction, while the minor axis captures dispersion perpendicular to it.$x_i$, $y_{\mathrm{I}}$ denote the coordinates of traditional village points, $\widetilde{{\mathrm{x}}_i}$, $\widetilde{y_i}$ is the coordinate deviation from the centroid, $n$ is the total number of traditional villages, and ${\sigma }_x$ and ${\sigma }_y$ are standard deviations along respective axes. By applying SDE, the directional tendencies and spatial dynamics of village distribution can be assessed, providing insights into their historical evolution and guiding future conservation efforts.

The GeoDetector model is a spatial analysis tool used to examine the relationship between geographical attributes and driving factors [68,69]. It can be applied to analyze the spatial heterogeneity of influencing factors on traditional villages in the Yellow River Basin. The calculation formula is as:

$$q=1-{\displaystyle \frac{{\sum }_{h=1}^L{N}_h{\sigma }_h^2}{N{\sigma }^2}}$$

(6)

where the q-value measures the explanatory power of an independent variable on a dependent variable, ranging from [0, 1], with higher values indicating stronger explanatory power. $h=1,\dots ,L$ represents influencing factor classifications, $N_h$ and $N$ denote the number of units in layer $h$ and the entire study area, respectively, while ${\sigma }_h^2$ and ${\sigma }^2$ are the variance of variable $\mathrm{Y}$ within layer $h$ and the entire region.

Geographically Weighted Regression (GWR) extends the traditional Ordinary Least Squares (OLS) regression by incorporating spatial coordinates into the regression equation. This approach allows a more robust assessment of spatial heterogeneity [70]. The corresponding mathematical formulation is as:

$$y_i={\beta }_0\left(x_i,y_i\right)+{\sum }_{k=1}^p{\beta }_k\left(x_i,y_i\right)x_{ik}+{\epsilon }_i$$

(7)

where $y_i$ is the dependent variable, and $x_{ik }$ is the observed value of the independent variable. The geographic coordinates of the sample are given by $\left(x_i,y_i\right)$. The term ${\beta }_0$ is the intercept, ${\beta }_k$ represents the regression coefficients, and ${\epsilon }_i$ is the random error term. GWR incorporates spatial variability in regression coefficients, facilitating the identification of key determinants influencing the village distribution patterns and regional disparities.

3. Results

3.1. Spatial Distribution Characteristics

3.1.1. Spatial Distribution Patterns

The spatial distribution of traditional villages within the study area exhibits a significant clustering pattern. The observed mean nearest neighbor distance is 7.66 km, whereas the expected mean nearest neighbor distance is 18.99 km. The computed Nearest Neighbor Index (NNI) is 0.40, with a p-value of 0.000, indicating a statistically significant clustered distribution of traditional villages across the basin ($p<0.01$), as calculated using Formula (1). At the regional level, the upper reaches of the basin present a mean NNI of 0.39 ($p<0.01$), while the middle reaches exhibit an NNI of 0.53 ($p<0.01$), with both values confirming significant clustering trends. In contrast, the lower reaches demonstrate a mean NNI of 1.13, with a p-value exceeding 0.05, suggesting a more random distribution pattern in this region (

Table 2. Spatial distribution patterns of traditional villages across different basin regions.

Basin Region	ra/km	re/km	R	p-Value	Type
Upper Reaches	8.50	21.69	0.39	0.000	Clustered
Middle Reaches	7.31	13.82	0.53	0.000	Clustered
Lower Reaches	13.25	11.80	1.12	0.304	Random
Overall	7.66	18.99	0.40	0.000	Clustered

).

3.1.2. Kernel Density Estimation Analysis

A fixed search radius of 30 km was selected for the Kernel Density Estimation (KDE) analysis to balance sensitivity to local clustering with the visualization of regional trends, as calculated using Formula (2). This parameter was derived through empirical trials. It effectively captures both high-density and medium-density clusters of traditional villages. As a result, it can reveal the spatial heterogeneity of their distribution across the Yellow River Basin. The analysis employed the default quartic kernel function to ensure a smooth and reliable surface estimation. The findings reveal one high-density cluster and two medium-density clusters (see Figure 2). The high-density cluster is concentrated in Jincheng City, Shanxi Province, in the middle reaches of the Yellow River. This region, situated in southeastern Shanxi near the southern edge of the Taihang Mountains, exhibits a strong clustering pattern. The first medium-density cluster is found in Haidong City and Huangnan Tibetan Autonomous Prefecture in Qinghai Province, along the upper reaches of the Yellow River at the edge of the Qinghai–Tibet Plateau. Villages in this region are distributed in a belt-like pattern along the river’s main course and its tributaries. The second medium-density cluster is in Jinzhong and Lüliang, Shanxi Province, situated in the central to southwestern part of the province along the Loess Plateau and the banks of the Yellow River, displaying a relatively high density. Additionally, the traditional villages in Linfen and Yuncheng in Shanxi, Weinan and Yulin in Shaanxi, and Luoyang and Sanmenxia in Henan, exhibit lower densities and a more scattered distribution pattern. These spatial patterns suggest that conservation strategies should prioritize clustered protection in the middle reaches, river-buffered protection in the upper reaches, and resilience-enhancing measures in the lower reaches.

