A Study on the Spatial, Structural, and Cultural Differentiation of Traditional Villages in Western Henan Using Geographic Detectors and ArcGIS

Abstract

Traditional villages are an important cultural heritage left by China’s agrarian civilization and serve as a testament to the historical development of the Chinese nation. The study of spatial and cultural differentiation in traditional villages is significant for their future preservation and development. Existing studies predominantly adopt a macro perspective, focusing on large-scale regions, and lack investigations from a micro perspective in medium- and small-scale areas. This study utilizes ArcGIS 10.8 for spatial analysis, multi-factor geographic detectors, and cultural geography spatial zoning methods to explore the spatial structure and cultural differentiation of 305 traditional villages in western Henan. The results indicate that the distribution of traditional villages in this region is significantly clustered and uneven, primarily concentrated in specific districts of Sanmenxia and Luoyang. Per capita GDP and the distance to roads are negatively correlated with the distribution of traditional settlements, reflecting the positive impact of lower economic levels and remote locations on village preservation. The spatial layout of traditional villages in western Henan exhibits clustering patterns, with cultural zoning characterized by distinct residential forms. This study, through the analysis of the spatial structure characteristics and influencing factors of traditional villages in the western Henan region, provides a new perspective on the formation and evolution of traditional villages, revealing the cultural differentiation characteristics of western Henan. The research results offer directional guidance for the conservation strategies of traditional villages in western Henan and provide a decision-making reference for cultural heritage conservation practices in similar regions.

1. Introduction

1.1. Research Background

Traditional villages refer to settlements that were established early, possess rich traditional resources, and have significant historical, cultural, scientific, artistic, social, and economic value, warranting protection [1]. They serve as crucial carriers of China’s material and intangible cultural heritage and are praised as “living fossils” of agrarian civilization. Their preservation and development are crucial for the inheritance of rural culture, the maintenance of the national spirit, and the sustainable growth of rural tourism and the economy. Since the economic boom and rapid urbanization post-reform, the survival of traditional villages is gradually shrinking or even disappearing. Studies indicate that the total number of natural villages in China decreased from 3.63 million in 2000 to 2.71 million in 2010. The number of traditional villages dropped from 5000 in 2000 to under 3000 by 2013 [2]. The preservation of traditional villages is urgent. Since the establishment of the People’s Republic of China, traditional villages have faced numerous pressures, making their protection urgent. Since the implementation of the Cultural Relics Protection Law of the People’s Republic of China in 1982, the government has introduced various laws and policies aimed at preserving the spatial layout, historical features, and cultural environments of these villages. As of 17 December 2012, a joint investigation by several ministries identified 8155 national-level traditional villages for protection, alongside 1032 provincial-level traditional villages announced in Henan Province.

1.2. Research Questions

Due to China’s vast territory and diverse topography, the formation of traditional villages in different regions has been influenced by various factors. Research has revealed distinct distribution characteristics and the varying significance of influencing factors, which are primarily based on regional, geographical, and cultural differences. Since 2012, protective research on traditional villages has gradually been carried out across the country. Liu Dajun et al. [3] found that the spatial distribution of traditional villages in China exhibits significant regional characteristics, with four prominent clusters located in northwest Yunnan, southeast Guizhou, the Central Plains, and southern Anhui–western Zhejiang. In terms of spatial distribution, traditional villages in Southwest China [4,5], South China [6,7], and Central China [8,9] are primarily clustered in distribution, but the density of this distribution is uneven. In contrast, the spatial distribution of traditional villages in North China [10,11] and Northwest China [12] is relatively concentrated, with most villages exhibiting a compact group layout. In addition to the different research findings across the aforementioned regions, many studies have indicated that the number of traditional villages in China shows a distinct pattern, with significantly more villages in the southern regions than in the northern regions and a noticeable variation in village numbers among provinces [13]. These studies confirm that the distribution pattern of traditional villages in China is the result of both natural geographical factors—such as topography, climate, river systems, and agricultural development conditions—and human geographical factors, including economy, population, transportation, and urbanization [14]. The influence of these factors varies during the formation process of traditional villages [15]. Kastenholz et al. [16], using Portuguese traditional villages as an example, pointed out that social, emotional, and symbolic experiences are important factors influencing traditional villages. Sun et al. [17] studied Hakka traditional villages in northeastern Guangdong and found that terrain, water systems, and socio-economic conditions are the key factors influencing the spatial distribution of these villages. Other studies have indicated that villages along the Yellow River in Henan Province exhibit a clustering pattern from southeast to northwest. The spatiotemporal evolution of relationships between villages, as well as their proximity to water sources, are closely related to climatic fluctuations, river course changes, hydraulic projects, and socio-cultural factors [18]. Studies in Northeast China indicate that rising temperatures and changes in precipitation patterns significantly affect the irrigation demands of farmland, increasing the pressure on water resources in villages, which in turn impacts the relationships between them [19]. Research by Ran et al. [20] demonstrated that hydraulic projects and river channel adjustments have altered the water quality and flow of rivers, affecting the distribution of water resources in river-adjacent areas. This, in turn, influences the availability of water sources for local villages and their socio-economic activities. The example of Kancun illustrates that the spatial evolution of traditional villages is a concrete manifestation of their cultural development [21]. This process of cultural transmission not only enhances internal cohesion within the village but may also strengthen connections with other villages through cultural activities and festivals. The interaction of various socio-cultural factors is key to understanding and preserving traditional villages.

Research on the classification of traditional settlements and dwellings, as well as regional differences in China, has now shifted towards regional division and genealogical studies. The research results focus on typical areas, specifically the typical cultural phenomena in cultural core zones. The cultural characteristics within traditional settlements are clear and distinct. The macro-level division of regions has matured, completing the construction of residential lineages in typical areas and the demarcation of cultural settlement zones [22]. The distinction of traditional village regions is primarily based on three key elements: ethnicity, culture, and geographical space, along with the interrelationships among them. From a cultural perspective, the representative cultural characteristics for differentiating traditional village regions can be categorized into four main aspects: 1. Terms of address; 2. Language; 3. Historical memory; and 4. Religious beliefs. These combined factors contribute to the regional distinctions of traditional villages [23]. Wang Wenqing et al. [24] argue that the cultural division of traditional settlements should be combined with certain natural geographical divisions, summarizing three principles: 1. Comprehensive principles; 2. Ontogenetic principles; and 3. Principles consistent with the utilization of residential culture and the development of the geographic environment. Yang et al. [25] studied the spatial characteristics of cultural landscapes to illustrate the spatial patterns of rural cultural special zones and “culture”, identifying the spatial characteristics of rural cultural landscapes and rural tourism. Shi Shuo [26] posits that cultural zoning aims to examine cultural interactions and their main features from a macro perspective. It should be noted that the distinctions between cultural zones are not entirely separate; their boundaries are often quite vague, and there is often a certain range of intersection or transition zones between cultural areas. Additionally, the internal diversity within each culture remains, highlighting regional differences. Cultural zoning serves as an interpretation of traditional settlement division from a macro perspective, integrating geographical divisions.

