Spatial Distribution Characteristics and Influencing Factors of Key Rural Tourism Villages in China

Abstract

Key villages of rural tourism have become an important carrier for the high-quality development of rural tourism. The precise identification of the spatial distribution characteristics and influencing factors of rural tourism key villages is of great value in promoting the quality upgrading of rural tourism in China and realizing the goal of rural revitalization strategy. The aim is to realize the complementary coordination and integration of urban and rural areas, promote rural transformation and increase farmers’ incomes. Taking three batches of 1199 key rural tourism villages in China as research samples, the nearest neighbor index, disequilibrium index and kernel density methods were used to reveal the spatial differentiation characteristics of key rural tourism villages and the influencing factors were further analyzed using the geographical connection rate and buffer analysis. The results showed that (1) the key villages of rural tourism demonstrated a typical agglomeration in the spatial distribution, with the Hu Huanyong line as the obvious boundary. (2) The very high-density agglomeration centers were mainly located in the Beijing–Tianjin–Hebei region and the Yangtze River Delta region, while the high-density agglomeration zones were mainly located in the North China Plain on the east side of the Taihang Mountains and the middle and lower reaches of the Yangtze River Basin. (3) The spatial distribution characteristics of hot regions were in the central and eastern regions; however, the cold regions were in the northwest and northeast regions. (4) The spatial distribution characteristics of key rural tourism villages were the result of the interaction and coupling of multiple factors. The key villages of rural tourism were mainly distributed in plain and hilly areas with dense river networks, dense populations, high levels of economic development, developed transportation and suburban areas.

1. Introduction

Rural tourism relies on rural natural and human landscape resources and integrates leisure and entertainment, ecological sightseeing, and farming experience. As an important carrier for the development of the rural characteristic economy and the inheritance of culture, key rural tourism villages have a leading role and typical demonstration effect in upgrading the quality of rural tourism. They play an important role in rural revitalization and urban-rural integration [1]. However, with the development of blowouts in rural tourism, a series of problems emerged, such as a low level of infrastructure, lack of prominent themes, serious homogenization, and destruction of resources and the environment. In this context, the development mode of rural tourism in China urgently needs to be transformed into characteristic and high-quality, and the key villages of rural tourism need to be upgraded [2]. The Ministry of Culture and Tourism of China announced three batches of 1199 key rural tourism villages (excluding Hong Kong, Macao, and Taiwan) in 2019, 2020, and 2021 and established the status of rural tourism development and rural tourism key village construction in rural economic and social development. The development and construction of key rural tourism villages are of great significance for the development of the rural characteristic economy, the integration of rural land resources, the balance of the urban-rural economic gap, the promotion of the integration of three industries, and the promotion of rural revitalization [3]. The key villages of rural tourism play an important role and typical demonstration effect in upgrading the quality of rural tourism and gradually become an important way to promote rural economic development and industrial revitalization [4]. The spatial form, location scale, and distribution characteristics of rural tourism destinations are favored by the academic circles [5], and have become the mainstream in current research on the spatial structure of rural tourism destinations. Existing studies have explored the spatial distribution and influencing factors at the provincial and municipal scales [6,7], especially relatively mature model villages, model villages, and characteristic villages of rural tourism as research objects [8,9]. The results show that the research spatial distribution can optimize the spatial structure layout, rationally allocate the tourism spatial resources, and provide guidance and countermeasures for the precision of rural tourism poverty alleviation, brand-building, and sustainable development. In view of the fact that the influencing factors mostly focus on the research of tourism competitiveness [10], driving mechanism [11], development status, mode, and strategy [12,13,14], the results show that the influencing factors can reflect the force on the factors of key rural tourism villages, guide the scale, quality, development direction and mode of key tourism villages, and then promote the sustainable development of rural tourism. In addition, the discussion on the influencing factors of rural tourism spatial differentiation from the perspective of geographical space is rarely involved, especially in the key tourism villages. Therefore, from the macro scale, this paper takes key tourism villages as the research object to explore the spatial distribution characteristics and influencing factors of rural tourism, which has very important guiding significance for correctly understanding the development status of rural tourism, understanding regional resource allocation, and guiding rural tourism poverty alleviation.

As a special tourism mode, rural tourism has been widely studied by scholars. Rural tourism activities appeared in Western countries in the middle of the 19th century and took shape after the 1980s. In recent years, foreign studies on rural tourism have mainly focused on the supply motivation and contradiction between the supply and demand of rural tourism, innovation of the operation mode of tourist destinations, tourism consumption mode, tourism venture capital, and sustainable development of rural tourism [15,16,17]. The results involved were all based on the development quality of rural tourist destinations and rural revitalization. Some scholars have tried to explore the interaction between rural tourism and social development from the spatial structure characteristics and spatial evolution of rural tourism destinations [18,19,20]. In recent years, researchers have analyzed the influencing factors of the development of tourism destinations from the promotion of rural tourism and related entrepreneurs of the rural tourism industry [21,22]. Research on the static layout of the spatial evolution process is more in-depth, while the dynamic evolution research is relatively deficient. The horizontal comparative study of different spaces is sufficient, while the analysis of spatial evolution stages and the research on spatiotemporal differentiation law are relatively weak [23]. ArcGIS and network analysis were used to prove the applicability of the centrality index in assessing spatial characteristics and proposed strategies for integrating rural tourism [24]. The rural development stages in the Annapurna area of Nepal and the spatial hierarchical structure characteristics of rural settlements under the influence of tourism were studied [25]. The buffer zone of different types of scenic spots certified that the area within 5 km of the scenic spot was suitable for rural tourism development [26]. Rural tourism destinations 50~100 km away from the city have a strong attraction for urban residents [27]. The European Union has put forward the European Rural Tourism Management Plan, which provides support for rural tourism in many fields and aspects, and takes rural tourism as an important means to promote the reconstruction and development of rural economy [28]. Lóránt analyzed the development of rural tourism from the perspective of natural conditions, made a comprehensive study of Finland and Hungary, and believed that climate change would have an impact on tourism in coastal and mountainous areas [29]. Miller posited that the main factors affecting the development of rural tourism in the United States are environmental facilities, residents, and economic spillover effects [30]. In 2021, the Italian government put forward the National Rural Revitalization Plan (Piano Nazionale Borghi), divided into two lines of intervention, to achieve rural revitalization. This paper mainly takes pilot villages as the object, combines the protection of cultural heritage with the revitalization of social economy, develops rural tourism, improves the employment environment, and reduces the loss of rural population [31]. Regarding developing countries, the Government of South Africa sees rural tourism as a major means of promoting the standard of living of people living in rural tourist communities through activities such as wildlife watching and gambling [32]. Furthermore, the conclusions about the spatial differentiation characteristics and influencing factors of rural tourism key villages can be applied to other developing countries and provide effective reference value for the spatial layout optimization and sustainable development of rural tourism destinations.