3.1.3. Spatial Autocorrelation Analysis

Spatial autocorrelation analysis was performed in ArcGIS 10.8 to examine the distribution patterns of traditional villages within the Yellow River Basin. A fixed distance band of 30 km was applied, based on the average nearest neighbor distance and aligned with the bandwidth applied in Kernel Density Estimation (KDE). The analysis yielded a Moran’s I index of 0.90492 with a p-value of 0.000, indicating a statistically significant and strong spatial clustering pattern, as calculated using Formula (3). This statistically significant result ($p<0.01$) indicates a strong positive spatial autocorrelation, suggesting a highly clustered distribution of traditional villages within the Yellow River Basin.

To better capture the intrinsic spatial clustering patterns of traditional villages, a hotspot analysis was conducted using the Getis–Ord Gi* statistic (Formula (4)). The analysis applied the Fixed Distance Band conceptualization method without field weighting. The results, shown in Figure 3, illustrate the local spatial autocorrelation characteristics across the study area. Among 4679 township units analyzed, 402 townships were identified as significant hotspots at the 99% and 95% confidence levels (Gi $p<0.05$), representing 8.6% of the study area and covering 63.9% of all traditional villages. In contrast, 975 townships were classified as significant cold spots at the 99% and 95% confidence levels (Gi $p<0.05$), accounting for 20.8% of the total area. The spatial distribution of these hot and cold spots closely aligns with the kernel density clustering pattern, further confirming the spatial clustering characteristics of traditional villages.

Primary hot spot clusters, with a 99% confidence level, are concentrated in the Jincheng–Jinzhong–Lüliang–Changzhi region of Shanxi Province and the Haidong–Huangnan region of Qinghai Province. Secondary hot spot clusters, at a 95% confidence level, are mainly distributed in Linfen–Yuncheng–Taiyuan (Shanxi), Yulin–Weinan–Yanan (Shaanxi), and Xining (Qinghai). In contrast, primary cold spot clusters, at a 99% confidence level, are located in Xi’an–Xianyang (Shaanxi), Dingxi (Gansu), Anyang–Xinxiang (Henan), and Heze (Shandong). Secondary cold spot clusters, at a 95% confidence level, are distributed in Kaifeng–Puyang–Zhengzhou (Henan), Baoji (Shaanxi), Shizuishan–Yinchuan (Ningxia), and Lanzhou–Qingyang–Tianshui–Pingliang (Gansu).

Overall, the results indicate that hot spot clusters are predominantly found in the southern upstream and eastern midstream regions, whereas cold spot areas are mainly concentrated in the northern upstream, southwestern midstream, and western downstream regions. This distribution pattern suggests a higher concentration of traditional villages in the central and eastern parts of the basin compared with the northern and western areas, forming a “dense in the central and eastern regions, sparse in the northern and southern regions” spatial distribution pattern. These findings underscore the need to prioritize conservation efforts in core hotspot areas, while implementing resilience-enhancing strategies in cold spot regions to mitigate loss and fragmentation.

3.1.4. Directional Distribution of Traditional Villages

This study utilized the standard deviational ellipse (SDE) method, as calculated using Formula (5), and centroid shift analysis to examine the spatiotemporal dynamics and evolutionary trends of traditional villages in the Yellow River Basin, as presented in

Table 3. Standard deviational ellipse (SDE) parameters of traditional villages in the Yellow River Basin.

Batch	Mean Longitude (°)	Mean Latitude (°)	Standard Deviation Along X-Axis (km)	Standard Deviation Along Y-Axis (km)	Eccentricity	Azimuth (°)
1	109.1376	36.5129	4.15	1.35	0.946	88.1°
2	109.6112	35.9510	3.81	1.64	0.903	88.1°
3	109.2033	36.6733	4.10	1.61	0.919	86.6°
4	109.8793	36.6048	3.89	1.40	0.933	86.9°
5	110.4838	36.0648	3.61	0.97	0.964	87.8°
6	108.0534	36.0019	4.54	1.25	0.961	87.8°

and Figure 4. The findings reveal that the initial centroid was located in the northern section of the river’s middle reaches, gradually expanding southward before contracting towards the central-western region. Overall, the centroid’s movement trajectory follows a “north–south–east–south–west” path.

As shown in Table 3, the orientation of the ellipses across different batches predominantly aligns in an east–west direction, reflecting a strong association with the longitudinal flow of the Yellow River. As the number of batches increases, the length of the ellipses’ major axis extends, indicating an expansion in the distribution range of the villages. Conversely, the minor axis initially lengthens and then shortens, implying an increasing degree of spatial clustering. These patterns reflect a directional yet constrained spatial evolution, shaped by the east–west flow of the Yellow River and regional development dynamics. The alignment of traditional villages along river corridors underscores the river’s guiding role in historical settlement. Meanwhile, the narrowing of the minor axis suggests increasing spatial consolidation. These findings highlight the need to strengthen conservation efforts along historical settlement belts that follow the course of the Yellow River.

3.2. Factors Influencing Spatial Distribution

3.2.1. Variable Selection

In 2012, China introduced the “Traditional Village Evaluation and Certification Index System (Trial)” to provide a scientific basis for the preservation and development of traditional villages. This system encompassed three core criteria: traditional village architecture, village location and layout, and the intangible cultural heritage inherent in the village. In 2018, the evaluation framework was revised, refining the assessment criteria into five key dimensions: historical accumulation, village environment, spatial morphology, traditional architecture, and folk culture. These modifications provide a more comprehensive representation of the diversity and complexity inherent in traditional village conservation.

