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Journal of Resources and Ecology >

2025 , Vol. 16 >Issue 4: 1241 - 1256

DOI: https://doi.org/10.5814/j.issn.1674-764x.2025.04.026

Special Column: Ecotourism and Rural Revitalization

Spatiotemporal Correlation between Agglomeration of Homestay and Environmental Field: Insights from Wuyuan, China

  • NIAN Bohan , 1 ,
  • FENG Xinghua , 1, * ,
  • JIANG Lizhen 1 ,
  • XU Liting 1 ,
  • LI Jianxin 2
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  • 1. School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
  • 2. Jiangxi Institute of Economic Development, Jiangxi Normal University, Nanchang 330022, China
*FENG Xinghua, E-mail:

NIAN Bohan, E-mail:

Received date: 2024-10-03

  Accepted date: 2025-01-18

  Online published: 2025-08-05

Supported by

The National Natural Science Foundation of China(42201187)

The Jiangxi Provincial Department of Education Graduate Innovation Fund Project(YJS2023012)

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Abstract

Regional clustering has become a distinctive feature of the homestay industry in terms of spatial distribution in many countries. However, identifying and quantifying the advantageous locations suitable for the agglomeration of the homestay industry at the micro-scale is still in the exploratory stage. Therefore, with the support of multi-source data and the spatial entropy weight superposition method, drawing on concepts from physics such as gravitational and magnetic fields, and based on relevant location theories, the environmental field strength and its quantitative model are proposed to evaluate micro-locations suitable for homestay development. Finally, a case study was conducted in Wuyuan, China. The results show that the homestay industry is mainly distributed along traffic lines and rivers, which is greatly affected by tourism resources and rural settlements, and the degree of agglomeration is gradually increasing; The environmental field strength is closely related to the accessibility of infrastructure and the development of tourism resources, and the overall structure tends to be networked, polycentric and “core-periphery”; there is a positive linear correlation between the distribution of homestay industry and environmental field strength. This study provides a basis for optimising decision-making related to the sustainable planning and site-selection of tourism destinations and the homestay industry.

Key words： homestay; environmental field; spatiotemporal correlation; bivariate spatial autocorrelation; Wuyuan

Cite this article

NIAN Bohan , FENG Xinghua , JIANG Lizhen , XU Liting , LI Jianxin . Spatiotemporal Correlation between Agglomeration of Homestay and Environmental Field: Insights from Wuyuan, China[J]. Journal of Resources and Ecology, 2025 , 16(4) : 1241 -1256 . DOI: 10.5814/j.issn.1674-764x.2025.04.026

1 Introduction

A homestay refers to a small accommodation facility that relies on people’s idle residential resources. The homestay host participates in receiving guests and provides them with natural scenery, regional culture, home atmosphere and other experiences (Kontogeorgopoulos et al., 2015). In recent years, with the rise of suburban peripheral tourism, the unique advantages of the homestay industry in the process of building beautiful villages, promoting urban-rural integration, and realizing rural revitalization have gradually attracted the attention of the state and local governments. Driven by the market and policies, the homestay industry has shown a trend of agglomeration and expansion. The transformation from the traditional loose “agritainment” model to the development of industrial clusters has become an important starting point for the protection, development and rejuvenation of vast rural areas (Liu et al., 2020; Zheng and Huang, 2022). However, the spatial heterogeneity caused by locational factors makes the development potential of homestays vary across regions (Adamiak et al., 2019; Eugenio-Martin et al., 2019). As a result of the homogenisation caused by the excessive influx of social capital and the false prosperity of the industry caused by the dislocation of policies, the unbalanced and inadequate development of rural homestays is still prominent (Guo, 2022). Accurately identifying the locational factors of the agglomeration development of the homestay industry and, on this basis, seeking the advantageous agglomeration location of the homestay industry and making reasonable plans will help to avoid the subsequent weakness caused by blind investment and resource mismatch and effectively improve the investment efficiency and development vitality of the homestay industry (Puciato, 2016; Fei et al., 2019). Especially in the context of the upgrading of cultural and tourism consumption in the post-pandemic era, the supply of tourism products increasingly emphasizes diversification and personalization, and the sharing economy has become the realistic development logic of the key economic form of the supply-side structural reform of China’s tourism industry, setting higher requirements for the scientific and rational planning of the homestay industry as a model of “shared accommodation” (Battino and Lampreu, 2019; Zhao et al., 2020; Pan et al., 2021). Therefore, revealing the location law of the homestay industry holds great practical significance for the government and corporate investors to clarify the location criteria of homestay projects, optimise the overall layout of homestay areas, and realise the optimal allocation of tourism resource endowments.
Industrial locational selection is a dynamic allocation process involving resources, production factors and enterprises to determine the optimal location in the geographical space of the flow, transfer and combination (Galbraith et al., 2008). With the shift in the focus of modern world economic development from manufacturing to the service industry, the identification and regulation of service industry locations have gradually become the focus of scholars and governments in China and elsewhere (Balsa-Barreiro et al., 2019). Based on central place theory, rent theory, consumer behaviour theory, and the retail gravity model, among other theories and quantitative research methods (Reilly, 1929; Alonso, 1964; Wolpert, 1970; Mulligan, 1984), related research on this topic has examined the factors influencing service industry locational selection, the evolutionary mechanism of the service industry spatial structure, and the relationship between consumer behaviour and the service industry layout from economic, geographical, and sociological perspectives (Coffey and Shearmur, 2002; Yang et al., 2012; Singh et al., 2022). The accommodation sector plays a pivotal role in the service industry, serving not only as a vital spatial carrier and component of recreational spaces for tourists (Tussyadiah and Pesonen, 2016), but also as a showcase for displaying the level of development in regional tourism industries in conjunction with other industry factors and resources (Yang and Cai, 2016). Research on the spatial distribution and site selection of the accommodation industry has followed the development of industrial layout theory, progressing from location theory, market theory, and industrial agglomeration to the emergence of the theory of the new economic geography. The results of research on the layout characteristics, pattern evolution and impact mechanisms of the traditional accommodation industry at different spatial scales and functional types. For example, a spatial model of the distribution of the hotel industry was proposed based on six major locations within the city, including historic blocks, transition zones, scenic areas, railway stations, intersections, expressways and airport edges (Ashworth, 1989). Some scholars have argued that the key factors influencing the layout of hotels are their location, price, size, and services offered. Moreover, they emphasised that the clustering of hotels in a specific area can be achieved only through distinguishing characteristics and features (Urtasun and Gutiérrez, 2006). The author also noted that the location of tourist hotels should not only follow the general principles of urban planning but also focus on the temporal and spatial behaviour of tourists (Bao, 1991). Existing research has extensively explored the holistic characteristics of spatial distribution in the accommodation sector at macro levels such as regions and cities, often incorporating case analyses. However, there is a common oversight in regard to microlevel locational considerations as the focal point for industrial siting decisions, which directly shapes broader spatial configurations. Consequently, it is imperative to pay more scholarly attention to the microscale site selection of accommodation establishments (Egan and Nield, 2000). Compared to the traditional accommodation sector, emerging lodging formats such as homestays exhibit noteworthy differences in crucial factors such as operational strategies, site selection, and property procurement. These differences directly impact spatial configuration patterns and emphasize the pivotal role of microlevel locational decisions in determining the success or failure of operations (Kaufman and Weaver, 1998). Scholars around the world have paid attention to the farreaching impact of the vigorous development of the homestay indus-try and short-term rental platforms on the urban economy and different interest groups (Bhuiyan et al., 2013; Karki et al., 2019). The spatial heterogeneity of the competition or complementary relationship between homestays and the traditional accommodation industry has gradually become the focus of discussion (Varma et al., 2016; Gutiérrez et al., 2017; Dogru et al., 2019). Research on the location of a homestay from a spatial perspective has focused mostly on single or multiple influencing factors related to a homestay’s own attributes and the surrounding environment, and such research has conducted important explorations in Seoul, London, Barcelona, New York and other cities (Dudás et al., 2017; Ki and Lee, 2019; Lagonigro et al., 2020; La et al., 2021). Scholars in China have observed the strong geo-graphical agglomeration features of homestays, as well as their critical role in rural revitalization. They often explore the spatial distribution characteristics of homestay tourism clusters and industrial and economic cooperation relation-ships, combining different regional development advantages to achieve sustainable development and multiparty benefits (Long et al., 2018; Ma et al., 2022; Zheng and Huang, 2022; Bi et al., 2023). Nevertheless, in terms of the research scale, the main focus is still on the provincial and urban levels, despite the observed shift towards a microlevel perspective. Overall, current research on the location layout of home-stays has made progress, but microscale analysis remains understudied, and there is a lack of supply and spatial equilibrium analysis of the development environment of the regional homestay industry based on a dynamic perspective.
With the rapid development of big data acquisition technologies, locationbased points of interest (POI) data have emerged as a more timely and more accurate alternative to traditional data collection methods. Such data have preliminarily been utilized in the study of geographical phenomena (Jiao and Xiao, 2022). Based on the research above, this study takes Wuyuan County, a region that has long regarded tourism as the leading industry of the social economy, as an example to quantitatively identify and describe the evolutionary characteristics of the spatial structure of the homestay environmental field, reveal the evolution of the spatiotemporal correlation characteristics of the residential industry agglomeration location and the environmental field, and provide a reasonable and effective quantitative analysis framework for understanding the locational selection logic of the homestay industry. Exploring the location law and planning guidance of the collaborative residential industry and environment and developing holistic tourism and realizing rural revitalization in other tourism resourcerich cities hold great significance. Therefore, the specific research objectives of this paper are as follows: 1) To identify the spatiotemporal evolutionary process of the homestay industry in Wuyuan County using spatial analysis methods. 2) To introduce a model for evaluating environmental field strength and employ micro geographical data to delineate the regional variations in the homestay environmental field and reveal its spatial differentiation process. 3) To examine the relationship between the agglomeration development of the homestay industry and the evolution of environmental field by employing the bivariate spatial autocorrelation method and to provide insightful policy recommendations to optimize the rational distribution of the homestay industry.

