Introduction

Traditional villages are complex living cultural heritages containing traditional production and life. The Recommendation concerning the Safeguarding and Contemporary Role of Historic Areas (Nairobi Recommendation), adopted by UNESCO in 1976, states that: “Each historic area and its surroundings should be considered as a whole as an interrelated unity, whose coherence and identity depend on the association of its components, which include human activities, buildings, spatial structures and the surrounding environment”. In China, a large number of surviving traditional villages show a harmonious coexistence of cultural and natural Their sites are mostly selected based on the natural advantages place of mountains, topography and rivers, and are built as rural settlements integrating “forests, water, fields, villages and mountains”. Strengthening the protection, development and utilization of traditional villages is not only the key to inheritance excellent traditional Chinese culture, but also an important part of promoting rural revitalization.

Chinese traditional villages are an important carrier of traditional culture and a community of villagers’ social life, and are known as “the living fossil of rural history and culture”. The ecological value, humanistic value, aesthetic value and social value of traditional villages give them potential for tourism development and utilization. In recent years, with the leaping development of transportation infrastructure in southwestern China, all kinds of original and peaceful traditional villages have been continuously explored by the market and discovered by tourists, becoming niche destinations and holy places for leisure and slow travel. The construction of transportation road network has accelerated the formation of “fast tour and slow travel” mode. Tourists in the journey time is getting shorter and shorter, in the villages’ time is getting longer and longer. Traditional villages rely on resource endowment characteristics and transportation accessibility advantages to develop rural tourism1, which has gradually become an important way to gather resources2 and drive the diversified development of tourism3.

Currently, research on traditional villages mostly focuses on spatial layout4, pattern evolution5and mechanism formation6. These studies often based on landscape genetics theory7, cultural ecology theory8, symbolic interaction theory9, tourism gaze theory10, sustainable development concepts, etc., using POI, geodetector11, entropy weight method, etc., to explore the evolutionary pattern of traditional villages12. In terms of the association between traditional villages and tourism attractiveness, it mainly focuses on the influence of rural tourist attractions on tourists’ willingness to revisit13, the spatial association between traditional villages and rural tourism14, as well as the evaluation system of the integration of cultural tourism in traditional villages15. In terms of research themes, the main focus is on tourism development strategies for living heritage16, the well-being of communities brought about by the development of tourism in traditional villages17, and the impact of village transformation on traditional livelihoods18. These studies have important implications for clarifying the relationship between traditional villages and the development of the tourism industry. As the construction of transportation infrastructure continues to extend to Southwest China, it is becoming more and more obvious that the increasingly developed transportation has a facilitating effect on the tourism of traditional villages. However, there is a lack of research on the relationship between tourism development in traditional villages and the road transportation network, as well as a lack of systematic explanation of the dynamics and interactions among traditional villages, tourism attractiveness, and transportation accessibility. Therefore, the study adopts ArcGIS, the modified gravity model and the research method of transportation accessibility to construct a model of the power mechanism between the spatial pattern of traditional villages, the attractiveness of regional tourism, and the transportation accessibility, and tries to clarify three aspects of the problem: (1) What is the spatial distribution of the 724 traditional villages in 88 districts and counties in Guizhou Province, and what is the correlation among the spatial distribution of traditional villages, tourism attractiveness and transportation accessibility. (2) What is the strength of the relationship between the spatial distribution of traditional villages and their tourist attractiveness as well as their transportation accessibility, and what factors influence this state. (3) What kind of mutual influence factors and driving elements are there between the spatial distribution of traditional villages, tourism attractiveness and transportation accessibility. This study will provide an important reference for understanding the tourism development value of traditional villages and promoting the sustainable development of rural tourism.