China’s rural tourism activities started late, began in the early 1970s, and gradually rose in the 1990s. Domestic scholars’ research has mainly focused on the development mode [33], the path of rural tourism [34], the evolution of tourism’s impact perception, and the attitude of rural tourism community residents [35,36]. However, there have been a few studies on the spatial structure and influencing factors of rural tourism destinations. The provinces, cities, and individual villages were investigated to explore the spatial structure, spatial evolution, and spatial optimization of rural leisure tourism destinations [37,38,39,40,41]. The resource endowment basis and natural environment conditions are important factors affecting the geographical distribution of rural tourism [42,43]. Economic level, traffic location, tourist market, policy orientation, and operating environment also affected the spatial characteristics and structural types of key rural tourism villages to varying degrees [44,45,46,47,48]. Overall, researchers have extensively discussed rural tourism from different perspectives and accumulated rich theoretical research results. However, most of the existing studies were focus on the micro level, lacking an in-depth exploration of the spatial distribution characteristics of rural tourism destinations and their influencing factors from the macro scale. It is rare to consider the natural, cultural, and economic factors in the overall research framework of key rural tourism villages. In China, few studies have been performed on the spatial differentiation characteristics and influencing factors of rural tourism villages, especially the key villages of rural tourism. Therefore, it is necessary to study the spatial distribution characteristics of the three batches of key rural tourism villages in China.

Therefore, the spatial distribution characteristics with three batches of rural tourism key villages in China were analyzed as the research object. The results found that the spatial distribution of rural tourism key villages had obvious clustering characteristics. Then, the influencing factors of rural tourism key villages were analyzed in the context of China’s natural environment base, cultural tourism resources, and socioeconomic development level. The aim is to optimize the development and integration of rural tourism resources, give full play to regional advantages, create distinctive rural tourism places, and drive the high-quality development of surrounding tourism villages. Meanwhile, it provides classification guidance and scientific guidance with practical significance for the selection and construction of key rural tourism villages and provides a quantitative scientific basis for the sustainable development of rural tourism. In addition, we also discuss how to use the spatial distribution characteristics of “key villages” to develop a distinctive road of rural tourism and help the rural revitalization strategy, especially to optimize the spatial layout of rural tourism key villages, explore the development potential of rural tourism and improve the infrastructure and tourism supporting facilities for the construction of rural tourism key villages.

2. Materials and Methods

2.1. Data Sources

The data from the three batches of 1199 key rural tourism villages in 2019, 2020, and 2021 were published on the official website of the Ministry of Culture and Tourism of China (https://www.mct.gov.cn/, accessed on 6 September 2021) (excluding Hong Kong, Macao and Taiwan). The Baidu coordinate picking system (https://maplocation.sjfkai.com/, accessed on 12 October 2021) was used to sort the geospatial information of key rural tourism villages in China. The spatial vector map was made from the standard map. The map review number of GS (2019) 1822 was downloaded from the standard map service website (http://bzdt.ch.mnr.gov.cn/, accessed on 20 October 2020) of the China Service Bureau of Surveying, Mapping and Geographic Information. China’s 250 m resolution DEM digital elevation data and river system data were obtained from the Data Center for Resources and Environmental Sciences of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 16 September 2020). The limits of the four major regional plates in China refer to the results of the literature [49]. Relevant data of key rural tourism villages, regional economic development data, and 5A-level tourist attractions were from the website of the China Statistical Bureau (http://www.stats.gov.cn/in, accessed on 20 September 2020) 2020, regional statistical yearbooks, and relevant statistical bulletins.

2.2. Methods

2.2.1. Nearest Neighbor Index

The nearest neighbor index can be used to analyze the proximity of key tourism villages in geographical space and measure the spatial distribution type of point elements [50]. The spatial distribution pattern of key tourism villages can be found more accurately by using ArcGIS software spatial statistical tools. The calculation formula is as follows:

$$R=\frac{{\overline{r}}_i}{{\overline{r}}_E}$$

(1)

In this equation, R indicates the nearest neighbor index, and ${\overline{r}}_i$ indicates the average of the distance r_i between the nearest neighbor and the theoretical nearest neighbor distance. When R < 1, the distribution of key villages of rural tourism tends to be concentrated; when R > 1, the distribution of key villages of rural tourism is uniform; when R = 1, it is random. ${\overline{r}}_E$ is calculated as follows:

$${\overline{r}}_E=\frac{1}{2}\sqrt{\raisebox{1ex}{$n$}\!\left/ \!\raisebox{-1ex}{$A$}\right.}$$

(2)

In this equation, An indicates the study area and n indicates the number of key rural tourism villages in the study area.

2.2.2. Geographic Concentration Index

The geographic concentration index is mainly used to measure the concentration degree of the spatial distribution of samples in the geographical space. The geographic concentration index can be used to determine and evaluate the agglomeration of key rural tourism villages in the actual spatial distribution. When the G value is larger, the key rural tourism villages are concentrated in a certain region; in contrast, the smaller the G value is, the more dispersed the distribution [51]. The calculation formula of G is as follows:

$$G=100\times \sqrt{\sum _{i=1}^n{\left(\frac{P_i}{M}\right)}^2}$$

(3)

In this equation, G represents the geographic concentration index; P_i represents the number of key rural tourism villages in the ith province; M indicates the total number of rural tourism key villages; and n indicates the total number of provinces.

