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Article

Construction of a Symbiotic Traffic Network of Traditional Villages in the Western Hunan Region of China Based on Circuit Theory

by
Jiayun Peng
1,
Meng Tang
1,
Qin Li
1,
Lin Yang
1,
Lin Qiao
1,
Wenhai Xie
1,2,* and
Chunshan Zhou
3,*
1
School of Civil Engineering and Architecture, Jishou University, Zhangjiajie 427000, China
2
Rural Planning and Development Research Center of Wuling Mountain Area, Zhangjiajie 427000, China
3
School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(13), 5468; https://doi.org/10.3390/su16135468
Submission received: 12 April 2024 / Revised: 22 June 2024 / Accepted: 25 June 2024 / Published: 27 June 2024
(This article belongs to the Section Sustainable Urban and Rural Development)
Browse Figures
Versions Notes

Abstract

:
The concentrated and contiguous development of traditional Chinese villages is imminent, and the realization of their symbiotic and coordinated development has become both a priority and a challenge. Taking 370 traditional villages in Western Hunan as the research object, a GIS spatial analysis method was used to identify and extract the sources of traditional villages in Western Hunan, construct traffic resistance surfaces, identify traffic corridors and traffic nodes based on circuit theory, and construct and optimize the symbiotic traffic network of traditional villages in Western Hunan. The results show that the symbiotic traffic network of traditional villages in Western Hunan is composed of 47 traditional village sources, 77 traffic corridors, and 68 key traffic nodes, which are concentrated in the northwest and southeast of Western Hunan, showing a spider-like structure. The distribution of traditional village sources shows the characteristics of a high distribution in the north and a low distribution in the middle, and the traffic resistance surface shows spatial characteristics of being high in the north and low in the south. Four traffic corridor optimization principles and five key traffic node restoration strategies are proposed. This study provides a new concept underlying, and method for, the planning and construction of the symbiotic transport network of traditional villages in Western Hunan, promoting the centralized protection and utilization of traditional villages and the coordinated symbiotic development of regions and assisting in the implementation of the rural revitalization strategy.

1. Introduction

Traditional villages have rich historical and cultural values, which represent a valuable cultural heritage that cannot be reproduced and a significant cultural research value. In the process of regional development, the historical and environmental changes caused by rapid industrialization and urbanization have brought significant impacts and challenges to a large number of these traditional villages possessing important historical and cultural values [1,2]. In recent years, the protection and utilization of traditional villages have received increasing attention because these actions are of significant value and importance to the inheritance and promotion of traditional villages [3].
Most of the research hotspots in this subject have been focused on the cultural landscape, increases in tourism activities, and holistic protection [4,5,6]. Most of the existing studies identified and classified the characteristics of traditional villages based on the principle of holistic protection viewed from the perspective of policy and development [7] or have proposed renewal strategies for traditional villages [8] and linkage development models for village groups [9]. With advances in modernization, research into the protection and utilization of traditional villages is gradually integrating traditional village resources, transforming the original “point-type”, single-type protection and utilization into multi-type and multi-regional village cluster protection and utilization [10]. The construction of a symbiotic transport network can strengthen the radiative driving role of an influential, single traditional village, improve the connectivity and accessibility of traditional villages, and realize the centralized continuous protection and utilization of traditional villages in the region. Therefore, the rational construction of symbiotic transport networks of traditional villages and the enhancement of their holistic attractiveness and sustainable development ability [11] are of great significance for the implementation of strategies such as coordinated regional development and rural revitalization.
Previous research on traditional villages has mainly analyzed the influencing factors, the village network structure, and constructed traditional village connection models using geographical detector models [12,13], the GWR model [14], GIS network analysis [15], space syntax [16], gravity models [17], etc. However, there is a lack of relevant studies that either consider the collaborative development between traditional villages at the regional spatial level or examine how to quantitatively identify their spatial structural characteristics. Based on this, and combined with the needs of cultural ecological protection and economic development, it has become increasingly urgent to improve the construction of symbiotic transport networks in the region.
Circuit theory is principally used in the construction of ecological networks, which are used to model the connections between different landscapes. This model uses the random walk characteristic of an electric charge to link circuit theory with ecology. The construction of ecological security patterns based on circuit theory has formed a basic paradigm of “source identification–resistance surface construction–corridors extraction–key point identification” [18]. A series of elements in the ecological network is very similar to the symbiotic transport network, namely: ecological sources correspond to traditional village sources, ecological resistance surfaces correspond to traffic resistance surfaces, and ecological corridors correspond to traffic corridors. This study attempts to apply circuit theory to the construction of a symbiotic traffic network and considers the outstanding historical and cultural value of traditional villages from the perspective of the holistic protection and utilization of traditional villages. In addition, potential traffic corridors, traffic pinch points, barriers, and other pattern building elements for promoting the centralized protection and utilization of traditional villages are identified.
Located in the northwestern part of Hunan Province in China, Western Hunan is endowed with unique natural resources, a rich tapestry of traditional villages, and a diverse array of ethnic minorities. Hunan Province is also an area of 20 contiguous poverty-stricken counties [19]. With the increasing frequency of economic activities and the imbalance in regional development, traditional villages in Western Hunan urgently need symbiotic transport networks. This study applies the method of ecological security pattern construction to the new field of symbiotic transport networks in traditional villages and promotes the centralized, continuous protection and utilization of traditional villages in Western Hunan from the perspective of holistic protection. Therefore, the main objectives of this study are as follows: (1) to determine the sources of traditional villages in Western Hunan, based on the spatial analysis of kernel density; (2) the selection of multiple resistance factors to construct the traffic resistance surfaces of traditional villages in Western Hunan; (3) the quantitative identification of traffic corridors, traffic pinch points, and barriers in the western Hunan region based on circuit theory; and (4) taking into consideration the current state of the traffic networks in Western Hunan and the requirements of master planning, to optimize the symbiotic traffic networks in Western Hunan.