Study area, research methods, and data sources

Study area

Guizhou is located in the southwest of China, its geographical position adjacent to Hunan in the east, Guangxi in the south, Yunnan in the west, and Sichuan and Chongqing in the north. It’s one of the most typical areas in the world for the development of karst landforms, and it’s also a mountainous province, which is in the second ladder of China. In Guizhou Province, there are the Dalou Mountains in the north, the Miao Mountains in the central section, the Wuling Mountains in the northeast, and the Wumeng Mountains in the west, with an average elevation of about 1100 m, spanning the Yangtze River and the Pearl River system. The forest coverage of the province reaches 62.81%, which have beautiful mountains, rivers and pleasant climate. Guizhou Province has 9 cities, which including 88counties and districts. There are 17 ethnic minorities living in the there, and the Miao ethnic group is the most populous among them. There are three main reasons why Guizhou was chosen as the case site for the study. First, Guizhou is one of the most typical regions in the world for karst landscapes. The typical karst landscape and the historical and cultural habitat of multi-ethnic settlement have given birth to 724 Chinese traditional villages. Secondly, the mountainous area of Guizhou accounts for 92.5%, and there is an old saying that “Guizhou is eight mountains, one water and one field”. Traditional villages with regional characteristics are overlapping and interlocking along rivers, mountains, rivers, hills and basins in Guizhou, they constitute rural tourism resources. Thirdly, in recent years, with the continuous improvement of road transportation in Guizhou and the deepening construction of the tourism brand of the “mountain park province”, the traditional villages that used to be little-known have become niche destinations, as shown in Fig. 1.

Fig. 1
figure 1

Location of the study area. (created by ArcGIS, version 10.4, http://www.esri.com/).

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Data sources

The data sources used in this article mainly comes from five aspects. (1) The data for different batches of traditional villages comes from the website in Ministry of Housing and Urban–Rural Development of the People’s Republic of China. The first batch of traditional village lists was announced in 2012, the second batch was announced in 2013, the third batch was announced in 2014, the fourth batch was announced in 2016, and the fifth batch was announced in 2019. (2) The geographical coordinates of traditional villages are derived from the Baidu Pickup Coordinate System (https://api.map.baidu.com). (3) The transportation network data in Guizhou comes from the National Geographic Information Cloud Service Platform (https://www.gscloud.cn). (4) The total number of tourists and total income in various cities of Guizhou are sourced from the official websites of the governments of 9 cities. (5) The mileage of highways between various cities are mainly obtained from the distance traveled by vehicles from the starting and ending points of the Gaode map (https://www.amap.com/).

Research methods

Analytical framework

In order to deeply explain the correlation and power mechanism among the spatial distribution pattern of traditional villages, tourism attraction and transportation accessibility, the study adopts ArcGIS combined with tourism attraction utility model and road network accessibility analysis method, and selected DEM, elevation, acreage, demographic, tradional village, Intangible Cultural Heritage, A tourist attraction, hotels, total tourist arrivals, passenger terminal, transportation network density, miles of roads in operation as 12 factors, and constructed a model to explain the power mechanism among the three dimensions. Then used geographically weighted regression and Spearman’s correlation analysis to construct an interpretive model of the power mechanism between the three dimensions, as shown in Fig. 2.

Fig. 2
figure 2

Analytical framework.

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Coefficient of Variation (CV): The CV is used to measure the degree of difference sample indicators in different regions, and this article is used to reflect the degree of difference in traditional villages among 9 cities. The equations are:

$$CV = \frac{{\sqrt {\mathop \sum \nolimits_{i = 1}^{n} \left( {X_{i} - \overline{X}} \right)^{2} } }}{n}/\overline{X}$$
(1)

In this formula, Xi represents the number of traditional villages in the ith city; n is the total number of cities in Guizhou; \(\overline{X }\) is the average number of traditional villages in each city. The larger the CV value is, the more remarkable the spatial differences are.

The Herfindahl Index is a comprehensive index used to measure concentration, which is used in this article to reflect the degree of regional agglomeration in traditional villages. The equations are19:

$$H=\sum_{i=1}^{n}{\left(\frac{{X}_{i}}{T}\right)}^{2}$$
(2)

In this formula: Xi represents the number of traditional villages in the ith city; T represents the total number of traditional villages in Guizhou Province. The closer the H value approaches 1, the higher agglomeration degree of traditional villages is; The closer the H value approaches 0, the lower agglomeration degree of traditional villages is. When 0.18 < H, the agglomeration degree of traditional villages are high oligopoly. When 0.1 < H < 0.18, the agglomeration degree of traditional villages are low oligopoly. When H < 0.1, the agglomeration degree of traditional villages is competitive.

The standard deviation ellipse method of spatial offset degree can reflect the nature of spatial distribution such as centrality, spatial extent, and evolution direction of geographical elements based on parameters such as the center, long and short axes, azimuth, and flatness of the ellipse20,21. In this study, the above method is adopted to measure the degree of offset about traditional villages in terms of spatial distribution.The equations are:

Center of gravity:

$$\overline{X }=\frac{\sum_{i=1}^{n}{x}_{i}}{n}$$
(3)
$$\overline{Y }=\frac{\sum_{i=1}^{n}{y}_{i}}{n}$$
(4)

In this formula, xi and yi are the geographical coordinates of the research unit i; n is the total number of research units; \(\overline{X }\) and \(\overline{Y }\) are the coordinate center of the elliptical.