In summary, the formation of traditional settlements is not confined to a single time frame but is the result of historical evolution and dynamic development. Although the weight of individual influencing factors varies across different regions, their categories do not differ significantly. Based on previous research conclusions, this study focuses on three areas of inquiry. The first part explores the spatial distribution characteristics and randomness of traditional settlements in different regions using the ArcGIS platform [27]. In recent years, spatial analysis and GIS technology have been widely applied across multiple fields. In the context of urban logistics and mobility services, Apostolopoulos and Kasselouris [28], through a case study of the Thriasio logistics center in Greece, demonstrated that GIS tools play a crucial role in the study of spatial and urban patterns in urban environments. In the field of sustainable urban planning, Liu et al. [29] explored the current applications, future demands, and potential of GIS models in sustainable urban mobility planning. Specifically, in heritage conservation, spatial analysis and GIS technologies can simulate the potential impacts of various conservation strategies and management measures on historical areas, providing scientific decision-making support for preservation efforts [30]. Moreover, the application of GIS technology and cultural geography in the study of traditional villages has also been addressed, particularly regarding the spatial structure and cultural differentiation of these villages. For instance, a study in the Xiangxi region utilized the nearest neighbor index and geographic concentration index in ArcGIS to examine the characteristics of village distribution and their influencing factors [31]. Another study in Jiangsu Province explored patterns of distribution change in traditional villages, using tools such as Artificial Neural Networks (ANN), Voronoi diagrams, and Moran’s I index while introducing the GeoDetector method to investigate the relationships between various factors and village spatial distribution [32]. These studies highlight the significant role of GIS and GeoDetector tools in modern cultural geography research, providing theoretical support and technical guidance for this study. The second part employs GeoDetector [33] to investigate the relative significance of various influencing factors during the formation and development process. As a novel geographic spatial analysis tool, GeoDetector can reveal the interactions and complex relationships between different factors. In the western Henan region, this tool can be particularly useful for studying the coupling relationship between economic development, social change, and cultural geographic features. The third part, from a cultural geography perspective, examines the influencing factors and spatial differentiation affecting the protection and focus areas of traditional settlements. Overall, research in different directions plays a positive role in the protection and development of traditional settlements in various regions.

1.3. Research Objectives

Although existing research has conducted extensive macro-scale analyses on the spatial distribution characteristics of traditional villages, there is still a lack of in-depth studies on the micro-scale characteristics, influencing factors, and the cultural differences reflected in the distribution patterns of traditional villages in medium- to small-scale regions. Building on existing research methods for traditional villages, this study focuses on the spatial distribution patterns of traditional villages in western Henan, delving into the multiple influencing factors and exploring the rich cultural diversity in this area. The aim is to provide a scientific basis for the effective preservation and sustainable development of traditional villages. Based on this, this study proposes the following key questions: 1. What are the spatial distribution characteristics of traditional villages in the western Henan region and their main aggregation patterns? 2. How does the spatial distribution of traditional villages interact with various influencing factors? 3. How is the cultural differentiation of traditional villages manifested across different landforms and cultural regions?

2. Data and Methods

2.1. Data Sources

This study is based on a total of 305 traditional villages published at the national and Henan provincial levels since 2012 (traditional villages, originally referred to as ancient villages, are those established before the Republic of China (1949). In September 2012, during the first meeting of the Expert Committee on the Protection and Development of traditional villages in China, it was decided to change the term from “ancient villages” to “traditional villages” to emphasize their cultural value and significance in heritage preservation. (The selection criteria for traditional villages are based on the “Traditional Village Evaluation and Recognition Index System (Trial)” issued in 2012 by the Ministry of Housing and Urban-Rural Development of China and other departments). In this index system, each province (or municipality directly under the central government) scores traditional villages based on their own current conditions, following the evaluation criteria. The scoring includes various factors such as the age of the village, the preservation and quality of historical buildings within the village, the overall integrity of the village, its scientific and cultural value, the natural environmental value (the harmonious coexistence of the village with its beautiful natural surroundings or traditional pastoral landscapes), and the significance of the intangible cultural heritage that the village embodies. Ultimately, these comprehensive factors are used to select traditional villages). The data are sourced from the Ministry of Housing and Urban-Rural Development and the Henan Provincial Department of Housing and Urban-Rural Development (

Table 1. Distribution statistics of traditional villages in western Henan.

Region Name	Number of Villages	Percentage (%)	Density/(10 Thousands·km2)
Luoyang	146	47.86%	52.23
Sanmenxia	127	41.63%	45.43
Dengfeng	26	8.52%	9.30
Gongyi	6	1.99%	2.14
Total/Average	305	100.00%	27.28

and

Table 2. Statistics on the distribution of traditional villages in each district and county.

Name	Number of Villages	Ranking	Percentage	Cumulative Percentage	Name	Number of Villages	Ranking	Percentage	Cumulative Percentage
Lushi	58	1	19.01%	19.01%	Yanshi	8	12	2.62%	92.73%
Song	41	2	13.44%	32.45%	Gongyi	6	13	1.96%	94.69%
Dengfeng	26	3	8.52%	40.97%	Luolong	5	14	1.63%	96.32%
Shanzhou	26	4	8.52%	49.49%	Yichuan	3	15	0.98%	97.27%
Luoning	22	5	7.21%	56.70%	Yiyang	3	16	0.98%	98.25%
Xinan	21	6	6.88%	63.85%	Yima	3	17	0.98%	99.23%
Mengjin	20	7	6.55%	70.13%	Hubin	2	18	0.77%	100%
Lingbao	20	8	6.55%	76.68%	Laocheng	0	19	0.00%	100%
Mianchi	18	9	5.90%	82.58%	Xigong	0	20	0.00%	100%
Ruyang	12	10	3.93%	86.51%	Chanhe	0	21	0.00%	100%
Luanchuan	11	11	3.60%	90.11%	Jianxi	0	22	0.00%	100%

). Longitude and latitude coordinates were converted using MapLocation (https://maplocation.sjfkai.com/ (accessed on 12 December 2023)), and the boundaries of the study area were extracted through the Alibaba Cloud Data Visualization Platform (http://datav.aliyun.com (accessed on 12 December 2023)) and formatted using MapShaper (https://mapshaper.org/ (accessed on 12 December 2023)). The DEM (Digital Elevation Model) [34] data are sourced from the Chinese Academy of Sciences data cloud platform (https://www.gscloud.cn/ (accessed on 12 December 2023)), while road and water system data are obtained from the OpenStreetMap platform (https://www.openstreetmap.org/ (accessed on 12 December 2023)). GDP (Gross Domestic Product) [35] data come from the statistical yearbooks published by various cities. After preprocessing the above data, we used ArcGIS 10.8 to create a study area map of the western Henan region (Figure 1), along with distribution maps of the traditional settlements and relevant data analysis.

2.2. Research Methods

2.2.1. ArcGis Spatial Analysis Method

To analyze the spatial distribution types and patterns of traditional villages within the study area (such as random distribution, uniform distribution, or clustered distribution), we selected the Moran’s I index, nearest neighbor index [36], geographic concentration index [36], imbalance index, and kernel density analysis [37] as our research methods.

