Investigating spatial patterns and determinants of tourist attractions utilizing POI data: A case study of Hubei Province, China

Abstract

Exploring the spatial distribution characteristics of tourist attractions and the influencing factors is of significant importance for destination development, yet little relevant research has been conducted. This study explores the spatial patterns and determinants of tourist attractions using Hubei Province of China as a case based on the POI (Points of Interest) data, combined with standard deviation ellipse, GeoDetector method and so on. The results show that: (1) The distribution of tourist attractions in Hubei Province is concentrated in Wuhan and Huanggang. (2) The overall spatial patterns of tourist attractions in Hubei Province show a trend of “overall dispersion, partial concentration”, with the direction of northwest-southeast. (3) The permanent population, passenger traffic volume, per capita GDP, and the added value of the tertiary industry are the primary factors influencing the spatial distribution of tourist attractions in Hubei Province. Additionally, topography and river systems factors also impact their distribution. This study provides critical information for theory and practice in terms of tourism resources optimization.

1. Introduction

In the post-industrial era, the tourism industry has gradually emerged as a long-term catalyst for urban transformation and industrial structure optimization. Among them, tourist attractions, as essential platforms of tourist activities and various tourism products, are the foundation for the cornerstone of the industry's growth. The spatial distribution patterns of tourist attractions hold significant importance regarding the sensible exploitation of tourism resources and the sustainable growth of regional tourism [1,2]. Since the importance of tourist attractions were recognized worldwide, various types of tourist attractions have been expanding continuously. While enriching various tourism resources, this expansion has also caused problems such as resource waste and poor management in some tourist attractions [3].According to the “Several Opinions on Further Promoting Tourism Investment and Consumption” issued by the General Office of the State Council of the People's Republic of China (State Council of China [2015] No.62), since 2015, mechanism for the withdrawal of tourist attractions has been implemented, and some “problematic tourist attractions” have been delisted, warned, and rectified. With the implementation of the mechanism for the withdrawal of tourist attractions, the original attractions have changed, profoundly impacting tourists' travel choices [4,5]. Based on this, Conducting research into the spatial patterns of tourist attractions aids in gaining a deeper understanding of the state and trends of the local tourism industry. It helps identify the spatial interaction relationship among attractions, optimize the utilization and allocation of tourism resources, and possesses scholarly and pragmatic worth in contributing to the sustainable progression of the tourism industry.

Existing research on tourist attractions has provided important decision-making references for regional tourism development, which can be categorized into three themes. One main area of focus is the study of factors influencing tourist attractions. As a service industry that caters to people's spiritual and social needs, tourism shares similar operating and marketing strategies with commercial [6,7] products and services [8]. From the perspective of the attraction itself, research focuses on the fairness and accessibility of tourism resources distribution [[9], [10], [11]], insights into competitive markets [12], and attraction marketing and branding [8,13]. From the perspective of tourists, researchers apply psychological and sociological analytical methods to investigate tourist preferences and individual behaviors [6,7] in order to propose differentiated approaches to improving tourist attractions. For example, Yu et al. [14] used text mining technology to analyze city tourism reviews from the TripAdvisor website, assessing the impact of social media commentary on tourists' decisions regarding attractions and the influence of these reviews on the overall urban tourism experience.

Another important stream of research is the economic, social, cultural, and environmental effects of tourist attractions. Regarding economic effects, the expansion of the tourism sector is poised to invigorate the proliferation of associated industries, encompassing hospitality, lodging, travel services, and recreational activities [15]. Therefore, the impact on the local area is mainly in the creation of employment opportunities [16], increase in tax revenue and commercial benefits [17], and other related aspects. Regarding social and cultural effects, the tourism industry plays a significant role in cultural preservation and heritage management within a region. It profoundly influences changes in local customs, values, and traditions [18]. In terms of environmental effects, relevant research focuses on exploring the balance between attraction development and protection, mainly involving the environmental impact of tourist attractions on the surrounding areas, such as the sustainable development of natural resources, environmental pollution, and carbon footprint [19,20].