Building upon prior research [71,72,73,74,75,76,77,78,79], the key factors influencing the designation of traditional villages can be broadly grouped into two dimensions: natural environment and socioeconomic conditions. Natural factors, such as elevation, slope aspect, precipitation, temperature, river proximity, river network density, normalized difference vegetation index (NDVI), net primary productivity (NPP), and farmland productivity reflect environmental suitability, agricultural potential, and ecological support capacity for traditional settlements. These elements collectively form the physical foundation and spatial clustering of traditional villages. Socioeconomic factors, including GDP, population density, road network density, distance to county centers, and density of cultural heritage sites, capture regional development levels, infrastructure accessibility, and cultural policy attention. Together, these variables represent both the environmental constraints and the socio-political drivers that shape the spatial distribution, conservation, and development potential of traditional villages.

Based on these factors, this study selected 17 indicators across 8 dimensions: topography, climate, ecology, hydrology, economy, population, transportation, and culture (

Table 4. Indicators and calculation methods of influencing factors.

Variable	Dimension	Indicator	Calculation Method	Unit
Independent variable		traditional village density	Number of traditional villages in each prefecture-level city within the basin	count
Dependent variables
Natural environmental factors	topography	elevation (x1)	Average elevation of each prefecture-level city within the basin	m
		topographic relief (x2)	Elevation height difference between the highest and lowest points of each prefecture-level city within the region	m
		slope (x3)	Average slope of each prefecture-level city’s terrain, representing the inclination of the surface	°
		aspect (x4)	Average aspect of each prefecture-level city, expressed as the angle between the direction of surface inclination and the true north	°
	climate	average annual temperature (x5)	Average annual temperature for each prefecture-level city, interpolated from spatial climatic grid data	℃
		average annual precipitation (x6)	Average annual precipitation for each prefecture-level city, interpolated from spatial climatic grid data	mm
	ecology	normalized difference vegetation index (NDVI) (x7)	Normalized difference vegetation index, using the maximum value composition method	-
		net primary productivity (NPP) (x8)	Net primary productivity, carbon storage per unit area	kg·C/m2
		ecosystem service value (x9)	Total value of four ecosystem services: provisioning, regulating, supporting, and cultural services	10,000 yuan/km2
		potential crop yield (x10)	Estimated cropland production potential based on the GAEZ model, considering five major crops: wheat, corn, rice, soybean, and sugarcane	kg/ha
	hydrology	river network density (x11)	Ratio of river length to regional area	km/km2
		distance to river (x12)	Average distance from traditional villages to the nearest river	km
Social and economic factors	economy	GDP (x13)	GDP per unit area, derived from land use types, nighttime light intensity, and residential density, spatialized into a 1 km spatial grid	10,000 yuan/km2
	population	population density (x14)	Population density per unit area, derived from land use types, nighttime light intensity, and residential density, spatialized into a 1 km grid	people/km2
	transportation	distance to county-level center (x15)	Average distance from traditional villages to the county-level administrative center	km
		road density (x16)	Ratio of road length to regional area	km/km2
	culture	cultural heritage protection unit density (x17)	Number of national and provincial cultural heritage protection units per prefecture-level city	count

). The dependent variable was defined as the number of nationally designated traditional villages within each prefecture, serving as a proxy for spatial concentration. To address potential multicollinearity among the variables, variables with a variance inflation factor (VIF) greater than 10, specifically for slope and topographic relief, were excluded. This ensured the independence of variables and enhanced the reliability of the model estimation results. This study analyzed the spatial heterogeneity of traditional villages within the Yellow River Basin at the prefectural level. To achieve this, the geographical detector and Geographically Weighted Regression (GWR) model were employed. This method helped uncover the core challenges, regional disparities, and spatial characteristics influencing the conservation and sustainable development of traditional villages.

3.2.2. Results of the Analysis of Influencing Factors

As discussed in Section 3.1.3, traditional villages in the Yellow River Basin exhibit a positive spatial autocorrelation, justifying the use of Geographically Weighted Regression (GWR) to further examine influencing factors. To conduct GWR analysis, the geographical detector method was first used to identify the key factors influencing the spatial distribution of traditional villages. Using the natural breakpoint method, the independent variables were grouped into four to five categories. GeoDetector was then used to explore factor interactions and calculate the q-values for each variable, as defined in Formula (6), evaluating each factor’s explanatory power and its contribution to spatial differentiation across the basin (

Table 5. GeoDetector results for factors influencing the spatial distribution of traditional villages in the Yellow River Basin.

Variable	Detection Factor	q	p-Value
x1	elevation	0.149	0.086 *
x2	topographic relief	0.133	0.050 *
x3	slope	0.148	0.016 **
x4	aspect	0.108	0.183
x5	average annual temperature	0.072	0.398
x6	average annual precipitation	0.102	0.200
x7	normalized difference vegetation index (NDVI)	0.110	0.315
x8	net primary productivity (NPP)	0.190	0.006 ***
x9	ecosystem service value	0.047	0.518
x10	potential crop yield	0.235	0.003 ***
x11	river network density	0.179	0.005 ***
x12	distance to river	0.572	0.000 ***
x13	GDP	0.148	0.093 *
x14	population density	0.042	0.561
x15	distance to county-level center	0.682	0.000 ***
x16	road density	0.141	0.037 **
x17	cultural heritage protection unit density	0.106	0.027 **

Note: * indicates significance at the 0.1 level ($p<0.1$), ** at the 0.05 level ($p<0.05$), and *** at the 0.01 level ($p<0.01$).