Elliptical rotation angle:

$$tan\theta =\frac{\left(\sum_{i=1}^{n}\Delta {x}_{i}^{2}-\sum_{i=1}^{n}\Delta {y}_{i}^{2}\right)+\sqrt{{\left(\sum_{i=1}^{n}\Delta {x}_{i}^{2}-\sum_{i=1}^{n}\Delta {y}_{i}^{2}\right)}^{2}+4\sum_{i=1}^{n}\Delta {x}_{i}^{2}\Delta {y}_{i}^{2}}}{2\sum_{i=1}^{n}\Delta {x}_{i}\Delta {y}_{i}}$$
(5)

In this formula θ is the azimuth of the rotation axis; \(\Delta {x}_{i}\) and \(\Delta {y}_{i}\) are the differences between the coordinates of unit i on the x and y and the average center coordinates.

Standard deviation of x and y:

$${\sigma }_{x}=\sqrt{\frac{{\sum }_{i=1}^{n}{(\Delta {x}_{i}^{2}cos\theta -\Delta {y}_{i}^{2}sin\theta )}^{2}}{n}}{ \sigma }_{y}=\sqrt{\frac{{\sum }_{i=1}^{n}{(\Delta {x}_{i}^{2}sin\theta -\Delta {y}_{i}^{2}cos\theta )}^{2}}{n}}$$
(6)

In this formula: \({\sigma }_{x}\) and \({\sigma }_{y}\) represents the standard deviation of the research unit along the x and y.

Global Moran’s I is a comprehensive statistical index employed to gauge the spatial autocorrelation of spatial data, thereby determining the presence of significant spatial patterns within a dataset22. The formula is as follows23:

$$Moran^{\prime}s I = n\frac{{\mathop \sum \nolimits_{i = 1}^{n} \mathop \sum \nolimits_{j = 1}^{n} w_{ij} \left( {x_{i} - \overline{x}} \right)\left( {x_{j} - \overline{x}} \right)}}{{\mathop \sum \nolimits_{i = 1}^{n} \mathop \sum \nolimits_{j = 1}^{n} w_{ij} \mathop \sum \nolimits_{i = 1}^{n} \left( {x_{j} - \overline{x}} \right)}}$$
(7)

where n represents the number of observational units, xi denotes the value of the itℎ observational unit, and \(\overline{x }\) denotes the average of all observational unit values. wij is an element of the spatial weight matrix that illustrates the extent of spatial connectivity between the itℎ and jtℎ locations. A positive value of the global Moran’s index indicates positive spatial autocorrelation, suggesting that similar values are spatially proximate, whereas a negative value implies negative spatial autocorrelation, in which dissimilar values tend to cluster spatially. If the global Moran’s index approaches zero, the spatial distribution exhibits a random pattern, and no significant spatial pattern is present. When Z > 1.96 or Z e < − 1.96(α = 0.05) rejecting the null hypothesis, which indicates significant spatial autocorrelation in traditional villages24.

Local Moran’s I, also known as clustering and outlier analysis, can be used to calculate the hotspots, cold spots, and spatial outlier functions of different spatial units. The specific formula is:

$${I}_{i}={Z}_{i}\sum_{j\ne 1}^{n}{w}_{ij}{Z}_{ij}$$
(8)

In this formula, Zi and Zj are the standardized forms of observed values for region i and j, Wij is the spatial weight of the observed region, and n is the number of research objects. The local spatial correlation can be divided into four quadrants: where H-H represents a higher level of self and surrounding areas; L-L represents a low level of oneself and the surrounding area, while H-L represents a high level of oneself and being surrounded by low-level areas; L-H indicates low self level and being surrounded by high-level areas.

Tourism attractiveness utility

This study used a modified gravity model to measure the tourism attractiveness of traditional villages in Guizhou. The equations are25:

$${R}_{ij}={k}_{ij}\frac{\sqrt{{P}_{i}{V}_{i}}\sqrt{{P}_{j}{V}_{j}}}{{D}_{ij}^{2}}$$
(9)
$${k}_{ij}=\frac{{g}_{i}}{{g}_{i}+{g}_{j}}$$
(10)

In these formulas, Rij represents the degree of tourism economic connection between city i and j; P and V represent the total tourism revenue and the total number of red tourism people in different cities; D is the highway mileage between city i and j; kij is the correction coefficient; g represents the number of traditional villages.