2.2.3. Disequilibrium Index

The disequilibrium index is an index reflecting the distribution of point-like elements, which can be used to analyze the balanced distribution of key rural tourism villages in various provinces and regions in China. In addition, the Lorenz curve was introduced in this paper to further reflect the imbalance of key villages. The Lorenz curve represents the cumulative proportion of variables and ranks them from low to high [52]. The calculation formula is as follows:

$$S=\frac{{\displaystyle \sum _{i=1}^n}{Y}_i-50(n+1)}{100n-50(n+1)}$$

(4)

In this equation, S indicates the disequilibrium index; n indicates the number of provinces and regions; and Y_i indicates the number of key villages in each province and is sorted from largest to smallest.

2.2.4. Kernel Density Estimation

In statistics, kernel density estimation is a nonparametric method of estimating the probability density function of a random variable, where a known density function is averaged over the observed data points to create a smooth approximation. Kernel density estimation can reflect the characteristics of spatial dispersion or concentration by testing the regional spatial variation of sample point density [53]. As a result, the dispersion of the concentration and area samples can be determined. The calculation formula is as follows:

$$f\left(x\right)=\frac{1}{nh}{\displaystyle \sum }_{i=1}^{n}k\left(\frac{x-xi}{h}\right)$$

(5)

In this equation, f(x) indicates the estimated value of the kernel density of key rural tourism villages; n indicates the number of key villages of rural tourism; x−x_i indicates the distance from the estimated point x to the sample x_i; h > 0 indicates the bandwidth; and k indicates the spatial weight function.

2.2.5. Geographic Connection Rate

The geographical connection rate was used to analyze the impact of tourism resource endowment on the spatial distribution of key rural tourism villages in China. Meanwhile, 5A-level scenic spots were selected as the measurement index of tourism resource endowment. The geographical connection rate ranged from 0 to 100. The larger the value, the higher the degree of spatial coincidence between key rural tourism villages and tourism resource endowments, and the closer the spatial connection between them. The calculation formula is as follows:

$$V=100-\frac{1}{2{\displaystyle \sum _{i=1}^n}\left|x_i-y_i\right|}$$

(6)

In this equation, V indicates the geographical connection rate; x_i indicates the proportion of the number of key rural tourism villages in the ith province to the total number of key rural tourism villages in China; and y_i indicates the proportion of the number of 5A scenic spots in the ith province to the total number of 5A scenic spots in China.

2.2.6. Spatial Autocorrelation Analysis

Spatial autocorrelation analysis is used to determine whether there is a correlation between a point element and its adjacent points in space, and it is an important index to measure the spatial correlation. The index can evaluate the importance of the index by calculating Moran’s I index value, Z score and p value. The global Moran index can also be used to determine whether the spatial distribution of elements in a region reflects an aggregated, discrete, or random pattern [54]. The $\mathit{Getis}-{\mathit{OrdG}}_i^{\ast }$ index was selected to study the spatial distribution of hot and cold areas of key rural tourism villages [55]. The formula for calculating Moran’s index is as follows:

$$I=n\frac{{\displaystyle \sum _{i=1}^n}{\displaystyle \sum _{j=1}^n}{W}_{\mathit{ij}}(X_i-\overline{X})(X_j-\overline{X})}{{\displaystyle \sum _{i=1}^n}{\displaystyle \sum _{j=1}^n}{W}_{\mathit{ij}}{\displaystyle \sum _{i=1}^n}{(X_i-\overline{X})}^2}$$

(7)

In this equation, X_i and X_j represent the number of key rural tourism villages in regions i and j, respectively; W_ij indicates the spatial position weight matrix; when i and j are adjacent, W_ij = 1; otherwise, when i and j are not adjacent, W_ij = 0; $\overline{X}$ represents the average value; and n represents the total number of samples. Moran’s value range is [–1, 1]; the value of +1 meaning strong positive spatial autocorrelation, to 0 meaning a random pattern, and to −1 indicating strong negative spatial autocorrelation. The standardized formula of the $Getis-Ord{G}_i^{*}$ measure is as follows:

$$Z\left(G_i^{*}\right)=G_i^{*}-E\left(G_i^{*}\right)/\sqrt{Var\left(G_i^{*}\right)}$$

(8)

In this equation, $E\left(G_i^{*}\right)$ and $Var\left(G_i^{*}\right)$ indicate the theoretical expectation and variance of $\left(G_i^{*}\right)$, respectively. If $Z\left(G_i^{*}\right)$ is positive and significant, it indicates that location i belongs to high-value spatial agglomeration and belongs to “hot spot area”; if $Z\left(G_i^{*}\right)$ is negative and significant, the number of location i is lower than the mean value and belongs to “cold spot area”, indicating low-value spatial agglomeration.

3. Results

3.1. Spatial Distribution Characteristics

3.1.1. Spatial Distribution Equilibrium

In 1935, Mr. Hu Huanyong put forward the population demarcation line, namely the Tengchong–Heihe Line, also known as the Hu Huanyong Line. It is a dotted line stretching from Heihe to Tengchong, dividing the area of China into two roughly equal parts [56]. As shown in Figure 1, there were significant differences in the number of key rural tourism villages in different regions. A total of 969 key rural tourism villages (80.82%) were distributed east of the Heihe–Tengchong Line, while 230 key rural tourism villages (19.18%) were distributed west. From the analysis of both sides of the Hu Huanyong Line, the key villages of rural tourism in China show a spatial distribution trend of “dense in the east and sparse in the west”, showing obvious characteristics of meridional regional differentiation.

From the perspective of the “four major plates” of China’s regional coordinated development strategy, the “four plates” are the western region, the northeast region, the eastern region, and the central region. At present, China’s regional coordinated development strategy includes promoting the development of the western region to form a new pattern, accelerating the revitalization of the old industrial base in Northeast China, promoting the rise of the central region, and realizing the “four major plates” for the optimal development of the eastern region [57]. The distribution of key rural tourism villages in China is unbalanced (

Table 1. Statistics on the number of key rural tourism villages in China’s four major sectors.

