Optimizing Spatial Layout of Campsites for Self-Driving Tours in Xinjiang: A Study Based on Online Travel Blog Data

Abstract

Within the economic and cultural context of Xinjiang, the tourism industry has rapidly developed as a strategic pillar of the national economy, with the self-driving tour market emerging prominently. However, the uneven spatial layout and insufficient service facilities of self-driving camps limit their development potential. This study aims to enhance the attractiveness of tourism in Xinjiang and improve the visitor service experience by constructing an evaluation system for the layout of self-driving camps based on online travel blog data, utilizing methods such as literature review, surveys, ArcGIS spatial analysis, and web text analysis. The Delphi method and entropy weight method were applied to determine the weights of the influencing factors. The findings reveal spatial imbalances in the layout of Xinjiang’s self-driving camps and propose eight preferred scenic areas for camp location. This study also suggests sustainable development strategies. These insights and recommendations aim to optimize the layout of self-driving camps, enhance the tourism experience, and promote the sustainable development of Xinjiang’s tourism industry.

1. Introduction

1.1. Background

Over the past decade, with rapid economic advancement and diversification of people’s lifestyle demands, the tourism industry has emerged as a strategic pillar of the national economy, particularly in the Xinjiang region of China. According to the “China Statistical Yearbook (2012–2019)”, the annual growth rate of domestic tourism revenue between 2012 and 2019 was estimated to be around 10.6%. During this period, the total revenue of the tourism industry amounted to CNY 6.63 trillion, constituting approximately 4.56% of the Gross Domestic Product (GDP). Concurrently, the optimization and diversification of tourism products have been evident, with the number of A-level scenic spots increasing from 6042 in 2012 to 14,332 in 2021. Xinjiang, renowned for its unique natural landscapes and rich cultural heritage, has become a popular destination for self-driving tourists. According to the “China Self-driving, RV, and Camping Tourism Development Report (2022–2023)”, the proportion of self-driving tourists in 2022 accounted for 74.8% of the total domestic tourists, an increase of 18.7% from 2019. It is noteworthy that while not all of these 74.8% tourists necessarily opted for self-driving campsites, there exists a close correlation between self-driving tourism and self-driving campsites, indicating a significant rise in the demand rate for such facilities. However, the spatial distribution of self-driving tourism campsites in Xinjiang is uneven, with only 250 service areas and campsites by the end of 2020. These facilities are mainly located in Hami, Turpan, Urumqi, Ili, and Aksu. This concentration restricts the potential for tourism development, increases inconvenience for tourists, and poses potential safety risks. Therefore, optimizing the spatial layout of self-driving tourism campsites in Xinjiang is not only crucial for enhancing tourist experiences and ensuring their safety but also a key strategy for achieving the sustainable development of the tourism industry and promoting regional economic growth.

The development of the self-driving tourism market has evolved through the stages of inception, growth, and maturity. Initially characterized by individual attempts from travel enthusiasts and vehicle owners, early self-driving tours were aimed at adventure and leisure without a mature market operation mode [1]. With the increase in vehicle ownership and the pursuit of diverse travel methods, self-driving tourism began to gain wider attention, marking its growth phase. Destinations started offering more services to attract self-driving tourists [2]. In the maturity phase, the market saw a diversification of participants, including destinations, car rental companies, and travel planning services, with more varied routes and products catering to different preferences. Alongside the improvement of relevant policies and regulations, the market’s healthy development was ensured. Currently, the self-driving tourism market has entered its maturity phase [3], making the study and optimization of self-driving campsite layouts crucial to meet the growing market demand and promote the sustained prosperity of the self-driving tourism culture [4]. Therefore, one of the research objectives is to provide a scientific basis for the rational planning of self-driving camping sites, enabling a more accurate identification of the potential self-driving tourism hot spots. This involves assisting planners in strategically positioning campsites and enhancing the quality and professionalism of self-driving tour services, thereby augmenting the overall appeal of tourism. Another objective is to propose future development strategies through the optimal layout of self-driving camping sites.

