Spatial Pattern Analysis of Tourism Resources in Hainan Island Based on Ripley's K Function

Abstract

A quantitative analysis of Hainan Island's spatial structure was conducted based 10,260 tourism resources. This analysis focused on three aspects: type characteristics, scale, and spatial distribution. Various GIS spatial analysis methods, such as the nearest neighbor index, Ripley's K function for multi-distance spatial cluster analysis, and kernel density, were employed. The findings reveal that Hainan Island boasts abundant tourism resources, but there are significant variations in their types. The hierarchical scale structure of these resources resembles a pyramid, with fewer high-grade tourism resources and a larger number of low-grade ones. In terms spatial distribution, tourism resources are generally concentrated, and their distribution pattern varies with distance. The highest spatial concentration is observed at 30.5km. The northern region exhibits the highest density of tourism resources, while the northeast, central, and southern regions have relatively lower densities. Consequently, Hainan Island's tourism resource distribution forms multiple levels of hotspots. These research conclusions hold important reference and guiding significance for the further development and planning of Hainan's tourism industry.

Key words:

Tourism Resources

Spatial Structure

Hainan Island

Spatial Analysis

Ripley's K Function

1. Introduction

Tourism resources play a fundamental role in the development of tourism [1] and are crucial factors in attracting tourists to specific regions [2]. The spatial structure of tourism resources refers to the interrelationships and combinations of various tourism resources within a geographical area. It encompasses scenic spots, transportation routes, and designated areas, and is an essential aspect of analyzing the geospatial structure of tourism [3]. The development of tourism relies on the extent to which tourism resources are explored and utilized. Therefore, studying the spatial structure of tourism resources holds significant importance in guiding their exploitation and utilization [4]. By examining the spatial relationships, correlations, degree of spatial clustering, and scale of tourism resources, we can clearly define the characteristics and advantages of tourism resource development. This, in turn, facilitates the rational and comprehensive utilization of regional tourism resources [5].

Tourism resources are vital components within the tourism system, and researchers place significant emphasis on studying their spatial structure. In terms of research content, the focus primarily lies on the agglomeration effect of tourism resources [6], the characteristics of the grade scale of these resources [7], and the spatial network characteristics they exhibit [8]. Commonly employed research methods include GIS spatial analysis, geo-econometric models, and social spatial networks. Various techniques such as point pattern analysis, Gini coefficient and Lorentz curve, geographical concentration index, coefficient of variation and equilibrium, kernel density analysis, and fractal analysis are frequently utilized [9, 10, 11]. Smith S L J [12] summarized several mathematical and geographic methods for studying spatial structure, including standard distance, connectivity coefficient, average center point, tightness index, and nearest neighbor analysis. Zhang Jinhe [13] conducted a comprehensive analysis of the connectivity and accessibility of spatial tourism resource networks using six analytical techniques: nearest neighbor index, R index, β index, average path length index, accessibility index, and tightness index. Guedes and Jiménez [14] analyzed the relationship between the structure and spatial distribution of tourism resources in Portugal using GIS technology, exploring the spatial distribution elements of tourist attractions and the spatial distance connecting scenic spots. Nie et al. [15] employed GIS buffer analysis to comprehensively analyze "intangible cultural heritage" tourism resources in Yellow River Basin of China based on points (excellent tourist cities, 5A tourist attractions), lines (railways, national highways), and point-line relationships. Their aim was to summarize the spatial structure and resource attributes, providing decision-making references for the protection and strategic planning local intangible cultural heritage and its tourism development. Li Y and Zhang [16] analyzed the spatial pattern of high-quality tourism resources in the Beijing-Tianjin-Hebei region using methods such as nearest proximity, nuclear density estimation, accessibility analysis, and correlation analysis. Hart et al. [17] employed social network analysis to study the spatial structure of desert tourism resources and analyzed the interaction patterns of giraffe with tourists in different situations. Based on the characteristics of the spatial structure, three spatial structure optimization strategies were proposed: guiding balanced development through differentiated development paths, enhancing the leisure tourism service function core areas, and strengthening group development functions.

