Rural Tourism Agglomeration Characteristics in Jilin Province and Their Influencing Factors

Abstract

Rural tourism agglomerations are increasingly viewed as catalysts for diversified regional growth, integrated rural revitalization, and improved farmer prosperity. However, most studies focus on urban and developed regions, leaving spatial patterns and evolutionary mechanisms in underdeveloped rural areas poorly understood. This study takes Jilin Province, an economically lagging region, as an example, measuring rural tourism agglomeration using spatial analysis methods including the Gini coefficient, nearest-neighbor index, Ripley’s K function, kernel density, and buffer analysis. Results show that agglomeration is significant and strengthening over time, with clear regional variations. All types of rural tourism products exhibit an “increase followed by decrease” pattern across spatial scales, evolving from isolated “nodes” to continuous “areas”. Agglomeration is subject to triple constraints from natural, economic, and social dimensions. This study suggests that high-quality rural tourism development should leverage point–axis spillover from flagship scenic areas, promote surface expansion of characteristic villages and towns, and strengthen network connectivity through roads and talent-information channels.

1. Introduction

Rural tourism serves as an alternative tourism form for rural regeneration and also as a conservation tool [1]. It encompasses tourism activities occurring in rural areas, relying on local traditional culture and natural resources [2]. As an important link connecting urban and rural areas and promoting their integrated development, rural tourism is an increasingly prominent tool for promoting the modernization of agriculture and rural areas [3], increasing farmers’ incomes, and inheriting rural culture. Agglomerative development is a key consideration in responding to the demand for rural tourism, promoting cross-industry integration and efficient utilization of land resources and driving rural transformation and upgrades [4]. This process involves the optimal allocation of rural tourism resources, adjustment of the industrial structure, and independent adjustment and optimization of business models. To achieve multiple goals, such as ecological protection, economic benefits, social welfare, and industry self-improvement [5], the construction of rural tourism clusters needs to be closely linked with the local natural ecology, economic level, and social development, achieving mutual adaptation, collaborative development, and effective integration. With the increasing tension in rural land resources and the migration of rural populations to cities, rural agglomeration development has spread geographically. The spatial agglomeration effect is more effectively exerted through industrial agglomeration [6], enabling different formats of rural tourism businesses to establish key interconnection mechanisms and improve production efficiency by sharing resource, market, and infrastructure costs [7]. In addition, rural industrial agglomeration effectively connects various factors of production, drives innovation in rural tourism, improves related service facilities, promotes optimization and upgrades of rural industrial structures, creates an industrial spillover effect, and enhances the benefits of rural tourism [8]. Presently, various forms of agglomeration, such as characteristic small towns, key rural villages for rural tourism, and demonstration zones for leisure agriculture and rural tourism, promote local economic development [9], increase farmers’ income [10], and protect cultural heritage [11]. These forms have gradually become an important force in bridging the urban–rural gap, enhancing rural appeal and promoting rural social harmony and stability. Therefore, systematically exploring the development of rural tourism agglomeration is of great significance for promoting the development of rural tourism, realizing rural revitalization, and promoting common prosperity.

Rural tourism agglomeration, rooted in industrial agglomeration theory, represents its specific manifestation within rural contexts [12]. It involves the concentration of rural resources and production factors [13], leading to the accumulation of talent, facilities, land, capital, and other elements in particular locales [14], thereby providing crucial support for rural development [15]. Unlike generic industrial agglomeration, rural tourism agglomeration is distinctly place-based, leveraging localized cultural assets, natural landscapes, and agricultural heritage to create unique visitor experiences [16,17]. This process can foster positive outcomes such as enhanced regional competitiveness through brand co-creation, shared customer bases, and collaborative governance [18], ultimately serving as a vital lever for rural revitalization. However, while acknowledging the positive significance of rural tourism agglomeration, we must be vigilant about the potential consequences of the urban-rural transformation it triggers, especially the profound impact brought about by the gentrification process [19,20]. These include potential landscape degradation due to over-commercialization [21], the marginalization of local communities in decision-making processes [22], and the risk of cultural commodification which may undermine authenticity [23]. Particularly in the agglomeration development driven by rural tourism, the gentrification process often accompanies the rise of the experience economy, promoting the development of a permanent tourism model that integrates leisure, residential, and cultural consumption [24]. This phenomenon not only alters the original industrial structure and demographic composition of rural areas, but also exacerbates the tension between urban and rural cultures and social strata, and even leads to the marginalization of local communities in the development process [25].