1.2. Literature Review

1.2.1. Research on Tourist Satisfaction in Self-Driving Campsites

Multiple studies have explored the influence of car camping experiences on tourist satisfaction using the 4Es theory framework. D.C. found that aesthetics, entertainment, everyday experiences, and positive memories are crucial for satisfaction [5]. Lee’s research emphasized the importance of novel experiences, lifestyle changes, and creating stress-relieving emotional atmospheres for enhancing satisfaction [6]. Jia Chen’s study highlighted consumers’ moderate satisfaction with existing camping tourism products and suggested improvement recommendations [7]. Guo Q explored the relationship between campsite attributes, tourist experience, satisfaction, and the intention to camp again, providing valuable insights into understanding the related camping behavior of tourists [8]. Ryu’s research analyzed tourist preferences and campsite service satisfaction, concluding that the fundamental facilities of the campsite and surrounding landscape conditions are pivotal factors influencing tourist satisfaction and their willingness to revisit [9]. O’Neill M A focused on the service quality of nature tourism in Alabama, and proposed the key drivers of satisfaction and intention to revisit in order to help enhance the service level of nature tourism [10].

1.2.2. Research on the Sustainable Development of Self-Driving Campsites

With the growth of camping activities, W.R.C. observed an increasing preference among tourists for freedom in their camping experiences, favoring natural environments for camping. This trend impacts environmental sustainability, prompting the author to propose a framework for car camping practices that emphasizes sustainability [11]. Camping activities can lead to significant and typically localized environmental impacts, affecting soil, vegetation, wildlife, and water quality. Due to biophysical effects, the long-term degradation of campgrounds can also negatively affect visitor experiences [12]. Consequently, numerous scholars have conducted research on sustainable development strategies for campgrounds. From a strategic perspective, Guo Jiajia planned the self-driving campsite along the terrestrial section of the Silk Road. Guided by the principles of ecological protection and cultural heritage preservation, she effectively designed the Gansu section of the self-driving campsite, thereby promoting sustainable camping development in the region [13]. Lucivero M.Cam focuses on coastal tourism, addressing the contradiction between the seasonal demand and environmental protection in the Mediterranean coastal regions. He proposes an open camping model that, through the use of flexible and movable structures, meets the needs of a large number of tourists while achieving sustainable development [14]. Arredondo J R employed containment strategies and a Spatial Autoregressive (SAR) model to assess the impact of campgrounds on nature reserves. The findings indicate that by adjusting campground types and topographical features, the sustainability of campgrounds can be enhanced [15]. Eagleston H studied the long-term impact of open campgrounds on nature reserves. It was found that there were significant changes in vegetation cover and soil erosion at campgrounds. Therefore, it is suggested to adopt more sustainable development methods, such as choosing campgrounds with strong resistance, limiting the expansion of campgrounds, and stopping the felling of trees, to reduce the impact on the environment [16]. Del Moretto D conducted a study on how to provide sustainable transportation systems for three campgrounds in the Tuscany region. By comparing two solutions, diesel-powered tourist trains and electric tourist trains, the study revealed their respective advantages, opportunities, and existing weaknesses and threats, contributing to the sustainable development of the campgrounds [17]. Wang, T conducted a study on the unmanaged Kurodake campsite in the Daisetsuzan National Park, examining the issues of soil erosion and overcrowding, and proposed future management strategies. Detailed topographical maps and questionnaire surveys suggest that formal management and reservation systems need to be introduced to ensure the sustainable use of the campsite [18].

1.2.3. Research on Choice Factors in the Site Selection of Self-Driving Campsites

Since the outbreak of COVID-19, camping as a form of leisure tourism has become more and more popular, and the research on the factors affecting the choice of camping sites has become increasingly important [19]. White D D categorizes campsite selection criteria into location characteristics, social conditions, ecological impacts, landscape features, and personal privacy. The most important factors are geographical location and social conditions, followed by privacy [20]. Sung Do H distributed questionnaires to campers at the Mangsang Auto Campground on the East Sea coast of the Korean Peninsula to collect data. The research found that the campground’s success was primarily due to its modest and straightforward facilities while maintaining cleanliness, convenience, and comfort. Furthermore, the study indicated that campers often prefer more secluded locations when choosing a campsite, taking into consideration the protection of personal privacy [21].