2 Construction of the micro-location model of the homestay environmental field

The locational selection of the homestay industry, which is an emerging and vibrant sector in the realm of tourism, has emerged as a significant internal driver of local tourism development. It not only exerts enduring effects on practical concerns such as cultural heritage preservation, residents’ income augmentation, and environmental governance but also assumes a critical agenda within disciplines such as geography, urban and rural planning, and regional economics. Nevertheless, despite the rapid convergence and development trends observed in the homestay industry, the processes of incubation, formation, and sustainable development of this sector have to be fully comprehended. Undeniably, the tourism industry has experienced rapid and extensive agglomeration at the global scale. Academic research on tourism industry agglomeration began in the 1990s. Based on the conceptual theory of industrial agglomeration, the “diamond theory model” first noted that tourism is one of the most suitable industries for agglomeration development. The fundamental purpose of agglomeration is to obtain a competitive advantage in regional tourism, and six influencing factors are proposed: Tourism factors, market demand, related industries, the enterprise structure, the government and opportunity (Poter, 1998). Based on the inherent nature of tourism as a service sector that caters to lifestyle needs, some scholars argued that government control, market stimulation, transportation guidance, and technological advancement can either facilitate or restrict the site selection decisions of location actors in the static and dynamic contexts of tourism (Fang et al., 2008). In explicating the spatial location of tourism industry agglomeration, scholars emphasized the networking and relational dynamics among tourism industries. They contended that local networks, social capital, and trust are pivotal for achieving competitive advantage within tourism industry clusters (Braun, 2005). With the continuous deepening of location theory, a large number of scholars have incorporated diverse factors such as socio-culture, public services and innovation into the analytical framework for the location of tourism industry agglomeration (Martínez-Pérez et al., 2016; Cao and Long, 2022; Sabalenka et al., 2022). Notably, research on the location of traditional mainstream accommodation clusters, represented by hotels, has shifted from early qualitative analyses focusing on non-spatial factors like investor decision-making, branding, and operational scale, to the comprehensive application of multidisciplinary quantitative methods such as Geographic Information Systems (GIS) and spatial econometrics. In this process, these methods have been used to deeply explore the spatial agglomeration effects, typological differentiation, and distance decay properties of the accommodation industry. The proposal of numerous theoretical, empirical, and operational models for hotel location selection has created conditions for assessing the suitability of hotel industry locations (Yang et al., 2014; Fang et al., 2021). Among them, the agglomeration model highlights that market demand, environmental resources, infrastructure support, cost-effectiveness, policy support, as well as the competitive and cooperative environment are the primary factors influencing the layout of the hotel industry. This holds equally important guiding significance for the location selection of the emerging homestay industry (Kalnins and Chung, 2004). In the era of holistic tourism, characterized by a comprehensive approach to tourism development, the post-modern transformation of tourist travel patterns presents new demands on the structure of the tourism industry and the wider social fabric. To achieve integrated cultural and tourism development, harnessing the potential of regional diversity as a valuable resource has become a prevalent concept embraced by governments at all levels to address the evolving needs of the contemporary era and to stimulate economic growth (Qian et al., 2016; Ma-toga and Pawłowska, 2018). The essence of the homestay industry lies in its local character, which implies the need to not only fulfil tourists’ demands for comfortable accommodations, entertainment, and leisure but also provide them with a dual experience of local cultural and ecological distinctiveness to create a sustainable profit-making ability for both the homestay hosts and the local communities in which they operate (Liu and Cheung, 2016). Thus, guided by the principles of characteristic, comfort, and profitability, the development of related businesses and integration with upstream and downstream industry chains based on the “characteristic homestay+” model, ultimately moving towards cluster branding, is an inevitable requirement for the high-quality development of the homestay industry. Based on the characteristics of the homestay industry and the “locality theory” and considering the actual situations of various cases, it is believed that the macro-factors promoting the aggregation and development of the homestay industry mainly include natural ecological environment, convenient facilities, tourism development environment, and market size environment. Through the interaction of these macro-location factors, a “environmental field” conducive to the aggregated development of homestays is formed within tourist destinations. Homestay operators and tourists gather based on shared value pursuits and experience needs. Meanwhile, the aggregation and diffusion of the homestay industry also exert a feedback effect on the environmental field, promoting its continuous optimization and adjustment.
Therefore, drawing on the concepts of gravitational and magnetic fields in physics, the concept of the environmental field is introduced to assess a region’s capacity to attract or foster the homestay industry. Specifically, each point within a region corresponds to a specific strength of the environmental field, where a higher field strength indicates greater appeal for homestay industry positioning. As shown in Figure 1, the abstract concept of the environmental field is expressed at the micro-level by mapping it onto the natural ecological environment, facility configuration environment, tourism development environment, and market scale environment as well as other factors within the region. Building upon these foundations and integrating the scientific validity and measurability of data, the environmental field location model of the homestay industry is proposed, thus concretizing the elements of the environmental field. 1) Regarding natural landscape resources, previous research indicates an increasing trend among boutique homestay locations to veer away from crowded areas and to seek out pristine ecological environments, thereby privatising the natural comfort of the surroundings. In this study, we characterise the natural landscape environment through the distribution of river water systems and vegetation coverage (Wang et al., 2022); 2) Regarding living convenience facilities, from the “traditional home” to the “commercial home”, as a product of consumption upgrading, the fundamental attributes of the homestay to provide and create a lifestyle for tourists have not changed. The improvement in infrastructure and public service facilities has become the key to improving the quality of accommodation for the host. Therefore, the accessibility of resources such as transportation, shopping, medical care and catering is an important factor affecting the concentration of homestays (Sun et al., 2022; Bi et al., 2023); 3) Regarding core tourism resources, high-level tourist attractions serve as the centrepiece of the spatial structure of regional tourism and have a significant spillover effect on nearby boutique homestays. Additionally, favourable policies and conditions in key rural tourism destinations play an important role in the selection of homestay locations. Moreover, considering the limited number of county-level scenic spots, reflecting the distribution of scenic sites showcasing the endowment of regional tourism resources can positively contribute to attracting clusters of boutique homestays (Xia et al., 2018); 4) Regarding population conditions, certain population sizes and densities form the fundamental requirements for the development and growth of the tourism market (Kaufman and Weaver, 1998).
Figure 1 Identification logic of the micro-location model of the homestay industry