Current research mainly focuses on (1) the causes and effects of industrial agglomeration. Marshall (1890) was the first to systematically study and propose the phenomenon of industrial agglomeration, pointing out that industrial agglomeration can bring about external economies of scale [26]. Jackson & Murphy (2002) introduced this idea into the field of tourism and pointed out that the natural advantages of the tourism industry are an important reason for its agglomeration [27]. Domestic scholars such as Lv Li (2022) further pointed out that rural tourism agglomeration not only optimizes the efficiency of resource allocation but also strengthens regional tourism competitiveness through brand co-construction, shared customer sources, and collaborative governance, becoming an important lever for promoting rural revitalization [28]. (2) Measurement models and empirical research on industrial agglomeration. Scholars have studied industrial agglomeration by constructing different measurement models, including qualitative research methods such as the diamond model [29] and in-depth interviews [30,31], as well as quantitative measurement methods such as location quotient (LQ) [32], the Herfindahl–Hirschman Index (HII) [33], and the spatial Gini coefficient (G) [34], to determine the spatial concentrations and evolutionary trends in rural industries. In recent years, some researchers have attempted to incorporate mobile phone signaling, POI big data, and nighttime light data into models to improve spatiotemporal accuracy [31]. (3) Influencing factors, mechanisms, and effects analysis of industrial agglomeration. Wang Xiaomeng (2015) analyzed the positive impacts of rural industrial agglomeration on local economic development, farmers’ incomes, employment opportunities, and other aspects, as well as potential issues such as increased environmental pressure and overexploitation of resources [35]. Fanelli et al. (2022) believe that differentiated local cultural characteristics also have a significant impact on rural agglomeration [36]. The stronger the local cultural identity, the more inclined it is towards small-scale, networked flexible clusters, which provides a theoretical basis for the study of rural tourism from a local perspective [36]. Despite these advancements, from a regional perspective, most existing studies on tourism agglomeration focus solely on urban areas, particularly in economically developed regions such as the eastern coastal areas. There is a notable lack of attention to the northeast region, especially to economically underdeveloped areas with net population outflows, such as Jilin Province. In terms of research subjects, most studies primarily focus on single elements such as key rural tourism villages and star-rated rural tourism destinations, and there is a lack of systematic research on the collaborative evolution of diverse business forms such as typical rural tourism enterprises, key villages and towns, and rural homestays [37]. In terms of time series, studies have mostly been based on cross-sectional data, focusing solely on characterizing whether there is agglomeration and the degree of this agglomeration, with insufficient tracking of the dynamic process, life cycle, and sustainability of rural tourism agglomeration.

Therefore, a local perspective is adopted in this study, taking Jilin Province as the case study area. By exploring the distribution and evolution patterns of different rural business forms such as high-level rural tourism operators, key rural tourism villages and towns, and rural homestays across spatiotemporal scales, it investigates the characteristics and influencing factors of rural tourism agglomeration development in economically underdeveloped areas. Subsequently, optimization pathways are proposed to promote the development of high-quality rural tourism in Jilin Province. This research is comprehensive, objective, and scientific. This approach aims to provide a more nuanced and contextually grounded understanding of rural tourism agglomeration.

2. Research Area and Data Sources

2.1. Overview of the Research Area

Jilin Province (Figure 1), located in the central part of Northeast China, is an important agricultural production base. In terms of natural resources, the convergence of the Songnen Plain and the remaining ranges of Changbai Mountain has resulted in fertile black soil. The abundant water resources of the Songhua, Tumen, and Yalu Rivers, along with diverse biological resources, provide a base for agricultural development and rural tourism. In terms of the cultural environment, Jilin Province is a multi-ethnic region where ethnic groups such as the Manchu, Korean, Mongolian, and Hui have lived together for generations. Jilin Wula Manchu Yangge, Yanbian agricultural music and dance, Changbai Shamu Bang Chant, and Northeastern Errenzhuan have been successively included in the national intangible cultural heritage list. In 2016, the first provincial survey of rural tourism resources was conducted in Jilin Province, resulting in the first “Rural Tourism Development Report of Jilin Province.” In 2021, Jilin Province issued the “Implementation Opinions on Promoting the High-quality Development of Rural Tourism,” proposing the creation of 10 rural tourism clusters across the province. By pooling resources and concentrating efforts, it aims to promote leapfrog development in rural tourism. In 2023, the formation of a hundred-billion-CNY rural tourism industry cluster was proposed in the “Action Plan for Building a Trillion-Yuan Tourism Industry in Jilin Province (2023–2025)”. Benefiting from its long-standing agricultural history and culture, a favorable policy support environment, and a vast rural leisure market, rural tourism in Jilin Province has developed rapidly in recent years, becoming an exemplary model of industrial revitalization and common prosperity in Northeast China.