1.2.4. Research on the Site Selection of Self-Driving Campsites

Qiu Xiaoyun employs a comprehensive research approach to investigate the site selection problem of tourist road campsites. By defining service facilities and summarizing key factors for site selection, the author integrates the centroid method and analytic hierarchy process to propose an efficient site selection strategy. Using the Songhua Lake area in Jilin Province, China, as a case study, the study validates the practicality and effectiveness of the method. It provides an innovative site selection model aimed at assisting decision-makers in selecting tourist road campsites considering diverse factors and offers theoretical guidance and application references for site selection in other regions [22]. Li C conducted a multifaceted study on the balance between the supply and demand of tourism facilities, focusing on enhancing regional comprehensive benefits. Firstly, they established a comprehensive evaluation index system to assess self-driving campsite locations from both supply and demand perspectives. Secondly, they integrated various data sources to ensure accuracy and used a combined subjective and objective method to determine the weights of the evaluation indices. Utilizing GIS technology, they conducted an in-depth assessment of the self-driving campsite locations in Xinjiang and proposed layout optimization strategies. Suggestions were made for Xinjiang to develop an operational map for the self-driving campgrounds and provide comprehensive facilities and services. This study provides a scientific basis for the planning and management of the self-driving campsites in Xinjiang and offers insights into the fields of tourism geography and regional planning [23].

1.2.5. Literature Summary

Through an in-depth review and comprehensive analysis of the relevant domestic and international literature, this study aims to explore an evaluation method that aligns more closely with the practical needs of tourists. Although some studies have evaluated campsite layouts, few scholars have employed the method of analyzing data from online travel blogs and integrating it with the analytic hierarchy process (AHP), especially in the context of campsite layouts within tourist-dependent areas. The uniqueness of this approach lies in its ability to adopt the perspective of tourists by analyzing their actual experiences and feedback in online travel blogs to determine various evaluation indicators for campsite layouts in tourist-dependent areas. This method not only provides an accurate reflection of the actual situation of campsite layouts in these areas but also caters to the needs and expectations of the tourists. Therefore, this study aims to explore and establish an innovative evaluation method to assess the campsite layouts in Xinjiang, China, from a comprehensive, objective, and practical standpoint. Through this approach, valuable data support and scientific insights will be provided for the planning and management of campsite layouts in Xinjiang’s tourist-dependent areas, thereby facilitating the rational utilization of tourism resources and contributing to the sustainable development of the regional tourism economy.

2. Data Sources and Study Area

2.1. Data Source

2.1.1. Online Travel Blog Data

In today’s era of information and digitization, the tourism industry, as an information-intensive sector, has seen the widespread dissemination and sharing of tourists’ real experiences and word-of-mouth evaluations through the Internet, particularly with the popularization of the Internet. Self-driving tours have become a popular way of traveling, and many travelers are willing to share their experiences and insights by writing online travel blogs after completing their self-driving trips. This development has provided a unique and rich source of data for studying self-driving tours, the layout of self-driving campsites, and their influencing factors. Previous studies [4,13,22,23,24,25] have mostly relied on traditional methods such as questionnaires or on-site observations to collect data, which, to some extent, may not fully capture the real-time experiences and perceptions of the tourists during self-driving tours, and the selection of survey samples may be biased. By contrast, online travel blogs, as a spontaneous expression of personal real experiences, can more objectively reflect a tourists’ actual feelings, preferences, and the various factors that may influence their choices during self-driving tours. Therefore, analyzing the influencing factors of self-driving campsites using online travel blogs can reveal the underlying motivations behind tourist behavior and provide more accurate and realistic data support.

To capture the authentic experiences and preferences of the tourists visiting Xinjiang, we systematically collected online travel blogs published on major tourism websites. Utilizing custom-developed web scraping techniques, data were harvested from various popular travel forums and sharing platforms from 1 January 2019 to 31 August 2023. This approach enabled the accumulation of thousands of travel blogs, encompassing the detailed descriptions of well-known tourist spots in Xinjiang, travelers’ experiences, evaluations, and recommendations. The collected data underwent preprocessing, including text cleaning, deduplication, and preliminary categorization, to facilitate the subsequent analysis.

2.1.2. Official Statistical Data

In addition to the data from online travel blogs, this study extensively gathered data from official statistical yearbooks released by the Xinjiang government, covering indicators such as tourism revenue, the number of visitors, and the development of tourism facilities. Additional data regarding traffic flow were provided by transportation survey stations. From the national geographic service information platform, we acquired points of interest and road network data for all A-grade scenic spots in Xinjiang, top-tier hospitals, gas stations, national highways, provincial roads, and expressways.