The third stream of research on tourist attractions concerns their spatial patterns. At a macro level, scholars focus on investigating the laws, evolution processes, characteristics, and patterns of spatial agglomeration and diffusion of tourist attractions [21,22]. Liu et al. [23] unveiled the degree of associations between different attractions and the paths and patterns of tourist flows. Some studies explored whether the spatial patterns of tourist attractions exhibit a centralized, dispersed, or core-periphery model [24], and its relationship with the urbanization process [25]. At a micro level, researchers focus on two aspects: the internal spatial organization patterns of tourist attractions and the distribution trends of tourist destinations based on visitor choice. The spatial structure of tourist attractions refers to the layout and interrelationships among different areas within the attraction, exerting a profound influence on its evolution and management [26]. For instance, studies investigated the patterns and efficiency of spatial organization within tourist attractions [27], the impact of the internal spatial layout on the tourist experience [28], and the strategies and methods of tourism attraction spatial planning and management, including attraction layout design, tourist flow management, and environmental protection [29]. In terms of spatial research from the perspective of visitors, it mainly focuses on the recommendation of tourism routes and popular attractions. For example, Zhou et al. [10] proposed an improved agglomerative nesting spatial clustering and spatial-temporal inference model for tourism route recommendation, which achieves significant improvements in recommendation accuracy and personalization compared to traditional tourism route recommendation algorithms.

In addition, the methods, subjects and data of researching tourist attractions are constantly expanding. Regarding the methodology, scholars have employed various methods, such as tourism behavior research methods [30], spatial statistical analysis methods [29], spatial simulation modeling methods [31], and network analysis [13,32], to explore the spatial patterns of tourist attractions. Regarding the subject, classification is based on the level of the tourist attractions, such as A-level, AA-level, and AAA-level tourist attractions [33,34], or the natural, cultural, and historical characteristics of the attraction, including world cultural heritage sites, natural reserves, national parks, characteristic towns, and rural tourism [25,35,36]. Classification can also be based on the area of the attraction and the visitor capacity, including large and small tourist attractions [37]. Regarding the data, scholars have gradually moved from traditional economic census data and survey questionnaires [38] to emerging data sources such as remote sensing data, points of interest (POI), and network data [30,39].

In general, the relevant research results of tourist attractions are relatively abundant, among which the research on the spatial pattern of tourist attractions has attracted more and more attention, which offers essential guidance for the strategic enhancement of travel resources and the enduring progression of the tourism industry within a region. However, current research on the spatial patterns of tourist attractions mainly focuses on specific attractions and often relies on traditional statistical data. Among emerging data, POI data is a type of geographic data that includes information about the names and locations of various elements, which has been widely employed in urban geography research. The application of POI data in research is still limited, despite its advantages of large data volume, easy acquisition, good timeliness, and high accuracy. Thus, there is a need for a comprehensive analysis, utilizing emerging data sources such as POI, especially at the regional level.

Hubei Province in China has abundant natural and cultural tourism resources, with a rapidly growing tourism industry that is typical of the region's development. Given this, this study endeavors to examine the spatial distribution and influential factors of tourist attractions at the urban scale in Hubei Province, China. By employing POI data and analytical methods such as point density analysis, standard deviation ellipses, and the GeoDetector, the study seeks to offer actionable decision-making support. This support is intended to promote a thorough understanding and judicious management of tourism resources, contributing to the industry's sustainable and high-quality growth.

2. Methodology

This study comprehensively applied spatial analysis methods to analyze the geospatial configurations and impetuses behind tourist attractions in Hubei Province. Firstly, the study selected the method of point density analysis to investigate the spatial pattern of tourist attractions based on the POI data and research needs, and demonstrated the degree of spatial agglomeration through visualization techniques. Building on this, the standard deviation ellipse technique was applied to predict the development trajectory and trends of tourist spots in Hubei province. Finally, the geographic detector model was utilized to examine the factors influencing the spatial pattern of tourist attractions and quantitatively analyze the sources of spatial heterogeneity in tourist attraction patterns, as well as the impact of different factors on these patterns. Through this series of methods, the study comprehensively and thoroughly analyzed the regularity and influences on the geospatial distribution of tourist attractions, providing robust backing for grasping the spatial configurations of these sites.