Accessibility of road

This article uses network analyst combined with road network data in Guizhou Province to analyze the accessibility of tourists to traditional villages. In the calculation process of network analysis method, firstly, the collected data was uniformly transformed into raster graphic files, and a network dataset was constructed. Then, using the network analysis tool in ArcGIS to set the range of isochronous circles, and inputed the distance impedance values, routes and other attributes to calculate the accessibility of traditional villages.

Results

Spatial structure characteristics of traditional villages

The study classify and organize 724 traditional villages from five batches, and create a radar chart to visually the quantity structure of traditional villages in Guizhou Province at different times, as shown in Fig. 3a–e.

Fig. 3
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(a–e) Structural radar maps of five batches of traditional villages in 9 cities in Guizhou Province.

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From Fig. 3a–e, it can be seen that regardless of the period or batch, Qiandongnan has the highest number of traditional villages, ranking first among the nine cities, while Guiyang and Bijie have the lowest number of traditional villages. As shown in Fig. 3a–e, in the first, second, third, and fourth batches, the number of traditional villages in Tongren is second only to Qiandongnan. As can be seen from Fig. 3e. In the fifth batch, the number of traditional villages in Qiannan exceeded that of Tongren, ranking second.

To visualize the evolutionary characteristics of traditional villages in Guizhou Province, five batches of traditional villages were imported into ArcGIS 10.4, and the distribution of different batches of traditional villages in 88 counties was presented, as shown in Fig. 4a–e.

Fig. 4
figure 4

(a–e) Evolution characteristics of traditional villages in five batches of Guizhou Province.

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As shown in Fig. 4a–e, overall, the five batches of traditional villages are mainly concentrated in the southeast of Guizhou, and the number is relatively concentrated in the central and northeastern parts of Guizhou. As shown in Fig. 4a, c, and d, it can be seen that in the first, third, and fourth batches of traditional villages, the number of traditional villages in Xixiu District of Anshun City is relatively concentrated.

Spatial imbalance of traditional villages

Coefficient of variation

To measure the degree of difference in traditional village indicators among 9 cities in Guizhou Province, the study used formula (1) to calculate the coefficient of variation values of traditional villages in 9 cities, as shown in Table 1.

Table 1 Coefficient of variation of traditional villages in Guizhou Province.
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From Table 1, it can be seen that the coefficient of variation in Qiandongnan is the highest, which indicates that the traditional villages in Qiandongnan have the highest volatility, it means the number of traditional villages in Qiandongnan deviates the most from the mean. And the coefficient of variation in Anshun City is the lowest, which indicates that the volatility of traditional villages in Anshun City is the smallest, it means the number of traditional villages in Anshun City is closest to the mean.

Herfindahl coefficient value

To measure the comprehensive index of concentration of traditional villages in 9 cities in Guizhou Province and reflect the degree of regional agglomeration of traditional villages in China, the Herfindahl coefficient was calculated using formula (2), as shown in Table 2.

Table 2 Herfindahl coefficient value of traditional villages in Guizhou Province.
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From Table 2, Qiandongnan’s H > 0.18, it indicates that traditional villages have a high degree of agglomeration, and the agglomeration type belongs to the high oligopoly type. Except for Qiandongnan, the H of the other 8 cities is less than 0.1, it indicates that the clustering degree of traditional villages in these 8 cities is low, and the clustering type is competitive type.

Standard deviation ellipse

To reflect the centrality, spatial range, and evolutionary direction of the geographical elements of five batches of traditional villages, this study combined formulas (4), (5), and (6) to draw the standard deviation ellipse of traditional villages using ArcGIS 10.4, as shown in Fig. 5 and Table 3.

Fig. 5
figure 5

Standard deviation ellipse of five batches of traditional Chinese villages in Guizhou Province.

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Table 3 Parameters related to the standard deviation ellipse of five batches of traditional villages in Guizhou.
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From Fig. 5, it can be seen that the first batch of traditional Chinese villages in Guizhou Province are mainly distributed along the southeast-northwest direction, and are mainly distributed in the southeast of Guizhou and Tongren. The second batch is mainly distributed along the southeast-northwest direction, and is mainly distributed in Qiandongnan and Tongren. The third batch is mainly distributed along the southwest- northeast direction, and is mainly distributed in Qiandongnan, Anshun, and Tongren. The fourth batch is mainly distributed along the southwest-northeast direction, and is mainly distributed in Qiandongnan, Tongren, and Anshun, with a small portion distributed in Zunyi. The fifth batch is mainly distributed along the southeast-northwest direction, and is mainly distributed in Qiandongnan and Anshun, with a small portion distributed in Zunyi and Qiannan.