2.2. Data Sources and Preprocessing

Based on relevant lists published by national and provincial authorities and using Python 3.12 to access big data resources, duplicate data were merged and organized, and 1550 different types of rural tourism resources were selected from the research area. Using the Baidu Maps API V3.0 coordinate picking tool, 1550 rural tourism resources were searched and individually calibrated to establish a basic geographic information database. To clarify the composition of these resources, they were categorized into three primary business types prevalent in Jilin Province’s rural tourism sector (

Table 1. Categories of rural tourism resources.

Category	Quantity	Description
Key rural tourism enterprises	249	High-quality (Grade 3A and above) rural tourism enterprises, including leisure farms, agro-ecological parks, and specialized resorts.
Key Rural Tourism Villages/Towns	189	Villages and towns officially recognized for their cultural heritage, ecological resources, and tourism development potential.
Rural Homestays	1112	Small-scale, family-run accommodations located in rural areas, providing lodging and often local experiential activities.

): 249 rural tourism operating units of 3A and above, 189 key rural tourism towns and villages, and 1112 rural homestays. The resources were categorized as follows: 249 rural tourism operating units of 3A and above, 189 key rural tourism towns and villages, and 1112 rural homestays. The standard ellipse analysis revealed that the average centroid of 1550 rural tourism resources is mainly concentrated in central Jilin Province. The overall spatial distribution direction is generally from southeast to northwest, which is consistent with the direction of development of the “East–West Tourism Double Loop” tourism industry. Kernel density estimation indicated that rural tourism resources in Jilin Province are generally distributed and partially concentrated. At the macro regional level, rural tourism resources are distributed. At the micro level, four main centers, forming four main centers in Changchun, Tonghua, Baishan, and Songyuan and several sub-centers in Jilin, Liaoyuan, and Yanbian (Figure 1). These centers cover all the types of rural tourism resources present in Jilin Province. Overall, the research objectives are representative.

The data for key rural tourism enterprises rated 3A or above used in this study were sourced from the Jilin Provincial Department of Culture and Tourism and the culture, radio, film, and tourism bureaus of various cities (prefectures). The key villages and towns for rural tourism were sourced from the Ministry of Culture and Tourism of China and the Jilin Provincial Department of Culture and Tourism. Due to the small number of graded homestays in Jilin Province, which is not representative, data on rural homestays were obtained from websites such as Meituan, Ctrip, and Qunar. Urban homestays within urban and rural built-up areas were removed through overlay analysis using ArcGIS 10.2 software. Data acquisition was completed by December 2023. The data on river systems, DEM elevation, transportation networks, etc., used in the analysis were sourced from the Geospatial Data Cloud and the Resource and Environmental Science Data Center of the Chinese Academy of Sciences. The data on scenic spots rated 3A or above were sourced from the Jilin Provincial Department of Culture and Tourism, while the economic and social data related to the agglomeration of rural tourism in Jilin Province were sourced from the “Jilin Provincial Statistical Yearbook (2016–2023)” and the statistical bulletins on national economic and social development of various regions.

2.3. Methods

This study utilizes comprehensive research methods such as the Gini index [3], nearest neighbor index [38], Ripley’s K function [27], kernel density analysis [39], and buffer overlay analysis [40] to explore the overall evolution characteristics of rural tourism agglomeration development in Jilin Province, as well as the spatial agglomeration patterns and influencing factors of rural tourism within different business types (

Table 2. Statistical analysis models and their geographical significance.

Index	Model	Model Interpretation	Geographical Significance
Gini Index	$G={\sum }_{i=1}^{n}PilnPi/lnN$	G is the Gini Index of rural tourism resource points; $P_i$ is the i-th rural tourism resource point accounting for the overall proportion; n is the number of regions and N is the total number of regions.	It is used to characterize the equilibrium of distribution at the macro level and identify the overall agglomeration trend. The value of G ranges between 0 and 1. The closer G is to 1, the more concentrated the distribution of rural tourism operating units is. Conversely, the closer G is to 0, the more dispersed the distribution of rural tourism resource points is.
Nearest Neighbor Index	$R={\displaystyle \frac{r_1}{r_E}}={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n} d_i/{\displaystyle \frac{1}{2}}\sqrt{A/n}$	$R$ is the nearest neighbor index, $n$ is the number of rural tourism resource points, and $A$ is the area of the study region.	It is used to represent the point distribution pattern at the micro level and quantify the intensity of local agglomeration, and to determine their spatial distribution type. When $R$ = 1, the distribution is random; when $R$ < 1, the distribution is agglomerated; and when $R$ > 1, the distribution is uniform.
Ripley’s K Function	$L(t)=\sqrt{{\displaystyle \frac{A\sum _{i=1}^n \sum _{j=1,j\ne 1}^n k(i,j)}{\pi n(n-1)}}}$	$A$ represents the area of the region, $n$ represents the number of points, and $k(i,j)$ represents the weight. The maximum and minimum values of $L\left(t\right)$ are defined as the upper and lower bounds, respectively. The value of$L\left(t\right)$ is calculated for each scale.	It is used to analyzes the agglomeration or dispersion characteristics through multi-scale analysis, revealing the scale dependence of the distribution of rural tourism resource points. An $L\left(t\right)$ value above the upper bound indicates a significant agglomerated distribution. An $L\left(t\right)$ value below the lower bound indicates a significant uniform distribution. A value between the upper and lower bounds indicates a random distribution. The maximum value of $L\left(t\right)$ reflects the characteristic spatial aggregation scale of the sample points.
Kernel Density Analysis	$f_h(x)={\displaystyle \frac{1}{nh}}{\displaystyle \sum _{i=1}^n} ({\displaystyle \frac{x-x_i}{h}})$	$f_h\left(x\right)$ is the kernel density estimate, n is the number of rural tourism resource points, $(x-x_i$) represents the distance from $x$ to $x_i$, and h > 0 is the bandwidth.	It is used to characterize the aggregation status of rural tourism resource points within a specified area. The larger the kernel density estimate value, the more concentrated the distribution.