Area	Eastern China	Central China	Western China	Northeastern China	China
Number of the first batch	99	64	130	27	320
Proportion of the first batch	30.94	20.00	40.62	8.44	100
Number of the second batch	209	136	274	61	680
Proportion of the second batch	30.74	20.00	40.29	8.97	100
Number of the third batch	62	42	78	17	199
Proportion of the third batch	31.16	21.10	39.20	8.54	100
Number of three batches	370	242	482	105	1199
Proportion of three batches	30.86	20.18	40.20	8.76	100

). With respect to its batches of tourism key villages, the number of tourism key villages belonging to the four major segments of the region all show a trend of first increasing and then decreasing. Compared with the first batch, the growth of the second batch of tourism key villages belonging to the four major boards is very significant; for example, the number of tourism key villages in the eastern region increased from 99 to 209. Compared with the first two batches, the number of tourism key villages in the third batch belonging to the four major boards is the lowest. The reason is that there are standards for the selection of key villages for rural tourism [58]. The key rural tourism villages announced in 2019 have attracted wide attention from the industry, and the key rural tourism villages have become influential rural tourism brands, setting a model benchmark for rural tourism and rural revitalization with villages as units [59]. Therefore, the number of key rural tourism villages was to increase dramatically in 2020. However, by 2021, there were few villages that met the selection criteria for key tourism villages or were actively improving. Therefore, the number of key rural tourism villages in 2021 was relatively small. However, in terms of the proportion of tourism key villages belonging to the four major boards, the regions are stable in all three batches, and the proportion they account for is basically the same. The number of key tourism villages in the western region is the largest, with a total of 482 (40.20%); the number in the eastern region is 370 (30.86%). The distribution of key tourism villages in the central and northeastern regions was relatively scarce, accounting for 20.18% and 8.76%, respectively.

From the provincial perspective, the geographical concentration index G of the three batches of key rural tourism villages in China was 18.34. If three batches of 1199 key rural tourism villages were evenly distributed in 31 provinces and regions in China, the numbers of key tourism villages in each province and region were approximately 39, with G₀ 18.11. G > G₀indicates that the distribution of key tourism villages was relatively concentrated at the provincial scale.

In Figure 2, the Lorenz curve of the distribution of key rural tourism villages in China showed an obvious convex form, indicating an unbalanced state. The number, percentage, and cumulative percentage of key rural tourism villages in China’s provinces and regions show the same changing trend (

Table 2. Statistics on the number of key rural tourism villages in China’s provinces and regions.

Area	First Batch	Second Batch	Third Batch	Total Number	Proportion (%)	Cumulative Proportion (%)
Xinjiang	15	41	11	67	5.59	5.59
Zhejiang	14	26	7	47	3.92	9.51
Jiangsu	13	26	7	46	3.84	13.34
Guizhou	12	26	7	45	3.75	30.78
Hubei	11	27	7	45	3.75	45.04
Jiangxi	12	25	7	44	3.67	34.45
Yunnan	13	23	7	43	3.59	16.93
Fujian	11	26	6	43	3.59	52.04
Sichuang	12	23	7	42	3.50	27.02
Hebei	11	24	7	42	3.50	37.95
Anhui	12	22	7	41	3.42	23.52
Hunan	11	23	7	41	3.42	48.46
Shandong	10	24	7	41	3.42	61.88
Shaanxi	11	23	6	40	3.34	41.28
Guangxi	11	22	7	40	3.34	55.38
Guangdong	10	22	7	39	3.25	68.31
Gansu	12	20	6	38	3.17	20.10
Henan	10	21	7	38	3.17	65.05
Beijing	9	23	6	38	3.17	73.98
Heilongjiang	10	21	6	37	3.09	58.47
Xizang	9	21	5	35	2.92	79.73
Chongqing	9	20	6	35	2.92	82.65
Liaoning	9	21	5	35	2.92	85.57
Ningxia	9	20	5	34	2.84	76.81
Jilin	8	19	6	33	2.75	88.32
Shanxi	8	18	7	33	2.75	91.08
Qinghai	8	20	5	33	2.75	93.83
Inner Mongolia	9	15	6	30	2.50	70.81
Hainan	8	16	5	29	2.42	96.25
Tianjin	7	11	5	23	1.92	98.17
Shanghai	6	11	5	22	1.83	100.00
Total	320	680	199	1199	100.00	100.00

). In the three batches, the number of key rural tourism villages in China’s provinces and regions experienced an inverted “V” trend of first increasing and then decreasing. On the one hand, the key villages of rural tourism announced in 2019 year attracted wide attention from the industry, which had good social repercussions, and the key role of demonstration initially emerged [58]. On the other hand, 2020 year was a decisive year for poverty alleviation and a key year for the full realization of a well-off society [60]. Therefore, the number of key tourism villages announced in 2020 was large, aiming to promote the rural revitalization strategy and promote the sustainable development of rural tourism. The imbalance index S of key rural tourism villages was calculated to be 0.11. Through Formula (4), the S value was low, indicating a certain number of key rural tourism villages in all provinces and regions.

3.1.2. Spatial Distribution Pattern

ArcGIS10.5 was used to calculate the nearest neighbor index (NNI) of the location elements of key rural tourism villages in 31 provincial-level administrative regions, and the calculated area was set to be 9.478057 million km² (excluding Hong Kong, Macao, and Taiwan) according to the results of the second national land survey in China Land Resources and Utilization. The average observed distance was 32,126.38 m, while the expected average distance was 44,454.94 m, and the NNI was 0.72. As shown in

Table 3. Analysis parameters of the average nearest neighbor distance of key rural tourism villages in China.

Area	Number	Nearest Neighbor Index	Z Value
China	1199	0.72	−18.37 **
Eastern China	370	0.29	−26.17 **
Central China	242	0.35	−19.41 **
Western China	482	0.56	−18.56 **
Northeastern China	105	0.28	−14.03 **

Note: ** shows that the Z-test is passed at 1% significance.

, the Z value was −18.37, the p-value of the significance test was 0.00, and the confidence level was 99%. The NNIs of the eastern, central, western, and northeastern plates were 0.29, 0.35, 0.56, and 0.28, respectively, which were lower than those of the whole nation.