Global Spatial Autocorrelation: This is an important metric for measuring the similarity of spatial data, typically calculated using Moran’s I. By analyzing the degree of spatial correlation among data values, it helps determine whether there is clustering or dispersion in the space, providing critical insights into the overall spatial structure and patterns.

Nearest Neighbor Index: This method assesses the clustering or randomness of spatial distribution by calculating the ratio of the average distance between sample points under random distribution conditions to the expected distance. It helps in gaining a preliminary understanding of the distribution pattern of the sample data.

Kernel Density Estimation: This non-parametric method estimates the probability density function of spatial data, effectively highlighting high-density areas. It aids in visually presenting the distribution characteristics of the sample data and identifying potential hotspot regions.

Geographic Concentration Index: This method quantifies the degree of concentration of a phenomenon in space and is used to assess the uneven distribution of resources or phenomena in specific geographical areas. It provides a quantitative measure for understanding the spatial distribution of particular phenomena and reveals issues related to regional development imbalances.

Inequality Index: This index measures the degree of inequality in spatial distribution and is commonly used to analyze the distribution of resources, population, or economic activities. By employing this method, we can identify potential inequalities in spatial distribution.

Using ArcGIS 10.8 and Microsoft Excel as data analysis platforms, this study employs the spatial analysis tools in ArcGIS 10.8 to analyze the spatial distribution characteristics of traditional settlements in the western Henan region based on multiple factors, including natural conditions (elevation, slope, aspect, sunlight, water systems), social factors (GDP of districts, roads), and historical and cultural conditions. The analysis explores the conditions for their formation, preservation, and current status. The research methods and related calculation formulas are shown in

Table 3. Spatial analysis research methods.

Analytical Methods	Formula	Formula Description
Moran’s I	$\mathrm{I}={\displaystyle \frac{\mathrm{n}}{{\mathrm{S}}_0}}·{\displaystyle \frac{\sum _{\mathrm{i}}^{\mathrm{n}}{\sum }_{\mathrm{j}}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}\mathrm{j}}\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)\left({\mathrm{x}}_{\mathrm{j}}-\overline{\mathrm{x}}\right)}{\sum _{\mathrm{i}}^{\mathrm{n}}\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)²}}$ (1) ${\mathrm{I}}_{\mathrm{i}}={\mathrm{Z}}_{\mathrm{i}}\sum _{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{i}\mathrm{j}}{\mathrm{Z}}_{\mathrm{j}}$	The global Moran’s I index assesses the degree of spatial autocorrelation across the entire study area. n represents the number of spatial units in the study area, xi and xj are the observed values of spatial units i and j, $\overline{\mathrm{x}}$ is the mean of the observed values, wij represents the spatial weight matrix, and S0 is the sum of the spatial weight matrix. Its value ranges from −1 to 1, where positive values indicate clustering of similar values, negative values suggest dispersion of dissimilar values, and values close to 0 imply a random spatial distribution with no significant autocorrelation. Zi and Zj represent the means of the observed values for spatial units i and j, respectively, and Wij represents the spatial weight.
Nearest Neighbor Index (NNI)	$\mathrm{R}={\displaystyle \frac{{\overline{\mathsf{\gamma }}}_1}{{\overline{\mathsf{\gamma }}}_{\mathrm{E}}}}=2\sqrt{\mathrm{D}}$ (2)	${\overline{\mathsf{\gamma }}}_{\mathrm{E}}$ is the average theoretical nearest neighbor distance, ${\overline{\mathsf{\gamma }}}_1$ represents the actual nearest neighbor distance, and D is the point density. The distribution of points can be divided into three states: uniform, random, and clustered. When R = 1, the distances between the points are neither too uniform nor too clustered, showing a random pattern; when R > 1, the distances between the indicated points show a uniform pattern; when R < 1, the distances between the indicated points show a clustering pattern.
Geographic Concentration Index	$\mathrm{G}=100\mathrm{X}\sqrt{\sum _{\mathrm{i}=0}^{\mathrm{n}}\left({\displaystyle \frac{{\mathrm{X}}_{\mathrm{i}}}{\mathrm{T}}}\right)²}$ (3)	G is the geographic concentration index for traditional villages, where Xi represents the number of traditional villages in the i-th study area. T is the total number of traditional villages. n denotes the number of administrative districts at the city level within the study area, and G0 is the geographic concentration index for the average distribution of traditional villages across cities. The G value ranges from 0 to 100, with a higher G value indicating a more concentrated distribution of traditional villages, while a lower G value indicates a more dispersed distribution.
Inequality Index	$\mathrm{S}={\displaystyle \frac{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{Y}}_{\mathrm{i}}-50(\mathrm{n}+1)}{100\mathrm{n}-50(\mathrm{n}+1)}}$ (4)	Yi is the cumulative percentage of the number of traditional villages in each administrative district, ranked from largest to smallest within the study area. S is the Inequality Index, where 0 < S < 1, S = 1 indicates that traditional villages are completely concentrated in one city-level administrative district, and S = 0 indicates that traditional villages are evenly distributed across all cities.
Kernel Density Estimation (KDE)	$\mathrm{K}\mathrm{D}\mathrm{E}\left(\mathrm{x}\right)={\displaystyle \frac{1}{\mathrm{N}}}{\sum }_{\mathrm{k}=1}^{\mathrm{N}}{\displaystyle \frac{1}{\mathrm{h}}}\mathrm{K}\left({\displaystyle \frac{\mathrm{x}-{\mathrm{x}}_{\mathrm{k}}}{\mathrm{h}}}\right)$ (5)	KDE(x) stands for kernel density estimation. K(x) is the kernel function, x − xk represents the distance between the estimation point and the sample point, N is the number of observation points, and h (h > 0) denotes the bandwidth, which represents the range of observation points (determined using the empirical rule in this study).

.

2.2.2. Multi-Factor GeoDetector Analysis Method

GeoDetector is a spatial analysis method for influence factors, which is primarily used to examine the spatial differentiation of a single variable and the coupling of the spatial distributions of two variables to detect potential causal relationships between them. The novelty of this technology lies in its ability to provide a method for comprehensively analyzing and interpreting the spatial distribution of traditional villages from multiple dimensions and perspectives. Through GeoDetector, it is possible not only to analyze the spatial distribution of traditional villages but also to further explore the dominant factors influencing these distributions, such as the natural environment, demographic economics, and transportation infrastructure. This in-depth analysis contributes to the formulation of more systematic cultural preservation and planning strategies, which are crucial for promoting the sustainable development of regional culture. The application of GeoDetector allows researchers to examine issues related to the protection and development of traditional villages from a broader perspective, thereby facilitating the formulation and implementation of relevant policies. This method assesses the interactive relationship between two factors by comparing the explanatory power of single-factor and two-factor joint effects on the spatial differentiation of the dependent variable [27,38]. The formulas are shown in

Table 4. Geographical detector analysis method.