).

3. Results

3.1. Overall Agglomeration Characteristics

To assess the overall spatial concentration of rural tourism resources, we calculated the Gini coefficient and nearest neighbor index annually from 2016 to 2023. As shown in Figure 2, the Gini index remained above 0.70 during 2016–2023, indicating a highly concentrated and increasingly agglomerated pattern. The high and rising value of this index reflects the continuous strengthening of rural tourism resources in Jilin Province, as well as their high degree of spatial agglomeration. Meanwhile, the calculation results of the average nearest neighbor index indicate that all R values for each year are less than 1 (ranging from 0.78 to 0.72) and decrease over time, further statistically confirming that rural tourism resources in Jilin Province exhibit a significant agglomerated distribution, and the degree of agglomeration is continuing to increase. From the perspective of the evolutionary mechanism, under the combined effects of increased market demand, policy dividends, and the rural revitalization strategy, advantageous factors such as transportation accessibility, land supply elasticity, service facility support level, and capital return rate continue to be concentrated in specific rural areas, exhibiting an initial “factor–location” coupling advantage. In addition, this spatial polarization generates positive feedback through economies of scale and rural network externalities, manifested as increased tourist volumes, decreased operating costs, and extended industrial chains, thereby attracting more labor, capital, technology, and information to continuously flow in, further strengthening the agglomeration potential of advantageous regions and leading to a spiraling expansion in the development gap between rural tourism in different regions.

From the perspective of regional heterogeneity (

Table 3. Comparison of Gini index of rural tourism in Jilin Province.

Year	2016	2017	2018	2019	2020	2021	2022	2023
Changchun	0.688	0.692	0.731	0.792	0.807	0.841	0.857	0.862
Jilin	0.736	0.742	0.767	0.861	0.875	0.881	0.909	0.898
Siping	0.190	0.236	0.787	0.818	0.826	0.853	0.872	0.874
Liaoyuan	0.718	0.723	0.699	0.685	0.765	0.772	0.831	0.862
Tonghua	0.819	0.826	0.874	0.902	0.909	0.921	0.939	0.930
Baishan	0.108	0.353	0.677	0.825	0.827	0.912	0.928	0.931
Songyuan	0.395	0.365	0.358	0.349	0.782	0.798	0.856	0.849
Baicheng	0.826	0.821	0.822	0.819	0.835	0.878	0.922	0.892
Yanbian	0.739	0.815	0.907	0.952	0.953	0.954	0.959	0.961
Standard Deviation	0.260	0.260	0.200	0.186	0.098	0.083	0.062	0.038