).

Fourteen variables with a q-value greater than 0.1 or statistical significance of the p-value were selected using the GeoDetector method, including distance to county-level center, distance to river, potential crop yield, net primary productivity (NPP), river network density, slope, elevation, GDP, road density, topographic relief, normalized difference vegetation index (NDVI), aspect, cultural heritage protection units density, and average annual precipitation.

The Geographically Weighted Regression (GWR) method, as specified in Formula (7), was used to further analyze the spatial heterogeneity and influence of these 12 selected variables on the spatial distribution of traditional villages. In the GWR analysis, the Gaussian kernel method was used, and an adaptive bandwidth was chosen as the kernel type. The optimal bandwidth was determined using the Akaike Information Criterion (AICc) to improve the model fit and robustness. The results indicate a good overall fit of the model, with an R² value of 0.486 and an AICc value of 199.72, indicating strong explanatory power. Furthermore, GWR coefficients were calculated for different regions, and the mean values for the upper, middle, and lower reaches of the Yellow River Basin were summarized (

Table 6. Average regression coefficients of influencing factors across different sections of the Yellow River Basin.

Variable	Upper Reaches	Middle Reaches	Lower Reaches
elevation	0.348	0.394	0.173
aspect	0.212	−0.001	0.038
average annual precipitation	0.371	0.733	0.546
normalized difference vegetation index (NDVI)	0.111	−0.031	−0.082
net primary productivity (NPP)	−0.347	−0.110	0.180
potential crop yield	−0.558	−0.615	−0.410
river network density	0.195	−0.387	−0.661
distance to river	−0.087	−0.027	−0.010
GDP	−0.041	0.275	0.546
distance to county-level center	−0.128	−0.407	−0.499
road density	0.681	0.732	0.592
cultural heritage protection unit density	0.187	0.102	0.068

). The findings reveal significant regional variation in factor sensitivity, confirming the presence of clear spatial heterogeneity across the different sections of the river basin.

Overall, natural environmental factors have a greater impact in the upper reaches of the Yellow River Basin. In contrast, economic and transportation factors play a more dominant role in the middle and lower reaches, reflecting the distinct regional characteristics of village development. Elevation and average annual precipitation exhibit the strongest impact on village distribution in the middle reaches, while NDVI and NPP show a negative correlation in the lower reaches, indicating that the ecological environment plays different roles in traditional village distribution across different regions. Potential crop yield has the greatest impact in the middle reaches (−0.615), whereas river network density demonstrates the strongest inhibitory effect in the lower reaches (−0.661). GDP’s impact is most pronounced in the lower reaches (0.546), suggesting that economic development is a key driver of traditional village distribution in this region. Additionally, road density consistently exhibits a positive effect across all regions, with the strongest impact observed in the middle reaches (0.732). Meanwhile, the distance to county-level centers has a stronger negative effect in the lower reaches (−0.499), indicating that villages tend to cluster closer to urban areas. Furthermore, the cultural heritage protection unit density has the highest positive impact on traditional village distribution in the upper reaches (0.187), but this influence gradually weakens in the middle and lower reaches.

3.3. Spatial Heterogeneity Analysis

3.3.1. Topography

The geographical environment plays a crucial role in shaping the formation and spatial distribution of agricultural civilization settlements. Factors such as elevation (DEM), soil type, land use, and precipitation collectively influence the spatial distribution of traditional villages (Figure 5a–d). Elevation variations affect hydrothermal conditions, soil properties, and transportation accessibility, all of which influence the village location choices. With reference to the global five-type landform classification and the regional topographical features of the Yellow River Basin, elevation was categorized into five levels. The analysis in Figure 5a reveals the following distribution of traditional villages across different elevation zones: low-altitude areas (<500 m) contain 87 traditional villages, plains and low mountain areas (500–1500 m) host the largest number with a total of 574 villages, hilly and mid-mountain areas (1500–2500 m) contain 129 villages, mountainous areas (2500–3500 m) have 94 villages, and plateau regions (>3500 m) have only 4 villages. These results suggest that regions with greater topographical diversity tend to have a higher density of traditional villages within the basin.

The GWR analysis results indicate that elevation generally has a positive influence on the distribution of traditional villages (Table 6). However, in certain localized areas, such as Inner Mongolia, Shuozhou, and Xinzhou in Shanxi Province, elevation negatively impacts the distribution of traditional villages. The negative impact observed in Inner Mongolia, Shuozhou, and Xinzhou can be attributed to the high-altitude conditions in northern China, which are characterized by colder climates, lower precipitation, and poor soil conditions. These factors restrict agricultural productivity and hinder population settlement, leading to a lower number of traditional villages. In contrast, traditional villages are more densely distributed in mid-mountain areas and transition zones between mountain and river valley plains, such as the Guanzhong Plain in Shaanxi and the Jin-Nan hilly regions of Shanxi. These areas feature diverse topography, moderate elevation, rich ecological environments, and complex climatic conditions, all of which support various agricultural practices and contribute to a higher density of traditional villages. Furthermore, mid-to-high altitude regions often provide natural isolation from external influences, enhancing their defensive characteristics and reducing the impact of modern development. This isolation has helped preserve traditional villages more effectively [80].