Moreover, to gain a comprehensive understanding of the layout of the self-driving campgrounds in Xinjiang, we reviewed related academic papers, research reports, and news media coverage. These sources aided in constructing an indicator system for the selection and layout of the self-driving campgrounds in Xinjiang, offering extra support for the analysis.

2.2. Overview of The Study Area

The Xinjiang Uygur Autonomous Region, commonly referred to as Xinjiang, is located in the northwest of China and stands as the country’s largest provincial-level administrative region, covering an area of approximately 1.66 million km², accounting for one-sixth of China’s total land area [26]. Spanning the central part of Asia, Xinjiang borders Gansu and Qinghai to the east, Tibet to the south, and shares international borders with Russia, Kazakhstan, Kyrgyzstan, Tajikistan, and Afghanistan to the northwest, holding a significant geopolitical position. The region’s terrain is varied and complex, featuring vast plains, continuous mountain ranges, profound basins, and abundant rivers and lakes. It is characterized by a diverse climate, predominantly showing the features of a temperate continental arid climate, with distinct seasons and ample sunlight. Xinjiang governs 14 prefecture-level administrative units, including 4 prefecture-level cities, 5 regions, and 5 autonomous prefectures. It is home to multiple ethnic groups, including the Han, Uyghur, and Kazakh, making it one of the most ethnically diverse areas in China. It serves as a crucial energy base in China, with rich natural gas and oil resources [27]. In addition, Xinjiang’s agricultural sector is robust, producing essential commodities such as cotton, grapes, and tomatoes [28]. In recent years, Xinjiang has actively developed its tourism industry, leveraging its unique natural landscapes and rich cultural heritage to attract numerous domestic and international visitors. The region boasts a wealth of tourist resources, including spectacular natural scenery, unique folk cultures, and rich historical sites. According to the latest statistics, Xinjiang has 513 A-level tourist attractions, of which 17 are 5A level and 124 are 4A level [29], as shown in Figure 1. Renowned tourist destinations include the Heavenly Lake of Tianshan, Kanas Lake, the ancient city of Turpan, the Flaming Mountains, and the Ili Kazakh Autonomous Prefecture, drawing many visitors each year.

3. Determining the Standard and Criterion Layers Based on Online Travel Blogs

3.1. Preprocessing and Word Frequency Analysis of Online Travel Blogs

In this study, using “self-driving tour in Xinjiang” as the keyword, we searched for online travel blogs on the Mafengwo and Ctrip travel websites, spanning from 1 January 2019 to 31 August 2023, and collected a total of 300 online travel blogs. After screening, we excluded overly simple, purely pictorial, or purely video travel blogs. Travel blogs by the same author at different times or locations were not processed, whereas those by the same author at the same location and time, but updated separately according to each day’s itinerary, were merged and treated as a single complete online travel blog. Thus, this chapter ultimately studied 197 authors of online travel blogs on the self-driving tours in Xinjiang, with a total of 208 online travel blog texts.

Text information was imported into the Rost CM 6 software version 5.8.0 for high-frequency feature word analysis. High-frequency words and behavioral feature words were extracted, filtering out the high-frequency words that are irrelevant to landscape perception and experiential perception (e.g., “as”, “only”, “think”, “did”, and “then”). Then, we organized and summarized the statistics, selecting the top 54 high-frequency words.

3.2. Construction of Criterion and Index Layers

According to the word frequency analysis in

Table 1. High-frequency vocabulary.

Number	Keyword	Frequency	Number	Keyword	Frequency
1	Driving	157	28	Recommend	51
2	Distance	148	29	Bonfire	48
3	National Road	135	30	Authentic	47
4	Fresh Air	134	31	Country Roads	45
5	Rest Room	117	32	Road Conditions	42
6	Parking Space	109	33	Consult	41
7	Accommodation	98	34	Team Building	41
8	Shower	98	35	Enthusiastic	41
9	Tent	92	36	Local Snack	39
10	Brand	88	37	Time	39
11	Barbecue	87	38	Air Conditioner	37
12	Navigate	82	39	Clamor	35
13	Greenery	79	40	Starry Sky	35
14	Local Specialties	77	41	City	33
15	Reception	76	42	Handicraft	32
16	Tour Guide	76	43	Performances	32
17	Enthusiasm	73	44	Trash Can	30
18	Landmark	73	45	Quiet	28
19	Pavilion	71	46	Road	27
20	Clean	67	47	Open-Air Cinema	23
21	Camping	67	48	Traffic Jam	22
22	Supermarket	64	49	Peaceful	20
23	Highway	63	50	Healing	20
24	Scenic Area	61	51	Parent–child Relationship	19
25	Help	59	52	Water Supply	13
26	Weather	58	53	Environmental Protection	11
27	Charging Station	54	54	Get Lost	9

, an in-depth study was conducted on the online travel blogs of the self-driving tours in Xinjiang, and the criteria layer of the model was comprehensively refined, including the location, transportation, social, natural, and support service factors.