), there are significant differences in the Gini Index of rural tourism development across various regions in Jilin Province from 2016 to 2023. Firstly, a high agglomeration state was established in the central region, represented by Changchun and Jilin, due to its first-mover advantages such as a high economic level, proximity to the tourist market, and well-developed infrastructure. Subsequently, the growth rate slowed down, entering a stage of “high fluctuation”. Secondly, Tonghua and Yanbian in the eastern region relied on the brand tourism potential of the Changbaishan large eco-tourism circle, showing a high initial value in early 2016. After 2018, the growth rate decreased, presenting an “inverted U” evolutionary trend, indicating the marginal diminishing effect after resource capacity approached saturation. Furthermore, Siping, Liaoyuan, and Baishan, with prominent late-mover advantages, had Gini coefficients of only 0.190, 0.718, and 0.108, respectively, in 2016, which jumped to 0.818, 0.685, and 0.825 by 2019, and then stabilized in the range of 0.85–0.93. Lastly, Baicheng and Songyuan in the western region of Jilin Province showed a diverging trend. The coefficient for Baicheng fluctuated between 0.826 and 0.892, maintaining high agglomeration, while Songyuan’s coefficient remained below 0.4 until 2019 and then surged to 0.782 after 2020, exhibiting a leapfrog transition directly related to the policy-driven development of Songyuan’s tourism industry and the implementation of major projects. Meanwhile, the inter-regional standard deviation continued to converge, changing from 0.260 in 2016 to 0.038 in 2023 and exhibiting a monotonically decreasing trend, indicating that the development of rural tourism in Jilin Province is gradually transitioning from polarization imbalance to coordination balance. From a policy perspective, influenced by the provincial-level “One Main and Six Duals” high-quality development strategy, administrative barriers and frictions in factor flow have been effectively weakened, promoting benign competition and cooperation among regions in terms of resource input, service standards, and brand building.

3.2. Agglomeration Characteristics of Different Business Formats

To reveal the agglomeration characteristics and evolutionary patterns of different rural tourism business types across multiple spatial scales, we applied Riple’s K function using Crimestat3.3 software. This method allows us to identify significant agglomeration scales and compare spatial patterns across the three business types: key rural tourism enterprises, key villages/towns, and homestays. The results (Figure 3) indicate that the L(t) values of rural tourism in Jilin Province as a whole and for each business type are consistently above the upper boundary L(t)max within the range of 0–180 km, exhibiting a significant agglomeration distribution. The curve shape generally presents an inverted U or inverted M pattern, increasing first and then decreasing. However, there are differences in peak position, peak number, and fluctuation amplitude among different business types, reflecting the spatial scale effect under different combinations of factors and location constraints.

3.2.1. General Characteristics

In the overall sample, the L(t) curve fluctuates and rises within the range of 0–120 km, reaching the first peak at 120.56 km, then slightly falling back and forming the second peak at 160.05 km, constituting a typical “bimodal” structure. This suggests that rural tourism in Jilin Province exhibits significant “hotspot–sub-hotspot” clusters at two spatial scales of 120 km and 160 km. Beyond 160 km, the L(t) value begins to systematically decrease, but it remains higher than L(t)max, indicating that the long-distance diffusion effect is limited and the polarization pattern is generally stable. This result echoes the previous conclusion of high polarization and weak diffusion based on the Gini index.

3.2.2. Characteristics of Different Business Formats

• The L(t) curve for key rural tourism enterprises exhibits a smooth parabolic shape, with an inflection point at 135.17 km and a peak intensity of 157.45 km. Within the range of 0–135 km, the L(t) value increases monotonically, indicating that facility-based formats primarily rely on central city tourist sources and transportation accessibility, exhibiting a layered agglomeration pattern. Beyond 135 km, the curve exhibits a gradual moderate decline, suggesting that business units have a limited dependence on markets beyond 150 km and that their spatial expansion demonstrates a “core–periphery” attenuation pattern.
• The L(t) curve shape for key villages and towns is similar to that of key rural tourism enterprises, but the peak slightly advances to 130.52 km, and its intensity is slightly lower (126.37 km). From a location perspective, key villages are mostly located within the transportation circle area 100–150 km from the city center, which allows them to rely on urban tourist sources while utilizing relatively inexpensive land and ecological resources, exhibiting advantages driven by both policy and resources. The curve gradually declines after 130 km, indicating that in the future, measures such as transportation improvements and brand promotion are needed to extend the radiation radius.
• In contrast to the unimodal patterns observed for operating units and key villages, the L(t) curve for rural homestays exhibits a distinct inverted M shape, with the first peak appearing early (19.35 km), followed by a rapid decline in the range of 19–94 km, and then double peaks at 115.62 km and 155.49 km. This multi-peak structure indicates that homestay location is characterized by “multi-scale nested” features. Suburban homestays within 20 km rely on city weekend tourism, exhibiting high-density, small-scale agglomeration, while the secondary peaks at 95–120 km and 150–160 km are close to the peaks of key rural tourism enterprises and key villages and towns, respectively, forming a typical “scenic area-dependent” agglomeration. The amplitude of the curve’s fluctuation is significantly higher than that of other formats, reflecting that homestays are more sensitive to location and market segmentation and that the related location selection behavior is more discrete and heterogeneous.

3.3. Analysis of Influencing Factors

To systematically examine the factors influencing agglomeration, we selected indicators across three dimensions: physical geographical factors, economic level, and social development. These factors were chosen based on existing literature [41,42] and their relevance to rural tourism clustering.