2. Materials and Methods

2.1. Overview of the Research Area

Western Hunan typically refers to the entire “West of Hunan Province”, which includes the Xiangxi Tujia and Miao Autonomous Prefecture, Zhangjiajie City, Huaihua City, and the western counties of Shaoyang. Western Hunan serves as a critical junction connecting the eastern and western regions of China, and unites the Yangtze River economic belt with the South China economic zone. This area is distinguished by its pronounced geographical characteristics and holds considerable strategic importance.
Western Hunan is located at the confluence of the Wuling and Xuefeng Mountains and is a typical mountainous terrain. The landform is dominated by mountains and hills, with plains and streams, alluvial plains on both sides, and widespread karst. Western Hunan has a subtropical monsoon humid climate with hot summers and cold winters, four distinct seasons, and abundant rainfall and hot weather in the same season. Under the influence of natural factors such as climate, hydrology, and geology, some local areas are prone to geological disasters such as landslides and debris flows.
Western Hunan is rich in historical and cultural heritage, including the World Geopark, and global cultural heritage, such as the Zhangjiajie UNESCO Global Geopark of China and the Laosicheng site in Yongshun County, Hunan. Western Hunan is also home to the Tujia, Miao, Dong, and other ethnic minorities, with unique intangible cultural heritage, such as Tujia brocade and Miao silver jewelry.
The research scope of this study is the Xiangxi Tujia and Miao Autonomous Prefecture, Zhangjiajie City, and Huaihua City, with a total area of 52,550 km2 across 24 districts and counties and 681 townships. By 2022, 370 traditional villages in Western Hunan have been listed in the national traditional villages list, accounting for 56.23% of the total number of traditional villages in Hunan Province. There are a large number of traditional villages in Western Hunan, which are spatially clustered; however, most of them are established in a relatively closed and isolated geographical environment. The construction of symbiotic transport networks between traditional villages is conducive to their sustainable development in Western Hunan (Figure 1).

2.2. Data Source and Pre-Processing

In this study, 370 traditional villages in Western Hunan were studied, and the data used principally include digital elevation model (DEM) data, traditional village point data, provincial and county administrative boundary data, and road network data. The digital elevation model was obtained from the geospatial data cloud and used to calculate the slope, the terrain ruggedness index, etc., with a spatial resolution of 30 m. Data on traditional villages were obtained from the Chinese Traditional Villages Directory of the Ministry of Housing and Urban-Rural Development to identify the sources of traditional villages. Provincial and municipal administrative boundary data and road data were obtained from the China Geographic Information Resources Directory Service System, and the road data included national roads, provincial roads, and city-level roads, with a ratio of 1:250,000. The rock strength data, traffic facility point POI data, and geological disaster point data were obtained from the Ecological Network of Chinese Geographical Sciences; the river network data were obtained from the Center for Resources, Environment and Data Science of the Chinese Academy of Sciences; and the data relating to ecologically sensitive areas were obtained from the geographic remote-sensing ecological network. Land-cover-type data were shared via the data website FROM-GLC10 [20] with a resolution of 10 m, which were used to construct the traffic resistance surface. In order to maintain the same spatial resolution for data from different sources, all data were uniformly resampled to 30 m. The projection coordinates were set to WGS 1984 UTM Zone 49N (Table 1).

2.3. Method Framework

The research method of this study included four steps (Figure 2). First, spatial analysis of kernel density method was used to identify and extract the traditional village sources in Western Hunan. Second, multiple factors were selected to construct the traffic resistance surfaces. Then, the traditional village traffic corridors and traffic nodes were identified in Western Hunan, where the nodes included traffic pinch points and traffic barriers. Finally, combined with the existing traffic network, the corridors were optimized, and the symbiotic traffic network of traditional villages in Western Hunan was constructed.