2.1. Study area

Hubei Province is situated in the central region of China, located in the middle reaches of the Yangtze River Economic Belt, with geographical coordinates between 29°01′53″N to 33°6′47″N and 108°21′42″E to 116°07′50″E, encompassing an expansive territory of 185,900 square kilometers (Fig. 1). Due to its unique geographical location, Hubei Province has significant transportation and location advantages, with frequent political, economic, and cultural exchanges throughout history, and diverse cultural and scenic resources such as the Yellow Crane Tower, Wudang Mountain, the Three Gorges of the Yangtze River, and the Longzhong Scenic Area. In terms of natural conditions, Hubei Province occupies the transitional belt from the second to the third tier of China's topopraphy. The terrain and landscape are diverse, with mountains encircling the eastern, western, and northern sides, while the central part is characterized by low-lying terrain. Overall, it presents an incomplete basin. Furthermore, Hubei Province is characterized by a subtropical monsoon humid climate, abundant water, and plant and animal resources, and is known as the “Province of a Thousand Lakes”, with widespread distribution of natural landscapes such as the East Lake Scenic Area in Wuhan and the Shennongjia Forestry District. In the list of national demonstration zones for all-for-one tourism released by the Ministry of Culture and Tourism, Hubei Province has eight tourist attractions on the list.

Fig. 1.

Basic spatial information of Hubei Province.

From 2015 to 2019, Hubei Province witnessed a growth in the number of tourists received, increasing from 510 million to 606 million, representing a growth rate of 18.82 %. The total tourism revenue also increased from CNY 430.88 billion to CNY 692.74 billion, with a growth rate of 60.77 %. The proportion of tourism revenue to the national gross domestic product increased from 14.2 % in 2015 to 15.25 % in 2019. In 2021, the General Office of the People's Government of Hubei Province disseminated directives concerning the operational specifics of a suite of strategies designed to bolster the recuperation and advancement of the cultural and travel sector. These measures included financial support, funding assistance, and brand development, aimed at promoting the post-pandemic recovery and high-quality development of Hubei. These initiatives fully leverage the power of culture and tourism to stimulate the province's growth. In 2022, the General Office of the People's Government of Hubei Province issued the “14th Five-Year Plan for Tourism Development in Hubei Province”, emphasizing the theme of promoting tourism development, building a world-renowned tourism destination, and shifting the focus from scale-driven growth to improving quality in the entire tourism sector.

Note: This figure shows the location of Hubei Province in China, the administrative divisions within Hubei Province, and the spatial distribution of points of interest (POIs) in tourist attractions in Hubei Province.

2.2. Data

The POI data was obtained from the Amap Open Platform (https://lbs.amap.com/) and Python was used for data retrieval. The study area for POI data acquisition was the Hubei Province. Using the keywords “tourism” and “attractions”, 2563 pieces of tourism attraction data were obtained after eliminating duplicate and irrelevant data that did not meet the research needs. Since the coordinate system for data obtained from the Amap Open Platform is GCJ02, the original coordinates were converted to WGS84 using QGIS software, and a vector database of tourism resources in Hubei Province was established.

In addition, the administrative division data required for the study was sourced from the National Geomatics Center of China (NGCC) database (www.ngcc.cn/ngcc). The Socioeconomic data were obtained from the “2021 Hubei Statistical Yearbook” and the “2021 National Economic and Social Development Statistics Bulletin of Hubei Province”. The images of tourist attractions were obtained from Baidu Baike (https://baike.baidu.com) and the People's Government of Enshi Tujia&Miao Autonomous Prefecture website (www.es.gov.cn).

2.3. Methods

2.3.1. Point density analysis

Point density analysis transforms spatially discrete points into a comprehensive and abstract representation of data, which is used to investigate the spatial patterns of geographic phenomena. In theoretical terms, a neighborhood is delineated around the nucleus of each grid cell, and the point density is ascertained by aggregating the quantity of touristic sites within the neighborhood and then partitioning this sum by its expanse [40]. The formula for point density analysis is expressed as:

$$D=N/S$$

where D represents the point density value of tourist attractions, N is the number of tourist attractions within the defined neighborhood, and S is the area of the defined neighborhood.

The point density method is employed to analyze the spatial distribution characteristics of tourist attractions in Hubei Province. By conducting point density analysis on POI data, researchers can identify spatial clustering areas of tourist attractions, thereby inferring the distribution of tourism resources in that region. This type of analysis helps to discover spatial correlations, hotspots, and blank areas among tourist attractions, thereby providing a scientific basis for tourism planning, resource integration, and marketing. Meanwhile, point density analysis can also assist researchers in identifying factors that influence the geographic dispersion of scenic spots, such as transportation facilities, natural resources, population density, and so on, thereby delving deeper into the formation mechanism and development trend. These analytical results are profoundly important in directing the evolution of the indigenous travel sector, amplifying the allure of touristic sites, and optimizing the allocation of tourism resources.