While there have been numerous research findings on the spatial structure of tourism resources, qualitative studies tend to outnumber quantitative studies, and there is a scarcity of research on the multi-scale spatial distribution patterns. Tourism resources exhibit different spatial structure models various scales. In many ways, these diverse spatial structure models hold significant importance for the development of regional tourism resources and tourism itself [18]. The study of appropriate scale distribution patterns of spatial geographical objects [19] plays a crucial role planning, layout, and site selection. guides the formulation of sustainable development strategies, thereby promoting the rational and comprehensive utilization of regional tourism resources and facilitating regional economic development. When examining spatial distribution patterns, spatial geographic objects or events can be abstracted as point processes. Currently, a common approach to studying the spatial distribution patterns of point processes is through point pattern analysis [20]. Ripley's K function, a distance-based point pattern analysis method, employs a mathematical model based on the distances between all events within the study area. As a density-based method, it reveals the relationship between spatial point patterns and scale across multiple scales. Consequently, is frequently utilized for effective landscape pattern analysis at various scales [21].

This paper aims to develop a scientific framework for analyzing spatial structures. It utilizes Ripley's K function in point pattern analysis and GIS spatial analysis technology to examine the relationship between the quantity structure and spatial distribution of tourism resources on Hainan Island. The study investigates the spatial distribution patterns of tourism resources at various scales and determines the appropriate feature scale. Additionally, it explores and analyzes the geographical location and spatial distribution patterns of tourist scenic spots, with the goal of providing a scientific basis for tourism spatial layout.

2. Methods and Data Sources

2.1. Study Area

Hainan Island is located in the northern latitude range of 18°10' to 20°10' and the eastern longitude range of 108°37' to 111°03’. aped like an oval snow pear, the island has a long axis that runs from northeast to southwest, measuring approximately 290 kilometers in length (Fig. 1a). Its width from northwest to southeast is about 180 kilometers, covering a total area of 33,900 square kilometers. Hainan Island is the second largest island in China after Taiwan. The island boasts a coastline that stretches for 1823 kilometers and is dotted with 68 large and small harbors. The central part of the terrain characterized by high and low areas, with mountains, hills, plate, and plains forming a circular layered landform. Surround this central region are coastal areas that slope towards the south. Hainan Island falls within the East Asian monsoon region and experiences a tropical oceanic monsoon climate. The weather is warm and hot throughout the year, accompanied by abundant rainfall. The annual average temperature is 24.7℃, and the annual average rainfall reaches 922.7 mm (Fig. 1b). The island's forest coverage rate is 59.2%, and its natural vegetation includes tropical monsoon rainforests, tropical rainforests, evergreen broad-leaved forests, mangroves, coniferous forests, scrublands, and grasslands. Hainan Island is the only island in China that possesses a tropical rainforest. The island is crisscrossed by numerous rivers, which flow from the central mountainous or hilly areas in all directions towards the sea, creating a radial water system. Prominent rivers include the Nandu River, Chang River, and Wanquan River. Hainan Province is home to more than 30 ethnic groups, including the Han, Li, Miao, and Hui. Over thousands of years, the distinct customs and traditions of these ethnic groups have given rise to a unique and Miao culture. With its exceptional natural scenery, tropical island charm, and rich ethnic heritage, Hainan Island serves as an ideal location for studying and developing tourism resources.

Fig. 1

The administrate division and topography of Hainan Island

2.2. Data source

In this study, the research group utilized the "Hainan Tourism Resource Management Information System" to conduct field investigations. A comprehensive dataset consisting of 10, detailed information was collected for Hainan Island. This information includes the name, type, administrative region, coordinates, score, grade, and other relevant details of each tourism resource unit. The spatial coordinate system employed for all data is theCS2000 national geodetic coordinate system, utilizing the Gauss-Kruger projection. The classification of tourism resources in this study is based on the Draft Classification Standard of Hainan Province Tourism Resources. This standard has been revised through the classification, investigation, and evaluation of tourism resources (GBT 18972 − 2017). Additionally, it incorporates the main category of coastal landscape to highlight the unique characteristics of Hainan. The classification system comprises nine main types, 25 subtypes, and 135 basic types (Table 1).