2.4. Circuit Theory and Its Development

Circuit theory requires a small amount of data, can quickly identify all possible corridors, can determine the width of the different spatial positions of the corridors [21], and can reveal the key areas on the corridors, namely the pinch points and barriers. However, it also has certain limitations, such as the identification of corridor needs that should be combined with the minimum cost path (LCP). Circuit theory is a geographical information network construction method proposed by McRae in 2006 [22] that has been widely used in the construction of ecological security patterns, with its core function being the identification of ecological corridors, pinch points, and barriers. Current innovations in ecological network construction based on circuit theory have mostly focused on method optimization [23] and the identification of key areas for ecological restoration [24], and interdisciplinary applications have emerged [25]. This study applies the construction method of ecological networks to the construction of traditional village symbiotic traffic networks and provides a scientific basis for the construction of traditional village symbiotic traffic networks in Western Hunan, based on circuit theory. The research steps principally determine the traditional village sources, construct the traffic resistance surface, identify the traffic corridors, and then, based on them, identify the traffic pinch points and traffic barriers.

2.5. Research Methods

2.5.1. Identification and Extraction of Traditional Village Sources

The scientific identification of sources is the basis for the construction of symbiotic transport networks. This study utilized 370 traditional villages as the research objects and adopted spatial analysis using the kernel density method [26] to scientifically identify and extract traditional village sources in Western Hunan.
The results of the spatial analysis of the kernel density method can reflect the agglomeration place and degree and the spatial distribution form of the traditional villages in Western Hunan, as well as the influencing degree on the surrounding areas. The denser the villages, the greater the possibility of centralized continuous protection and utilization, and the higher the nuclear density value. The calculation formula for the spatial analysis of kernel density is as follows [27,28,29]:
Densit = 1 ( r a d i u s ) 2 ∑ i = 1 n 3 π × p o p i ( 1 − ( d i s t i r a d i u s ) 2 ) 2
For d i s t i < radius, where i = 1, …, n represents the input traditional village points, with a total of 370 data elements. If i is located within the radius of the new predicted point (x,y), only the points in the sum are included; p o p i is the population field value of the traditional village point i , which is an optional parameter and refers to the volume of the space enclosed by the smooth surface [30] covered above and the plane below each traditional village point. d i s t i is the distance between the traditional village point and the new predicted point (x,y).
According to the characteristics of the study area and the size of the grid, the search radius was determined to be 1 km, and spatial analysis of the kernel density was carried out. When combined with natural conditions, such as the elevation and water system, as well as morphological angles, such as the spatial distribution and administrative boundaries of traditional villages [31], the region with a high nuclear density was selected as the source of traditional villages.

2.5.2. Selection and Construction of Traffic Resistance Surface Index

The identification of traffic corridors is often affected by certain natural factors. The traffic resistance surface is the prerequisite for the construction of traffic corridors and the identification of traffic pinch points and barriers. The higher the traffic resistance, the more challenging it becomes for traditional villages to develop a symbiotic relationship [32]. Conversely, lower traffic resistance values facilitate the development of symbiotic relationships between traditional villages [33].
Based on the actual situation in Western Hunan [34] and the existing research results of relevant scholars [35,36,37,38,39,40], this study selected eight resistance factors, namely slope, rock strength, distance from the river, distance from geological disaster points, distance from ecologically sensitive areas, distance from roads, distance from traffic facility points, and land cover type, as the construction index of the traffic resistance surface (Figure 3). The analytic hierarchy process (AHP) was used to determine the weight of each resistance factor, the resistance value was classified [41], and the weighted superposition of each resistance factor was carried out to obtain the final traffic resistance surface (Table 2).

2.5.3. Traffic Corridor Construction Based on Circuit Theory

As an important channel for the circulation of elements, energy, and materials among various sources, a traffic corridor can connect relatively dispersed and independent traditional villages. This is of great significance for enhancing the integrity and connectivity of traditional villages in the study area.
In the construction of the traditional village symbiotic traffic network based on circuit theory, the traffic resistance surface is similar to the conductive surface and has the flow mode of material elements that is similar to the charge flow. Through the identification of the adjacent traditional village sources, the cost-weighted distance and the minimum cost path were calculated [42]. In the process of path calculation, the area with higher resistance was avoided, and the lower resistance was assigned to the optimal simulated path that could promote the traffic corridor [43]. On the minimum cumulative resistance surface, the corridor represents the resistance trough between the two adjacent “sources” and the low resistance channel that is most easily connected. Its calculation formula is as follows [44]:
M C R = f m i n ∑ j = n i = m D i j × R i
where MCR represents the minimum resistance cumulative value; f represents the positive correlation between the minimum cumulative resistance and the traffic flow between traditional villages and the transfer efficiency between them; D i j represents the actual distance from the source patch j to the source patch i of traditional villages; and Ri represents the resistance coefficient of grid i to the construction of traffic corridors in the traditional village sources.
After a number of tests, the threshold value of the corridor’s current width was determined to be 10,000. Circuit theory was used to identify the minimum cost distance between traditional village sources [45], delimit the traffic corridor, and carry out the visual representation of the corridor.