The center of gravity of various batches of traditional villages in Guizhou from 2012 to 2019 in Fig. 5 first moved from southeast to northwest, and finally from northwest to southeast. And the center of gravity of the standard deviation ellipse all fell in the area of Qiandongnan, which indicates that the traditional villages in Qiandongnan ranked first in Guizhou Province.

In Table 3, the larger the area of the standard deviation ellipse, the wider the distribution of traditional villages in Guizhou Province, and vice versa. The short half axis of the standard deviation ellipse represents the range of data distribution. The shorter the short half axis is, the more pronounced the centripetal force presented by the data is. From Table 3, it can be seen that in the ratio of area, the fourth batch > the third batch > the fifth batch > the first batch > the second batch. Among the five batches, the fourth batch of traditional villages is the most widely distributed, followed by the third, fifth, and first batches, and the second batch has the narrowest distribution range. In the short half axis ratio, the fourth batch > the fifth batch > the third batch > the second batch > the first batch. The fourth batch of traditional villages had the highest degree of dispersion.

Overall, the five batches of traditional villages are mainly distributed in the eastern and southwestern regions of Guizhou Province, with the vast majority distributed in Qiandongnan Anshun, Tongren, and Zunyi, while a small number are distributed in other cities.

Spatial correlation

To explore the spatial correlation characteristics of traditional villages in Guizhou Province, according to formula (7), five batches of traditional villages were imported into ArcGIS 10.4 to obtain the global Moran index value, the Z-score and P-value are shown in Table 4.

Table 4 Global Moran index for the First to Fifth Batches of Traditional Villages in Guizhou Province.
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From Table 4, it can be seen that the global Moran index of the five batches of traditional villages is greater than zero and shows a gradually increasing trend, it indicates that traditional villages in Guizhou Province are positively correlated in space. That is, the closer the global Moran index of traditional villages is to 1, the higher the degree of positive correlation with space; The Z-scores of each batch of traditional villages are all greater than 1.96, and the P-values are all less than 0.05, rejecting the null hypothesis, it indicates that there is a significant spatial autocorrelation in traditional villages in Guizhou Province.

The global Moran index can only reflect the spatial correlation characteristics of traditional villages in Guizhou Province as a whole, therefore, this study further applies the local spatial autocorrelation of formula (8) to analyze the local spatial characteristics of the batches of traditional villages in Guizhou Province.

According to formula (8), traditional villages in Guizhou are imported into ArcGIS 10.4 to obtain the spatial agglomeration distribution area of traditional villages in Guizhou Province, as shown in Fig. 6a–f and Table 5.

Fig. 6
figure 6

(a–f) Localised Moran index of five batch traditional villages in Guizhou Province.

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Table 5 Distribution of localised spatial clusters of traditional villages in Guizhou Province.
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According to Fig. 6a–f and Table 5, it is found that traditional villages exhibit an H-H, H-L, L-H, and L-L model. Overall, the spatial distribution of traditional villages in Guizhou Province is dominantly characterized by H-H, L-L clustering.Followed by L-H clustering, with a small amount of H-L local spatial clustering distribution observed in the analysis of traditional villages in the second, fourth, and fifth batches. Among them, The H-H aggregation area is mainly distributed in Qiandongnan, while the third and fourth batches are slightly distributed in Tongren and Anshun. Qiandongnan is a typical multi-ethnic region and also the most densely populated area of the Miao and Dong ethnic groups in China. The administrative region has preserved unique ethnic symbols such as residential architecture, food culture, folk festivals, song and dance art, and traditional skills, and has built a traditional village cluster with ethnic minority cultural characteristics. Tongren also promotes the gathering of traditional village elements due to the unique ethnic culture generated by the settlement of ethnic minorities such as Miao, Dong, and Tujia. Anshun, on the other hand, was influenced by the “military garrison system” of the Ming Dynasty in China and retained the customs constrained by historical policies in terms of village layout, residential architecture, and ethnic costumes, thus forming a traditional village group with historical and cultural characteristics. The L-L aggregation area is mainly distributed in Guiyang and Bijie, with a small amount distributed in Zunyi and Qiannan. As the capital of Guizhou Province, Guiyang has rapid economic development, strong cultural inclusiveness and high urbanization rate, while Bijie, Zunyi and Qiannan are spatially adjacent to Guiyang and driven by the radiation of the center of Guiyang, with good development of its urbanization, infrastructure, etc. Therefore, the gathering of traditional villages in these four regions is relatively scattered, thus forming a “low-low” gathering pattern.