2.3.2. Standard deviation ellipse

The standard deviation ellipse visualizes the correlations and differences between different variables, exploring the direction distribution, trend, and range of an abstracted point data feature [41,42]. This method calculates the mean, variance, and covariance matrix of each variable in a multivariate data set to determine an ellipse that represents the variability of the data set. The formula for calculating the standard deviation ellipse is:

$${SDE}_x=\sqrt{\frac{\sum _{i=1}^n{\left(x_i-\bar{X}\right)}^2}{n}}$$

$${SDE}_y=\sqrt{\frac{\sum _{i=1}^n{\left(y_i-\bar{Y}\right)}^2}{n}}$$

where $x_i$ and $y_i$ are the positional coordinates of individual elements, $\bar{X}$ and $\bar{Y}$ are the arithmetic mean centers, ${SDE}_x$ and ${SDE}_y$ are the centers of the ellipse.

The application of the Standard deviation ellipse method not only facilitates the revelation of spatial distribution patterns of tourist attractions but also aids researchers in identifying potential spatial correlations and clustering phenomena. By analyzing the shape, size, and orientation of the standard deviation ellipse, researchers can further interpret possible spatial interactions and influence relationships among tourist attractions. This contributes to understanding the spatial distribution characteristics among tourist attractions, including concentrated distribution areas and dispersed distribution areas, which are of significant importance in tourism planning and resource allocation. This study, combining POI data with Geographic Information System (GIS) technology, furnishes a more exhaustive comprehension of the geographical dispersion patterns of tourist attractions. It offers scientific evidence and practical guidance for tourism planning, resource distribution, and promotional efforts, thereby advancing the enduring progression of the travel sector.

2.3.3. GeoDetector

GeoDetector [43] is used to detect the spatial differentiation and driving forces of research objects. This method is less limited by other assumptions, and its results are more objective and realistic, which has received increasing attention from researchers. The model is as follows:

$$q=1-\frac{1}{N{\sigma }^2}\sum _{h=1}^L{N}_h{\sigma }_h^2$$

where q is the influence detection value of the influencing factor on the density of tourist attractions in Hubei Province; $N_h$ is the number of units included in the detection element; N is the total number of units in the study area; ${\sigma }_h^2$ and ${\sigma }^2$ are the variances of the Y values (scenic spot density values) of the detection element layer and the entire study area, respectively. The q value is distributed within [0, 1], and when the q value is smaller than or equal to 0, it indicates that the factor has less or no impact on the spatial patterns of tourist attractions in Hubei Province; when the q value is larger than or equal to 1, it indicates that the factor has a greater impact on the spatial patterns of tourist attractions in Hubei Province.

The Geodetector model is a multivariate statistical analysis method that comprehensively considers the impact and interaction of multiple independent variables on the dependent variable to analyze the complexity of geographic phenomena. Therefore, when analyzing the influencing factors of tourist attraction density in Hubei Province, the Geodetector model can provide a comprehensive perspective to explore the mechanisms of each factor. In the study, social and economic factors, as well as natural factors, are used as independent variables, while tourist attraction density is used as the dependent variable. The Geodetector model is employed to analyze the relationships between these factors. Through sensitivity analysis of different factors, it is possible to determine which elements exert the most pronounced influence on tourist attraction density and their interaction effects. This aids government and planners in formulating sound strategies for the growth of tourism and furnishes a factual foundation for pertinent decision-making processes.