2.5.4. Traffic Node Identification Based on Circuit Theory

A “traffic node” is a node that plays a key role between traditional village sources and is an important part of the traffic network. “Traffic nodes” include traffic pinch points and traffic barriers [46]. The protection or repair of these nodes will have a significant impact on the connectivity of the traffic network [47].
The term “traffic pinch point” refers to an area with a high current density on the traffic corridor [48]; the resistance value of this area is lower than that of the surrounding area, and it is more likely that the traffic corridor between the traditional village sources will pass through this area. Traffic pinch points with a high resistance or that overlap with traffic barriers can seriously affect the connectivity of the traffic network [49]; therefore, the maintenance of the integrity of traffic pinch points is critical. The pinch point mapper tool in the Linkage Mapper toolbox, Circuitscape 4.0, was employed. In addition, considering the significant number of traditional village sources, the “all to one” mode under raster centrality was selected. Using the weighted cost distances of 90, 120, 150, 180, 210, and 300 as the corridor width, the current density values of the traffic network under different corridor-width thresholds were obtained using iterative calculation. After a number of tests, the threshold of the corridor width was determined to be 150. The current density values were divided into five categories using the natural breakpoint method, and the highest current density value was taken as the traffic pinch point in the study area.
The term “traffic barrier” refers to a node with a high resistance value which affects the connectivity of key locations in the traffic network. The Barrier Mapper tool in the Linkage Mapper toolbox was used to identify the traffic barriers, whereby the node with a relatively high original connection can be detected through selection of the improvement score relative to the least-cost distance percentage (referred to as the LCD option). The LCD option indicated that the connectivity of the region was good, although it was blocked. If this option was not selected, nodes with high improvement scores can be detected, indicating that the region was completely blocked and that the transport network connectivity in the region could be improved through the repair of these nodes [50,51,52]. In this study, two modes were selected for calculation at the same time; the detection radius was set as 30, 60, 90, 120, and 150 and the moving window method was used to search and detect the cumulative current recovery value. This means that the pixel value in the center of the search window was replaced with the value of the minimum cost distance between the traditional village sources. The improvement value of the minimum cost distance per unit was used to characterize the improvement in connectivity after the removal of barrier points [50]. Considering the rounding error and the cumulative current recovery value, the detection radius was determined to be 60. The cumulative current recovery value was divided into five categories using the natural breakpoint method, and the highest category of the cumulative current recovery value was taken as the traffic barriers in the study area.

3. Results and analysis

3.1. Results of Determining the Sources of Traditional Villages

In Western Hunan, the total area of traditional village sources is 6573.64 km2, accounting for 12.51% of the total area. A total of 47 village sources were established (Figure 4). The largest source is 960.17 km2, accounting for 14.69% of the total area, and is located to the south of Yongding District, Zhangjiajie City. The smallest area of the sources is 4.74 km2, accounting for 0.07% of the total area, located in Longshan County, Xiangxi Tujia and Miao Autonomous Prefecture. From the perspective of holistic spatial distribution, the traditional village sources in the northern and southern regions are numerous and cover extensive areas, while those in the central region are fewer in number and occupy smaller areas. The distribution in the northern and southern parts forms a longitudinal strip from the southwest to the northeast. This is consistent with the results of Li Bo, etc. [53]. From the county distribution perspective, the sources are concentrated in 10 counties, namely Longshan County; Baojing County; Huayuan County; Guzhang County; Fenghuang County, the Xiangxi Tujia and Miao Autonomous Prefecture; Tongdao Dong Autonomous County; Jingzhou Miao and Dong Autonomous County; Xupu County; Huitong County, Huaihua City; and Yongding District, Zhangjiajie City, of which the Tongdao Dong Autonomous County source number distribution is the largest, accounting for 13% of the total number of sources. On the whole, the size, quantity, and distribution of traditional village sources in Western Hunan are affected by geographical factors such as the elevation and water system. In Western Hunan, traditional villages are mostly distributed in areas of small undulating terrain with a relief of 290–490 m and in watershed distribution areas within the buffer zone of 4 km from the rivers. The traditional villages are distributed in Xuefeng and Wuling Mountain in large numbers and with a concentrated distribution, which is similar to the research results of most scholars [54,55,56,57]. In areas with relatively gentle terrain, traditional villages sources are vast and expansive, exhibiting a blocky distribution. In contrast, in areas with significant variations in terrain, the number of traditional village sources is limited, and the areas are small, exhibiting a scattered distribution. This is similar to the results of Jiao Sheng, etc. [58].