Aspect exerts a stronger influence on traditional village distribution in the upper reaches of the Yellow River Basin, but this effect weakens in the middle and lower reaches. (Table 6). In the upper reaches, village locations are largely shaped by solar radiation and climatic adaptability. In contrast, soil type and land use patterns become the primary determining factors in the middle and lower reaches. As illustrated in soil type and land use maps (Figure 5b,c), the upper reaches are predominantly covered by alpine soil, saline–alkali soils, and arid soils. These soil types have low agricultural productivity and limited fertility. As a result, grasslands are the dominant land use type, and traditional villages are primarily concentrated along the riverbanks, relying on natural pastures and small-scale agriculture. In the middle reaches, leached and semi-leached soils, which offer higher agricultural productivity, are more prevalent. These soil types support the cultivation of staple grain and cash crops. Land use in this region is mainly composed of farmland and forest, creating concentrated agricultural zones where the availability of arable land directly drives village clustering. The lower reaches feature a more diverse range of soil, including alluvial, leached, and hydromorphic soils, all of which are highly fertile with favorable irrigation conditions, making agriculture the dominant land use. However, prolonged river deposition has resulted in low-lying terrain with a higher risk of flooding. This has led to a decline or relocation of traditional villages and consequently resulted in lower village density.

3.3.2. Climate

This study found that precipitation plays a significant role in the habitability of traditional villages, while temperature has a minimal effect. This suggests that regional temperature variations do not play a decisive role in village distribution. Instead, precipitation is crucial for agricultural production and water resource availability. Overall, precipitation in the Yellow River Basin positively affects the spatial distribution of traditional villages, with the strongest impact in the middle reaches, particularly in Xi’an, Tongchuan, Xianyang, and Weinan in Shaanxi Province.

As shown in Figure 5d, traditional villages are distributed across different precipitation zones as follows: low-precipitation areas (<300 mm) contain 37 villages, moderate-precipitation areas (300–600 mm) host 664 villages, and high-precipitation zones (600–1000 mm) host 186 villages. Since traditional village formation is closely tied to agriculture, water availability is a key factor shaping their distribution. In low-precipitation areas, scarce water resources limit agricultural activities and daily life, leading to a sparse village distribution. Moderate-precipitation regions, mainly in Shaanxi, Shanxi, and Henan in the middle reaches of the Yellow River, provide stable agricultural conditions, making them the core areas for traditional villages. Although high-precipitation zones benefit from abundant rainfall, the associated risks of flooding and wetland formation restrict the clustering and expansion of villages.

3.3.3. Ecology

Figure 6a–d illustrate how ecological conditions, agricultural productivity, and natural resources collectively shape the spatial distribution of traditional villages. The GWR analysis reveals that NDVI exerts a weak positive influence in the upper reaches, but negatively impacts village distribution further downstream. This suggests that areas with dense vegetation, often located in mountainous or conservation zones, are less suitable for large-scale village settlements due to development restrictions. Similarly, regions with dense vegetation are often designated as ecological protection zones or forested areas, further limiting village distribution. NPP negatively affects village distribution in the upper and middle reaches but has a positive impact in the lower reaches. This indicates that in the upper and middle reaches, high NPP areas are linked to ecological conservation areas or dense natural vegetation, which are less suitable for agricultural development. In contrast, in the lower reaches, high NPP corresponds to areas of high agricultural productivity, which supports village formation.

Potential crop yield negatively influences village distribution across all regions, with the strongest effect observed in the middle reaches. This is likely due to high productive agricultural zones often being targeted for urbanization and modern agricultural expansion. This promotes large-scale, intensive farming or non-agricultural uses over traditional village settlements. Ecosystem service value does not significantly increase the distribution of traditional villages, indicating that settlement formation depends not only on ecological suitability but also on livelihood conditions. Areas with a high ecosystem service value often face land use restrictions, limiting their potential for agriculture, economic development, and infrastructure expansion, resulting in a lower village density. This highlights the inherent tradeoff between ecological conservation and human settlement.

3.3.4. Hydrology

Water is the foundation of human civilization, serving both as a crucial resource and a natural transportation corridor. Rivers and tributaries play a key role in shaping the spatial distribution of traditional villages by facilitating trade, resource circulation, and cultural exchange. As shown in Figure 7, traditional villages are primarily located on elevated terrain along the riverbanks. In the upper reaches, high altitudes, low precipitation, and high evaporation rates limit large-scale village clustering. In the middle reaches, a well-developed river network, particularly in the Loess Plateau and major river valleys, supports the highest village density. In contrast, in the lower reaches, frequent flooding and soil salinization restrict agricultural development and limit village expansion.