3. Results

3.1. Overall spatial pattern of tourist attractions

Fig. 2(a) shows the overall spatial patterns of tourist attractions in Hubei Province. Generally, all cities in Hubei Province have some tourist attractions distributed and relatively abundant in number, but with noticeable differences between regions. The distribution of touristic sites in Hubei Province is predominantly focused in the eastern and western sectors, with a comparatively lower number of attractions in the central region compared to the east and west. Specifically, Wuhan City has the most extensive distribution of tourist attractions and ranks first in terms of the number of attractions, with a total of 500. As a national historical and cultural city, Wuhan has a long history and has been the center of military and cultural activities in modern times, resulting in a large number of natural and cultural landscapes. Apart from Wuhan, Huanggang City also has a considerable quantity of tourist attractions, with a total of 420. Some cities have a similar number of tourist attractions, such as Yichang, Jingzhou, Xiangyang, Enshi Autonomous Prefecture, and Shiyan, with 199, 192, 190, 181, and 172 tourist attractions, respectively. Yichang is known as the “Hydropower Capital of China”, while Jingzhou and Xiangyang have been important commercial and cultural exchange regions since ancient times due to their strategic locations. Enshi Autonomous Prefecture and Shiyan City have relatively high elevations and complex terrains, resulting in a large quantity of tourist attractions in the regions. In addition, Xiantao, Huangshi, and Jingmen cities have tourist attractions distributed between 106 and 147, while the number of attractions in other cities is less than 100.

Fig. 2.

Spatial pattern of tourist attractions in Hubei Province

Note: This figure displays the spatial patterns of Hubei Province, including (a) distribution of quantity; (b) point density; (c) standard deviation ellipse; (d) river system.

3.2. Density spatial pattern of tourist attractions

Utilizing the point density analysis tool in ArcGIS 10.2 spatial analysis, a point density analysis map of tourist attractions in Hubei Province was generated to analyze the overall distribution pattern. As shown in Fig. 2(b), the spatial patterns of tourist attractions in Hubei Province present a trend of “overall dispersion and local concentration”, with some core areas serving as the distribution centers expanding outward. According to the central place theory, the spatial patterns of tourist attractions can be explained as different levels of central places, each of which provides different ranges and types of tourism facilities and services. Larger central places offer more extensive tourism facilities and services, such as hotels, restaurants, shopping centers, etc., while smaller central places offer more limited facilities and services, such as scenic spots and farm stays. In addition, areas with better transportation may be more competitive in the tourism industry, as visitors can more easily reach those areas. Therefore, Wuhan is the most significant high-density area.

From the perspective of the eastern part of Hubei, Wuhan, together with its neighboring cities, Huanggang, Ezhou, and Huangshi, form a high-density area of tourist attractions distribution. This area has a dense population and a favorable geographical location, with frequent political, economic, and cultural exchanges with neighboring provinces, resulting in a wide distribution of tourist attractions. In the central and western parts of Hubei, the density of tourist attractions is low in cities such as Suizhou, Jingmen, Xiaogan, Xianning, Tianmen, Qianjiang, and Xiantao, with only a few areas having a relatively lower density. Xiangyang, Yichang, and Jingzhou also have some areas with lower density, but all have high-density areas within the region, and the density decreases continuously from this center. From the perspective of cities in the western part of Hubei, the density of tourist attractions in Shiyan, Shennongjia Forestry District, and Enshi Autonomous Prefecture is generally low. This region has a low population, high elevation, and complex terrain, culminating in a comparatively modest tally of touristic sites, predominantly consisting of natural vistas.

3.3. Pattern direction and center of tourist attractions

Using the standard deviation ellipse tool within ArcGIS 10.2 software, an evaluation of the directional and trend aspects of the spatial arrangement of touristic sites in Hubei Province was undertaken, and a standard deviation ellipse chart illustrating the spatial arrangement of these attractions was generated. As shown in Fig. 2(c), the direction of the standard deviation ellipse for touristic sites in Hubei Province runs from northwest to southeast, with obvious directionality, indicating strong development potential and momentum in the western region. In recent years, Xiangyang and Yichang, as two provincial sub-center cities, have shown robust economic growth and tourism development momentum, transitioning from “one principal and two deputies” to “one principal and two wings” under policy guidance and support. Yichang, known for its famous attractions such as the Three Gorges Scenic Area of the Yangtze River and Changyang Qingjiang Gallery, is located in the core area of the Western Hubei Ecotourism Circle and has made rapid progress in tourist attractions projects, tourist volume, and tourism revenue, benefiting from a favorable business environment and policy guidance. In addition, during this period, the X-axis radius of the standard deviation ellipse was 230.94 km, the Y-axis radius was 105.11 km, and the distribution center was located at 113°11′20″E longitude and 30°46′53″N latitude, in Jiuzhen town, Tianmen city, at the center of the Jianghan Plain, with a deviation angle of 98.58°.