3.3.1. Impact of Physical Geographical Factors

Natural environments such as fresh air, clean water sources, and flat terrain conditions are the basic resources for rural tourism and are prerequisite for its sustainable development. In this study, an overlay analysis of rural tourism resources in Jilin Province was conducted based on water and terrain elements using ArcGIS 10.2 software. Steps included continuous buffering of water area and elevation in increments of 0.5 km and 50 m, respectively, and calculating the number of rural tourism resource points within each buffer zone. Subsequently, through raster calculation, vectorization processing, and data analysis, the quantitative relationship between rural tourism agglomeration and water and terrain elements in Jilin Province was determined (Figure 4). The results indicate that ① Rural tourism resources in Jilin Province generally exhibit a clear hydrophilic characteristic. A total of 30.97% of rural tourism resource points are clustered within a 0.5 km buffer distance from water bodies, and 74.45% of rural tourism resource points are distributed within a 2.5 km buffer zone. Water resources are not only the lifeblood of agricultural production but also an important mode for core rural tourism attractions such as water sports, leisure fishing, and ice and snow experiences. The abundance and quality of water resources directly affect the development of rural tourism activities and the quality of tourism experiences. In water-resource-rich areas such as Songhua and Chagan Lakes, rural tourism agglomeration areas with water as the theme are more easily formed. These areas can attract more tourists, providing more employment opportunities and income for the local area. ② The scale of rural tourism resources increases with elevation, first increasing and then decreasing. Within an elevation range of 200 m, the scale of rural tourism resources gradually increases with elevation, with 41.61% of rural tourism resource points located in this range. Around an elevation of 200 m, there is a relatively concentrated area of rural tourism. These areas usually have a milder climate and a suitable ecological environment, with a denser population and relatively higher levels of economic development, all of which provide a comfortable environment and human resource support for rural tourism development. Above 200 m, the distribution of rural tourism resource points rapidly decreases with increasing elevation, with 82.27% of rural tourism resource points located within an elevation range of 450 m. Beyond an elevation of 450 m, although the ecological quality is superior, development is significantly limited due to transportation accessibility and construction costs.

3.3.2. Impact of the Combination of Economic Level Elements

The foundation of the regional economy serves as the fundamental driving force for the commercialization and industrialization of rural tourism resources. Villages in economically developed regions are able to offer more diversified tourism products and services, enhancing the market competitiveness of rural tourism and promoting its agglomerative development. Per capita GDP and the mileage of graded highways were used in this study to characterize the economic level of rural tourism agglomerative development (Figure 5). By employing the fishnet tool, 5 km × 5 km grid division was conducted in Jilin Province. Spatial analysis techniques were then utilized to perform density mapping of various indicators, which were subsequently rasterized. After assigning the density values of each indicator to the center point of each grid, correlation and regression analyses were performed using SPSS 22.0 software. The Pearson correlation coefficient was employed to assess the linear relationships, and a two-tailed t-test was used to determine statistical significance. At a significance level of α = 0.01, the Pearson correlation coefficients for per capita GDP and the density of graded highways were 0.897 and 0.904, respectively (both p < 0.01), indicating a significant correlation between agglomerative development of rural tourism and the level of regional economic development. ① Per capita GDP serves as a crucial support and prerequisite for the implementation of tourism activities. Its density is an important indicator for measuring the scale of and potential demand for rural tourism markets in a region, and it is also the core driving force behind the agglomerative development of rural tourism. When the density of per capita GDP is between 20,000 and 50,000 CNY/km², rural tourism resource points exhibit a clear trend of agglomerative development. Although the marginal effect decreases after 50,000 CNY/km², the agglomerative intensity remains high. ② Roads serve as a bridge connecting markets and rural tourism destinations. The density of the road network is a key indicator for measuring the level of local rural tourism development and the convenience of communication between hosts and guests, forming the cornerstone of rural tourism agglomerative development. When the density of graded highways reaches 2 km/km², rural tourism begins to exhibit a trend of agglomerative development. Subsequently, for every additional 1 km/km² in density, the agglomerative density increases by approximately 0.7 homes/km². Improving the transportation network not only reduces spatial and temporal distances but also strengthens external economies of scale by reducing logistics and information costs.