Analytical Methods	Formula	Formula Description
GeoDetector	$\mathrm{q}=1-{\displaystyle \frac{\sum _{\mathrm{h}=1}^1{\mathrm{N}}_{\mathrm{h}}{\mathsf{\sigma }}_{\mathrm{h}}^2}{\mathrm{N}{\mathsf{\sigma }}^2}}$ (6)	h = 1, 2, …, l represents the partitions of variable Y or factor X. ${\mathrm{N}}_{\mathrm{h}}$ and N denote the number of units in the h layer and the entire region. ${\mathsf{\sigma }}_{\mathrm{h}}^2$ and ${\mathsf{\sigma }}^2$ are the variances of Y for the h layer and the overall region, respectively. The value q indicates the degree of influence that this factor or combination of factors has on the spatial differentiation of traditional villages; a larger q-value signifies a greater level of influence.

. Based on the unique topography, geomorphology, and socio-cultural conditions of the western Henan region, significant influencing factors were selected in conjunction with the studies by Guo Shuaolong et al. [4] and Sun Yuheng et al. [39], as shown in

Table 5. Influencing factors of spatial distribution of traditional villages.

Factor Type	Influencing Factors	Indicator	Indicator Meaning
Natural factors	Terrain factors	X1: Elevation/m	Extract the elevation of the village location
		X2: Slope/°	Extract the slope of the village location
		X3: Slope aspect/°	Extract the slope aspect of the village location
	Rivers factors	X4: Rivers	Extract the river distribution in the study area
Social factors	Economic factors	X5: Per capita/yuan	Extract the per capita GDP of the study area
		X6: Roads	Extract the road distribution in the study area
		X7: High way	Extract the distribution of Highway in the study area

.

2.2.3. Cultural Geography Spatial Zone Method

Cultural geography spatial analysis focuses on studying the spatial combinations, distributions, and developmental evolution of human cultures [40]. This method emphasizes the spatial characteristics of cultural phenomena on the Earth’s surface, such as distance, orientation, and topological relationships, which form the fundamental spatial relationships between geographic entities. The research scope includes not only the geographic distribution of culture but also aspects such as cultural landscapes, origins and dissemination, relationships with the ecological environment, and cultural evaluations of the environment. Through spatial analysis, it is possible to reveal the spatial patterns and structures of cultural phenomena, understand the impact of human activities on the geographic environment, and reflect on these changes in cultural practices [41]. Geographic information system (GIS) analysis further enriches the methods and techniques of spatial analysis by storing, managing, processing, and analyzing spatial data of geographic phenomena, including spatial data modeling, querying, and visualization.

3. Results

3.1. Analysis Results of the Spatial Distribution of Traditional Villages

3.1.1. Results of the Spatial Distribution Correlation Analysis

Based on Formula (1), the calculation yields a Moran’s I index of 0.271255, an expected index of −0.047619, a variance of 0.018453, a Z value of 2.347363, and p = 0.018907. The results indicate the following:

• The value of Moran’s I ranges from −1 to 1. When Moran’s I is greater than 0, larger values indicate a stronger spatial correlation. If Moran’s I is 0, it signifies a random spatial distribution. Conversely, negative values (less than 0) indicate spatial dispersion, with smaller values suggesting more pronounced dispersion. The p-value represents the probability, where smaller p-values enhance the confidence level of the results. The Z-value, indicating the deviation of a dataset, ranges between −2.58 and 2.58. It is categorized into seven intervals: Z < −2.58; −2.58 to −1.96; −1.96 to −1.65; −1.65 to 1.65; 1.65 to 1.96; 1.96 to 2.58; and Z > 2.58. The combination of the Z-value and p-value supports the reliability of the research findings;
• The spatial Moran’s index for the western region of Henan is greater than zero and exceeds the expected value, suggesting a positive spatial correlation in the distribution of traditional villages in this area. Since Z < 2.58 and p > 0.01, the results demonstrate that the spatial distribution of traditional villages in the western region of Henan is not random (Figure 2a). These data provide important clues for further research.

According to Formula (2), the average theoretical nearest neighbor distance (average observed distance) is 3.31 km, the average actual nearest neighbor distance (expected average distance) is 4.69 km, and the nearest neighbor ratio is 0.706. Since R < 1, the Z score is −9.803714, with a significance level of p = 0.000000 < 0.01, indicating that the traditional villages in the western region of Henan exhibit a clustered distribution type (Figure 2b).

3.1.2. Analysis of Spatial Distribution Clustering

Assuming that the 305 traditional villages are evenly distributed, each county-level city and district would have an average of 13.86 villages. Using Calculation Formula (3), we obtain G = 30.87368828 and G0 = 21.32660245, with G > G0.

The Lorenz curve is used to show the average distribution of traditional villages in the study area. The horizontal axis is the name of each district and county, and the vertical axis represents the cumulative proportion of the distribution of traditional villages. The closer the curve is to the diagonal line, the more uniform the distribution is, and the farther it is from the diagonal line, the higher the concentration is. The uniform distribution curve is represented by red, and the Lorenz curve is represented by black. According to Formula (4), the imbalance index S = 0.568 indicating that the spatial distribution of traditional villages across the 22 county-level administrative regions in western Henan is uneven, primarily concentrated in Lushi County, Song County, and Dengfeng City (Figure 3).

3.2. Relationship Between the Spatial Distribution of Traditional Villages and Influencing Factors

• Relationship between Traditional Villages and Geographic Elevation (

  Table 6. Relationship between traditional village distribution and geographic elevation.

  	Elevation Range (m)	Quantity/Percentage
  	89~300	43/14.09%
  	301~500	78/25.57%
  	501~800	129/42.29%
  	801~1000	35/11.47%
  	1001~1300	16/5.24%
  	1301~2367	4/1.34%

  Source: The DEM (Digital Elevation Model) data are sourced from the Chinese Academy of Sciences data cloud platform (https://www.gscloud.cn/). The traditional village data were sourced from the national-level traditional villages announced by the Ministry of Housing and Urban-Rural Development of China and the provincial-level traditional villages published by the Henan Provincial Department of Housing and Urban-Rural Development with changes made by the authors.

  ).

2.

The Relationship between Traditional Villages and Terrain Slope (

Table 7. Relationship between the distribution of traditional villages and terrain slope.

	Slope Range	Quantity/Percentage
	≤3°	143/46.88%
	3~5°	63/20.65%
	5~15°	97/31.80%
	15~25°	3/0.67%
	≥25°	0

Source: The DEM (Digital Elevation Model) data are sourced from the Chinese Academy of Sciences data cloud platform (https://www.gscloud.cn/), and the slope is calculated using the ArcGIS platform with changes made by the authors. Note: <3° flat plains, central parts of basins; 3~5° foothill areas, sloping plains at the foothills, shallow hills, valleys, etc.; 5~15° mountain foothill areas, around basins, hilly regions.

).

3.

The Relationship between Traditional Villages and Slope Aspect (

Table 8. Relationship between the distribution of traditional villages and slope aspect.