3 Materials and methods

3.1 Research area

Wuyuan County, located in northeast Jiangxi Province, is part of Shangrao city. Since the turn of the 21st century, Wuyuan has experienced rapid development in tourism due to its exceptional ecological resources and rich cultural heritage. The rural tourism model known as the “Wuyuan model” has garnered significant attention from scholars.
By focusing on the theme of culture and ecology, the transformation of the tourism industry has effectively enhanced environmental sustainability and improved overall tourism quality. The establishment of a modern comprehensive transportation system has facilitated the efficient movement of people, goods, information, and services. Furthermore, collaboration among different spatial forms and functional areas has fostered the construction of a distinctive tourism brand while driving economic growth. Gradually overcoming the traditional challenges faced by mountainous agricultural counties, Wuyuan has earned prestigious titles, such as China’s leading county for tourism strength. It has been a national pilot area for rural holidays, and it was one of the first national global tourism demonstration areas. As an international rural tourist destination with strong consumer demand and policy support, Wuyuan’s homestay industry plays a pivotal role in the county’s development. In 2023, the homestay industry in Wuyuan generated over 1.3 billion yuan in annual comprehensive revenue, indirectly creating employment opportunities for more than 20000 individuals. In strengthening its position as “China’s most beautiful countryside”, integrating cultural and tourism resources through homestays is crucial for enhancing residents’ income opportunities. Therefore, it was reasonable to select Wuyuan County as a representative to evaluate the suitability of the location of the homestay agglomeration area, which provided a reference for the development and construction of the homestay agglomeration area of the same type of rural tourism destination (Figure 2).
Figure 2 Study area and the homestay distribution in Wuyuan

3.2 Data sources and data processing

Homestay Data: For this study, data on homestay establishments in Wuyuan County were collected using the popular and well-rated homestay section of the Qunar website. The Python software platform was used to scrape information such as homestay names, locations, opening dates, and contact information. The data collection took place on December 31, 2022. To ensure the operational status of the homestays and to avoid sample omissions, the application programming interface (API) of the Baidu Maps platform, with “homestay” as the keyword, was used for data verification and cross-referencing. In addition, telephone interviews were conducted to further improve the accuracy of the homestay addresses, the room rates, and other data. After removing records with significant missing information, 23, 127, and 947 valid samples were obtained for 2010, 2016, and 2022, respectively.
The data regarding the environmental factors influencing homestays were primarily obtained through the official data interface API of the Amap platform, a leading mapping service provider in China. Using Python software, various categories of POI data for Wuyuan County in 2010, 2016, and 2022 were acquired, geocoded to obtain coordinates of latitude and longitude, and subsequently calibrated to generate diverse spatial datasets encompassing different factors. Data on transportation networks at various levels, the distribution of river systems, and administrative boundaries were sourced from the official websites of the Ministry of Natural Resources and the Department of Natural Resources of Jiangxi Province. Digital elevation model (DEM) data were downloaded from the geographical spatial data cloud website (http://www.gscloud.cn). Data on national level and provincial level key tourism villages and A-grade rural tourism sites and scenic areas were gathered from the official websites of the Ministry of Culture and Tourism of China and the Department of Culture and Tourism of Jiangxi Province, respectively. The population density data were based on LandScan global population gridded data from 2000 to 2022, with a resolution accuracy of 1 km. GIS spatial analysis techniques were utilized to resample and estimate the population density within the study area.

3.3 Research methods

3.3.1 Average nearest neighbour analysis

The nearest neighbour distance method uses the average distance between each point element and its nearest neighbour point element to calculate the nearest neighbour index. This index is used to analyse the spatial distribution pattern of homestay points to determine whether they exhibit clustering, uniformity, or randomness (Wang et al., 2022). The calculation formula for this method is as follows:
rE=12n/S
R=rirE
where R represents the nearest neighbour index; ri is the actual nearest neighbour distance; rE is the theoretical nearest neighbour distance; n is the number of homestays; and S is the total area of the region. The distribution pattern of the homestay industry can be determined based on the value of R. When R=1, the homestay industry tends to be evenly distributed. When R<1, this industry tends to be agglomerated.

3.3.2 Coefficient of variation of Voronoi polygon

Voronoi polygon is a basic data structure about spatial proximity. In order to verify the accuracy of the nearest neighbor index, the coefficient of variation (CV) of Voronoi polygon area is used to test the calculation results (Li et al., 2022). The calculation formula is as follows:
CV=S/M
where CV is the coefficient of variation;Sis the standard deviation of the area of the Voronoi polygon; and M is the average value of the area of the Voronoi polygon. When 33%≤CV≤64%, the points are randomly distributed; when CV > 64%, the points are condensed distribution; when CV < 33%, the points are uniformly distributed.

3.3.3 Multi-scale spatial cluster analysis

Under different spatial distance scales, there are changes in the spatial distribution characteristics of elements. Ripley’s K-function is a distance-based point pattern analysis method. By analysing the density of expected point elements and the density of actual point elements in a circle with a fixed point as the centre and different radius r, the spatial agglomeration intensity of point elements at different spatial scales can be characterized (Reilly, 1929). The calculation formula is as follows:
$K(d)=A \sum_{i=1}^{n} \sum_{j=1}^{n} \frac{w_{i j}(d)}{n^{2}}$
where i, j =1, 2, …, n, i≠j; n is the number of homestays in the study area; d is the distance scale; wij(d) is the distance between homestays i and j in the range of distance d; and A is the area of the study area. To better explain the actual spatial pattern, Besag and Diggle (1977) proposed the L function instead of the K-function:
       Ld=Kdπ−d
The plot of L(d) versus d can be used to examine the scale-dependent distribution pattern of homestays. In the formula, when L(d) >0, the homestay point obeys an aggregated distribution at this spatial scale, L(d) = 0 indicates a random distribution, and when L(d) < 0, the distribution is diffuse. A Monte Carlo test was used to test the significance of the L function, and 99 random simulations were used to determine the confidence interval’s upper (L(d)max) and lower (L(d)min) curves. The maximum deviation from the confidence interval is the aggregation intensity index, and the d value corresponding to the aggregation intensity peak can be used to measure the aggregation scale.

3.3.4 Kernel density estimation

Kernel density is an exploratory tool for measuring the spatial distribution density of regional elements and can intuitively present the agglomeration characteristics and distribution trend of point elements in geospatial space (Wang et al., 2013). The specific calculation formula is as follows:
fx=1nh∑i=1nkx−xih
where f(x) is the kernel density estimate;  kx−xih is the kernel function; n is the number of homestays in the study area; h is the broadband; and (x-xi) represents the distance from the estimated point x to the sample point xi. In this paper, 1 km2 cellular grid is selected as the output unit, and 4 km is selected after multiple debugging steps of the search radius. The Jenks natural breakpoint method is used to classify and obtain the homestay industry agglomeration centres for each year.