The location factor is one of the important indicators determining the construction of campsites [24]. Currently, the construction models of the campsites in China are mainly divided into three types: urban-based, tourist attraction-based, and independently developed [30,31]. Through the word frequency analysis, we found that the high-frequency words, such as “city” and “scenic area”, highlight tourists’ attention to the location of the campsites. For the campsites based on cities, the location factor considers the city’s GDP and consumption capacity. For the campsites based on tourist attractions, the location factor considers the number of A-grade tourist attractions in the surrounding area.

Transportation factors play an important role in the development of the self-driving tourism market, considering the convenience of transportation to the self-driving campsites and the level of regional transportation development [32]. The high-frequency words in the word frequency analysis (e.g., “country roads”, “driving”, “national road”, “distance”, and “highway”) emphasize the importance of transportation factors. These factors include road accessibility and the quality and density of road networks. Good road quality can reduce the travel time and costs and enhance travel experiences, whereas a dense road network structure implies that more tourist destinations can be reached quickly and directly from a campsite.

Social factors refer to the social environment and structure that influence the selection and operation of self-driving campgrounds, encompassing population characteristics, labor market, education and cultural levels, social security situation, and the acceptance of local residents. The high-frequency words in the word frequency analysis (e.g., “reception”, “enthusiasm”, and “team building”) reveal the effects of social factors on campground selection and operation. Furthermore, the high-frequency words, such as “authentic”, “open-air cinema”, “local characteristics”, “handicrafts”, and “performances”, reflect the tourists’ attention to the local culture and characteristics. These factors may be supported and promoted by local policies, thereby influencing the selection and operation of the campgrounds. Considering these social factors is crucial for determining whether a campground receives social support and acceptance, as well as its ability to harmoniously integrate into the local society [33].

Natural factors encompass the natural environmental characteristics of the area where the self-driving campgrounds are located, such as the topography, climate conditions, water resources, vegetation coverage, and biodiversity. Through the word frequency analysis, we noticed that the high-frequency words, such as “fresh air”, “greenery”, and “starry sky”, highlight the importance of the natural environment. The richness and diversity of natural resources are often key to enhancing the attractiveness of self-driving campgrounds and determining the types of leisure and outdoor activities that can be supported in the area [25].

Support services are equally essential for the prosperity of the self-driving tourism market. The high-frequency words in the word frequency analysis (e.g., “restroom”, “parking space”, and “charging station”) reflect tourists’ demand for support services. They include the distribution and quality of gas stations, repair services, shopping centers, and dining establishments. The completeness of support services not only provides necessary driving support and convenience for self-driving tourists but also significantly enhances the overall travel experience, attracting more tourists to participate in self-driving tours.

On the basis of the principles of integrity, systematicity, scientificity, adaptability, and operability, this study comprehensively considers the research findings of experts and scholars in the field of self-driving campground site selection through the examination of the relevant literature, which includes keywords such as “self-driving tour”, “self-driving campground site selection”, “campground”, and “car campground”, and by integrating literature review analysis with the research findings discussed earlier in this paper [34,35,36]. Furthermore, many researchers have surveyed existing self-driving tourism campsites on-site. This study identifies and refines 20 preliminary factors under the criteria layer of location, transportation, social, natural, and support service factors. These factors are detailed in

Table 2. Evaluation index system for site selection of self-driving campgrounds in Xinjiang.