3.1.3. Spatial Distribution Density

Thirty-one provincial-level administrative regions in China (excluding Hong Kong, Macao, and Taiwan) were taken as the scope for analysis. Meanwhile, ArcGIS10.5 software was used to analyze the kernel density of the first, second, and third batches of rural tourism key villages and all three batches of villages. Based on previous studies, Jenks’s natural discontinuity classification method [61] was used to set the same parameters for analysis according to the ArcGIS10.5 software. According to the size of the kernel density value, the region was divided into very low-density area, low-density area, medium-density area, high-density area, and very high-density area. However, the size of the kernel density value presented by each batch is not the same. It can explain in more detail which interval the kernel density value is in and compare the differences of three groups of key rural tourism villages more clearly. The spatial distribution density map of rural tourism key villages is shown in Figure 3, revealing that it varies significantly in different regions in China.

The first batch of key rural tourism villages formed two very high-density agglomeration centers, two high-density agglomeration belts, and two high-density agglomeration centers in space. The very high-density agglomeration centers were located in the Beijing–Tianjin–Hebei region and the Yangtze River Delta region. The high-density agglomeration zones were mainly located in the North China Plain, Guanzhong Plain, the lower reaches of the Yangtze River Basin, and the junction of Sichuan, Guizhou, and Chongqing. The fourth high-density centers were in the Huangshui Valley, the central part of Ningxia, the northern part of Guangxi, and the eastern part of Hainan Island. Furthermore, four low-density core areas have been formed, including the southern Tibetan area centered on Lhasa in Tibet, the western area centered on Dunhuang in Gansu, the Junggar Basin at the northern foot of the Tianshan Mountains in Xinjiang, and the Changbai Mountain area in the eastern part of the Liaoning and Jilin Provinces. The second batch of key rural tourism villages has formed two very high-density agglomeration centers, one north–south high-density agglomeration belt, and three high-density agglomeration centers. The two very high-density agglomeration centers were consistent with the first batch, and the high-density agglomeration zones were mainly located in the North China Plain, the central part of Shaanxi Province, the border area between Guizhou and Chongqing, the middle and lower reaches of the Yangtze River Basin, the hilly areas of Shandong Province and the Wuyishan region. Compared with the first batch, the three high-density agglomeration centers decreased in the central part of Ningxia and increased in the southern foot of Changbai Mountain in eastern Liaoning. The third batch of key rural tourism villages has formed four very high-density agglomeration centers, one high-density agglomeration belt, and three high-density agglomeration centers. Compared with the first two groups, the four very high-density agglomeration centers are mainly located in the Huangshui Valley and the eastern part of the Taihang Mountains. The high-density agglomeration zones were mainly located in the North China Plain, the junction of Sichuan, Guizhou and Chongqing, and the middle and lower reaches of the Yangtze River Basin. Compared with the first batch, the three high-density agglomeration centers were reduced in the Huangshui Valley, central Ningxia, and northern Guangxi and increased in the central Shaanxi and Wuyishan areas.

From the kernel density distribution of three batches of key rural tourism villages in China, two very high-density agglomeration centers, one north–south high-density agglomeration belt, four high-density agglomeration centers and several low-density areas were scattered in lumps. Two very high-density agglomeration centers are in the Beijing–Tianjin–Hebei region represented by Beijing and Tianjin and in the Yangtze River Delta region at the junction of Jiangsu, Zhejiang, Anhui, and Shanghai. A north–south high-density agglomeration zone was mainly located in the North China Plain, the central part of Shaanxi Province, the junction of Guizhou and Chongqing, the middle and lower reaches of the Yangtze River Basin, Shandong hills, and Wuyi Mountains.

Overall, the very high-density area of the kernel density analysis of the key rural tourism villages was mainly located east of the Heihe–Tengchong Line, which was consistent with the developed social economy and dense population. However, owing to the poor natural environment, low level of economic development, low population density, and poor traffic accessibility, the key villages of rural tourism were sparsely distributed in the west.

3.1.4. Spatial Distribution Correlation

The global autocorrelation of the spatial distribution of rural tourism key villages is shown in

Table 4. Global Moran’s I index of key rural tourism villages in China.

Batch	Global Moran’s I Index	Expectation Index	Variance	Z Value	p-Value
First batch	0.206	−0.029	0.006	3.086	0.002
Second batch	0.156	−0.029	0.006	2.437	0.015
Third batch	0.212	−0.029	0.006	3.203	0.001
The merger of three batches	0.185	−0.029	0.006	2.824	0.005

. The Global Moran’s I index of the first, second, and third batches of rural tourism key villages were 0.206, 0.156, and 0.212, respectively. The corresponding normal statistical values Z were 3.086, 2.437, and 3.203, and the test effect was significant, indicating a significant positive spatial correlation. Based on previous studies, Jenks’ natural discontinuity classification method [61] was used to divide the cold spot area (−1.34~−1.29), sub-cold spot area (−1.28~−1.01), insignificant (−1.00~−0.74), sub-hot spot area (−0.73~1.40), and hot spot area (1.41~2.61) according to the Z value (Figure 4). It can explain in more detail in which range the hotspot analysis is and more clearly compare the differences between the three batches of key rural tourism villages.

The hot spots of the first batch of key rural tourism villages were mainly distributed in Beijing, Tianjin, Jiangsu, Zhejiang, and Shanghai, while the sub-hot spots were distributed in Hebei, Shandong, Anhui, Jiangxi, Fujian, Hainan, and Ningxia. The sub-cold spots were in Shaanxi, Gansu, and Xinjiang. The cold spot areas were in the Qinghai–Tibet Plateau, the Sichuan Basin, and the Inner Mongolia Autonomous Region. The hot spots of the second batch of key rural tourism villages were consistent with those of the first batch. The sub-hot areas were basically stable, and Guangdong Province became a sub-hot area. Gansu Province in the sub-cold spot area was changed to the cold spot areas. The hot spots of the third batch of rural tourism key villages were consistent with those of the first two batches, in addition to Gansu Province in the sub-cold spot area changing to the cold spot areas. Moreover, the Inner Mongolia Autonomous Region was transformed into a cold spot area. According to the total amount of three batches of key rural tourism villages, the hot spots were concentrated in the Beijing–Tianjin region and Yangtze River Delta region, while the cold spots were mostly concentrated in the Qinghai–Tibet Plateau and the Sichuan Basin.

According to the total amount of three batches of key rural tourism villages, the hot spots were concentrated in the Beijing–Tianjin region and Yangtze River Delta region, while the cold spots were mostly concentrated in the Qinghai–Tibet Plateau and the Sichuan Basin.