3.3.5 Bivariate spatial autocorrelation

The bivariate spatial autocorrelation model can reveal the spatial distribution correlation and dependence characteristics of the agglomeration degree of the homestay industry and the micro-locational factors of various types of environments (Anselin, 1995; Ord and Getis, 1995). The calculation formula are as follows:
$B \text {-Moran's } I=\frac{\sum_{i=1}^{n} \sum_{j=1}^{n} W_{i j}\left(x_{i}-\bar{x}\right)\left(y_{i}-\bar{y}\right)}{S^{2} \sum_{i=1}^{n} \sum_{j=1}^{n} W_{i j}}$
$I_{i}=Z_{i} \sum_{j=1}^{n} W_{i j} Z_{j}$
where B-Moran’s I is the bivariate global spatial autocorrelation coefficient, n is the total number of sample units; Wij is the spatial weight matrix; x is the independent variable; y is the dependent variable; and S2 is the variance of all samples. It is the bivariate local spatial autocorrelation coefficient, and Zi and Zj are the variance standardized values of i and j in the sample unit, respectively.

3.3.6 Construction of the evaluation model of environmental field strength of the homestay industry

The agglomeration location of the homestay industry is a place where it is highly feasible to develop homestays, and it is also a region that is highly attractive to homestay investors. By systematically reviewing the research trends in the geographical location of the tourism homestay industry and clarifying the theoretical framework of location studies, this study constructs a micro-location model for the homestay industry (Table 1). The key aspect is the introduction of the concept of field strength to establish the homestay environmental field, which describes the varying strengths and weaknesses of different locations in nurturing the potential for the development of homestay industry clusters in the region. The factors influencing the geographical location of the homestay industry and its locational attributes can be modelled using a vector grid, which enables the spatial distribution analysis of statistical data. This approach ensures the standardization of vector and raster data. Subsequently, methods involving buffer zones, overlay assignment, and the cost-weighted distance are employed to measure the environmental factors within the locational attributes. The individual environmental factors are then standardized using the range method, and their weights are determined using the entropy weight method. Finally, through the summation and analysis of these indicators, the final environmental field strength of homestay is obtained.
Table 1 Homestay environmental factor definitions and measurement methods
Locational factors Environmental factors Micro-location measurement indicators Indicator measurement methodology Description of the indicator measurement methodology
Natural landscape environment Landscape resources River distribution Buffer analysis Assignment of buffers by distance from the locational factor
Forest cover Assignment stacking analysis The magnitude of the role of the influencing elements of the locational factors was first assigned, and an overlay analysis was carried out using GIS software
Convenient living facilities Transport facility
environment		 Accessibility of bus stations, highway interchanges, high-speed rail stations,
and parking lots		 Cost-weighted distance algorithm Calculation of the shortest weighted distance from each grid to a destination grid by applying the shortest path method to the raster data
Shopping facility
environment		 Accessibility of department stores,
supermarkets, general markets,
and convenience stores
Medical facility
environment		 Accessibility of hospitals, clinics,
and pharmacies
Catering and
entertainment
environment		 Accessibility of Chinese restaurants, Western restaurants, coffee shops,
bars, and tea houses
Core tourism resources Tourist attraction
environment		 Distribution of A-class scenic spots Buffer analysis Assignment of buffers by distance from the locational factor
Scenic spot environment Distribution of scenic spot POI
Rural tourism
Environment		 National and provincial key villages for rural tourism and A-class rural tourism spots
Population
conditions		 Population environment Population distribution Assignment stacking analysis The magnitude of the role of the influencing elements of the locational factors was first assigned, and an overlay analysis was carried out using GIS software

4 Results

4.1 Spatiotemporal evolutionary process of Wuyuan homestay industry

4.1.1 Overall evolutionary characteristics

According to Table 2, throughout the study period, the average nearest neighbour distance of Wuyuan homestays at each time interval was consistently smaller than the expected value. Furthermore, the nearest neighbour index R was consistently lower than 1 and significant at the 1% level, suggesting the presence of a clustered spatial configuration. Based on the changes in the R value, it is apparent that the R value was substantially greater in 2010 and gradually decreased thereafter. This trend suggests a continuous increase in the spatial agglomeration level of homestays in Wuyuan County.
Table 2 Spatial distribution types of homestays in Wuyuan
Year Average nearest
neighbour distance (m)		 Expected nearest
neighbour distance (m)		 Nearest neighbour index Z-value P-value Distribution type
2010 1418.95 3185.92 0.445 -5.09 <0.001 Agglomeration
2016 695.04 2146.85 0.324 -14.58 <0.001 Agglomeration
2022 176.34 973.73 0.171 -48.21 <0.001 Agglomeration
To further assess potential inaccuracies of the nearest neighbour index, the Voronoi coefficient of variation (CV) was utilized. The CV values for 2010, 2016, and 2022 were notably high levels, i.e., 161.97%, 249.48%, and 311.47%, respectively. Significantly exceeding the critical value of 64%, these verification results indicate a typical county level distribution of agglomerated Wuyuan homestays. In 2010, there were a mere 23 homestays in Wuyuan County. The concentration of these accommodations was primarily influenced by the pioneering development of the ancient village one-day tour route situated along the eastern line. These homestays were strategically positioned near the leading tourist villages that benefitted from their abundant resources and comparative transportation advantages. Through the active implementation of the ideology that “green mountains and clear waters are as valuable as mountains of gold and silver” and the further advancement of the strategy to “develop holistic tourism and construct the most beautiful rural areas”, Wuyuan County has gradually witnessed the manifestation of ecologically based economic benefits. Consequently, the tourism industry system has experienced consistent improvement, leading to a continuous transformation of the tourism model and ultimately igniting the vigour of homestay tourism. Subsequently, by 2022, homestays in Wuyuan County skyrocketed to a total of 947, reflecting massive expansion since 2010. The increasing spatial agglomeration of the homestay industry contributes to the formation of new regional tourism and vacation attractions and drives the establishment of homestay brands. However, an excessive concentration of homestays within a locality may intensify competition among similar types of homestays and aggravate problems such as homogeneity and an imbalance of supply and demand. Therefore, it is necessary to optimize the spatial arrangement of homestays through structural adjustment to fully develop resource endowments and maximize comprehensive benefits.

4.1.2 Evolution of the scale of spatial agglomeration

The multi-distance spatial clustering statistical analysis of homestays in Wuyuan County in 2010, 2016, and 2022 was conducted using Ripley’s K-function in CrimeStat software to identify the multi-scale spatial agglomeration characteristics of Wuyuan homestays.
The results show that the L(d) indices at most time points were greater than the maximum expected from a random distribution, and this difference was statistically significant (Figure 3). The L(d) indices show an increasing trend, followed by a decreasing trend with distance, and the overall curve gradually changed from a relatively flat shape to an unimodal distribution over time. However, significant variations were observed in the peak values of L(d) and the corresponding spatial distance scales over different periods. Specifically, the L(d) curve of homestays in 2010 shows a relatively flat pattern, indicating spatial agglomeration within a range of 5.4 km. Beyond this threshold, homestays appeared to be randomly distributed. In particular, a bimodal structure was observed at 0.4 km and 2.2 km, with peak values of L(d) at 2.01 and 2.04, respectively. In 2016, posi-tive L(d) values were observed at different spatial scales, indicating spatial agglomeration. The highest level of agglomeration occurred at a spatial distance of 1.72 km, with an L(d) value of 3.39, indicating a further increase compared to 2010. In 2022, the highest level of agglomeration was reached at a spatial distance of 1.78 km, corresponding to an L(d) value of 4.15, which was slightly greater than that in 2016. Overall, with the establishment of the Wuyuan rural tourism resort, the favourable development foundation and immense market prospects have consistently stimulated the expansion of the spatial agglomeration scale and the enhancement of agglomeration intensity within the homestay industry. However, the agglomeration characteristics are still pronounced in the small scale range of 0-3 km.
Figure 3 Ripley’s K-function of homestays in Wuyuan in 2010, 2016 and 2022

Note: L(d)csr denotes the theoretical value of the L-function under complete spatial randomness.