First-Level Index	Number	Second-Level Index	Index Attribute
Location Factors	Z1	GDP of the City	Positive
	Z2	Per Capita Disposable Income of the City	Positive
	Z3	Resident Consumption Capacity	Positive
	Z4	Total Tourism Revenue	Positive
	Z5	Total Number of Tourists	Positive
	Z6	Number of Surrounding A-grade Tourist Attractions	Positive
Transportation Factors	Z7	Number of Main Traffic Arteries	Positive
	Z8	Tourist Passenger Volume	Positive
	Z9	Tourist Hotspot Routes	Positive
Social Factors	Z10	Land Costs	Negative
	Z11	Degree of Local Policy Support	Positive
	Z12	Labor Costs	Negative
Natural Factors	Z13	Geological Disaster Situation	Negative
	Z14	Altitude	Qualitative
	Z15	Weather Conditions	Qualitative
	Z16	Climate Conditions	Qualitative
	Z17	Water Source Conditions	Positive
Support Service Factors	Z18	Number of Gas Stations	Positive
	Z19	Number of Car Repair Shops	Positive
	Z20	Number of Tertiary Hospitals	Positive

.

4. Construction of Layout Evaluation System for Road Trip Camps

4.1. Selection of Evaluation Site

Currently, the site selection and development models of the self-driving tourism campsites in China can be roughly divided into three categories. The first approach involves the development model attached to existing scenic spots, where the site selection and construction of campsites are closely integrated with the existing natural resources and tourism facilities of the scenic spots. The second model relies on urban development, where the site selection and development of campsites make use of the comprehensive tourism service functions of the tourist hotspot cities. Third is the independent development model, where campsites are independently selected and developed without relying on any existing tourism resources.

Among the three models, the model that relies on existing scenic spots dominates the site selection and construction of self-driving campsites. This model has distinct advantages. First, it can maximize the use of the existing infrastructure and service facilities of the scenic spots, thereby significantly reducing additional capital investment. Second, as a provider of mature tourism products, scenic spots can provide self-driving tourists with diversified and high-quality camping tourism services. Third, the brand effect and popularity of the scenic spots can be borrowed by campsites, which not only help enhance the market reputation of campsites but also strengthen the overall service reputation through high-quality tourism services. Fourth, leveraging the market influence of the scenic spots can effectively expand the customer base, achieve the integrated operation of market resources, and promote the dual improvement of economic and social benefits [37].

Xinjiang has rich tourism resources, currently with 573 A-level scenic spots, 120 4A-level scenic spots, and 17 5A-level scenic spots. Therefore, choosing the construction model of campsites based on scenic spots in Xinjiang has significant advantages.

As of January 2024, according to the data provided by the Xinjiang Uygur Autonomous Region Department of Culture and Tourism, the Xinjiang Uygur Autonomous Region has 17 5A-level scenic spots. The names and locations of these 17 scenic spots are listed in

Table 3. Xinjiang 5A-level scenic spots.

Number	Scenic Spot Name	Location
1	Tianshan Tianchi Scenic Area	Changji Hui Autonomous Prefecture
2	Grape Valley Scenic Area	Turpan City
3	Kanas Lake Scenic Area	Burqin County, Altay Prefecture
4	Nalati Tourist Scenic Area	Ili Kazakh Autonomous Prefecture
5	Karakoram Scenic Area	Ili Kazakh Autonomous Prefecture
6	Bayinbuluke Scenic Area	Bayingolin Mongol Autonomous Prefecture
7	Bosten Lake Scenic Area	Bortala Mongol Autonomous Prefecture
8	Zepu Jinhu Yang Scenic Area	Zepu County, Kashgar Prefecture
9	Tianshan Grand Canyon Scenic Area	Urumqi City
10	Baisha Lake Scenic Area	10th Division of Xinjiang Production and Construction Corps
11	Keketuohai Scenic Area	Fuyun County, Altay Prefecture
12	Sailimu Lake Scenic Area	Bortala Mongol Autonomous Prefecture
13	Jiangbulake Scenic Area	Changji Hui Autonomous Prefecture
14	World Devil City Scenic Area	Karamay City
15	359 Cultural and Tourism Area	Alar City
16	Kashgar Old City Scenic Area	Kashgar Prefecture
17	Pamir Tourism Area	Kashgar Prefecture

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4.2. Qualitative Data Scoring Rules

In the evaluation of site selection for self-driving campgrounds, the following data processing standards have been established to effectively evaluate and score data that are difficult to obtain or quantify [38].

Table 4. Qualitative data scoring standard.