Tourism attractiveness

To measure the tourism attractiveness of traditional villages, this study takes the total tourism income, total tourism visits, per capita tourism expenses, and travel distance between different regions in 2023, and using formulas (9) and (10), to calculate the tourism attractiveness values Rij of 9 cities in Guizhou, and then imported Rij into ArcGIS 10.4 for visualization, as shown in Fig. 7.

Fig. 7
figure 7

Tourism attractiveness of traditional villages.

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As seen in Fig. 7, the tourism attractiveness of each city presents centering on Guiyang and radiating to the five cities under the Central Economic Circle. That is, Guiyang and Zunyi, Guiyang and Qiandongnan, Guiyang and Anshun, Guiyang and Qiannan, Guiyang and Bijie have larger values of tourism attractiveness. It indicates that Guiyang has the strongest degree of tourism economic ties with these five cities. Among them, the tourism attractiveness between Guiyang and Zunyi is especially the highest, indicating that the degree of tourism economic connection between Guiyang and Zunyi is the strongest. In addition, the tourism attractiveness values of Guiyang and Qiandongnan follow Guiyang and Zunyi are in the middle. This indicates that Guiyang and Qiandongnan have stronger tourism attraction and closer tourism economic ties between the two cities. It can be seen that the number of traditional villages affects the tourism economic links between regions and further affects the development of the tourism industry between regions.

Transportation accessibility

To analyze the road network accessibility of 724 traditional villages, the study used network analysis method combined with road network data from Guizhou Province to obtain the road network accessibility of 88 districts and counties of Guizhou Province, as shown in Fig. 8.

Fig. 8
figure 8

Transportation accessibility of traditional villages in Guizhou Province.

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As shown in Fig. 8, traditional villages are mainly centered around Qiandongnan, forming six isochronous circles. Among them, traditional villages in Qiandongnan have the best accessibility, with a reach time between 1.5 and 2 h. Secondly, the accessibility of traditional villages in most areas of Qiannan, Guiyang, and Tongren is second only to Qiandongnan, with a reach time of 2–3 h. Thirdly, in some areas of Anshun, Zunyi, and Tongren, traditional villages can reach within 3–3.5 h. Fourthly, the accessibility of traditional villages in most areas of Qianxinan, Liupanshui, Bijie, and a small part of Zunyi is poor, with a reach time of over 4 h. It can be seen from Fig. 8 that, there is a positive relationship between the concentration of traditional villages and accessibility. That is, the more concentrated the traditional villages are, the better the accessibility.

Analysis of factors affecting relevance

The spatial distribution pattern of traditional villages can be affected by geographic location, elevation, slope direction and population factors. For example, in Guizhou, the Miao people tend to live on the hillsides and the Buyi people live on the riversides. At the same time, traditional villages, as an important rural tourism resources, will affect the distribution of tourists as well as influence the connection between tourist destinations. The generation of tourism attraction is closely related to the endowment of tourism resources, the degree of perfection of tourism service facilities, and the level of development of tourism economy. In addition, the tourism system is the result of the interaction between tourism sources, tourism destinations and tourism corridors. Transportation is seen as the medium between tourism sources and destinations and as a prerequisite for the development of tourism, it is both an important “engine” for tourism development and a key driver for enhancing the centrality of tourism destinations26. The magnitude of the interaction nodes in a transportation network is called transportation accessibility27. The convenience of transportation affects tourists’ willingness to travel28, and the accessibility of transportation affects tourists’ satisfaction29, in addition, the degree of integration of transportation and tourism affects the attractiveness of tourist destinations, and also affects the exchange and integration of passenger, capital, information, and material flows, and then affects the efficiency and level of inter-regional tourism cooperation30. Adeola and Evans argue, transportation accessibility of tourist attractions refers to the convenience of tourists to reach tourist attractions through various modes of transportation, covering transportation cost, transportation time, transfer efficiency31. Arinta et al. argued that congestion due to insufficient transportation capacity will significantly lengthen the time to reach tourist attractions and reduce accessibility32.