3.3.3. Impact of the Combination of Social Development Elements

Social factors such as population density, institutional innovation, and the scenic area environment can enhance rural cohesion and promote the formation and development of rural agglomeration. In this study, the social development impact of rural tourism agglomeration development is characterized using the population density and the density of high-grade scenic areas (Figure 6). The kernel density values of these two factors were extracted using a 5 km × 5 km fishing net unit, and Pearson correlation and regression analyses were conducted using the agglomeration density of rural tourism destinations. The results show that ① At a significance level of α = 0.01, the Pearson correlation coefficient for population density is 0.874 (p < 0.01), indicating a significant correlation between rural tourism agglomeration development and permanent urban resident population density. Permanent urban residents constitute the main participating group and customer base for rural tourism activities. Their density is important for measuring the scale of the rural tourism market and the demand potential, and it is also the core driving force for promoting the development of rural tourism agglomeration. When the urban population density in a region is between 20,000 and 50,000 people per square kilometer, rural tourism destinations exhibit a clear trend of agglomeration development. A high population density not only directly increases leisure demand but also provides sufficient local labor and a diverse service supply for rural tourism operations. ② The distribution of rural tourism resource points in Jilin Province shows a significant spatial convergence characteristic with high-grade scenic areas. At a significance level of α = 0.01, the Pearson correlation coefficient for population density is 0.946 (p < 0.01), and the regression equation fit R² is 0.889. There are between 20 and 40 scenic areas rated above the 3A level per square kilometer, and rural tourism destinations exhibit a clear trend of agglomeration development. High-grade scenic areas often have convenient transportation conditions and a well-established infrastructure, as well as a relatively stable customer market. The associated brand awareness and resource spillover effects can effectively promote the agglomeration of rural tourism resources. The functional complementarity and sharing of customer flow between scenic areas and villages significantly reduce the market cultivation period and accelerate the formation and expansion of rural tourism clusters.

4. Discussion

Promoting the integrated development of urban and rural areas and facilitating the spatial agglomeration of rural tourism can activate rural resource elements, enhance agricultural labor productivity, and increase farmers’ income through multiple channels. This is of great significance for underdeveloped regions to comprehensively promote the rural revitalization strategy [43] and achieve common prosperity for all [44]. Based on existing research, this study uses multi-scale spatial analysis methods to reveal the spatio-temporal pattern, business type differentiation, and formation mechanism of rural tourism agglomeration in Jilin Province. The research finds that the spatial distribution of rural tourism resources shows significant non-equilibrium characteristics under the influence of multiple factors such as natural conditions, economic foundation, and social environment.

Overall, rural tourism in Jilin Province exhibits a “high polarization and weak diffusion” trend, indicating a high degree of agglomeration that is continuing to strengthen, albeit with uneven development among regions. The central region, with its advantages in economy, resources, and market, demonstrated a high degree of agglomeration in the early stages of rural tourism development. However, the eastern region has prominent late-mover advantages, and its differences with the central region are gradually decreasing. The agglomeration characteristics of the western region exhibit a leapfrog development trend, but, overall, it is still in the early stages of agglomeration development. The standard deviation between regions has decreased from 0.260 to 0.038, reflecting a gradual transition from spatial differences to coordinated convergence under provincial policy coordination and market environment.

Analysis of the agglomeration characteristics of different business types reveals that various rural tourism business types exhibit a service agglomeration distribution across different spatial scales, with a clear “initial increase followed by a decrease” trend. The spatial distribution scales of key rural tourism enterprises and key rural tourism villages are similar, with both reaching their maximum agglomeration intensity at around 130 km, indicating that these two business types are constrained by transportation radius and the market neighborhood, exhibiting a layered layout. The spatial agglomeration intensity of rural homestays fluctuates significantly, reaching a peak at 19.35 km and forming a double-peak structure (peaks at 115.62 km and 155.49 km), indicating that homestay location selection is highly sensitive to micro-location, landscape uniqueness, and customer source stability.

Analysis of influencing factors indicates that, in terms of natural conditions, the spatial agglomeration of rural tourism in Jilin Province exhibits a pronounced tendency towards water and land proximity, with significant agglomeration characteristics observed within a 2.5 km buffer zone around water bodies at an altitude of around 200 m. Analysis of economic development levels reveals that economically developed areas can provide rural areas with more diversified tourism products and services, promoting the development of rural tourism agglomeration. When the per capita GDP density is between 20,000 and 50,000 CNY/km² and the density of graded highways reaches 2 km/km², the development of rural tourism begins to trend towards agglomeration. Analysis of social environmental factors shows that when the regional urban population density is between 20,000 and 50,000 people/km² and there are between 20 and 40 3A-grade or above scenic spots per km², characteristics of spatial rural tourism agglomeration are evident.

In addition, it is necessary to be vigilant about the possible negative effects of excessive agglomeration [45]. Regions such as Changchun and Jilin have entered a high agglomeration plateau period, with resource congestion, environmental pressure, and the risk of homogenization competition gradually emerging; while in the western region, there are still challenges such as insufficient infrastructure, weak brand effect, and talent loss. Moreover, the gentrification trend of rural tourism and its social spatial impact on local communities have not been fully explored in this study, which will be a key focus of our subsequent research.