	Slope Aspect Range	Quantity/Percentage
	N	43/14.09%
	NW	37/12.13%
	NE	32/10.49%
	W	47/15.40%
	S	42/13.77%
	SW	39/12.78%
	SE	38/12.45%
	E	28/8.89%

Source: The DEM (Digital Elevation Model) data are sourced from the Chinese Academy of Sciences data cloud platform (https://www.gscloud.cn/), and the slope aspect is calculated using the ArcGIS platform with changes made by the authors.

).

4.

The Relationship between Traditional Villages and Rivers (

Table 9. Relationship between the distribution of traditional villages and rivers.

	Distance Interval	Quantity/Percentage
	0~5 km	115/37.70%
	5~10 km	85/27.86%
	≥10 km	105/34.44%

Source: The hydrological data are sourced from the OpenStreetMap platform (https://www.openstreetmap.org/ (accessed on 12 December 2023)). The boundary data for the study area were sourced from the Alibaba Cloud Visualization Platform (http://datav.aliyun.com), with changes made by the authors.

).

5.

The Relationship between Traditional Villages and Landforms (

Table 10. Relationship between the distribution of traditional villages and landforms.

	Landform Type	Quantity/Percentage
	Rocky mountainous areas	189/61.96%
	Alluvial plains	30/9.83%
	Loess plateaus	36/11.80%
	Loess hilly landforms	50/16.41%

Source: Landform data come from Henan Provincial Geological Bureau (https://dzj.henan.gov.cn/), with changes made by the authors.

).

6.

The Relationship between Traditional Villages and GDP (

Table 11. Relationship between the distribution of traditional villages and per capita GDP.

	GDP Range (¥)	Quantity/Percentage
	42,000~47,000	3/13.63%
	47,000~65,000	5/22.72%
	65,000~81,000	4/18.18%
	81,000~100,000	6/27.27%
	100,000~120,000	3/13.63%
	120,000~150,000	1/4.57%

Source: GDP data come from Henan Provincial Bureau of Statistics (https://tjj.henan.gov.cn/). The boundary data for the study area were sourced from the Alibaba Cloud Visualization Platform (http://datav.aliyun.com), with changes made by the authors.

).

7.

The Relationship between Traditional Villages and Major Roads (

Table 12. Relationship between the distribution of traditional villages and roads.

	Distance Interval	Quantity/Percentage
	0~5 km	139/45.57%
	5~10 km	83/27.21%
	≥10 km	83/27.21%

Source: The road data are sourced from the OpenStreetMap platform (https://www.openstreetmap.org/). The boundary data for the study area were sourced from the Alibaba Cloud Visualization Platform (http://datav.aliyun.com), with changes made by the authors.

).

4. Discussion

4.1. Influencing Factors and Spatial Distribution

4.1.1. Single Influencing Factor and Spatial Distribution

The western Henan region is located in the western part of Henan Province, bordered by the Yellow River to the north and adjacent to Shaanxi and Shanxi provinces to the west. Its complex terrain features loess landscapes, rocky mountains, and alluvial areas, with significant elevation changes from west to east, marking the transition from the second to the third tier of China’s topography [42]. Key rivers like the Yellow River, Luo River, and Yi River flow through the area. Economically, Luoyang ranked 45th among prefecture-level cities in China in 2020. The region boasts a developed transportation network, with Luoyang being the first non-capital city in central and western China to operate a subway. Sanmenxia City, located in the Yellow River’s Golden Triangle at the junction of Henan, Shanxi, and Shaanxi, serves as a vital transportation hub. This geographic pattern significantly influences social development in western Henan, shaping the formation and evolution of traditional villages. Therefore, this study correlates seven influencing factors from (Table 5) with the distribution of traditional villages, showing (

Table 13. Detection results of the significance (q value) of each influencing factor by GeoDetector.

	Elevation	Slope	Slope Aspect	GDP	Highway	Roads	Rivers
q	0.029050415	0.004007718	0.002141939	0.129966925	0.017023103	0.004209968	0.028572164
p	0.000	0.000	0.008193393	0.000	0.000	0.01059635	0.000

Note: A larger q-value indicates a stronger driving force of the factor, while a smaller p-value indicates a more significant result.

) the explanatory power in the following order: GDP density (0.12997) > elevation (0.02905) > distance to rivers (0.02857) > highway density (0.01702) > provincial road density (0.00421) > slope (0.00401) > slope aspect (0.00214).

The research results indicate that among the seven influencing factors, GDP density has the most significant impact on the spatial distribution of traditional villages, followed by elevation and rivers, while slope and aspect have the least influence. The economic factor, being the most influential, suggests that the spatial distribution and development of traditional villages in the study area are strongly affected by the level of economic development. Combining Table 2 and Table 11, it is found that there are 99 national and provincial traditional villages distributed in Lushi County and Song County, accounting for 32.45% of the total. However, these counties rank in the lowest GDP range, indicating that areas with a higher number of traditional villages tend to have lower socio-economic development levels. On the other hand, the low economic and road density in these areas may facilitate the effective protection of traditional villages. As secondary factors, elevation and rivers provide ample water sources and arable land for residents while also facilitating transportation and agricultural production. The study area shows little influence from slope and aspect, indicating that the site selection for villages is not highly dependent on sunlight and orientation.

1.

Elevation

The complex terrain of the western Henan region makes elevation a key factor influencing village distribution [43]. Different elevations have also had a profound impact on the lifestyle and social–cultural structure of villages. High-altitude areas, characterized by steep terrain, limited arable land, and inconvenient transportation, are not conducive to the formation and sustained development of traditional villages. Additionally, villagers often use rough, locally sourced materials to construct their homes. The mountainous soil is rocky and difficult to excavate, further limiting the development of architectural styles and the expansion of village sizes [42]. In contrast, the mid-elevation range of 501–1500 m, with relatively abundant resources and less external disruption during rapid urbanization, helps preserve traditional cultural customs and the original appearance of the village and serves as a protective barrier and primary distribution area for traditional villages [44]. This aligns with the findings of Kang Jingyao et al. [45]. There are 180 traditional villages distributed in the middle altitude range of 501–1500 m in western Henan, accounting for 59.00% (Table 6). According to the “Luoyang County Chronicle” [46], from the Qianlong period, low-altitude plains have developed transportation and agriculture, resulting in a much higher density of traditional villages. Villages can rely on arable land resources for sustainable development, and agricultural activities have led villagers to form unique cultural customs, such as festivals, rituals, and cooperative farming. Over time, this has gradually resulted in unified or similar socio-cultural characteristics within the region [18]. The concentrated development of villages and frequent interactions further promote cultural integration within the region, resulting in a cultural distribution pattern with distinct local characteristics. However, since the 1980s, societal development has significantly impacted this distribution, with only 39.66% of traditional villages remaining.

2.