The GWR analysis further reveals that the regression coefficient of river network density varies significantly across the basin—positive upstream, but negative in the central and downstream areas. In the upper reaches, areas with dense river networks have abundant water resources, which support agricultural irrigation, economic activities, and transportation, factors that foster village development. In contrast, in the middle and lower reaches, high river network density often corresponds to wetlands or river confluence zones that are prone to flooding, which can discourage settlement. For instance, in the river valleys and plains of Shanxi and Shandong, the historical risk of flooding has led villages to avoid areas with dense river networks. The influence of distance from rivers on the distribution of traditional villages is generally negative, meaning that villages tend to be located closer to water sources. This relationship is especially pronounced in the upper reaches, where villages show a stronger dependence on rivers. In the middle and lower reaches, however, the importance of proximity to rivers declines, and other factors take on a more significant role in shaping village distribution.

3.3.5. Economy

The GWR analysis results indicate that in the upper reaches, GDP per square kilometer is negatively correlated with traditional village distribution. This suggests that economic activities in these regions rely more on resource-based industries than traditional agriculture, limiting their role in village preservation. In contrast, GDP positively influences village distribution in the middle and lower reaches, where economic growth supports rural revitalization and cultural heritage conservation. In wealthier regions, governments allocate more resources to protect traditional villages, and rural tourism has become a key driver, integrating these villages into the modern economy rather than allowing them to be displaced by urbanization.

Based on the GDP levels, the Yellow River Basin can be categorized into five economic zones: low GDP, below-average GDP, average GDP, above-average GDP, and high GDP. As illustrated in Figure 8a, the proportion of traditional villages in these zones is 28.4%, 34.5%, 29.7%, 7%, and 0.5%, respectively. This distribution shows that traditional villages are scarce in above-average GDP and high-GDP areas, where economic development prioritizes industrial and commercial growth over agriculture. As the economic dependence on agriculture declines, rural populations increasingly migrate to urban centers, leading to population loss and reduced rural vitality. Overall, in less developed economic regions, the stable human–land relationship reduces disturbances to traditional villages. For instance, in the low GDP areas of the upper and middle reaches, such as the Qinghai–Tibet Plateau and the Loess Plateau, the complex terrain has limited large-scale modernization, allowing traditional villages to maintain their historical form and structure.

3.3.6. Population

The GWR analysis results show that population density has a limited explanatory power in predicting traditional village distribution. This may be attributed to increased population mobility and urbanization, which have reshaped spatial population patterns. Further analysis based on Figure 8b categorizes the distribution of traditional villages into five population density zones: sparsely populated areas (9%), low-density areas (20.2%), moderate-density areas (41.7%), high-density areas (28.7%), and very high-density areas (0.45%).

Traditional villages are primarily concentrated in moderate-density regions (41.7%). They are least prevalent in both very high-density areas (0.45%) and sparsely populated areas (9%). In high-density areas, economic development and urban expansion alter village structures, leading to their integration into urban systems and the potential loss of identity. Conversely, in sparsely populated regions, the lack of economic and social activities necessary to sustain traditional villages results in their decline. As shown in Figure 8c, the upper reaches of the Yellow River have a sparse distribution of traditional villages due to the complex terrain, restricted accessibility, and stronger environmental conservation. The middle reaches, with favorable agricultural conditions, host the highest concentration of villages. In the lower reaches, despite the high population density, rapid urbanization and land use changes weaken the direct influence of population factors on village sustainability.

3.3.7. Transportation

Transportation infrastructure significantly influences the socio-spatial formation and sustainable protection of traditional villages. The distance to county-level administrative centers negatively correlates with village distribution, indicating that villages become sparser as the distance increases. This effect is most pronounced within the lower basin of the Yellow River (−0.499), where urbanization and infrastructure development intensify the spatial impact of county centers, accelerating village transformation. In contrast, road density positively influences village distribution, highlighting the importance of transportation accessibility in sustaining rural settlements. A well-developed road network facilitates economic integration, enhances market connectivity, and supports agricultural industrialization. As shown in Figure 8d, 16.9% of traditional villages are located within 0–10 km of major transportation routes. These findings suggest that villages with better transportation access benefit from improved resource accessibility, trade opportunities, and economic resilience, enhancing their long-term sustainability.

3.3.8. Culture

National and provincial cultural heritage protection units reflect a region’s deep historical legacy, showcasing its unique ethnic culture and traditional lifestyles. This study finds that while the cultural heritage protection unit density positively impacts the distribution of traditional villages, its overall influence remains relatively weak compared with natural and economic factors. This suggests that historical and cultural richness alone is not sufficient to sustain a village’s presence, especially in regions experiencing rapid urbanization. Many heritage protection units are concentrated in urban centers or scenic zones, where policy interventions often fail to extend their impact to the surrounding rural areas. Additionally, a functional disconnect between the static protection of cultural relics and the dynamic preservation of living villages may weaken cultural influence in practice. Spatially, the strongest positive influence is observed in the upper reaches of the Yellow River, particularly in Qinghai, Gansu, and Ningxia. These areas, rich in historical heritage such as the Silk Road culture and ancient city ruins, benefit from the clustering effect of cultural heritage protection units. This concentration attracts greater resources and policy support, effectively promoting the conservation and development of traditional villages. In contrast, the middle and lower reaches experience weaker cultural heritage protection effects, as economic development and urbanization have a greater impact. Moreover, cultural heritage policies in these regions often prioritize urban monuments over rural landscapes, resulting in limited support for village-level protection. These findings suggest a need to integrate cultural protection with rural revitalization by adopting mechanisms such as living heritage corridors, village-based cultural mapping, and community-led heritage tourism. Such approaches are especially important in regions where cultural assets exist but remain underutilized in supporting the conservation and sustainable development of traditional villages.