3.4. Drivers of spatial patterns of tourist attractions

Numerous academics have undertaken investigations into the elements that shape the geographical configurations of touristic sites across various territories, providing valuable theoretical references for this study. Refering to the research of Liu and Hao [29] and combining the actual situation of Hubei Province, this study posits that economic and social factors will influence the spatial patterns of tourist attractions in Hubei Province. Among them, economic factors include tourism revenue, value added by the tertiary industry, and per capita GDP. Social factors include permanent population, passenger traffic volume, and per capita disposable income in urban areas. Using the GeoDetector method, this study explores the impact on the spatial patterns of tourist attractions, and uses GIS overlay analysis to superimpose river systems and elevation data with POI data to explore the inherent relationships between geographical factors and the spatial patterns of tourist attractions.

3.4.1. Socioeconomic factors

According to Table 1, the main factors influencing the spatial patterns of tourist attractions in Hubei Province are permanent population, passenger traffic volume, per capita GDP, value added of the tertiary industry, and tourism income, with q values of 0.953, 0.581, 0.524, 0.482, and 0.476, respectively. In addition, the influence of per capita disposable income in urban areas is relatively minor, indicated by a q value of 0.104.

Table 1.

GeoDetector analysis of the spatial patterns of tourist attraction.

Primary indicators	Secondary indicators	q value
	Permanent population	0.953
Social factors	Passenger traffic volume	0.581
	Per capita disposable income in urban areas	0.104
Economic factors	Tourism income	0.476
	Value added of the tertiary industry	0.482
	Per capita GDP	0.524

Economic factors exert a significant impact on the geographical configurations of touristic sites in Hubei Province. Rapid economic development can lead to increased economic vitality and frequent economic activities, resulting in more funds flowing into the tourism industry. Existing scenic spots will thus be better equipped, and the potential for tourism resources in the province will be continuously explored, promoting the expansion of the tourism industry. Analyzing the secondary indicators, it becomes apparent that per capita GDP exerts a more pronounced influence. Correspondingly, being the most economically advanced area in Hubei Province, Wuhan City hosts the highest quantity of touristic sites in the region.

Social factors also play a substantial role in shaping the spatial distribution of tourist attractions in Hubei Province. The continuous progress of society brings about rapid economic growth, and concurrently, it often promotes the continuous improvement of regional infrastructure, the continuous increase of population, and the improvement of residents' living standards. Under the joint effect of infrastructure improvement and residents’ aspirations for a better life, residents are more likely to flock to numerous tourist attractions for leisure. This will also drive further advancement in the local travel sector, resulting in the development of more mature touristic sites, expanding their radiation range, and forming a virtuous cycle. Permanent population is the most significant factor, which is in line with common understanding. Population is the foundation of industrial development, and cities in the province with a larger population, such as Wuhan, Huanggang, and Xiangyang, have a wide distribution of tourist attractions. And the influence of passenger traffic volume is also larger, indirectly reflecting the level of infrastructure in a certain region. Similarly, Xiangyang, Yichang, and Huanggang, where passenger traffic volume is high, have a large number of tourist attractions.

3.4.2. Natural factors

Topographic factors are important for the formation of natural landscapes, and regions with high terrain and unique topographic conditions often have abundant natural resources for tourism development. At the same time, topographic factors can also promote or hinder the formation and development of cultural landscapes. The basic condition for the formation of cultural landscapes is that there is a population engaged in political, military, economic, and cultural activities in a certain region and constantly developing, becoming a region with typical significance in a certain aspect. Topographic factors determine the transportation conditions of the region, and thus have a profound impact on population mobility and activities, leading to differences in the distribution of cultural landscapes. By overlaying the POI data of tourist attractions in Hubei Province with the 90 m resolution digital elevation map of Hubei Province, an analysis was conducted to explore the intrinsic relationship between the two. From Fig. 2(d), it can be seen that in the high-elevation areas of the northwest and southwest of Hubei Province, the distributional tendencies of touristic sites are relatively sparse, while in the low-lying eastern and central regions, the distributional tendencies of touristic sites are significantly denser. This indicates that there is a correlation between the distribution of tourist attractions and topographic factors, and tourist attractions tend to be distributed in areas with lower topographic fluctuations, where population mobility and activities are convenient and not limited by geographical conditions, resulting in more tourist attractions.