4.1.3 Evolution of spatial agglomeration intensity

The results show that in 2010, there were fewer high density areas, and the homestay agglomeration formed a “one main, one secondary” pattern, with the main core being in eastern Jiangwan Town and the secondary core being at the border of Xitou Township and Duanshen Township. Homestays were scattered in the central urban area and in towns with abundant tourism resources and developed populations in the central and northern regions, showing a clear orientation towards tourist attractions. In 2016, with the improvement in transportation infrastructure and the positive impact of the successful development of several A-level scenic spots, the number of homestays increased significantly. The scope and scale of existing agglomerations continued to expand, forming contiguous development along transportation arteries through contact diffusion. New clusters emerged mainly through leapfrog diffusion in the northern, northwestern, and western parts of the region. By 2022, the range and scale of homestay agglomerations in Wuyuan had significantly strengthened, with an expanded high density area. The overall expansion pattern exhibited both contact diffusion and leapfrog diffusion, with good spatial continuity. The spatial pattern shows an “east strong, west weak” characteristic. Overall, the distribution of Wuyuan homestays in different periods shows significant spatial heterogeneity, evolving from a single core agglomeration to a multi core hierarchical “point-axis” pattern over time. This form of agglomeration continued and intensified each year, ultimately forming a core density structure aligned with the direction of transportation arteries and river flows (Figure 4).
Figure 4 Kernel density map of Wuyuan homestays in 2010, 2016 and 2022

4.2 Assessment of the strength evolution of the environmental field for homestay development

Based on quantitatively assessing the environmental field indicators for homestays in Wuyuan County, the environmental potential energy of the homestay field at a 1 km² grid scale in Wuyuan County for the three time periods of 2010, 2016, and 2022 was derived by weighting and summing the field strength values of various locational factors. These values were then classified into five grades of low level, relatively low level, medium level, relatively high level, and high level according to the natural breakpoint method (Figure 5), to analyze their spatial-temporal evolutionary characteristics.
Figure 5 Spatial pattern characteristics of the Wuyuan homestay environmental field strength in 2010, 2016 and 2022
From the perspective of the natural landscape environment, the overall spatial pattern of the environmental field strength of Wuyuan homestays is relatively stable. The high field strength area of the evaluation score is consistent with the trend of the river, while the low field strength area is scattered or banded in peripheral areas of relatively high value or high areas. From the perspective of living facilities, the high field strength area has expanded year by year and occupied an absolute dominant position, and the middle and low value areas of field strength have been continuously compressed. With the development of Wuyuan’s social economy and transportation facilities, the accessibility of various living factors has rapidly improved. The areas with low convenience mainly exist in the northern, southwestern and eastern parts of Wuyuan. From the perspective of core tourism resources, the high value area of the environmental field strength is distributed in a patchy shape around the A-level scenic spot and has a high correlation with the distribution of homestays. With the development of various tourism resources, the overall field strength value tends to increase, showing a trend of being high in the north, low in the south, high in the east and low in the west. From the perspective of population distribution, the high value area of Wuyuan environmental field strength is centred on the central urban area, distributed along north‒south traffic arteries in strips, and scattered in the centre of each township. It shows point-axis development and spread over time. The environmental field strength is greater in the east than in the west and greater in the south than in the north. According to the comprehensive evaluation of the environmental field strength of homestays, it shows an increasing trend in the time series dimension, and the scope of the suitable development area of homestays continues to expand. From 2010 to 2022, the proportion of horizontal units with environmental field strength below the middle level decreased from 52.67% to 26.48%, and the proportion of horizontal units with environmental field strength above the middle level gradually increased from 47.33% in 2010 to 59.18% and 73.52% in 2016 and 2022, respectively. From the perspective of spatial evolution, the high value area of the environmental field strength of Wuyuan homestays expanded from the “resource chain” and “traffic chain” axis belt in 2010 to the “multi point” and “multi belt” unbalanced area in 2022, while the low field strength area experienced pattern evolution from agglomeration to large agglomeration and small dispersion, and the scope gradually decreased. Overall, the development of the environmental field strength in the Wuyuan homestay industry during the study period was favourable, with the high value areas of this field strength expanding in range and the low value areas tending to be distributed at the periphery, indicating an apparent “core- periphery” structure overall.

4.3 Analysis of the spatiotemporal correlation evolution of the degree of homestay industry agglomeration and the environmental field

Through the comprehensive evaluation of the development level of homestay environmental field strength in Wuyuan and the development status of homestay agglomeration, this study finds that the areas with high environmental field strength have a certain coincidence with developed homestay industry agglomeration areas. To further verify the interaction between the two, the bivariate spatial autocorrelation model is used to explore the evolutionary characteristics of the spatial correlation degree between the homestay industry agglomeration degree and environmental field strength. During the three years of the study period, the spatial autocorrelation index (Moran’s I) between the degree of industrial agglomeration of the homestay industry and environmental field was positive, i.e., 0.105, 0.167, and 0.222, indicating that there was a spatial positive correlation between the degree of industrial agglomeration of the homestay industry and environmental field. Within the distribution range of homestay points, the environmental field strength is high, and the development potential is good. In terms of the time series, Moran’s I increases year by year, showing a trend of continuous strengthening. These findings indicate that the degree of the spatiotemporal correlation between the two gradually increases.
The local indicators of spatial autocorrelation (LISA) clustering map (P<0.05) further illustrates the local spatial clustering characteristics between the environmental field of homestay and homestay points (Figure 6). The correlation levels between the indicators were divided into three main categories: positive correlation, negative correlation, and non-significance. Uniform criteria were used to further distinguish between them, with darker colors indicating a greater degree of correlation. From the perspective of specific locational factors, the LISA clustering map of natural landscape elements and homestay agglomeration shows that negatively correlated areas are mainly distributed in a strip-like pattern, with the degree of correlation gradually increasing over time. Positively correlated areas are mainly distributed around negatively correlated areas with weaker correlations. Strongly positively correlated areas are scattered in the eastern and northern regions, indicating that areas with abundant natural landscape resources are attractive to homestays but with less impact. According to the LISA clustering diagram of the elements of convenient living facilities, negative correlations dominate, with the central urban area as the main distribution area, spreading along the transportation axis. Additionally, highly positively correlated areas are mainly distributed in the inner areas of the negatively correlated areas, indicating that many homestays tend to be distributed in areas with perfect infrastructure and good living conditions. The LISA clustering analysis of core tourism resource elements reveals that negatively correlated areas exhibit scattered patchy distributions, while positively correlated areas gradually expand and dominate. However, the correlation degree is relatively low, and highly positively correlated areas are scarce, indicating that regions with core tourism resources are not fully utilized. This underutilization is particularly prominent in the western region. According to the LISA clustering diagram of the population condition elements, the correlation is mainly negative, and the highly negatively correlated area is located in a scattered distribution in the eastern and northern areas. The development level of homestay agglomeration is relatively high, and the field strength of the population condition field is relatively low. This kind of area meets the needs of homestays to pursue vacation and leisure. Over time, a positively correlated area developed in the central urban area and various townships, and the consumer market orientation of homestays gradually became obvious. From the comprehensive evaluation of the spatiotemporal correlation relationship between the homestay agglomeration and environmental field, the regions with a strong positive correlation between the environmental field strength and homestay agglomeration in the 2010-2022 period show an increasing trend. The agglomeration zone is distributed in the central and eastern lines of Wuyuan. These regions attract numerous homestay layouts due to their excellent natural landscape environment, convenient transportation network, rich tourism resources and deep market foundation. The weakly positively correlated areas are mostly distributed on the northern, southwestern and southeastern edges of Wuyuan, where there are few homestays and the environmental field strength is low. Due to the distance from the central urban area, the population is relatively sparse, the infrastructure is not perfect, and it is limited by environmental conditions. Thus, it is at a disadvantage in the process of homestay location selection. A strongly negatively correlated area refers to an area where the homestay layout is more dispersed and the environmental field strength is higher. Such an area is mainly distributed on the periphery of the strongly positively correlated area and the transition zone between the strongly positively correlated area and the nonsignificant area. In these areas, the environmental field strength is greater, but the layout of homestays is not sufficient. In western Wuyuan, due to the lack of overall tourism resources and immature tourism development routes, the layout of homestays is limited. The weakly negatively correlated area is primarily distributed on the outskirts of the strongly negatively correlated area and gradually disperses over time. Overall, the region exhibiting the most significant spatial correlation between the homestay industry and environmental field strength in Wuyuan County extends northwards, westwards, and eastwards from the central urban area as its core. The highly positively correlated area has expanded; however, it remains concentrated predominantly in the eastern line area with superior tourism resources and earlier tourism development. Although the negatively correlated area has contracted, its intensity of correlation has increased. Therefore, effectively leveraging locational advantages and tourism resources while actively guiding the layout and development of the homestay indus-try is a crucial priority for these regions.
Figure 6 Analysis of the evolution of the spatiotemporal correlation pattern of the homestay industry agglomeration and environmental field