4. Discussion

The complexity of traditional village conservation and development in the Yellow River Basin in China arises from the dynamic evolution of multiple influencing factors and pronounced spatial heterogeneity. By adopting a basin-wide and nature–society dual-coupling perspective, this study proposes differentiated development strategies based on the spatial distribution patterns and driving mechanisms identified through our empirical analysis.

First, the spatial clustering characteristics stress the strategic importance of the middle reaches, emphasizing the need for zoned and classified conservation. Empirical results show a clear overall clustering trend of traditional villages in the Yellow River Basin, with the middle reaches forming the core aggregation zone (Figure 2 and Figure 3), while the lower reaches display a more random and low-density distribution pattern (Table 2). The standard deviational ellipse analysis further suggests a north–south expansion in the spatial orientation of villages across designation batches, suggesting a phased shift in the conservation focus over time (Figure 4). Based on these findings, spatial planning should transition from a monocentric model to an integrated strategy of “core-led, edge-reinforced, and basin-linked” development. Priority should be given to cluster-based conservation and the establishment of linear cultural corridors in the middle reaches, while strengthening ecological buffer zones in the upper reaches and expanding conservation efforts for dispersed settlements in the lower reaches.

Second, significant regional differences in driving mechanisms call for a differentiated, region-specific response system. Empirical analysis shows that the distribution of traditional villages is influenced by a combination of natural and social factors, resulting in a structural pattern: ecological dependence in the upper reaches, economic embedding in the middle reaches, and cultural weakening in the lower reaches (Figure 5 and Figure 6; Table 6). Based on this, the study proposes a three-dimensional response strategy focusing on ecological adaptability, industrial support capacity, and cultural continuity: (1) In the upper reaches, attention should be directed toward improving the ecological foundation and environmental carrying capacity. Due to the high altitude, low temperatures, and limited water resources, villages in this region rely heavily on ecological stability. It is crucial to establish ecological redlines to prevent peripheral development from encroaching on the cultural spaces and to implement a dual-protection strategy that safeguards both ecology and culture. (2) In the middle reaches, the focus should be on enhancing the industrial transformation capacity of cultural resources. Areas rich in intangible cultural heritage should promote cultural and creative economies based on handicrafts, folk festivals, and traditional opera. The creation of culturally themed village clusters can support embedded protection by integrating industrial development with cultural preservation. (3) In the lower reaches, where the ecological pressure is high and the cultural cohesion is weak, the integration of rural tourism and modern agriculture should be prioritized to support resource-integrated development. This approach will help create a compound village system that merges agriculture, culture, and ecology.

Third, the cultural embedding mechanism varies significantly by region, requiring tailored institutional incentives. GWR analysis shows that cultural resources have the greatest positive influence on village distribution in the upper reaches, highlighting the importance of institutional support in peripheral areas. To strengthen cultural resilience, national-level cultural heritage zones should be established in these regions, focusing on digital archiving and the promotion of ethnic minority cultures. In contrast, in the middle and lower reaches, the strategy should center on the market integration of cultural resources. Tools such as digital exhibitions and festival-based economies can drive the transformation of cultural assets into marketable experiences, unlocking their revitalization potential and enhancing sustainable village development.

Fourth, the governance system must respond to both spatial sensitivity and digital capability. Empirical results show a significant negative correlation between the distance to county-level centers and the distribution of traditional villages (Table 6), indicating that these villages are highly vulnerable to the pressure of urban expansion and resource absorption. In high-density clusters such as Haidong, Huangnan, Jincheng, and Yulin, a comprehensive governance framework should be developed. This framework should be anchored in a digital platform and multi-stakeholder participation model. By leveraging digital technologies, such as remote sensing, blockchain-based property rights systems, and GIS tools, governance efforts can achieve precise cultural heritage protection and sustainable resource utilization.

Unlike previous research that focused on county or provincial level analyses, this study adopts a watershed-scale perspective, covering all 888 nationally designated traditional villages within the Yellow River Basin. By integrating the GeoDetector model to identify key influencing factors and the Geographically Weighted Regression (GWR) model to explore spatial variation, the study overcomes the spatial homogeneity limitations of traditional regression models. This approach enhances both the explanatory power and the model fit. Furthermore, in designing protection strategies, this study emphasizes a transition from macro-level distribution patterns to regionally customized and classified response strategies. It aligns spatial structure with regional development capacity, forming a closed-loop system in which theoretical insights, driving mechanisms, and policy recommendations reinforce each other. This framework offers strong policy relevance and replication potential for traditional village protection and revitalization.

5. Conclusions

This study makes several key contributions to the understanding and conservation of traditional villages in the Yellow River Basin. By adopting a basin-wide perspective, it overcomes the limitations of previous research focused solely on administrative boundaries, providing a more integrated and systematic framework for conservation and development strategies. The spatial analysis at the prefectural level across the upper, middle, and lower reaches ensures a more holistic approach, mitigating the fragmentation caused by provincial-level studies. Additionally, the application of the Geographically Weighted Regression (GWR) model captures spatial heterogeneity, revealing regional variations in the influencing factors and enabling more targeted conservation efforts.