The influence of river systems on the distributional tendencies of touristic sites is significant. Rivers provide essential geographic conditions for human production and living by ensuring water supply, commuting, and transportation needs, as well as serving as reservoirs during periods of uneven precipitation. Since ancient times, population centers have relied on rivers and lakes, making river systems an important factor in population aggregation. The relationship between the distributional tendencies of touristic sites and the population is closely related to river systems. From Fig. 2(d), it can be observed that areas with dense river systems, such as the Yangtze River and the Han River, have a wider distribution of tourist attractions. Conversely, regions with scarce river systems, such as the northwest and southwest, have relatively fewer tourist attractions. Therefore, for the distributional tendencies of touristic sites, river systems are also an important influencing factor, which correlates each other by affecting the distribution of the population and reinforcing the transportation and passenger transport conditions. Additionally, river systems themselves, as landscapes, can also foster different types of tourism resources, such as animal and plant resources. As a result, regions with dense river systems tend to have a higher density of tourist attractions.

4. Discussion

In the context of the increasingly prominent attention to tourism industry development and urban environment, there is a relatively limited amount of research on the distributional tendencies of touristic sites and influencing factors in a region [44,45]. This study utilized POI data to explore the distributional tendencies of touristic sites in Hubei Province, employing methods like point density analysis and standard deviation ellipse. Additionally, the GeoDetector was applied to scrutinize the elements that shape the geographical spread of touristic sites. On the one hand, this inquiry examines the spatial configuration and determinants of touristic sites within a particular region, thereby enhancing our grasp of the spatial dynamics propelling these locales and expand the research content of tourist attraction spatial patterns. On the other hand, the use of POI data in this study to explore the geographical spread and determinants of touristic sites in Hubei Province is advantageous in overcoming the shortcomings of traditional data, facilitating clarification of the current status of regional tourism resources, and providing reference for tailored development strategies.

It is worth noting that some scholars have also focused on the development and spatial agglomeration of tourist attractions [46], providing certain inspiration for the conduct of this study. The research revealed that in regions abundant in tourism resources and with a more developed economy, it is easier for numerous tourist attractions to emerge, which is consistent with the viewpoint of Wang et al. [47]. Meanwhile, tourist attractions exhibit a clear agglomeration trend in spatial distribution, with Wuhan city as the center forming a high-density cluster with surrounding areas such as Huanggang, Huangshi, and Ezhou. The possible reasons for this phenomenon may be ascribed to the strong economic growth of the central city in the region, relatively well-developed infrastructure providing more support for tourist attraction development, and the combined appeal of multiple attractions compared to a single attraction attracting more visitors, thereby enhancing the overall tourism carrying capacity of the region. This observation also validates the viewpoint of Yu et al. [48]. Furthermore, this study also identified the influence of factors such as transportation, socio-economic status, and natural geography on the distributional tendencies of touristic sites in Hubei Province. Tourist attractions are often located along transportation arteries or in areas with economic development, aligning with the findings of Truchet et al. [27].

However, existing studies have focused primarily on specific attractions [49], lacking an analysis of the distributional tendencies of touristic site within regions. Meanwhile, the traditional statistical survey data used in related research are limited in quantity and lack timeliness, while the emerging POI data, characterized by strong timeliness and large volume, are underutilized. Furthermore, Research mostly analyzes the factors affecting the spatial layout of tourist attractions from a single dimension of economy or environment, which has limited help in comprehensively guiding the development of regional tourism [50]. In comparison with existing literature, this study further corroborates the significant characteristics of tourist attractions regarding spatial distribution and spatial density. It also investigates the economic, social, and natural factors influencing their development, demonstrating the rational nature of the research. Therefore, this research delivers the following contributions: Firstly, it performs an exhaustive examination and categorization of the geographical spread attributes of scenic locales in Hubei Province by leveraging POI data, thereby partially enriching the utilization of POI data in pertinent scholarly endeavors. Secondly, it is crucial to thoroughly explore the significant impact of transportation accessibility, socio-economic factors, and natural geographical conditions on the layout of tourist attractions, in order to enhance the overall tourism quality of the region through element-driven approaches.