5 Discussion

5.1 The methodological advantages of the homestay industry micro-location model

The quantitative identification of the location of emerging tourism formats has always been a research hotspot in the field of human geography. Under the comprehensive influence of various factors, such as natural, economic, social and other characteristics, the locations suitable for the development of the homestay industry have undergone significant changes over time. However, not all regions possess the prerequisites for developing clusters of homestays (Long et al., 2018). Currently, many studies focus on finding and verifying the distribution characteristics of the homestay industry and the factors affecting its distribution. There is a lack of scientific analysis and quantitative evaluation of the locational factors and micro-location of the homestay industry through modeling, which limits the systematic understanding of the development patterns of homestay industry clusters and hinders practical guidance for the implementation of the industry. Therefore, based on the micro-location theory, this paper aims to address these gaps by focusing on a representative study area. By integrating the location theory of tourism industry agglomeration, the environmental factors affecting the homestay industry are clarified, and the environmental field model of the homestay industry is con-structed by combining the specific environmental factors with the concept of field potential energy. Finally, the best development area of the homestay layout is described and visualized on the grid scale by using geographic information system analysis techniques such as buffer analysis and cost weighting analysis, as well as spatial entropy weight superposition analysis. Compared with the previous research on the administrative region as the research unit (Ki and Lee, 2019; Sun et al., 2022), the grid scale as the research unit can focus on the environmental field strength in each grid in the study area, and then reflect the spatial and temporal correlation characteristics of the homestay industry agglomeration on the grid scale, which is conducive to a more accurate and comprehensive analysis of the development stages of homestay in different regions. In addition, by comparing with the existing research, we find that the micro-location pattern of the homestay industry is consistent with the spatial distribution of the actual homestay industry in Wuyuan, which proves that the research framework we proposed is scientifically reasonable and effective (Zhang et al., 2020).

5.2 Policy implications

Although governments at all levels are still using policies to continuously promote the construction of homestays in order to maximize the role of homestays in expanding domestic demand and achieving rural economic recovery, how to adopt a comprehensive plan to scientifically select sites and achieve sustainable development still needs to be resolved and discussed (Yusof et al., 2013; Kontogeorgopoulos et al., 2015). The existing research shows that the spatial development process of the homestay industry not only has the general characteristics of the location orientation, facility orientation and agglomeration orientation of the hospitality enterprises but also has the uniqueness of the location of the homestay such as the orientation of resource landscape and the orientation of residential areas, which is consistent with the results of this study (Qian et al., 2016; Wang and Ma, 2024). With the deepening of the rural revitalization strategy, the vast rural areas have been able to develop tourism rapidly based on their unique natural scenery and tourism resources, and the number of homestays has continued to grow under this development background. However, due to insufficient infrastructure support, these rural homestays often lack sustainable management capabilities (Kunjuraman and Hussin, 2017). Although the main urban area has outstanding advantages in transportation, communication, medical treatment, leisure and entertainment, and the high environmental field strength area occupies a large area, the homestay within its scope still needs to focus on the homogenization and vicious competition problems that may be caused by the lack of reasonable planning and standardized management (Chen et al., 2021). Based on the evolution and spatiotemporal correlation characteristics of the homestay industry agglomeration and the homestay environmental field in Wuyuan County, the following suggestions are proposed. In areas with high field strength and a high agglomeration of the homestays, it is necessary to deeply analyze its spatial texture and cultural accumulation. Pay attention to the integration and inheritance of regional culture, and create a high-quality homestay gathering area that takes into account both characteristic development and heritage protection. At the same time, it is necessary to avoid homogeneous competition and enhance the competitiveness and attractiveness of the homestay industry through differentiated development strategies. In areas with high environmental field strength but a low agglomeration of homestays, attention should be paid to improving policies to encourage the development of homestays and guiding the expansion of industrial space. At the same time, we should actively implement the strategy of talent training and attraction, improve the professional quality and service quality of homestay operators, and provide talent guarantee for the rapid development of homestay industry. For areas with low environmental field strength but a high concentration of homestays, efforts should be made to enhance the quality of transportation facilities construction, landscape environment quality and supporting industry quality in the region, while developing surrounding tourism resources and optimizing production space, living space and ecological space. By enhancing the potential energy of the overall environmental field, it provides space support and development momentum for the further development of the homestay industry. In addition, for all the areas where the homestay industry is cultivated and developed, we should pay attention to scientific planning and rational layout, and promote the development of the homestay industry in the direction of high quality, specialization and sustainability by strengthening government guidance and market supervision.

5.3 Limitations and future research

This study has certain limitations. Firstly, due to the complexity of factors influencing the spatial location of homestays, this research failed to cover detailed information on factors such as homestay grades, scales, and types during data collection. It is necessary to further enrich the research content by comparing the differences in location selection among homestays of different grades in the future; Secondly, when exploring the factors influencing the location selection of homestays, relevant social factors such as government support, characteristics of investment owners, and the willingness of community residents to transfer property rights were not fully considered. Future research can consider combining qualitative studies to construct a more comprehensive and accurate micro-level model of the homestay industry (Banki and Ismail, 2015; Zamzuki et al., 2023; Mei, 2024); Additionally, the development and construction process of homestays involves the agglomeration of tourism elements and spatial reconstruction. In the future, more precise data-driven empirical research needs to be conducted from multiple levels and perspectives to maximize the economic, social, and environmental benefits of the homestay industry. This maximization will ultimately enhance the competitiveness of rural tourism destinations.