Slope and Slope Aspect

Slope also has a significant impact on the distribution of traditional villages. Using the “Slope Analysis” tool in ArcGIS, we found that 67.53% of traditional villages are located on gentle slopes of 0–5°. The vast majority (99.33%) of traditional villages are situated in areas with slopes less than 15° (Table 7). Among them, the distribution of traditional villages with a slope of less than 3 degrees in western Henan accounts for the largest proportion, with 143 traditional villages distributed, accounting for 46.88%. Flat areas are not only easier for cultivation but also more favorable for habitation, thus attracting the formation and concentration of more villages. This conclusion, when combined with the earlier finding that traditional villages are predominantly located in mid-elevation areas, indicates that even in mid-elevation mountainous regions, traditional villages tend to be sited on gentle plateaus. This aligns with the research conducted by He Chuan et al. [47].

In the western Henan region, which emphasizes winter sunlight, north-facing slopes are considered the least favorable orientation. At the same time, the distribution of traditional villages on north-facing slopes may seem unreasonable. There are 112 traditional villages in the north, northeast, and northwest of western Henan, accounting for 36.71%. A closer analysis reveals that 14.09% of villages located on north-facing slopes are situated on gentle slopes of 0–5° (Table 8). In these cases, sunlight requirements are nearly irrelevant. Therefore, the impact of slope aspects on the distribution of traditional villages is minimal [48].

3.

Rivers

Water systems not only influence the geographical layout of traditional villages but also support the social life and cultural practices of these villages by creating a suitable ecological environment and resource distribution. This, in turn, contributes to the formation of cultural zones of traditional villages on a larger scale [49]. The proximity to major rivers significantly influences the distribution of traditional villages in the western Henan region. In the western Henan region, 200 traditional villages are distributed within a 10 km range of major rivers, accounting for 65.56% of the total, with 37.70% of villages located within 5 km of a river. Particularly in alluvial plains where river systems converge, the flat terrain and ample water sources lead to dense clusters of traditional villages, a finding consistent with historical records. Similarly, research on traditional villages in the Ganjiang River basin has found that lowland plain areas are more conducive to the continuity of these villages. This indicates that the formation and development of villages depend on the availability of water resources and the suitability of land [49]. Using ArcGIS for spatial analysis, we noted that traditional villages located more than 10 km from major rivers are often found in the Loess Plateau and higher mountainous areas, with 105 villages reaching 34.44% (Table 9). This suggests the presence of established groundwater utilization patterns in the region, supported by research from Fei Xianmei [50] and Ge Yipeng [42]. River systems, as natural barriers and resource foundations, promote the distribution and protection of traditional villages within the basin. They, along with economic development and historical cultural factors, shape the continuity and cultural expression of these villages.

4.

Landforms

The western Henan region features diverse landform types, primarily composed of medium to low rocky mountains, loess plateaus, loess hills, alluvial plains, and intermountain basins. This landform distribution significantly influences the location of villages. Historical records from the Qing Dynasty and the Republic of China indicate that the alluvial plains, alluvial impact zones, and intermountain basins around Sanmenxia and Luoyang, with their lower elevation and flat terrain, serve as major areas for traditional village distribution. Although the loess plateaus and hills have slightly higher elevations, their unique soil conditions and agricultural value also attract some village formations. In the lower mountainous areas, despite the undulating terrain and limited arable land, the rich natural resources lead to the establishment of several villages.

However, the analysis presented in (Table 10) shows that there are 189 traditional villages distributed in rocky mountainous areas, accounting for 61.96% of the total, which contrasts sharply with the aforementioned historical records. Li Gen et al. [51] suggest that economic development has led to an imbalance in the distribution of traditional villages. This viewpoint is consistent with the argument presented in Ge Yipeng’s paper [42].

5.

GDP and Roads

In addition to natural geographical factors, socio-economic factors have also significantly influenced the distribution of villages. GDP density is an important indicator for measuring the concentration or intensity of economic activities within a region and reflects the overall level of economic activity. A higher GDP density typically indicates that a region generates greater economic value per unit area and is often associated with higher levels of urbanization, as well as more comprehensive and widespread infrastructure development and coverage. The traditional villages within the study area are distributed across various counties, each with differing GDP levels. Therefore, the variation in GDP density provides a quantitative basis for analyzing the extent to which traditional villages are influenced by economic development throughout their growth.

Through overlay analysis using ArcGIS on elevation, GDP, water systems, and roads, we found that GDP (Table 11) density and distance to roads (Table 12) are negatively correlated with the number of traditional villages. There are 222 traditional villages within 10 km of the main roads in western Henan, accounting for 72.78% of the total. This indicates that as socio-economic development and urbanization accelerate, traditional villages in western Henan are severely eroded and destroyed. This viewpoint is supported by Wang Shaofei et al. [52] and Huang Shujian et al. [53]. In contrast, studies by Kang Jingyao et al. [45] and Feng Yafen et al. [54] found that economic development in Guangdong actually benefits the preservation of traditional villages, leading to a new path for their protection. Therefore, when formulating protection strategies, it is essential to fully consider the balance between GDP and the preservation of traditional culture to ensure the effective protection of traditional villages.

4.1.2. Multiple Influencing Factors and Spatial Distribution

To deeply analyze the combined types of comprehensive limiting factors affecting the spatial distribution of traditional villages and their degrees of influence, this study employs ArcGIS 10.8 and GeoDetector Excel 2018 Version analysis software, utilizing the interactive detection module within GeoDetector for quantitative assessment. Through interactive detection analysis, we observed that in the western Henan region, the explanatory power of any two influencing factors regarding the spatial differentiation of traditional villages significantly exceeds that of any single factor (

Table 14. The interactive detection results of various influencing factors based on GeoDetector.

Factor	Elevation	Slope	Slope Aspect	GDP	Highway	Roads	Rivers
Elevation	0.029050415
Slope	0.037999484	0.004007718
Slope Aspect	0.039767534	0.013212942	0.002141939
GDP	0.252375333	0.163010276	0.155434508	0.12996692
Highway	0.050955226	0.025689005	0.027072227	0.16082464	0.017023103
Roads	0.056563422	0.018866764	0.018074466	0.17857101	0.044408129	0.00420996
Rivers	0.125526966	0.056112644	0.046124897	0.31443457	0.068102223	0.07036238	0.02857216

). This finding reveals that the spatial distribution of traditional villages in western Henan results from the interplay and joint effects of multiple factors. Furthermore, by combining data from single-factor detection and interactive detection, we clearly found that while the influence of GDP density as a single factor (0.12996692) is significant, its impact is markedly enhanced in interaction with other factors. Notably, the interaction between GDP density and river density (0.3443457) demonstrates the greatest influence on the distribution of traditional villages in western Henan among all interactions.

This result aligns with the findings of Sun Yuhang [39], indicating that the interaction between GDP density and other factors is a dominant influence on the distribution of traditional villages in western Henan. Therefore, when studying the distribution of traditional villages in this region, it is essential to emphasize the interactions among multiple factors and to comprehensively consider how these interactions affect the distribution of traditional villages.