3.2. Analysis of Influencing Factors

3.2.1. Natural Environmental Factors

Elevation above Sea Level

The elevation determines the topographic relief of villages and directly affects the distribution regions of villages. There are differences in climate, vegetation, soil, and other natural environmental factors in different elevation areas, which have a direct impact on the location, scale structure, and agricultural production mode of the villages [62]. Because of the suitable climate, high vegetation coverage and good ecological environment in low-altitude areas, it is conducive to the site selection and construction of villages. The impact is that it is easier to gather population and form relatively large-scale gathering villages, which is conducive to agricultural production and life. However, the combination of water and heat, vegetation cover, and the soil growth environment in high-altitude areas are relatively unfavorable to the distribution of villages and the development of industries, resulting in the formation of rare and small-scale villages, and the production and life of agriculture are extremely inconvenient [63]. The distribution map of key rural tourism villages was analyzed by ArcGIS 10.5 and 250 m resolution DEM elevation data in China (Figure 5). According to the landform classification standard in China, it is divided into plains (0~200 m), hills (200~500 m), mountains (500~1000 m), Class I plateaus (1000~2000 m), and Class II plateaus (>2000 m) [64,65]. As shown in Figure 6, the number of key rural tourism villages located below 200 m above sea level reached 36.70%, and the number between 200 m and 500 m was up to 20.68%. However, the number of key rural tourism villages located within 500–1000 m and 1000–2000 m above sea level were relatively similar, 17.01% and 17.35%, respectively, while the number above 2000 m above sea level was only 8.26%. Overall, with the increase in altitude, the number of key rural tourism villages decreased.

River System

River systems are a necessary condition for the development of rural tourism and provide basic material guarantees for human production and life [63]. The key villages of rural tourism were mainly distributed in the middle and lower reaches of the Yangtze River, the upper and lower reaches of the Yellow River, and the middle and lower reaches of the Pearl River using the overlay analysis method (Figure 7). The river systems not only provided production and living water for villages but also regulated the transportation mode and climate. Moreover, suitable hydro-logical conditions provided a beautiful natural landscape for the development and construction of rural tourism villages [66]. Therefore, the river system is an important factor affecting the spatial distribution of key rural tourism villages in China.

3.2.2. Resource Endowment Factor

Tourism resource endowment is an important factor affecting the tourism development of a region [67]. Under the current evaluation and management standards of tourism resources in China, the level of tourism resource endowment is mainly measured by the level of scenic spots. Grade 5A scenic spots are typical representatives of high-quality, high-endowment, and high-value tourism resources and represent the brand a of regional tourism development [68]. The Ministry of Culture and Tourism announced 306 5A-level scenic spots in China in 2020. 5A-level scenic spots were selected as the measurement index of regional tourism resource endowment, and the impact of tourism resource endowment on the spatial distribution of key rural tourism villages was analyzed. The results show that the geographical connection rate L was 99.85, and there was a close relationship between them.

3.2.3. Socioeconomic Factors

Population Density

Population density is one of the main factors affecting the spatial distribution of key rural tourism villages. The larger the population density, the larger the tourism market capacity and the potential tourism market [69]. Population density is the number of people per unit of land area. High-density population gathering in economically developed areas, to a certain extent, determines the development and construction of key tourism villages, which has a significant radiation and driving effect on the development of surrounding villages. In Figure 8, because of the high population density, good economic foundation, and high degree of modernization, the key rural tourism villages were mainly distributed in the east area of the Hu Huanyong Line, especially in the eastern coastal areas. This caused the agglomeration effect of key tourism villages. Due to the large land area, sparse distribution of tourism villages, small population and fragile ecological environment of the remote areas of Xinjiang, Inner Mongolia, Tibet, and other provinces, the key rural tourism villages were sparsely distributed, accounting for a relatively low proportion.

Economic Development Level

The level of regional economic development determines the distribution, development, and construction of key rural tourism villages. The GDP of each region in 2020 with the number of key rural tourism villages was combined for overlaying analysis (Figure 9). The results show that the GDP of Guangdong, Jiangsu, Shandong, Zhejiang, and other eastern coastal provinces and the number of key rural tourism villages were at the forefront. There was a positive correlation between the level of economic development and the distribution of key rural tourism villages in these provinces.

Traffic Location Conditions

Transportation is one of the six elements of tourism linking tourism destinations and tourist sources, including railways, highways, aviation, and water transport [70]. The source market of rural tourism is mainly the surrounding cities facing self-driving tours. Thus, the distribution of key rural tourism villages was affected by road traffic factors. Referring to the standard of tourists’ strong sense of experience, the buffer radius is 20 km and 40 km, respectively, and tourists can ride and drive to the main road within one hour [71]. As shown in Figure 10, the 20 km buffer zone of the main highway traffic line covered 746 key rural tourism villages (62.2%), and the 40 km buffer zone covered 1014 key rural tourism villages (84.6%). This indicates that the traffic location conditions had a significant impact on the spatial distribution of key villages of rural tourism. Therefore, the development degree and time sequence of rural tourism along traffic roads were constantly improving and optimizing, which further promoted the agglomeration development of key rural tourism villages along traffic routes.

3.2.4. Tourist Market Factors

In recent years, with the rapid development of rural tourism, the public has been glad to choose the short, frequent, and fast way of vacation. Rural tourism has become an important tourist destination for urban residents to relax. According to previous studies, there is a low valley of rural tourism in the area about 50 km away from the tourist market, and there is a dense belt in the area about 70 km away from the city [72]. Therefore, taking prefecture-level cities and provincial capitals as the center, the buffer zone radius is set at 40 km and 80 km, respectively, and the buffer zone analysis map is drawn. As shown in Figure 11, 309 key villages of rural tourism were within the 80 km buffer zone of provincial capital cities, and 414 key villages of rural tourism were within the 40 km buffer zone of prefecture-level cities. Thus, 60.30% of the key villages were surrounded by buffer zones established by provincial capital cities and prefecture-level cities.

3.2.5. Policy Factors

The development and construction of rural tourism is inseparable from the support of national policies. In recent years, China and local governments have promulgated a series of rural tourism policies and regulations. These policies and documents effectively promoted the development of rural tourism.