6 Conclusions

Building upon the concept of the homestay environmental field, this study concentrates on the conducive ambient environment for the agglomeration and development of the homestay industry. A micro-location model for the homestay industry is established, with Wuyuan, a representative tourist city in China, serving as a case study. Using a hexagonal grid measuring 1 km2 as the fundamental spatial unit of analysis, this research employs quantitative geographical methods to measure and assess the spatiotemporal correlation and evolutionary features between the homestay industry agglomeration and homestay environmental field strength in Wuyuan County. The findings provide a scientifically sound basis for guiding the optimal distribution of the homestay industry and maximizing the exploitation of spatial resources. The results demonstrate that the homestay in Wuyuan County presents a typical agglomeration mode and continues to increase over time, the agglomeration model has undergone an evolution from a single central core to a multi-core, layered “point-axis” structure; The overall development of the environmental field strength in Wuyuan County is favorable. High-value areas tend to “overall” and “networked” development, while low-value areas tend to be marginalized. The natural landscape environmental factors exhibit minimal temporal variation. The high value areas of field strength of living facilities have expanded significantly, the high value areas of field strength core tourism resource elements have remained relatively stable, and the high value areas of field strength of population factors are concentrated in the central urban area; The agglomeration intensity within the homestay sector in Wuyuan County exhibits a positive correlation with the environmental field strength of homestays. Nonetheless, this correlation is predominantly discernible within specific locales, notably characterized by superior natural landscapes, well-established infrastructure, convenient amenities, abundant tourism assets, and dense population clusters. Conversely, regions demonstrating negative correlations manifest as clusters and bands encircling the positively correlated zones, with the correlation strength intensifying progressively over time.
This study systematically examines the evolution of location theory from a micro perspective, attempts to establish a micro-location model for the homestay industry, and investigates the interplay between the agglomeration development of the homestay industry and the environmental field strength of homestays. Overall, the correlation between the degree of agglomeration in the homestay industry and the environmental field strength varies with different location factors. On one hand, the spatial dynamics of the homestay industry exhibit multiple driving forces similar to those in traditional accommodation industries. On the other hand, due to being a product reflecting people’s yearning for a pastoral lifestyle in the post-industrial era, its spatial location requires full consideration of environmental complexity and comprehensive advantages. In summary, this study not only enriches the theoretical framework for location selection in the homestay industry, creates conditions for the comprehensive evaluation of the location of homestay clusters, and provides a decision-making basis for achieving the optimal spatial target positioning. At the same time, it also avoids the waste of resources and funds caused by blind investment in practice, which is conducive to achieving a win-win situation for the sustainable development of the homestay industry and the urban space development of tourist destinations.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Adamiak C, Szyda B, Dubownik A, et al. 2019. Airbnb offer in Spain— Spatial analysis of the pattern and determinants of its distribution. International Journal of Geo-Information, 8(3): 155. DOI: 10.3390/ijgi 8030155.

[2]
Alonso W. 1964. Location and land use:Toward a general theory of land rent. Cambridge, USA: Harvard University Press.

[3]
Anselin L. 1995. Local indicators of spatial association—LISA. Geographical Analysis, 27(2): 93-115.

[4]
Ashworth G J. 1989. Accommodation and the historic city. Built Environment, 15(2): 92-100.

[5]
Balsa-Barreiro J, Li Y, Morales A. 2019. Globalization and the shifting centers of gravity of world’s human dynamics: Implications for sustainability. Journal of Cleaner Production, 239: 117923. DOI: 10.1016/j.jclepro.2019.117923.

[6]
Banki M B, Ismail H N. 2015. Understanding the characteristics of family owned tourism micro businesses in mountain destinations in developing countries: Evidence from Nigeria. Tourism Management Perspectives, 13: 18-32.

[7]
Bao J G. 1991. A preliminary study of the significance of tourist spatial behavior pattern in the choice of hotel location. Human Geography, 6(3): 36-41. (in Chinese)

[8]
Battino S, Lampreu S. 2019. The role of the sharing economy for a sustainable and innovative development of rural areas: A case study in Sardinia (Italy). Sustainability, 11(11): 3004. DOI: 10.3390/su11113004.

[9]
Besag J, Diggle P J. 1977. Simple Monte Carlo tests for spatial pattern. Applied Statistics, 26(3): 327-333.

[10]
Bhuiyan M A H, Siwar C, Ismail S M. 2013. Socio-economic impacts of home stay accommodations in Malaysia: A study on home stay operators in Terengganu State. Asian Social Science, 9(3): 42. DOI: 10.5539/ass.v9n3p42.

[11]
Bi J W, Wang Y, Han T Y, et al. 2023. Exploring the effect of “home feeling” on the online rating of homestays: A three-dimensional perspective. International Journal of Contemporary Hospitality Management, 36(1): 182-217.

[12]
Braun P. 2005. Creating value to tourism products through tourism networks and clusters:Uncovering destination value chains. Proceedings of the OECD-Korea international tourism conference on global tourism growth:A challenge for SEMs. Seoul, South Korea: Organisation for Economic Co-operation and Development (OECD).

[13]
Cao K, Long S. 2022. Evolution of tourism industry agglomeration and its influencing factors based on counties in Xinjiang Uygur Autonomous Region. Economic Geography, 42(12): 205-213. (in Chinese)

DOI

[14]
Chen Q, Guan X, Huan T C. 2021. The spatial agglomeration productivity premium of hotel and catering enterprises. Cities, 112(1): 103113. DOI: 10.1016/j.cities.2021.103113.

[15]
Coffey W J, Shearmur R G. 2002. Agglomeration and dispersion of high-order service employment in the Montreal Metropolitan Region, 1981-1996. Urban Studies, 39(3): 359-378.

[16]
Dogru T, Mody M, Suess C. 2019. Adding evidence to the debate: Quantifying Airbnb’s disruptive impact on ten key hotel markets. Tourism Management, 72: 27-38.

[17]
Dudás G, Vida G Y, Kovalcsik T, et al. 2017. A socio-economic analysis of Airbnb in New York City. Regional Studies, 7(1): 135-151.

[18]
Egan D J, Nield K. 2000. Towards a theory of intraurban hotel location. Urban Studies, 37(3): 611-621.

[19]
Eugenio-Martin J L, Cazorla-Artiles J M, González-Martel C. 2019. On the determinants of Airbnb location and its spatial distribution. Tourism Economics, 25(8): 1224-1244.

DOI

[20]
Fang L, Xie Y, Yao S J, et al. 2021. Agglomeration and/or differentiation at regional scale? Geographic spatial thinking of hotel distributiona—A case study of Guangdong, China. Current Issues in Tourism, 24(10): 1358-1374.

[21]
Fang Y P, Yan X P, Chen Z N. 2008. The research of service location factors system. Economic Geography, 28(1): 44-48, 58. (in Chinese)

[22]
Fei L, Jiaming L, Hu Y, et al. 2019. The evaluation system and application of the homestay agglomeration location selection. Journal of Resources and Ecology, 10(3): 324-334.

DOI

[23]
Galbraith C S, Rodriguez C L, DeNoble A F. 2008. SME competitive strategy and location behavior: An exploratory study of high-technology manufacturing. Journal of Small Business Management, 46(2): 183-202.

[24]
Guo D. 2022. Research on the development B&B industry in Middle-Western Zhejiang region based on Internet+. Journal of Applied Science and Engineering, 26(4): 557-564.

[25]
Gutiérrez J, García-Palomares J C, Romanillos G, et al. 2017. The eruption of Airbnb in tourist cities: Comparing spatial patterns of hotels and peer-to-peer accommodation in Barcelona. Tourism Management, 62: 278-291.

[26]
Jiao H, Xiao M. 2022. Delineating urban community life circles for large Chinese cities based on mobile phone data and POI data—The case of Wuhan. ISPRS International Journal of Geo-Information, 11(11): 548. DOI: 10.3390/ijgi11110548.

[27]
Kalnins A, Chung W. 2004. Resource-seeking agglomeration: A study of market entry in the lodging industry. Strategic Management Journal, 25(7): 689-699.

[28]
Karki K, Chhetri B B K, Chaudhary B, et al. 2019. Assessment of socio-economic and environmental outcomes of the homestay program at Amaltari Village of Nawalparasi, Nepal. Journal of Forest and Natural Resource Management, 1(1): 77-87.