Table 1
Quantity and basic types of tourism resources in Hainan
Main Types	Subtypes	Fundamental Types
Geological landscapes	4	23
Water landscapes	5	13
Biological landscapes	2	8
Astronomical phenomena and meteorological landscapes	2	6
Marine & coastal landscape	2	7
Buildings and facilities	3	44
Ruins and remains	2	11
Tourism commodities	3	17
Human activities	2	6

2.3. Methods

2.3.1 Nearest proximity index

The Nearest Neighbor Index (NNI) is a measure that assesses deviations from a random distribution by calculating the ratio of the observed mean distance between each point and its nearest neighbor to the expected mean distance in a random model. NNI is calculated as follows:

$\:\text{N}\text{N}\text{I}=\frac{\left[\frac{\sum\:_{i=1}^{n}{d}_{i}}{n}\right]}{0.5*\sqrt{\raisebox{1ex}{$A$}\!\left/\:\!\raisebox{-1ex}{$n$}\right.}}$

In formula (1), d_i is the distance between the i th point and its nearest neighbor, n is the total number of tourist monomers, and A is the area of the study area. When NNI is less than 1, it means that the tourist attractions are clustered, when NNI is close to 1, it means that the tourist attractions are randomly distributed, and when NNI is greater than 1, it means that the tourist attractions are evenly distributed.

2.3.2 Multi-distance spatial cluster analysis

Multi-Distance Spatial Cluster Analysis is a method used to summarize the spatial correlation, either clustering or dispersion, within a specific distance range. It can be applied to study how the spatial aggregation or dispersion of individual tourism resources changes as the distance varies. The principle behind this analysis is to calculate the number of scenic spots within a circle, with a designated point as the center and a set distance and distance increment. As the calculated distance increases, the number of tourist resource points within the circle typically increases. If the average number of scenic spots within a certain calculated distance exceeds the average density of tourist resource points in the entire research area, the distribution of distances will be considered as a cluster distribution. The transformation L (d) function of K function is usually adopted, and its formula is as follows:

$\:L\left(d\right)=\sqrt{\frac{A\sum\:_{i=1}^{N}\sum\:_{j=1,j\ne\:i}^{N}k(i,j)}{\pi\:N(N-1)}}$

In the given context, the language appears to be describing a spatial analysis method for assessing the distribution pattern of tourism resource points in a region. Here's a breakdown of the key elements:

• A: Represents the region under consideration.

• N: Represents the tourism resource points within the region.

• d: Represents the distance threshold used to determine the weight of connections between points.

• k(i,j): Represents the weight or connectivity between two points i and j based on their distance.

The weight (k) is determined by whether the distance between two points is less than or equal to the threshold (d). If it is, the weight is set to 1, indicating a strong connection. If the distance is greater than d, the weight is set to 0, indicating no connection. To assess the distribution pattern, the observed value of k is compared to the expected value of k. If the observed value is higher than the expected value, it suggests a more clustered or agglomerated distribution of tourism resource points within the specified distance. Conversely, if the observed value is lower than the expected value, it indicates a more discrete or dispersed distribution compared to a random distribution. To obtain the confidence interval (L(d)) under the real scenario, a simulated random test is conducted. If L(d) falls above the confidence interval, it suggests a spatial agglomeration pattern of tourism resource points. If L(d) falls below the confidence interval, it indicates a random distribution pattern. Finally, if L(d) is significantly below the confidence interval, it suggests a uniform regular distribution pattern of tourism resource points. It's worth noting that this description seems to outline a theoretical framework or methodology rather than providing specific information about a particular region or dataset.