4. Discussion

On the whole, the development of key rural tourism villages in China is unbalanced in space, and the eastern region is obviously better than the western region, which is consistent with Ma [67]. From the perspective of the four major sectors of China’s regional coordinated development strategy, among 1199 identified key tourism villages, the eastern region of China accounts for 30.86%, the central region accounts for 20.18%, the western region accounts for 40.20%, and the northeastern region accounts for 8.76%. All regions should prioritize the protection and rational development of tourism. Thus, the planning and construction of rural tourism should highlight the pattern of geographical spatial differences that make full use of tourism resources and location advantages in different regions to promote the sustainable development of rural tourism. In the process of rural tourism development, especially in rural tourism destinations, due to the role of many factors, we should take full advantage of beautiful landscapes, natural environments, cultures, and other tourism resources [73]. Attention should be paid to the level of social and economic development in the surrounding areas, especially the characteristics of population distribution. In addition, we should activate the various functions of agriculture, such as rural ecological leisure, tourism, culture and other values. Blind development will overload many aspects of rural operation and may cause some damage to the ecological environment in some rural areas [74].

In terms of overall density, the high-density areas of key rural tourism villages are distributed east of the Heihe–Tengchong Line. The nuclear density distribution of key rural tourism villages has two obvious agglomeration areas, which are distributed in the eastern region with a developed economy and dense population. From different regional scales, the degree of aggregation in the east is the highest, followed by the middle, the northeast, and the west. We can find that the distribution of kernel density in the distribution of key tourism villages in China is independent and obviously unbalanced. In the process of developing key tourism villages in the future, priority should be given to areas with low kernel density, rich tourism resources, and high level of economic development. In terms of spatial correlation, the hot spots are concentrated in the eastern coastal areas, mainly in Beijing, Tianjin, Jiangsu, Shanghai, and Zhejiang, while the cold spots are mainly concentrated in the provinces represented by the Qinghai–Tibet Plateau and Sichuan Basin. Therefore, in future research and the selection of key rural tourism villages, the eastern coastal areas should be based on the existing development experience and problems, pay attention to the innovation of products and services, explore the development path of deep integration of cultural industry and rural tourism, and realize the high-quality and high-end development of rural tourism products and services. Western remote areas should face their own shortcomings, further improve their development policy, enhance their innovation ability, increase financial support for key rural tourism villages, pay attention to the diversification of the types of key rural tourism villages, and realize the transformation of rural tourism products from sightseeing to leisure and deep experience.

The natural geographical environment is the environmental basis for the sustainable development of key rural tourism villages. Most of the key tourist villages are in plain and hilly areas with low altitude and abundant rainfall. Agriculture has been developed in these areas, and a series of projects combining agriculture and tourism can be reasonably designed, such as “forestry + entertainment” and “breeding + farm life experience”. Based on respecting agricultural production and life, we should build rural agricultural tourism and leisure destinations. In addition, the development of tourism in key tourist villages located in areas with higher altitude and less rainfall often faces problems such as shortage of tourist market and small population. The government can increase capital investment and advertising, rather than large-scale transformation, to protect the original environment to attract tourists to outdoor extreme sports.

China’s rich tourism resources have laid the foundation for rural leisure tourism destinations, especially the key tourism villages, which are widely distributed in combination with tourism resources. According to the “National Eco-tourism Development Plan (2016–2025)”, China will become a big ecotourism country by 2025. Therefore, we should pay more attention to the protective development of the original ecological natural resources, rationally create representative regional tourism brands, develop characteristic tourism products, adjust measures to local conditions, and gradually promote the sustainable development of rural ecotourism.

The spatial distribution of key tourism villages is consistent with high-density population and economically developed cities, which shows that these key tourism villages need a vast population base and economic input to support. Greater population aggregation provides more potential tourists, thus strengthening the driving force of the tourism market. More urbanized areas generally have greater spending power, which can also enhance the driving force of the market. Travel preferences will be more diverse in a larger population base. Therefore, population density is also a socioeconomic factor that leads to more tourist-focused villages.

Most of the key tourist villages in China are in developed areas, such as the eastern coastal areas of China. This shows that the further development of the economy has provided more perfect infrastructure and service facilities for tourists. In addition, tourism investment in these areas is also relatively high. At the same time, a higher GDP increases per capita disposable income, which may stimulate tourists’ strong desire to spend. In contrast, economically underdeveloped areas are often unable to meet the development needs of key tourism villages. However, in the western region of China, the income of key tourism villages accounts for a large proportion of GDP. Therefore, the development of key tourism villages will effectively promote economic growth and increase the income of villagers.

The factors affecting the distribution of key tourism villages show that key tourism villages have a strong dependence on highway traffic. The quality of tourism accessibility directly affects the time and economic cost for tourists to reach key tourism villages, and then affects their decision-making and the development of key tourism villages. From the spatial distribution characteristics of key tourism villages in China, there are more key tourism villages in the surrounding areas of transportation hub cities because of their developed highway system and perfect transportation facilities. In areas with inconvenient transportation, even in areas with abundant tourism resources, there are few key tourism villages due to complex terrain, poor transportation infrastructure, and difficult development. For example, although Tibet and Qinghai are rich in tourism resources, their transportation infrastructure is poor. Therefore, the transportation facilities of key tourist villages should be improved in the future.

As far as kernel density analysis and hot spot analysis are concerned, this paper discusses the spatial distribution analysis of three batches of key tourism villages from a macro perspective. The results show that the high-density areas are concentrated in the Yangtze River Delta and Beijing–Tianjin–Hebei region, the hot spot areas are concentrated in the eastern coastal areas, and the cold spot areas are mainly concentrated in the Qinghai–Tibet Plateau and Sichuan Basin. Compared with previous studies [75,76], the nuclear density analysis is consistent with the hot spot area, but different from the cold spot area in the western inland area. From the perspective of batch combination, the three-batch combination analyzed in this paper is different from the previous two-batch combination. The distribution of hot spots and cold spots is almost unchanged, but the expansion range of sub-cold spots and sub-hot spots is different. In addition, this paper accurately summarizes the spatial distribution characteristics of three groups of key rural tourism villages, which are hot in the central and eastern regions and cold in the northwest and northeast regions. In terms of influencing factors, this paper only discusses the single influencing factors of the distribution of key tourism villages from a macro perspective and reveals the relationship between different influencing factors and the distribution of key tourism villages. Different from previous studies [76,77], based on the detection of geographical detector, the influencing factors are divided into different levels. In the use of two-way interaction detection, relevant policies, resource endowments, traffic accessibility and economic development level are strong influencing factors. Tourism reception capacity and market potential are moderate influencing factors. Terrain characteristics are relatively weak influencing factors. It can directly reflect which influencing factor has an effect on the key villages of rural tourism.