[29]
Kaufman T J, Weaver P A. 1998. Marketing efforts of bed and breakfast operations: Do they influence success? Journal of Travel & Tourism Marketing, 7(4): 61-78.

[30]
Ki D, Lee S. 2019. Spatial distribution and location characteristics of Airbnb in Seoul, Korea. Sustainability, 11(15): 4108. DOI: 10.3390/su11154108.

[31]
Kontogeorgopoulos N, Churyen A, Duangsaeng V. 2015. Homestay tourism and the commercialization of the rural home in Thailand. Asia Pacific Journal of Tourism Research, 20(1): 29-50.

[32]
Kunjuraman V, Hussin R. 2017. Challenges of community-based homestay programme in Sabah, Malaysia: Hopeful or hopeless? Tourism Management Perspectives, 21: 1-9.

[33]
La L, Xu F, Hu M, et al. 2021. Location of Airbnb and hotels: The spatial distribution and relationships. Tourism Review, 77(1): 209-224.

[34]
Lagonigro R, Martori J C, Apparicio P. 2020. Understanding Airbnb spatial distribution in a southern European city: The case of Barcelona. Applied Geography, 115: 102136. DOI: 10.1016/j.apgeog.2019.102136.

[35]
Li M, Ouyang W, Zhang D. 2022. Spatial distribution characteristics and influencing factors of traditional villages in Guangxi Zhuang Autonomous Region. Sustainability, 15(1): 632. DOI: 10.3390/su15010632.

[36]
Liu C, Dou X, Li J, et al. 2020. Analyzing government role in rural tourism development: An empirical investigation from China. Journal of Rural Studies, 79: 177-188.

[37]
Liu S, Cheung L T O. 2016. Sense of place and tourism business development. Tourism Geographies, 18(2): 174-193.

[38]
Long F, Liu J, Zhang S, et al. 2018. Development characteristics and evolution mechanism of homestay agglomeration in Mogan Mountain, China. Sustainability, 10(9): 2964. DOI: 10.3390/su10092964.

[39]
Ma H, Huang S, Wang M, et al. 2022. Evaluating tourist experience of rural homestays in coastal areas by importance-performance analysis: A case study of homestay in Dapeng New District, Shenzhen, China. Sustainability, 14(11): 6447. DOI: 10.3390/su14116447.

[40]
Martínez-Pérez Á, García-Villaverde P M, Elche D. 2016. The mediating effect of ambidextrous knowledge strategy between social capital and innovation of cultural tourism clusters firms. International Journal of Contemporary Hospitality Management, 28(7): 1484-1507.

[41]
Matoga Ł, Pawłowska A. 2018. Off-the-beaten-track tourism: A new trend in the tourism development in historical European cities. A case study of the city of Krakow, Poland. Current Issues in Tourism, 21(14): 1644-1669.

[42]
Mulligan G F. 1984. Agglomeration and central place theory: A review of the literature. International Regional Science Review, 9(1): 1-42.

[43]
Ord J K, Getis A. 1995. Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4): 286-306.

[44]
Pan H, Chen M, Shiau W L. 2021. Exploring post-pandemic struggles and recoveries in the rural tourism based on Chinese situation: A perspective from the IAD framework. Journal of Hospitality and Tourism Technology, 13(1): 120-139.

[45]
Poter M E. 1998. Clusters and the new economics of competition. Harvard Business Review, 75(6): 77-90.

[46]
Puciato D. 2016. Attractiveness of municipalities in south-western Poland as determinants for hotel chain investments. Tourism Management, 57: 245-255.

[47]
Qian C, Sasaki N, Shivakoti G, et al. 2016. Effective governance in tourism development—An analysis of local perception in the Huangshan Mountain area. Tourism Management Perspectives, 20: 112-123.

[48]
Reilly W J. 1929. Methods for the study of retail relationships. Austin, USA: Bureau of Business Research Press.

[49]
Sabalenka I, Krupenka Y, Ganski U, et al. 2022. Regional tourism clusters sustainability modeling taking into account sociocultural factors. Management Theory and Studies for Rural Business and Infrastructure Development, 43(4): 457-467.

[50]
Singh P, Arora L, Choudhry A. 2022. Consumer behavior in the service industry: An integrative literature review and research agenda. Sustainability, 15(1): 250. DOI: 10.3390/su15010250.

[51]
Sun S, Wang X, Hu M. 2022. Spatial distribution of Airbnb and its influencing factors: A case study of Suzhou, China. Applied Geography, 139: 102641. DOI: 10.1016/j.apgeog.2022.102641.

[52]
Tussyadiah I P, Pesonen J. 2016. Impacts of peer-to-peer accommodation use on travel patterns. Journal of Travel Research, 55(8): 1022-1040.

[53]
Urtasun A, Gutiérrez I. 2006. Hotel location in tourism cities: Madrid 1936-1998. Annals of Tourism Research, 33(2) 382-402.

[54]
Varma A, Jukic N, Pestek A, et al. 2016. Airbnb: Exciting innovation or passing fad? Tourism Management Perspectives, 20: 228-237.

[55]
Wang S, Deng Z, Chung F, et al. 2013. From Gaussian kernel density estimation to kernel methods. International Journal of Machine Learning and Cybernetics, 4(2): 119-137.

[56]
Wang W, Yang Q, Gan X, et al. 2022. Spatial distribution pattern and influencing factors of homestays in Chongqing, China. Applied Sciences, 12(17): 8832. DOI: 10.3390/app12178832.

[57]
Wang X, Ma Q. 2024. Spatial distribution pattern and influencing factors of bed-and-breakfasts (B&Bs) from the perspective of urban-rural differences: A case study of Jiaodong Peninsula, China. Chinese Geographical Science, 34(4): 752-763.

[58]
Wolpert J. 1970. Departures from the usual environment in locational analysis. Annals of the Association of American Geographers, 60(2): 220-228.

[59]
Xia B, Dong S, Ba D, et al. 2018. Research on the spatial differentiation and driving factors of tourism enterprises’ efficiency: Chinese scenic spots, travel agencies, and hotels. Sustainability, 10(8): 901. DOI: 10.3390/su10040901.

[60]
Yang Y, Luo H, Law R. 2014. Theoretical, empirical, and operational models in hotel location research. International Journal of Hospitality Management, 36: 209-220.

[61]
Yang Y, Wong K, Wang T. 2012. How do hotels choose their location? Evidence from hotels in Beijing. International Journal of Hospitality Management, 31(3): 675-685.

[62]
Yang Z, Cai J. 2016. Do regional factors matter? Determinants of hotel industry performance in China. Tourism Management, 52: 242-253.

[63]
Yusof Y, Muda M S, Amin W A, et al. 2013. Rural tourism in Malaysia: A homestay program. China-USA Business Review, 12(3): 300-306.

[64]
Zamzuki F A, Lola M S, Aruchunan E, et al. 2023. Assessing the sustainability of the homestay industry for the east coast of Malaysia using the Delphi approach. Heliyon, 9(11): e21433. DOI: 10.1016/j.heliyon.2023.e21433.

[65]
Zhang Z R, Wan T H, Yuan M. 2020. Study on the spatial distribution of homestay inns in Wuyuan and its influencing factors. Jiangxi Science, 38(1): 130-134. (in Chinese)

[66]
Zhao Y, Chau K Y, Shen H, et al. 2020. The influence of tourists’ perceived value and demographic characteristics on the homestay industry: A study based on social stratification theory. Journal of Hospitality and Tourism Management, 45: 479-485.

[67]
Zheng J, Huang L. 2022. Characterizing the spatiotemporal patterns and key determinants of homestay industry agglomeration in rural China using multi geospatial datasets. Sustainability, 14(12): 7242. DOI: 10.3390/su14127242.

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