3.2. The Results of Traffic Resistance Surface Construction

The traffic resistance surfaces in Western Hunan are divided into low resistance value regions, relatively low resistance value regions, medium resistance value regions, relatively high resistance value regions, and high resistance value regions, using the natural breakpoint method (Figure 5). On the whole, the spatial differences in traffic resistance surfaces in Western Hunan are relatively significant, showing as high in the north and low in the south. From the perspective of county distribution, the areas with a high resistance value are mainly located in Sangzhi County, the Wulingyuan District of Zhangjiajie City, Longshan County, Baojing County, Jishou City, Yongshun County, Mayang Miao Autonomous County, Yuanling County, Xupu County, and Tongdao Dong Autonomous County, Huaihua City, with an area of 6386.22 km2. The low resistance value areas are mainly located in Xinhuang Dong Autonomous County, Zhijiang Dong Autonomous County, and Hecheng District, Huaihua City, with an area of 4021.14 km2. The areas with a high resistance value are significantly affected by natural conditions, such as slope, rock strength, and distance from rivers, and most of them are located in ecologically sensitive areas or remote areas prone to geological disasters. The areas with low resistance value are also significantly affected by natural conditions, and the distances from traffic facilities and roads are relatively short.

3.3. Results of Traffic Corridor Construction

Based on circuit theory, this study identified 115 traffic corridors, the length of which ranged from 1.15 to 100.77 km, totaling 2699.47 km (Figure 6). The corridors in the south and north were densely distributed, while the corridors in the central region were generally long and dispersed. Based on the relevant studies in the literature [59,60,61], this study used the natural breakpoint method to divide traffic corridors into the following three categories: critical traffic corridors, important traffic corridors, and general traffic corridors. Among them, there are 58 critical traffic corridors with a total length of 505.96 km, accounting for 18.74%. The distribution of the numerous critical traffic corridors is extensive, characterized by short connecting distances and outstanding central connectivity. The traffic corridors from the northern source of Chenxi County to the northeastern source of Xupu County are the longest critical traffic corridors, accounting for 0.67% of the total length of all corridors. There are 47 important corridors, with a total length of 1423.76 km and an average corridor length of 30.29 km. Most of the important corridors pass through areas of plains and are less constrained by the terrain conditions. The longest important corridor is that located from the northeast of Hongjiang City to Huitong County, with a length of 50.73 km. There are 10 general corridors, with a total length of 769.75km, which account for 28.51% and are mainly distributed in the central region. The general corridors are used to connect distant sources and where there are fewer routes to choose; thus, the corridor is more likely to disappear under economic or traffic pressure. The longest general corridor is the corridor from Fenghuang County to Xinhuang Dong Autonomous County, with a length of 100.77 km.

3.4. Analysis of Results of Traffic Node Identification

3.4.1. Analysis of Pinch Point Identification Results

In Western Hunan, 896 traffic pinch points are identified, with a total area of 2752.01 ha. The smallest traffic pinch point area is 0.06 ha, which is located in Xinhuang Dong Autonomous County of Huaihua City. The largest traffic pinch point area is 41.30 ha, which is located in Hongjiang City, Huaihua City. The area of traffic pinch points is relatively small; however, their number is significant, with the majority located in important corridors (52.33%), and a substantial portion are located in general corridors (26.53%), while the number of traffic pinch points on critical corridors is relatively small (21.14%). From the perspective of county distribution, there are ten counties and districts with relatively large areas and a considerable number of traffic pinch points. These are Huitong County, Yuanling County, Hongjiang City, Xupu County, and the Hecheng District of Huaihua City, as well as Yongshun County, Baojing County, Fenghuang County, and Guzhang County in Xiangxi Tujia and Miao Autonomous Prefecture, and the Yongding District of Zhangjiajie City. The land-cover-types of traffic pinch points include forest land (48.80%), construction land (25.82%), water bodies (12.89%), cultivated land (8.86%), and grassland (3.63%). The principal land-cover-types are forest land and construction land, indicating that the land-cover-type of the traffic pinch points between the traditional village sources is most likely to be construction land, which is conducive to traffic construction. There are 607 traffic pinch points that overlap with traffic barriers, playing an important role in improving the connectivity between traditional village sources in Western Hunan and which should be the focus of protection or restoration activities (Figure 7) [48].

3.4.2. Analysis of Identification Results of Barriers

According to the identification results of traffic barriers, a total of 3902 traffic barriers were identified, with a total area of 22,819.50 ha, among which the smallest traffic barriers area was 0.06 ha, and the largest area was 434.40 ha, all of which were located in Huitong County, Huaihua City. A significant number and area of traffic barriers are concentrated in the southeast of Xiangxi Tujia and Miao Autonomous Prefecture and Huaihua City. From the perspective of county distribution, Huitong County, Yuanling County, Hongjiang City, Jingzhou Miao and Dong Autonomous Counties, Tongdao Dong Autonomous County in Huaihua City, Yongshun County, Fenghuang County, Baojing County in Xiangxi Tujia and Miao Autonomous Prefecture, as well as Yongding District, Zhangjiajie City, all have large areas of traffic barriers. According to the results of the construction of traffic corridors, the traffic barriers are mainly distributed on key (25.87%) and important traffic corridors (51.87%); meanwhile, the number of traffic barriers on general traffic corridors (22.26%) is relatively small. Therefore, in order to ensure the connectivity of traffic corridors, the repair of the traffic barriers is extremely significant. The land cover types of the traffic barrier areas include forest land (79.10%), cultivated land (15.43%), grassland (5.06%), and construction land (0.41%), and the land cover types for traffic barriers are mainly forest land (Figure 8).