In order to accurately investigate the spatial pattern of traditional villages and the influencing factors of tourism attractiveness and transportation accessibility, the study selected 12 factors of DEM, elevation, acreage, demographic, tradional village, Intangible Cultural Heritage, A tourist attractions, hotels, total tourist arrivals, passenger terminal, transportation network density, miles of roads in operation as the measurement data. The study categorized the above 12 indicators into three dimensions, spatial differentiation system, tourism support system, and transportation facilitation environment system, as shown in Table 6.The study first used the ordinary least squares (OLS) method for the measurement, and the results found that the variance inflation factor (VIF) of the 12 indicators were less than 7.5, and there were no redundant indicators.

Table 6 Impact indicators.
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Then the geographically weighted regression was used to measure it, and the study listed “total tourism income” as the dependent variable and the above three dimensions as the explanatory variables, among which the R2 of the spatial differentiation system, the tourism support system, and the transportation facilitation system were all greater than 0.5, which showed that the model fit was better in general, and the specific spatial fitting differences are shown in Fig. 9a–c.

Fig. 9
figure 9

GWR results of spatial differences in the impact of systems on tourism development.

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As can be seen from Fig. 9a–c, the standard error values of the three systems are less than 2.5, and the model indicators are relatively reliable. To further explore the relationship between the indicators, the study used the Spearman method to measure the correlation between them, and the results are shown in Fig. 10.

Fig. 10
figure 10

Heat map of Spearman square correlation analysis.

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As can be seen from Fig. 10, firstly, there is a significant positive correlation between Demographic and elevation, Intangible culture heritage and traditional village, and total tourist arrivals and hotels. It can be seen that the degree of traditional village agglomeration will affect the advantages and disadvantages of rural cultural resources and rural tourism resource endowment. The advantages and disadvantages of resource endowment will make the evolution and development of tourist destinations produce polarization effect and convergence effect, which also affects the tourism vitality and tourism attraction of tourist destinations. The regional superposition effect produced by traffic accessibility will accelerate the formation of the central position of the tourist destination, the core advantage, and then contribute to the tourist destination to produce regional radiation effect, and promote the formation of the tourism corridor with traffic as the medium. Secondly, there is a relatively significant correlation between transportation network density and grade A tourist attractions, total tourist arrivals and passenger terminal, total tourist arrivals, total tourist arrivals and evelvation, total tourist arrivals and demographic. It can be seen that road transportation, as an important public facility infrastructure for promoting the high-quality development of tourism destinations, play a role in enhancing the attractiveness of tourism destinations and promoting tourism economy exchanges between cities. It is evident that the pioneering and strategic nature of transport, in conjunction with the integrated, interrelated and open nature of the tourism industry, facilitates the flow of material, information, financial and passenger flows to tourist destinations. In short, “traveling” and “touring” have become the new realities of tourism. Convenient, comfortable and cost-effective transportation accessibility will reduce the attraction decay due to the problem of long distance, promote the willingness to travel and improve tourist satisfaction, which is important to promote the transformation and upgrading of traditional villages as well as the high-quality development of tourist destinations. Nowadays, connecting scenic spots through roads, linking attractions through highways, and integrating scenery through roads have become the main trend to accelerate the development of tourist destinations. The series of derivatives developed around the integration of traffic and tourism, such as the integration of bridge tourism, cruise tourism, train tourism, self-driving tour and other new products will continue to inject new vitality into the development of tourist destinations.

Discussion and conclusion

Discussion

First of all, this study focuses on the interaction and power mechanism between the spatial pattern of traditional villages, tourism attraction and transportation accessibility, which is great significance to revitalize the tourism value of traditional villages, promote the extension of communication and tourism fusion products to the rural areas, and enrich the layout of reverse tourism, niche tourism, and self-relaxation tourism products in rural areas. Secondly, through quantitative analysis combined with spatial presentation, the study shows the correlation between the spatial distribution characteristics of traditional villages, tourism attractiveness and transportation accessibility, which increases the importance of transportation factors compared with previous studies, and has certain reference significance for the further deepening of the integration of the road trip to promote the transformation and development of traditional villages in the future. Finally, the analytical framework established by the study helps to make up for the lack of attention to the tourism attractiveness and tourism value of traditional villages, which is particularly important for further deepening the exploration of the cultural tourism value of traditional villages, and also provides ideas for the implementation of measures such as diversifying the development of cultural tourism products and enriching the quality of supply services of rural tourism products in the future.