2.3.3. Kernel density Estimation

Kernel density Estimation is a non-parametric test method used to estimate unknown density functions in probability theory. It approximates the density distribution through a set of Gaussian distributions whose mean is a set of sample points conforming to the density distribution. Kernel density estimation method believes that geographical events have a high probability of occurring in areas with high spatial point density and a low probability of occurring in areas with low spatial point density. It creates a smooth continuous surface by establishing a smooth circular surface for each element point, calculating the distance between each element point and the reference location, and then summing all surface values of the reference location to establish the peak value or kernel of these points. The nuclear density decreases with the increase of the radiation distance from the center. The mathematical function of kernel density calculation is expressed as follows:

$\:f(x,y)=\frac{1}{n{h}^{2}}\sum\:_{i=1}^{n}k\left(\frac{{d}_{i}}{h}\right)$

Where, f(x,y) is the density estimation of the spatial position at (x,y); h is the bandwidth or smoothing parameter, which can be set according to the analysis scale and elements; d_i is the distance between the (x,y) position and the i th observation position; n is the observed value; k is the kernel function.

3. Results

3.1 Characteristics of tourism resources

Hainan Island, located in China, offers a wide range of tourism resources. These resources can be categorized into two main types: natural tourism resources and cultural tourism resources, based on the national classification standard of tourism resources (GBT 18972 − 2017). In terms natural tourism resources, Hainan Island boasts a total of 3,696 resources (Table 2). The majority of these resources are distributed among three categories: geographical landscape (1,640), water landscape (1,591), and biological landscape (253). Together, these three categories make up 94% of the natural tourism resources on the island. However, celestial and climatic tourism resources are relatively scarce, accounting for only 0.87% of the natural tourism resources. On the other hand, cultural tourism resources are more abundant on Hainan Island, with a total of 6,564 resources. The most prevalent categories within this classification are buildings and facilities (4,850) and historical sites (1,042). These two categories alone account for 89% of the cultural tourism resources available on the island. In contrast, tourist purchases (470) and humanistic activities (247) make up a smaller portion, comprising only 11% of the cultural tourism resources. Overall, Hainan Island offers a diverse array of tourism resources, including stunning geographical and water landscapes, rich biodiversity, historical sites, and various cultural attractions. Visitors to the island can explore its natural wonders and immerse themselves in its vibrant cultural heritage.

Table 2
Number and proportion of tourism resources in Hainan Island
Category	Main Types	Number	Proportion of the number in the category(%)	Proportion of the total number(%)
Natural tourism resources	Geological landscapes	1640	44.37%	15.98%
	Water landscapes	1591	43.05%	15.51%
	Biological landscapes	253	6.85%	2.47%
	Astronomical phenomena and meteorological landscapes	32	0.87%	0.31%
	Marine & coastal landscape	180	4.87%	1.75%
Humanity tourism resources	Buildings and facilities	4850	73.20%	46.83%
	Ruins and remains	1042	15.87%	10.16%
	Tourism commodities	470	7.16%	4.58%
	Human activities	247	3.76%	2.41%

3.2 Level scale of tourism resources

Hainan Island has a total of 177 tourism resources categorized into five levels, which accounts for 2% of the overall resources. Level 4 resources amount to 670, representing 7% the total. Tertiary resources make up 17% with a count of 1741. Secondary resources are 29% with 6 in number. The first-class resources, totaling 4696, account for 45%. This distribution indicates that the hierarchical structure of tourism resources in Hainan Island follows the pyramid scale hierarchy theory, with fewer high-grade resources and a larger proportion of low-grade resources (refer to Fig. 2).

Fig. 2

Hierarchical scale structure of tourism resources in Hainan Island

3.3 Spatial distribution characteristics of tourism resources

3.3.1 Overall distribution characteristics of tourism resources

According to the spatial distribution map of tourism resources in Hainan Island, tourism resources are densely distributed throughout the island. Humanistic tourism resources are particularly concentrated in the northern region and scattered in other areas. On the other hand, natural tourism resources are predominantly found in the central region. High-grade resources, on the other hand, are concentrated in the east and more sporadic in the west (Fig. 3).

Fig. 3

Spatial distribution of tourism resources in Hainan Island

3.3.2 Measurement of distribution type of tourism resources

The Average Nearest Neighbor tool in the ArcGIS 10.5 spatial statistics toolbox is utilized to calculate the Nearest Neighbor Index (NNI) and conduct a significance test for tourism resource points in Hainan Island. As indicated in Table 3, the tourism resources in Hainan Island exhibit a significant clustering pattern overall, with both natural landscapes and human landscapes displaying significant agglomeration distributions.