4.2. Spatial Structure Characteristics

4.2.1. Spatial Orientation

Provincial-level traditional villages in Henan are selected based on initial evaluations and recommendations from various cities, districts, and counties and are comprehensively assessed by the Expert Committee on the Protection and Development of Provincial Traditional Villages in Henan. The basis for this evaluation is the “Notice on the Issuance of the Traditional Village Evaluation and Identification Index System (Trial)” (Jian Cun [2012] No. 125) issued by the Ministry of Housing and Urban-Rural Development and other departments. National-level traditional villages in China, on the other hand, are evaluated by the National Committee for the Protection and Development of Traditional Villages, which reviews the traditional villages recommended by each province (or municipality directly under the central government) based on the jointly issued “Traditional Village Evaluation and Identification Index System (Trial)” by the Ministry of Housing and Urban-Rural Development, Ministry of Culture, State Administration of Cultural Heritage, and Ministry of Finance. However, from a geographical perspective, there are certain differences in the spatial distribution between provincial-level and national-level traditional villages. One of the objectives of this study is to explore the spatial distribution characteristics of these traditional villages. The standard deviational ellipse method can help identify whether the traditional villages exhibit spatial clustering and their distribution direction. While analyzing the clustering, directional analysis can also be combined with the previous analysis of influencing factors to further demonstrate the relationship between the spatial distribution in western Henan and factors such as topography, rivers, and roads. By analyzing provincial-level and national-level traditional villages separately, we can gain a deeper understanding of the spatial distribution characteristics and orientation of different levels of traditional villages in western Henan.

By analyzing the spatial distribution and orientation of national and provincial traditional villages in western Henan using the standard deviation ellipse tool in ArcGIS 10.8, we found that the distribution of national traditional villages (Figure 4) exhibits a certain orientation, with a larger X-axis length indicating a broader range in the X direction (

Table 15. Standard deviation ellipse analysis of traditional villages.

Level	Center Coordinate Point	X-Axis Length	Y-Axis Length	Ellipse Flattening	Direction Angle
Provincial	E 111°48′45.620″, W 34°22′47.138″	115.7109455	65.90549276	0.43043	73.45671
National	E 111°47′47.802″, W 34°21′55.022″	111.8572783	71.15957669	0.363836	73.13002
Total	E 111°48′31.781″, W 34°22′34.666″	114.8083699	67.20562289	0.414628	73.38216

). The standard deviation ellipse for provincial traditional villages (Figure 5) also shows a greater X-axis length and higher ellipse flattening, suggesting a wider distribution and more pronounced orientation. Both types display an overall spatial direction from southwest to northeast, with lower ellipse flattening indicating stronger clustering effects and orientation. This result aligns with the study by He Chuan et al. [47], which highlights that the spatial orientation of traditional villages is influenced by factors such as topography and river systems, which is consistent with our findings.

By combining the above analysis results with the topography of western Henan, we find that the residual ranges of the Qinling Mountains, such as the Xiaoshan, Xiong’er, and Funiu Mountains, extend eastward into the eastern Henan plain. The Yellow River, Luo River, and Yi River run alongside these mountains, with traditional villages primarily distributed among mid-to-low stony mountains, loess plateaus, loess hills, alluvial impact zones, and intermountain basins, forming a typical east–west topographic feature. This study provides a strong explanatory power for the standard deviation ellipse analysis results and further supports the finding that the distribution of national and provincial traditional villages in western Henan shows a spatial direction from southwest to northeast.

4.2.2. Spatial Structure

This study explores the overall distribution characteristics of traditional villages in western Henan, starting from the distribution within various counties and districts. The spatial distribution of traditional villages in this region is highly uneven, exhibiting a coexistence of concentrated and dispersed patterns. Specifically, Lu County in Sanmenxia and Song County in Luoyang are highly concentrated, with traditional villages in these two counties accounting for 32% of the entire study area. In contrast, the distribution of traditional villages in counties like Yiyang and Yichuan and urban areas such as Yima and Hubin is relatively sparse, comprising only 1.6% to 3.6% of the total in the study area. The distribution in Luanchuan, Yiyang, Luolong District, and Yanshi District is also relatively limited. Notably, traditional villages have not been found in the rural areas of Luoyang’s old city, Jianxi District, Xigong District, and Luolong District.

Further analysis of the spatial distribution of traditional villages within the study area reveals four high-density areas: the northwest of Shanzhou District, the western part of Mengjin District, the central area of Lushi County, and the northwest of Xin’an County. According to the kernel density analysis results, darker areas indicate a more concentrated distribution of traditional villages. The high-density area in the northwest of Shanzhou District is the most prominent, suggesting that this region serves as the primary concentration area within the study region. Thus, we define it as the core region of traditional village distribution in the study area. The other three areas (the western part of Mengjin District, the central area of Lushi County, and the northwest of Xin’an County), while also showing relatively concentrated distributions, are less significant compared to the core region and are therefore classified as three secondary distribution nodes. Outside these four primary traditional village distribution areas, there are many moderately dense regions (represented by green areas in Figure 6). These regions contain a certain number of traditional villages but do not exhibit a notably concentrated distribution; instead, they present a more dispersed regional distribution. Thus, we categorize this type of distribution as a regional pattern. By integrating the results of the kernel density analysis with the administrative divisions and geographical conditions of western Henan, we discovered that in the regions of Luoyang, Dengfeng, and Gongyi, villages exhibit a circular distribution pattern along rivers and mountains. In contrast, within the city of Sanmenxia, the distribution of villages appears as a linear pattern extending from the north of Shanzhou across the eastern part of Ling-bao, the western part of Luoning, and the central and western parts of Lushi County. Overall, the distribution pattern of traditional villages in western Henan displays distinct characteristics of “one core, three nodes, and multiple regions”.

4.3. Spatial Differentiation

The diverse landforms in western Henan, including stony mountains, alluvial plains, loess plateaus, and loess slopes (Figure 7), have nurtured a unique traditional village culture. According to statistical data, stony mountains are the predominant landform type in the region, housing the highest number of traditional villages, totaling 189, which accounts for 61.96%. The alluvial plains along the Luo and Yi Rivers contain 30 traditional villages, making up 9.83%. The loess plateau area at the junction of Shanxi, Shaanxi, and Henan has 36 traditional villages, representing 11.80%. In the loess slope area, there are 50 villages, accounting for 16.41%. This argument is further supported by the research of Duan Fangjun et al. [55].

4.3.1. Landform Types and Residential Forms

This study indicates that residential forms, village cultural zoning, and the distribution of specific cultural communities exhibit significant differences and regional characteristics under various landform types. As the primary landform type in western Henan, stony mountains host the highest number of traditional villages, reflecting a profound influence on traditional village culture. Additionally, the alluvial plains along the Luo and Yi Rivers, loess plateau areas, and loess slopes each nurture unique traditional village cultures. These findings align with the research of Ge Yipeng [42].

In the loess plateau area, residences are primarily cave dwellings; the loess slope area often features cliff dwellings; stony mountains are characterized by low-grade courtyard houses made of wood, stone, or a mixture of both; and the plain areas predominantly have high-grade brick–wood and brick–mud courtyard structures. The western Henan region features various types of landforms that make full use of the local geographical environment (Figure 8), resulting in a diversity of architectural styles and residential forms.