3.5. Construction of a Symbiotic Traffic Network of Traditional Villages in Western Hunan

3.5.1. Optimization of Traffic Corridor

In order to strengthen the connection and exchange between traditional villages and to promote the centralized, continuous protection and utilization of traditional villages [62,63], based on the results of traffic corridor identification combined with the layout planning of the highway network in Hunan Province (2021–2050) [64] and the existing transport network, four major traffic corridor optimization principles are proposed. These aim to improve the effectiveness of connectivity between traditional village sources and thereby enhance their circulation capacity, as follows:
(1) Eliminate the long-distance traffic corridors between traditional village sources, those with a high regional resistance value, and identify situations where there are no existing traffic routes between traditional village sources and where the traffic corridors exert only a weak influence on the connection between traditional village sources; (2) the traffic corridors constructed based on circuit theory and the current traffic routes that essentially coincide with the existing corridors are determined to be first-level corridors; (3) the traffic corridors representing a short distance between traditional village sources, low regional resistance values, and with a significant influence on the connection between traditional village sources are retained as second-level corridors; and (4) the traffic corridors that have a significant influence on the connection between traditional village sources are optimized into third-level corridors, based on the current traffic routes and natural conditions.
After optimization, 38 traffic corridors were eliminated and 77 traffic corridors were retained, of which 15 were classified as first-level corridors, 24 as second-level corridors, and 38 as third-level corridors. The longest traffic corridor is a first-level corridor of 81.18 km, the shortest traffic corridor is a second-level corridor of 1.13 km, the average length of the first-level corridor is 34.23 km, the average length of the second-level corridor is 8.05 km, and the average length of the third-level corridor is 25.98 km. From the perspective of spatial distribution, the first-level corridors are mainly concentrated in the central part of Western Hunan, and their distribution is relatively uniform. The secondary corridors are principally concentrated in the eastern part of Western Hunan, and their distribution is relatively dispersed. The third-level corridors are mainly concentrated in the northwestern area of Western Hunan, and their distribution is relatively concentrated.

3.5.2. Repair Strategy of Key Traffic Nodes

In this study, 68 key traffic nodes were identified through reference to the relevant literature and then superimposed onto the current traffic network, traffic barriers, traffic pinch points, and optimized traffic corridors [60,65]. The traffic nodes are mainly located in the first-level corridors (51.25%), some are located in the second-level corridors (23.75%), and some are in the third-level corridors (25.00%), of which 20 intersect with the current traffic network. The total area of the key traffic nodes is 78.01 ha, of which the largest area is 18.45 ha, the smallest area is 0.02 ha, and the average area is 1.15 ha. The key traffic nodes are relatively few in number and cover a small area, primarily concentrated in the southeastern region of Huaihua City. From the county distribution perspective, the traffic pinch areas are relatively large in 10 districts and counties, namely Huitong County, Hongjiang City, Yuanling County, Xupu County, Hecheng District in Huaihua City, Yongshun County, Baojing County, Fenghuang County, Guzhang County in Xiangxi Tujia and Miao Autonomous Prefecture, as well as Yongding District in Zhangjiajie City. The land cover types at key traffic nodes include forest land (68.49%), cultivated land (23.88%), grassland (6.67%), construction land (0.68%), and water areas (0.28%) (Figure 9).
This study combined various resistance factors to analyze the issues faced by 68 key traffic nodes. In addition, this study focused on addressing the following five major issues—high slope gradients, low rock strength, proximity to rivers, proximity to ecologically sensitive areas, and proximity to geological disaster points—through the proposal of corresponding repair strategies, thereby improving the resilience of transport networks to major disruptive risk events [66], as follows:
(1)
For terrain with significant slopes, more consideration should be given to the road direction in the process of traffic network construction, and road slope should be reduced through the oblique intersection of roads and contour lines or through conformation to contour lines;
(2)
For areas with low rock strength, the foundations should be solidified in a variety of ways;
(3)
For traffic routes close to rivers, the construction of river dams and protective guardrails should be considered in combination with the precipitation situation, including the retrofitting of critical traffic nodes with potential disruption risks [67];
(4)
For road traffic construction involving ecologically sensitive areas, bridge and tunnel projects should be adapted to pass without harm to the areas, and necessary research into these areas should be performed [68];
(5)
For key traffic nodes close to the geological disaster points, it is necessary to fully assess the impact of geological disasters on the region, reinforcing key nodes in the transport network before a disaster occurs [69]. If the safety of the region cannot be guaranteed, another site should be considered for the construction of the traffic routes. At the same time, in the subsequent use of the traffic network, attention should be paid to the road traffic supervision and maintenance of the key traffic nodes to improve road quality and enhance the network accessibility [70].