Table 3
Analysis of spatial agglomeration of tourism resources in Hainan Island
Type	Mean observation distance(m)	Expected average distance(m)	NNI	z score	Distribution type
Total	698.787	1241.149	0.563	-80.567	Significant aggregation
Natural tourism resources	1295.777	1970.618	0.657	-39.742	Significant aggregation
Humanity tourism resources	666.219	1534.458	0.434	-81.062	Significant aggregation

3.3.3 Multi-scale hot spot detection

In order to explore the hot spots of spatial distribution of tourism resources, Ripley's K function and kernel density index are respectively used to measure and analyze the following.

(1) Multi-distance spatial cluster analysis

The Ripley's K function, a spatial statistics tool available in the ArcGIS 10.5 spatial statistics toolbox, was utilized to calculate the multi-scale spatial distribution map of tourism resource points on Hainan Island (see Fig. 4). The analysis reveals that the L(d) curve for tourism resources on Hainan Island surpasses the Hi Conf (higher confidence interval), indicating an overall aggregate distribution pattern. As the distance increases, the L(d) curve progressively diverges from the d curve, reaching its maximum aggregation intensity at 30.5 km before gradually declining. At 86 km, the L(d) curve intersects with the d curve, signifying significant clustering of scenic spots within the 0-85.5 km range, with a characteristic spatial scale of 85.5 km. Subsequently, the L(d) curve begins to approach the upper confidence interval, and the aggregation intensity gradually diminishes.

Fig. 4

Multi-distance spatial distance cluster analysis of tourism resources in Hainan Island (Ripley's K function)

(2) Nuclear density analysis

Based on the measurement and analysis mentioned above, the distribution of known tourism resources exhibits a strong spatial aggregation. To identify the distribution hotspots, the kernel density tool in ArcGIS spatial analysis toolbox was utilized, employing the Kernel Density estimation formula mentioned earlier. The calculation radius for the nuclear density, also known as the search radius, was determined based on Ripley's K function analysis, which indicated that the highest degree of aggregation occurred at 30.5km. To generate the density map, four search radius values were selected: 10.5km, 20.5km, 30.5km, and 40.5km. Figure 5 illustrates the resulting density maps, showing the maximum nuclear density achieved at each search radius. The values obtained were 2.21 /km², 1.10 /km², 0.89 /km², and 0.70 /km² for the search radii of 10.5km, 20.5km, 30.5km, and 40.5km, respectively. Smaller search radius values provide more detailed information in the generated kernel density map but may obscure the overall features. On the other hand, larger search radii yield smoother density curves and higher generalization levels in the kernel density map, but they may overlook local features. Considering both the local characteristics and the overall pattern of density distribution, a search radius of 30.5km is chosen as the ideal value. Further analysis can be conducted to examine the kernel density characteristics and hotspots of different types and levels of tourism resources, considering both the local and overall density distribution patterns.

Fig. 5

Comparison of nuclear density at different scales of all tourism resources in Hainan Island

According to Fig. 6, the average kernel density of Hainan Island at an altitude of 30.5km is calculated to be 0.22 /km², with a standard deviation of 0.14. Figure 6 shows the re-rendered density map using standard deviation classification. It is evident from Fig. 6 that the high-density area is primarily concentrated in the northeastern region, specifically the Haikou - Ding'an - Chengmai core area. On the other hand, the density in the northeast, middle, and south regions is relatively low, encompassing the Qionghai core area, Wenchang core area, Baisha-Danzhou core area, and Sanya core area. Considering the geomorphic features of Hainan Island, the spatial distribution pattern of tourism resources on the island can be categorized into six multi-level hotspots in the north, middle, and south. These include the first-level hotspot Haikou, the second-level hotspots of Qionghai and Sanya Yazhou, and the third-level hotspots of Wenchang, Baisha-Danzhou, and Sanya Haitang.