1.

Cave dwellings

Cave dwellings in western Henan predominantly feature arrangements with 8, 10, or 12 cave rooms. These caves are organized according to orientation and function, each assigned a specific spatial hierarchy. The courtyard space is centered around a natural patio, which forms during excavation. From this central pit, additional caves are dug around it, with each cave facing and opening toward the patio. This pit acts as a unifying core, connecting each geographically spaced cave room into a cohesive whole (Figure 9).

2.

Cliff dwellings

Cliff dwellings are constructed based on the contours of the cliff face, with primary cave rooms surrounded by additional buildings. These adjustments are made in relation to the ideal layout of a residence, balancing functional needs with feng shui principles. The layout resembles a siheyuan (courtyard house), but the main room is replaced by a three-unit cave dwelling (Figure 10). The construction process is simpler compared to other types of residences; many are situated along linear cliff faces or “U”-shaped cliff profiles. The main room is transformed into a three-unit cave dwelling, complemented by east and west wing rooms and auxiliary buildings, achieving a harmonious integration of structures and cave dwellings. These dwellings are commonly found in topographically restricted ravine areas and regions of extreme poverty.

3.

Courtyard houses

Courtyard houses are characterized by a rectangular layout that is statistically aligned with the average water level values based on local historical records. Research indicates that a complete traditional courtyard house typically adheres to this standard. In the western Henan region, typical low-end courtyard houses can be considered a prototype of traditional residences (Figure 11). This layout includes the main room, east and west wing rooms, and auxiliary buildings, with the courtyard entrance located to the right of the southern auxiliary building, creating an overall rectangular shape.

4.3.2. Cultural Zoning

Based on the above characteristics, this study categorizes traditional villages in western Henan into two sub-regions: surface-type traditional village culture distribution and cave dwelling-type traditional village culture distribution. These are further divided into five specific cultural communities: mountainous surface traditional village culture community, plain surface traditional village culture community, pit cave traditional village culture community, cliff cave traditional village culture community, and courtyard cliff traditional village culture community (Figure 12).

4.4. Limitations of This Study

Previous studies utilizing ArcGIS and spatial analysis have achieved significant results in traditional villages, focusing on the classification of traditional settlements and residential types, as well as regional differences. While research has matured in macro-regional delineation and typical cultural phenomena in core cultural areas, there is a lack of studies analyzing smaller regions and the underlying influence of various factors on cultural differentiation. This research aims to explore the spatial structure characteristics and spatial differentiation of traditional villages in the western Henan region using ArcGIS, GeoDetector, and cultural geography, providing theoretical support for their protection and sustainable development. However, this study primarily relies on government-released data and the existing literature, which may suffer from issues of timeliness and completeness, potentially impacting the comprehensiveness and accuracy of the results. Additionally, cultural differentiation in traditional villages is influenced by various factors, including historical background, social structure, and economic development levels. Although attempts were made to classify cultural regions, not all nuances affecting cultural differentiation may have been fully captured. To address these limitations, future research could consider broader data sources, including local archives and field survey data, for more accurate insights. Employing diverse methodologies, such as system dynamics modeling and multidimensional spatial statistical analysis, may also enhance understanding of the formation and evolution of traditional villages. Furthermore, close collaboration with local governments and communities is recommended to implement and evaluate conservation measures, ensuring effective protection and sustainable use of cultural heritage.

5. Conclusions

This study focuses on 305 national and provincial traditional villages in western Henan, employing ArcGIS spatial analysis methods, GeoDetector factor influence analysis, and cultural geography spatial zoning methods to explore the spatial distribution characteristics, influencing factors, and the relationship between spatial distribution and cultural zoning of traditional villages. The research findings reveal the following:

• The spatial distribution of traditional villages in western Henan exhibits significant clustering and unevenness, with uneven distribution within various counties and considerable differences between them. The main concentrations are found in Lu County and Shanzhou District of Sanmenxia City, as well as Song County and Luoning County in Luoyang and Dengfeng City;
• Per capita GDP and distance from roads show a negative correlation with the distribution of traditional villages. In western Henan, the lower per capita GDP and the greater distance from roads in the mid-to-low mountainous and loess plateau areas provide a certain degree of protection for traditional villages, allowing them to be preserved;
• The overall spatial distribution of traditional villages in western Henan exhibits a clustered pattern characterized by “one core, three nodes, multiple regions.” In the western part, there is a north–south banded distribution, while in the eastern part, the layout follows a circular pattern along the Yellow River and the foothills of the mountains;
• Under different landform types, residential forms, village cultural zoning, and the distribution of specific cultural communities exhibit significant differences and regional characteristics. The traditional village culture in western Henan can be divided into two cultural sub-regions and five cultural communities.

This paper explores the spatial distribution, structural characteristics, and differentiation of traditional villages in the western Henan region. It conducts a multi-perspective analysis from humanistic, geographical, historical, and social viewpoints. Starting from the two categories of natural and social factors, it identifies three influencing elements: topography, hydrology, and economy, ultimately deriving seven impact indicators. This study employs geographic detectors to explore the driving forces behind spatial differentiation, revealing the influence of various factors on the spatial distribution of traditional villages. By analyzing the interactions among these factors, the research provides a solid theoretical foundation and data support for the formulation and implementation of preservation and sustainable development policies by governments or heritage conservation organizations. In the process of preservation or policy formulation, it is crucial to fully consider the influences of topography, economy, hydrology, and culture and to develop differentiated protection measures based on the unique circumstances of traditional villages. This study also emphasizes the value of the rich historical and cultural resources and local architectural art in the western Henan region. Although village distribution shows significant aggregation, rapid economic development and the urbanization process have left an indelible impact on traditional village development. Through systematic protection and development, it is possible to effectively promote and safeguard rural revitalization and ensure cultural security at the national and regional levels.

Secondly, this study combines spatial analysis, factor interaction detection, and cultural geography-based spatial zoning methods to investigate traditional villages in the western Henan region. This approach provides a more comprehensive and detailed analytical perspective. The use of GIS and GeoDetector offers a solid quantitative foundation for analyzing the spatial distribution of traditional villages, enhancing the credibility of the research. By thoroughly analyzing the impacts of various influencing factors on village distribution, the study further explains the potential mechanisms for village preservation under different economic, social, and environmental contexts, offering new insights into the formation and evolution of traditional villages. The cultural zoning of traditional villages in the western Henan region identifies multiple cultural communities, enriching the understanding of local cultural diversity.

In future work, we aim to focus on two research areas. One is to continue exploring how to effectively utilize ecological, cultural, and social resources while preserving traditional villages, aiming for a win–win situation between economic benefits and environmental protection. This will provide more comprehensive guidance for the preservation and sustainable development of traditional villages. The second area involves the application of digital technology in heritage conservation, investigating how to leverage digital technologies (such as virtual reality and augmented reality) for the protection and presentation of traditional villages. We also plan to conduct cross-regional comparisons to analyze the similarities and differences in spatial distribution, cultural transmission, and economic development of traditional villages in different areas, revealing the underlying factors and patterns.