This study also provides suggestions based on Hubei Province concerning the judicious evolution and geographical refinement of travel assets, specifically to augment the equilibrium of the regional scenic locales' distribution and to foster indigenous travel progression in tandem with pivotal influencing elements, ultimately achieving the goal of sustainable and efficient development of the overall regional tourist attractions. Currently, tourist attractions in Hubei Province are mainly concentrated in the eastern regions such as Wuhan, Huanggang, and Huangshi, which are densely populated areas, with less distribution in the central and western regions. In response to this situation, cities in western Hubei should actively tap the potential of tourism resources, develop various types of scenic spots based on their urban conditions, and increase publicity efforts while improving supporting tourism resources to better serve tourists. The primary factors affecting the distributional tendencies of touristic site include population, passenger traffic volume, per capita GDP, and the value-added of the tertiary industry. Therefore, in order to promote the sustainable development of tourist attractions, efforts should be made to improve the tourism transportation infrastructure, enhance spatial connectivity among various tourist attractions; strengthen publicity and promotion to increase the visibility and attractiveness of tourist attractions; and foster cooperation and alliances among regional attractions to achieve synergy and coordinated development. The research provides an effective method for understanding the spatial patterns and advancement trajectories of local scenic locales, and is capable of yielding strategic illumination for the orchestration and enrichment of analogous regional travel assets globally.

Although this research has achieved considerable advancements in scrutinizing the geospatial configurations and factors influencing tourist sites in Hubei Province using POI data, there are also limitations that need to be considered. Firstly, as the research on tourist attractions was based on point data and failed to carry out more detailed research according to the types and levels of tourist attractions. In addition, the selection of influencing factors can be more diversified by selecting more factors from different angles to support a comprehensive and scientific understanding of the interaction. Secondly, this research centers on Hubei Province as an exemplar case, and although the discoveries might furnish beneficial perspectives for refining the geographical arrangements of touristic sites in analogous regions, the generalization of results to other areas requires careful consideration of their applicability. Future research will delve deeper into areas such as clarify levels of tourist attractions, as well as expanding the application scenarios of case studies.

5. Conclusions

This study takes Hubei Province, an area abundant in tourism resources, as a case and investigates the spatial patterns and influencing factors that affect tourist attractions. Utilizing POI data, a rich and timely source of information, and applying GeoDetector techniques, this research enhances our comprehension of the spatial characteristics of regional tourist attractions and their driving forces. The conclusion is as follows: (1) Tourist attractions in Hubei Province are primarily clustered in Wuhan and Huanggang cities, highlighting a substantial disparity between the eastern and central-western regions. Specifically, the eastern cities exhibit a higher concentration of tourist attractions compared to their counterparts in the western region. (2) The spatial distribution of tourist attractions in Hubei Province exhibits a distinctive pattern characterized by global dispersion and local concentration, with Wuhan serving as the central hub. Specifically, a cluster of high-density tourist attractions is observed in the vicinity of Wuhan, encompassing Huanggang, Huangshi, and Ezhou. Additionally, another cluster of significant tourist sites is found in Xiangyang, Yichang, and Jingzhou. Notably, the spatial arrangement of tourist attractions in Hubei Province demonstrates clear directional tendencies, with the western region displaying notable potential and momentum for further development. (3) The spatial distribution of tourist attractions in Hubei Province is influenced by various key factors, including permanent population size, passenger traffic volume, per capita GDP, value added of the tertiary industry, as well as geographical features such as topography and river systems. Among these factors, the permanent population emerges as the most influential determinant in the region.

Funding

This work was supported by the Major Project of Philosophy and Social Sciences in Higher Education Institutions in Hubei Province of China (No. 22ZD015) and Project of Graduate Teaching and Education Reform (High-quality Teaching Cases), Zhongnan University of Economics and Law (CN) (No. JCAL202430).

Ethics declaration

Informed consent was not required for this study because it does not involve detailed privacy information of each person, nor does it involve biomedical research on them.

Data availability statement

Data will be made available on request.

CRediT authorship contribution statement

Yuehua Jiang: Writing – original draft, Investigation. Wei Huang: Writing – original draft, Methodology. Xinxing Xiong: Writing – original draft, Data curation. Boyang Shu: Supervision. Jixin Yang: Writing – review & editing. Minglong Li: Writing – review & editing. Xufeng Cui: Writing – review & editing, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.