In summary, the natural geographical environment affects the spatial differentiation characteristics of key tourism villages through tourism resources, and economic development, population size, and transportation networks affect their development through consumption level, tourism market demand, and tourism infrastructure, respectively.

5. Conclusions

In this paper, the spatial distribution characteristics of three groups of 1199 key rural tourism villages in China were quantitatively analyzed by the nearest index, geographic concentration index, and kernel density analysis methods. Furthermore, the influencing factors were further explored using the geographical connection rate and buffer analysis methods. The main conclusions are as follows.

• The spatial distribution of key rural tourism villages in China has an obvious regional differentiation and generally presents the spatial structure characteristics of “dense in the east and sparse in the west”, which may be related to the population density and the level of economic development in different regions. Meanwhile, the spatial distribution of key villages presents a cohesive distribution trend.
• In terms of spatial density distribution, the very high-density agglomeration centers were mainly located in the Beijing–Tianjin–Hebei region represented by Beijing and Tianjin and the Yangtze River Delta region at the junction of Jiangsu, Zhejiang, Anhui, and Shanghai, while the low-density areas mainly include the southern Tibet area with Lhasa as the center, the Junggar Basin at the northern foot of Tianshan Mountains in Xinjiang, and the Changbai Mountain area in the eastern part of the Liaoning and Jilin provinces. It can be seen from this that the kernel density distribution of key tourism villages in China is independent and obvious, with high density concentrated in the southeast and low density concentrated in the northwest. Its distribution shows a clear imbalance. Therefore, in the process of developing and constructing key tourism villages in the future, priority should be given to areas with low kernel density, rich tourism resources, and a high level of economic development.
• Tourism villages showed a significant spatial autocorrelation, and the key rural tourism villages in different regions represented spatial agglomeration characteristics. The hot spots were mainly concentrated in Beijing, Tianjin, Jiangsu, Shanghai, and Zhejiang, while the cold spots were mainly concentrated in the Qinghai–Tibet Plateau and Sichuan Basin. The distribution of cold and hot spots in the distribution of key tourism villages in China is independent and obvious, and its distribution shows a clear imbalance. In the process of selecting key tourism villages in the future, priority should be given to cold hot spot areas.
• The spatial distribution characteristics of key rural tourism villages were the result of the interaction and coupling of multiple factors. The analysis of topographic factors shows that the key villages of rural tourism are more distributed in the plain and hilly areas with relatively gentle and low altitude, which can provide different experiences for tourists, provide site selection for the development and construction of key tourism villages in the future, and promote the sustainable development of rural tourism. Consistent with the existing conclusions, the distribution of rural tourism key villages near the source of the river is denser, and with the increase in distance from the water area, the distribution of villages is more discrete, showing hydrophilicity. In densely populated areas, agricultural production activities are concentrated, which promotes the formation and development of rural tourism. In addition, there is a strong correlation between the level of economic development and the distribution of key villages. Residents in areas with higher level of economic development have higher requirements for rural tourism leisure and are more willing to pay for rural tourism services, thus promoting the sustainable development of rural tourism. The analysis of traffic location factors shows that the traffic accessibility of rural tourism destinations directly affects the accessibility of tourists. Most of the key tourist villages are located within the radiation range of 40 km of the highway, and citizens are more inclined to travel for short distances. The distance between the development of rural tourism destinations and urban centers has a positive impact on the development of tourism. The key villages of rural tourism are surrounded by the buffer zones established by provincial capitals and prefecture-level cities, which is consistent with the conclusions of previous studies. To sum up, these factors are important factors to promote the sustainable development of rural tourism.
• The research results of this paper have important theoretical significance and practical value for the full implementation of the rural revitalization strategy in the new era, the promotion of tourism supply side structural reform, and the improvement of rural tourism scale efficiency and sustainable development. Based on the analysis of this paper, the development of key rural tourism villages is unbalanced in space, and the eastern region is obviously better than the central and western regions. Therefore, it provides scientific guiding significance for optimizing the spatial structure layout of rural tourism key villages, rationally allocating tourism spatial resources and promoting the sustainable development of rural tourism. The high-density areas and hot spots of key rural tourism villages are mostly concentrated in the Yangtze River Delta and Beijing–Tianjin–Hebei region, and the proportion of areas east of Hu Huanyong Line is as high as 80.82%. In the future, it can provide reference for realizing the balance and heterogeneity of key rural tourism villages, aiming at promoting the sustainable development of rural tourism with high quality. In addition, from the perspective of traffic location, the 20 km buffer zone of the main highway traffic line covers 746 key rural tourism villages, accounting for 62.2%, which shows that the accessibility of rural tourism needs to be improved. Therefore, this is needed in order to strengthen the construction of rural tourism transportation network and build a sustainable development system for the accessibility of rural tourism destinations.
• There are some limitations or weaknesses in this study. First, the Chinese Culture and Tourism Bureau has published three lists of key rural tourism villages in China. The authors made a static analysis of the spatial dimension of key tourism villages, but not a dynamic analysis from the time series level. Therefore, follow-up studies can gradually expand the research perspective to the time dimension according to the publication of the directory. Second, in this study, we conducted a single preliminary exploration of the main factors affecting the formation of key villages for rural tourism. However, access to rural data is constrained, and we did not conduct a quantitative study on the coupling coordination among key tourism villages in different regions and the local environment, economy, population, transportation, and urbanization. These aspects need further study. To improve the pertinence and effectiveness, we will select different types of key rural tourism villages with representativeness to conduct field research and case studies and carry out special discussions and studies on the promotion of local rural tourism sustainable development and employment of local farmers in future research.