5. Conclusions

5.1. Main Conclusions

This study mainly emphasizes the spatial agglomeration characteristics and influencing factors of rural tourism in economically underdeveloped areas, providing important insights for the sustainable development of rural tourism. The main conclusions are as follows:

Rural tourism in Jilin Province shows a significant spatial agglomeration trend, with a high overall agglomeration degree that continues to increase. There is a clear regional heterogeneity, with the central region maintaining a high and stable level, the eastern region showing a marginal decrease, and the western region achieving leapfrog development driven by policies.

The response scales of multiple business types vary significantly: business units and key villages exhibit a unimodal ring structure, while rural homestays present a bimodal pattern of dependence on both the suburbs and scenic spots.

Agglomeration is influenced by three-dimensional factors of nature, economy, and society. The natural conditions dimension shows a significant preference for water and proximity to the ground. The economic level dimension is highly correlated with per capita GDP and road network density. The social environment dimension closely depends on population density and the spatial spillover of high-level scenic spots.

5.2. Suggestions

Currently, issues such as uneven spatial distributions, difficulties in integrating emerging rural tourism destinations, and short per capita travel radii are still barriers to rural tourism development in Jilin Province. To address these issues, the following optimization and regulation measures are proposed: ① Strengthening of the “point-like” radiation of high-grade scenic spots. The agglomeration effect caused by high-grade scenic spots is an important factor in the geographical spread of rural tourism. Especially in the western region of Jilin Province, where rural tourism is relatively dispersed, tourist sources at high-grade scenic spots should be used to drive the development and revitalization of surrounding rural tourism destinations. For this region in particular, it is possible to use the golden signboards of 5A-level scenic spots such as Chagan Lake and Nenjiang Bay as points to delineate their boundaries. Through measures such as unified identification, joint marketing, and inter-trip sharing, surrounding characteristic villages, towns, rural enterprises, homestays, and agritourism can be incorporated into the rural tourism route system, achieving the “coexistence of scenic spots and villages.” ② Promotion of the “plane-like” expansion of characteristic villages and towns in rural tourism. Analysis of various business elements shows that rural tourism villages and towns have become important players in the development of rural tourism. In contrast to single rural tourism destinations, in rural tourism villages and towns, goals such as the construction of infrastructure, public services, and other support services can be better concentrated on. At the same time, these areas can rely on the power of village (town) governments to attract returning entrepreneurs to participate in rural tourism. Townships with restrictions should be encouraged to formulate special plans for rural tourism development under the unified planning of the town government, coordinating land use, environmental protection, and resource control. Tourism cooperatives should be established at the village level to integrate idle homesteads and abandoned land to build shared homestays and study camps, transforming resources into assets and villagers into shareholders. ③ Strengthening of the “network-like” connectivity of roads. Research shows that the densities of grade roads, population, and per capita GDP are all significantly correlated with rural tourism agglomeration. Therefore, strengthening the connectivity between urban and rural areas, as well as between villages, and improving the economy, information services, and talent flow networks between urban and rural areas can effectively promote the flow and integration of factors between these areas and the development of high-quality rural tourism through the expansion and connectivity of networks.

5.3. Research Limitations and Future Directions

Although this study has made certain contributions, several limitations are worth noting. Firstly, the POI data and official directories used in the research may have biases in covering micro and small business entities and emerging business forms. Such data sources tend to focus more on larger and more stable enterprises, which may lead to insufficient representation of non-standardized and seasonal rural tourism business forms. Secondly, the study is based on cross-sectional data, which can reveal the spatial characteristics at a certain point in time but is difficult to fully capture the dynamic evolution process and internal causal mechanisms of rural tourism agglomeration. Thirdly, the study did not fully incorporate in-depth discussions on social and cultural dimensions such as community participation, cultural authenticity, and host-guest relationships, which are important factors influencing the sustainable development and spatial formation of rural tourism.

Future research can be further expanded in the following aspects: First, multi-source big data such as social media check-in data, online review information, and mobile phone signaling data can be introduced and combined with traditional geographic data to improve the accuracy and timeliness of business form identification and spatial measurement. Second, long-term tracking studies and multi-case comparisons can be conducted. Through diachronic analysis of rural tourism agglomeration areas at different development stages and in different regional types, their life cycles and evolution mechanisms can be revealed. In addition, particular attention should be paid to the social and spatial reconstruction effects brought about by rural gentrification, and the interaction and competition among external capital, new immigrants, and local communities in tourism development should be explored to gain a more comprehensive understanding of the socio-economic driving forces and cultural changes behind rural tourism agglomeration.