The main findings of the study are as follows:

• The traditional villages in the Yellow River Basin in China exhibit a clustered spatial distribution at the watershed scale, with significant spatial heterogeneity. While the upper and middle reaches show notable clustering, the lower reaches exhibit a more random, dispersed pattern. Kernel Density Estimation and hotspot analysis identify two major core clusters: Jincheng–Jinzhong–Lvliang in Shanxi and Haidong–Huangnan in Qinghai. The hotspot areas contain 63.9% of all traditional villages, forming a hierarchical spatial pattern, with dispersed villages in the upper reaches, dense clusters in the middle, and a lower density downstream.
• The spatial distribution of villages has evolved dynamically in response to the environmental conditions of the river basin. Standard deviation ellipse and centroid migration analyses show a dynamic shift in the distribution centers of the six batches of designated villages, following a “north–south–north–east–south–west” trajectory. The azimuth angle of the traditional village locations remains stable between 86.6° and 88.1°, mirroring the west–east orientation of the Yellow River.
• The analysis of the driving factors indicates that the proximity to county centers, river distance, and agricultural production potential are key determinants of village distribution, reflecting a strong reliance on ecological resources and spatial accessibility. The GWR analysis confirms that both natural and socio-economic factors jointly influence the spatial patterns of traditional villages. In the upper reaches, villages depend more on natural environmental conditions, with elevation and precipitation being particularly influential. In the middle and lower reaches, economic development and transport accessibility dominate village evolution. Factors like road density become more prominent in the middle reaches. Additionally, cultural heritage protection units have a stronger influence in the remote upper reaches, underscoring the role of institutional cultural support in preserving villages’ integrity.

5.1. Managerial Insights

Based on the findings of this study, we propose the following recommendations for policymakers and stakeholders to support the conservation and sustainable development of traditional villages in the Yellow River Basin:

• Develop a multi-center, multi-type protection system to support regionally differentiated governance. Traditional villages in the Yellow River Basin show distinct spatial and ecological characteristics. These differences require tailored protection strategies. In the upper reaches, the villages are mainly located in plateau and river valley areas. These areas face strong ecological constraints and rely heavily on cultural heritage. Efforts here should focus on ecological redline management. Emphasis should be placed on revitalizing cultural heritage in harmony with natural ecosystems to foster cultural and ecological coexistence. In the middle reaches, characterized by dense village clusters and rich cultural assets, strategies should prioritize large-scale, contiguous protection in the core areas. Efforts should integrate intangible cultural heritage resources, support the development of and cultivate distinctive industries, and build a synergistic culture–ecology–economy system. In the lower reaches, villages are more dispersed and subject to both flood risks and urban expansion. Protective strategies should focus on building ecological buffer zones and implementing rational land use planning to contain development boundaries and reduce protection pressure.
• Promote the development of “linear cultural corridors” to enhance spatial connectivity and cultural identity. The spatial distribution of traditional villages closely follows the Yellow River and its major tributaries, demonstrating a natural alignment for integrated cultural and ecological conservation. It is recommended to establish linear cultural corridors along the mainstream of the river and key tributaries, including the Datong River, Tao River, and Geshi River in the upper reaches, and the Fen River, Qin River, Daxiangzang River, and Changyuan River in the middle reaches. These corridors would enhance spatial continuity, reinforce regional cultural identity, and serve as axes for village cluster linkage, facilitating holistic protection and development.
• Establish a cross-regional watershed governance mechanism to enable systematic and coordinated protection. The Yellow River Basin spans multiple provinces and administrative regions, requiring coordinated governance beyond conventional administrative boundaries. A watershed-based governance framework should be adopted to promote cross-jurisdictional collaboration and policy coordination at the sub-basin level. This shift from an “administrative boundary” mindset to an “ecological logic” framework enables differentiated management, that aligns more closely with the natural landscape and supports integrated, multi-scalar conservation.
• Strengthen transportation and policy support to improve village resilience and development capacity. Findings from the GWR analysis indicate that road density and distance to county centers are key factors of traditional village distribution. Targeted infrastructure investment in the underdeveloped central and western regions is essential to improve and enhance external connectivity. At the same time, a robust cultural heritage protection policy framework should be established, supported by dedicated funding mechanisms to attract social capital and encourage sustainable rural revitalization and economic empowerment.

5.2. Future Research

Building on this study, several areas warrant further investigation to refine conservation strategies and enhance the sustainable development of traditional villages in the Yellow River Basin:

• The current analysis is conducted at the prefecture level, which restricts the ability to capture structural evolution within villages and finer scale spatial heterogeneity. Future research could employ clustering analysis to develop hierarchical conservation frameworks tailored to different village types, ensuring localized and context-sensitive protection measures.
• Socio-psychological and institutional factors such as cultural identity, resident participation, and policy responsiveness were not effectively quantified in this study. Future studies could employ methods such as questionnaire surveys, semi-structured interviews, and text mining to examine villagers’ cultural attitudes, behavioral intentions, and policy acceptance. These approaches would support the development of an integrated “behavior–institution–space” analytical framework, offering a more comprehensive foundation for preserving living heritage and informing policy design.
• This study relies on static data and does not capture the temporal dynamics or future trajectories of traditional village evolution. Future research could incorporate multi-period remote sensing imagery and panel data, applying time series analysis and multi-scale modeling to track dynamic changes and enable more targeted intervention. This would provide stronger support for building resilient and adaptive rural systems.