Fig. 6

Hot spots of tourism resources in Hainan Island

4. Discussion

Hainan Island is renowned for its abundant tourism resources, encompassing both natural and cultural attractions that are unique within our country. The distribution of these resources exhibits a concentrated pattern, primarily in the northern city of Haikou, the southern city of Sanya, the eastern coast, and the central mountainous area. The central mountainous region, the eastern coast, and Sanya City boast relatively concentrated natural tourism resources. On the other hand, Haikou City in the north possesses a developed social economy, a rich historical and cultural heritage, and abundant humanistic tourism resources. The development of tourism resources in Hainan has reached a certain scale, with the industry experiencing rapid growth and expanding opportunities for further development.

However, there exists an imbalance in the development of tourism resources in Hainan Province, primarily manifested in two aspects. Firstly, there is an overemphasis on nature while neglecting the importance of cultural resources. The development of natural tourism resources has reached a relatively mature stage. For instance, scenic spots like Nuoda emphasize ecological preservation, and coastal tourism resources such as Yalong Bay, Sanya Bay, Haitang Bay, Clear Water Bay, Perf Bay, Shenzhou Peninsula, Yingbin Peninsula, Longqi Bay, Longmu Bay, and Chizhi Bay have been well-developed.

Hainan also possesses numerous cultural tourism resources, including cultural relic gardens, ruins, and historical sites. Notable examples include Five Temple, Hai Rui Tomb, Qiongtai Academy, Dongpo Academy, Yazhou Ancient City, Qiujun Tomb, and various revolutionary memorial sites like the old site of Qiongya Column, the memorial statue of the Red Troop of Women, Jinniu Ling Martyrs Cemetery, Baisha Uprising Memorial Hall, Xiuying Ancient Fort, and Li Shuoxun Monument. Additionally, there are historical sites such as the residence of Huang Daopo (Shuinan Village, Yacheng), the former residence of Hai Rui, and the ancestral residence of the Song family. Intangible cultural heritages like Danzhou tunes, Li songs and dances, Lingao puppets, and national festivals also contribute to Hainan's cultural tourism offerings. However, the development of these resources primarily revolves around superficial sightseeing tours, lacking in-depth exploration, meaningful cultural connotation display, and immersive experiences. As a result, tourists' appreciation and recognition of these cultural attractions remain relatively low.

Based on the grade distribution and cluster distribution characteristics of tourism resources in Hainan Province, as well as the landform features of coastal, mountainous, and tropical rain forests, several tourism resource utilization areas can be formed. These areas include:

(1) Northern City Cultural Tourism Resource Utilization Area: This area focuses utilizing the cultural resources found the northern cities of Hainan Province. It may include historical sites, museums, traditional villages, and cultural events that showcase the unique heritage and traditions of the region.

(2) Southern Coastal Tourism Resource Utilization Area: This area capitalizes on the coastal resources in the southern part of Hainan Province. It may feature pristine beaches, water sports activities, marine parks, and resorts that cater to beachgoers and water enthusiasts.

(3) Central Mountain Tropical Rainforest Tourism Resource Utilization Area: This area highlights the natural beauty and biodiversity of the central mountainous region, which is characterized by tropical rainforests. It may offer opportunities for eco-tourism, hiking, wildlife observation, and nature-based activities.

(4) Eastern Mountain and Sea Cultural Resort Tourism Resource Utilization Area: This area combines the mountainous landscape with the coastal resources in the eastern part of Hainan Province. It may include cultural resorts, spa retreats, scenic viewpoints, and outdoor recreational activities that allow visitors to experience both the mountains and the sea.

(5) Western Historical and Cultural Tourism Resource Utilization Area: This area focuses on the historical and cultural resources found in the western region of Hainan Province. It may include ancient towns, temples, archaeological sites, and cultural festivals that showcase the rich history and heritage of the area.

By developing and utilizing the tourism resource circles, a series of tourism products of different natures can be created. These circles can be categorized into three main types:

(1) Ecological Utilization Circle of Tourism Resources in the Mountain Tropical Rainforest: This circle emphasizes the preservation and sustainable use of the natural resources found in the mountainous tropical rainforest areas. It may include eco-lodges, guided nature tours, research centers, and educational programs that promote environmental conservation and awareness.