4. Discussion

There are a large number of traditional villages in Western Hunan with distinct regional characteristics; however, they are often established in a relatively closed and isolated geographical environment. The construction of a symbiotic transport network of traditional villages is of practical significance for improving the connectivity of the transport network in Western Hunan, promoting the centralized, continuous protection and utilization of traditional villages and promoting the coordinated symbiotic development of regions. The circuit theory is commonly used for constructing ecological security patterns as well as for ecological restoration in key areas. In recent years, the direction and scope of research have gradually increased and broadened. For example, in order to alleviate the urban heat island effect, cold island networks have been constructed based on circuit theory [25], a thermal environment space network has been constructed in combination with circuit theory to improve the urban environment [71], heritage corridor networks have been constructed with the help of circuit theory [72], etc. These studies have fully considered the randomness of current movements and have made the application of circuit theory in the field of route planning more scientific; however, few studies have applied circuit theory to traffic-network planning. Based on circuit theory, this study has constructed the symbiotic traffic networks of traditional villages, expanded the application scope of this theory, and also provided new ideas for the centralized, continuous protection and utilization of traditional villages.
This study did not eliminate the deviation between the theoretical simulation and the actual circuit through the addition of a buffer zone and other methods at the periphery of the research area, so the results were affected by certain boundary effects [33]. Therefore, the establishment of various parameters in the identification of the traffic corridors and traffic nodes needed to be adjusted in a more precise and scientific way in the practice process based on the actual situation. This study principally considered the natural and social factors such as slope, rock strength, distance from rivers, proximity to ecologically sensitive areas, proximity to roads, proximity to geological disaster sites, proximity to transport facilities, land cover types, administrative boundaries, spatial forms of traditional villages, and master planning. In subsequent studies, the impact of socio-economic factors such as GDP and population on the construction of symbiotic transport networks will be further considered.

5. Conclusions

In this study, through the identification and extraction of the sources of traditional villages and the comprehensive consideration of multiple factors, the traffic resistance surface was constructed to ensure the scientific and effective extraction of traffic corridors. Based on circuit theory, the traffic connection between traditional villages was simulated to effectively identify traffic corridors, traffic pinch points, and traffic barriers [73]. Combined with the current traffic network and the master plan, the identified traffic corridors and key traffic nodes were optimized and repaired, a symbiotic transport network of traditional villages in Western Hunan was constructed, the holistic attractiveness and image of traditional villages were enhanced, the centralized and continuous protection and utilization were realized, and coordinated regional development was promoted. The following results were obtained:
(1)
The distribution of traditional villages was analyzed based on the spatial analysis of kernel density, and the sources of traditional villages in Western Hunan were obtained. Through the comparison of the distribution values of nuclear density with the administrative boundaries of traditional villages, 47 traditional village sources with a high connectivity value were identified. Among them, Yongding District in Zhangjiajie City, a southern traditional village source, represented the largest traditional village source. From the holistic distribution, the distribution of the sources shows the characteristics of “dense in the north and south, sparse in the middle”.
(2)
From the two dimensions of natural conditions and traffic conditions, eight types of resistance factors were selected as the construction index of the traffic resistance surface, and weighted superposition was carried out to identify which traffic resistance surface has a significant impact on the identification of traffic corridors. The traffic resistance surfaces in Western Hunan are generally high in the north and low in the south. The natural conditions are an important factor affecting the traffic resistance values.
(3)
Based on the circuit theory, 115 potential traffic corridors were identified, of which 58 were critical, 47 were important, and 10 were general. These were distributed in a radiating spider web to ensure connectivity between traditional village sources. A total of 3902 traffic pinch points and 896 traffic barriers were identified, including 607 overlapping areas between traffic pinch points and traffic barriers. These pinch points are the focus of protection and restoration activities.
(4)
Based on the current traffic network, identified traffic corridors, and master planning, four principles of traffic corridor optimization and five strategies for key traffic node restoration are proposed. A total of 77 traffic corridors were optimized, comprising 15 first-level corridors, 24 second-level corridors, and 38 third-level corridors, and 68 key traffic nodes were identified, jointly constructing a symbiotic transport network of traditional villages in Western Hunan.