This study also has certain shortcomings. Firstly, it lack of analyzing the causes of spatial differentiation from the perspective of historical geography. Secondly, the study lacks analyze the reasons why the non-equilibrium pattern of traditional villages formed from the perspective of migration of ethnic minorities. Thirdly, the study lacks analysis of policy guiding factors for the extension of traffic and tourism integration products to the countryside. In the future, based on Guizhou’s traditional village resource endowment, give full play to the comparative advantages of ethnic minority cultural heritage, and richly expand the integration of rural tourism, heritage tourism, cultural tourism, agricultural tourism and other products based on the transportation infrastructure will become the direction of attention.

Conclusion

The study used ArcGIS combined with a modified tourism attractiveness model and transportation network analysis tool to analyze the spatial pattern, tourism attractiveness, and transportation accessibility of 724 traditional villages in 88 counties in Guizhou Province, and established an explanatory model based on the three influencing mechanisms. Research has found that:

  1. (1)

    In terms of the spatial structure of traditional villages, the coefficient of variation and the Herfindahl coefficient are the highest in Qiandongnan, which indicates that traditional villages in Guizhou are mainly clustered in Qiandongnan. The centers of gravity of the standard deviation ellipses of the five batches of traditional villages also fall in the area of Qiandongnan. Traditional villages in Guizhou are mainly located in Qiandongnan in the H–H zone. L-L zone located in Guiyang, Bijie , and to a lesser in Zunyi and Qiannan. The reason is that Qiandongnan is the area where the Miao and Dong ethnic groups live in , and a large number of well-preserved Miao and Dong architectural complexes have been preserved in this area. The well-known Xijiang Thousand Houses Miao Village and Zhaoxing Thousand Houses Dong Village are both located in this area, the wind and rain bridges, drum towers that reflect the architectural characteristics of the Miao villages in the area have formed a spectacular architectural landscaping of minority villages. Guiyang is the capital city of Guizhou , due to the influence of modernization and urbanization, traditional villages reflecting rural characteristics in Guiyang are relatively scarced, while landmarks and neighborhoods reflecting the process of urban construction and development are more numerou.

  2. (2)

    (2)As far as the tourism attraction of traditional villages is concerned, Guizhou has formed an attraction structure centered on Guiyang , and the tourism attraction between the nine cities radiates around with the Qianzhong Economic Circle as the core. Among them, Guiyang and Qiandongnan, Guiyang and Anshun, Guiyang and Qiannan, Guiyang and Bijie, Guiyang and Zunyi have larger tourism attraction values. It can be seen that the number of traditional villages, as a kind of rural cultural tourism resource, affects the tourism and economic links between regions.

  3. (3)

    In terms of tourism traffic accessibility, traditional villages are mainly centered in Qiandongnan, forming six isochronous circles. Among them, the accessibility of traditional villages in Qiandongnan is the best. The accessibility of traditional villages in most areas of Qiandongnan, Liupanshui, Bijie and a small part of Zunyi is poor. It can be seen that the regional superposition effect generated by the accessibility of transportation will accelerate the formation of the core advantages of tourist destinations. This is best demonstrated by the fact that the “China Rural Tourism Highway No. 1”, formed around the traditional Miao and Dong villages in Qiandongnan, was awarded the “Top 10 Demonstration Cases of Integrated Development of Transportation and Tourism” by the Ministry of Culture and Tourism and the Ministry of Transportation of the People’s Republic of China.

  4. (4)

    In terms of correlates and drivers, the degree of clustering of traditional villages affects the advantages and disadvantages of rural cultural tourism resource endowment and is significantly related to regional tourism infrastructure such as hotels, Grade Ascenic spots, road network density, and passenger transportation stations. As one of the six elements of tourism, transportation is related to economic and social development, supporting the production and life essentials. Transportation and tourism feed into each other. Perfect transportation facilities can not only improve the quality of tourism and transportation services, but also promote high-quality development of the tourism industry. The rich traditional villages and cultural tourism resources attracted the extension of transportation facilities from the core city to the edge city. At the same time, the improving tourism infrastructure has greatly stimulated the public’s desire to travel, convenient and comfortable transportation will. And to a certain extent, overcome the attenuation of destination attractiveness due to long-distance commuting, and promote the management of transportation services to the scenic spots, improve the level of tourism public services and tourism service quality assurance.