(2) Coastal Plain Tourism Resources Circle: This circle focuses on the utilization of the tourism resources found in the coastal plain areas. It may include beach resorts, water parks, seafood restaurants, and cultural activities that cater to tourists seeking relaxation, recreation, and coastal experiences.

(3) Coastal Zone Tourism Resources Circle: This circle highlights the tourism resources found along the coastal zones of Hainan Province. It may include beachfront hotels, water sports facilities, marinas, and entertainment venues that offer a range of recreational activities and attractions for visitors.

By strategically developing and promoting these tourism resource utilization areas and circles, Hainan Province can attract a diverse range of tourists and offer them unique and memorable experiences based on the region's natural, cultural, and historical assets.

5. Conclusion

The study conducted spatial quantitative analysis using GIS methods such as the nearest neighbor index, multi-distance spatial cluster analysis, and kernel density analysis to examine the spatial distribution and structure of tourism resources in Han Island. The following conclusions were drawn:

(1) Hainan Island has a significant number of individual tourism resources, with humanistic tourism resources outnumbering natural tourism resources. The humanistic category includes buildings and facilities, while the natural category comprises geographical and cultural landscapes.

(2) The types of tourism resources in Hainan Island are diverse, covering both natural and cultural resources. Natural resources account for 75% of the subcategories, while human resources cover 100%. Among the basic types, 53.57% are natural resources, and 86.25% are human resources.

(3) Tourism resources in Hainan Island are densely distributed overall. Humanistic tourism resources are concentrated in the northern region, while natural tourism resources are concentrated in the central region. High-grade resources are primarily found in the east and sporadically the west. The largest spatial distribution pattern of tourism resources in Hainan Island extends up to 30.5 km. The high-density areas of tourism resources are mainly located in the north, while the northeast, central, and southern regions have relatively lower densities.

Based on the results of nuclear density calculations and the geomorphological characteristics of Hainan Island, the spatial distribution pattern of tourism resources can be classified into multiple levels of hotspots. The first-class hotspots, including Haikou City, Ding'an County, and Chengmai County, are situated in the northern region. The second-level hotspots, encompassing Qionghai City and Yazhou District of Sanya, are found the eastern and southern regions. The third-level hotspots, consisting of Wenchang County, Baisha County, Danzhou City, and Sanya Haitang District, are in the central, northeast, and southern regions.

This study aims to analyze and categorize the distribution characteristics of tourism resources, providing a foundation for future optimization of tourism resource planning, development, and utilization. It offers both theoretical and practical guidance in this regard. However, it is important to note that this paper only presents an initial analysis of the spatial pattern of tourism resources on Hainan Island. Further research and analysis are required to explore the relationship between tourism resources and natural, human, and social factors.

Author Contribution

Conceptualization, S.Z. and T.Z.; Methodology, S.Z.; Formal analysis, S.Z. and T.Z.; Investigation, S.Z.; Resources, T.Z. and S.Z.; Data curation, T.Z.; Writing—original draft, T.Z. and S.Z.; Writing—review & editing, T.Z. and S.Z.; Visualization, S.Z.; Supervision, S.Z.; Funding acquisition, T.Z. and S.Z. All authors have read and agreed to the published version of the manuscript.

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Funding

Declaration: This study was financially supported by National Natural Science Foundation of China (42501291; 42001243), Humanities and Social Science Project of the Ministry of Education, China (24YJAZH226), Natural Science Foundation of Shandong Province, China (ZR2022QD132; ZR2025MS570; ZR2020QD008), Social Science Planning Project of Shandong Province (24DGLJ35; 25CGLJ34), Qingdao Social Science Planning Research Project (QDSKL2401027) and Rural Revitalization Project of Ocean University of China(ZX2025006; ZX2024007).

Data Availability

Data will be made available from the corresponding author on reasonable request.

Conflicts of Interest:

The authors declare no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

This article does not contain any studies with human participants performed by any of the authors.

Yes