Author Contributions

Conceptualization, J.P. and W.X.; data curation, M.T. and W.X.; formal analysis, J.P. and C.Z.; funding acquisition, L.Q. and C.Z.; investigation, L.Y. and L.Q.; methodology, Q.L.; writing—original draft, J.P.; writing—review and editing, M.T., Q.L. and L.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant Number: 42261042, 42361032); Hunan Provincial Philosophy and Social Science Foundation (Grant Number: 22YBA152), the Outstanding Youth Project of Scientific Research Project of Hunan Provincial Department of Education (Grant Number: 22B0533); the National College Student Innovation and Entrepreneurship Training Program Project (Grant Number: S202310531023); and the Hunan College Students Innovation and Entrepreneurship Training Program (Grant Number: S202110531056).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Acknowledgments

We thank the editors and instructors for their insightful and constructive comments, and thank all the panelists for their hard work.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The research area.
Figure 1. The research area.
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Figure 2. The method framework.
Figure 2. The method framework.
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Figure 3. The traffic resistance surfaces in Western Hunan.
Figure 3. The traffic resistance surfaces in Western Hunan.
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Figure 4. The distribution of traditional village sources.
Figure 4. The distribution of traditional village sources.
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Figure 5. The traffic resistance surfaces.
Figure 5. The traffic resistance surfaces.
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Figure 6. The distribution of the traffic corridors.
Figure 6. The distribution of the traffic corridors.
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Figure 7. The distribution of traffic pinch points.
Figure 7. The distribution of traffic pinch points.
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Figure 8. The distribution of traffic barriers.
Figure 8. The distribution of traffic barriers.
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Figure 9. The symbiotic transport networks in traditional villages.
Figure 9. The symbiotic transport networks in traditional villages.
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Table 1. The description of data types and sources.
Table 1. The description of data types and sources.
DataData SourcesResolution
Digital Elevation Model Data (DEM)National Geographic Information Resources Directory Service system (https://www.gscloud.cn/, access on 25 March 2023)30 m
Data of traditional village pointsDirectory of Chinese traditional villages of the Ministry of Housing and Urban-Rural Development
(https://www.mohurd.gov.cn/, access on 25 March 2023)		Vector
Provincial and municipal administrative boundariesNational Geographic Information Resources Directory service system (https://www.gscloud.cn/, access on 25 March 2023)Vector
Road dataNational Geographic Information Resources Directory service system (https://www.webmap.cn/, access on 25 March 2023)Vector
Rock strengthEcological Network of Geographical Sciences 30 m
(www.csdn.store, access on 1 June 2023)		30 m
Transportation facility point POI dataGeographical Sciences Ecological Network
(www.csdn.store, access on 1 June 2023)		Vector
Geological disaster site dataEcological Network of Geographical Sciences
(www.csdn.store, access on 1 June 2023)		Vector
River network dataCenter for Resources, Environment and Data Science (https://www.resdc.cn/, access on 1 June 2023)Vector
Ecologically sensitive area dataGeographic remote sensing ecological network
(https://www.gisrs.cn, access on 1 June 2023)		Vector
Land cover typeFROM-GLC10
(http://data.ess.tsinghua.edu.cn/, access on 25 March 2023)		10 m
Table 2. The assignment and weight of the resistance factors.
Table 2. The assignment and weight of the resistance factors.
Resistance FactorGrading CriteriaResistance ScoreWeightsResistance FactorGrading CriteriaResistance ScoreWeights
Slope (°)0~8.1010.15Distance from
geological
disaster sites (m)		0~26.4290.15
8.10~14.91326.42~55.657
14.91~22.30555.65~114.075
22.30~31.797114.07~217.023
31.79~89.909217.02~354.741
Rock strength (Pa)7.00~35.0090.13Distance from traffic facility
points (m)		0~41010.06
35.00~60.977410~4303
60.97~9.515430~4405
91.51~147.003440~9907
147.00~267.61990~12279
Distance
from river (m)		0~7342.6890.07Distance from roads (m)0~1482.0810.13
7342.68~15,860.1871482.08~3458.183
15,860.18~25,552.5253458.18~5928.315
25,552.52~38,181.9235928.31~9245.347
38,181.92~74,895.3119245.34~17,996.659
Distance from ecologically sensitive area (m)0~101.9890.14Land cover typeCultivated land90.17
101.98~179.687Woodland7
179.68~262.235Grassland5
262.23~359.353Waters3
359.35~619.161Construction land1
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Peng, J.; Tang, M.; Li, Q.; Yang, L.; Qiao, L.; Xie, W.; Zhou, C. Construction of a Symbiotic Traffic Network of Traditional Villages in the Western Hunan Region of China Based on Circuit Theory. Sustainability 2024, 16, 5468. https://doi.org/10.3390/su16135468

AMA Style

Peng J, Tang M, Li Q, Yang L, Qiao L, Xie W, Zhou C. Construction of a Symbiotic Traffic Network of Traditional Villages in the Western Hunan Region of China Based on Circuit Theory. Sustainability. 2024; 16(13):5468. https://doi.org/10.3390/su16135468

Chicago/Turabian Style

Peng, Jiayun, Meng Tang, Qin Li, Lin Yang, Lin Qiao, Wenhai Xie, and Chunshan Zhou. 2024. "Construction of a Symbiotic Traffic Network of Traditional Villages in the Western Hunan Region of China Based on Circuit Theory" Sustainability 16, no. 13: 5468. https://doi.org/10.3390/su16135468

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