Qualitative Data	Score 10	Score 5	Score 1
Z7: Number of main traffic arteries	≥3	2	≤1
Z9: Hot tourist routes	≥3	2	≤1
Z11: Policy support	Encourages camp construction and issues relevant standards	Issues relevant to normative policies	No policies
Z12: Labor cost(CNY)	1620–1540	1700	1900
Z13: Geological disaster situation	Not in an earthquake zone and has never experienced major geological disasters	Located in an earthquake zone but no major disasters	Major disasters occurred
Z14: Altitude	500–2000 m	≤3000 m	≥3000 m
Z15: Days of environmental quality reaching the standard	≥250 days	200–250 days	≤200 days
Z16: Topography	Plains	Hills	Mountains
Z17: Water source conditions	>3	3	<3
Z18: Number of gas stations within two hours of accessibility	≥10	5–10	<5
Z19: Number of auto repair shops within two hours of accessibility	≥20	10–20	<10
Z20: Number of tertiary hospitals within two hours of accessibility	≥2	1	0

presents the details.

4.3. Basic Data

Z1 is in billion yuan, Z2 is in yuan, Z3 is in yuan, Z4 is in hundred million yuan, Z5 is in ten thousand people, Z6 is in units, Z8 is in vehicles, Z10 is in yuan per m², Z15 is in days per year, and Z16 is in days per year. Specific data are presented in

Table 5. Original data of scenic spots (Part 1 and Part 2).

Part 1
	Z1	Z2	Z3	Z4	Z5	Z6	Z7	Z8	Z9	Z10
Tianshan Tianchi Scenic Area	364.7	31553	20177	32.32	589.02	117	10	403221	5	5491
Grape Valley Scenic Area	526.56	26008	17097	38.25	704.19	35	5	374224	5	6266
Kanas Lake Scenic Area	399.71	26289	19783	71	844	2	1	43076	5	5287
Nalati Tourist Scenic Area	122.86	25300	17895	7.55	248	10	10	97608	5	5923
Karakoram Scenic Area	122.86	25300	14002	7.55	248	35	1	54367	5	5923
Bayinbuluke Scenic Area	1519.84	31214	22364	88.36	1574.71	2	5	134676	10	4238
Bosten Lake Scenic Area	481.66	30506	19073	224.3	2627	1	10	348965	5	3735
Zepu Jinhu Yang Scenic Area	1368.56	31597	21756	115.01	1501.4	6	1	23043	1	4501
Tianshan Grand Canyon Scenic Area	3893.22	46276	32117	391.11	5153.71	118	10	437937	10	8670
Baisha Lake Scenic Area	76	38287	25693	10.25	213.14	4	1	140211	10	4764
Keketuohai Scenic Area	82.32	25185	12978	74.95	851.51	15	1	87975	10	2450
Sailimu Lake Scenic Area	481.66	30506	17434	224.3	2627	45	5	17890	10	3735
Jiangbulake Scenic Area	2169.52	31562	14032	117.64	2208.04	36	1	43768	1	5418
World Devil City Scenic Area	1181.1	53903	33012	52.89	882.18	1	5	13454	5	5342
359 Cultural and Tourism Area	380.91	39307	21798	12.39	225.04	6	10	54336	1	4600
Kashgar Old City Scenic Area	1368.56	17245	10765	115.01	1501.4	6	10	42768	10	5563
Pamir Tourism Area	1368.56	17245	10765	115.01	1501.4	3	1	23000	1	2600
Part 2
	Z11	Z12	Z13	Z14	Z15	Z16	Z17	Z18	Z19	Z20
Tianshan Tianchi Scenic Area	10	5	5	5	88	35	5	10	10	10
Grape Valley Scenic Area	10	1	5	5	246	14	5	10	10	10
Kanas Lake Scenic Area	5	5	5	10	264	93	5	5	5	5
Nalati Tourist Scenic Area	10	5	1	10	160	93	10	5	10	5
Karakoram Scenic Area	10	5	1	5	160	93	1	10	5	5
Bayinbuluke Scenic Area	5	5	5	10	250	27	5	5	10	5
Bosten Lake Scenic Area	5	5	5	10	147	34	5	5	5	10
Zepu Jinhu Yang Scenic Area	5	5	5	1	149	10	5	5	5	5
Tianshan Grand Canyon Scenic Area	10	1	5	10	285	38	10	10	10	10
Baisha Lake Scenic Area	1	10	1	1	108	66	5	5	5	5
Keketuohai Scenic Area	1	5	1	10	114	62	1	5	5	1
Sailimu Lake Scenic Area	5	10	5	10	97	38	1	5	10	5
Jiangbulake Scenic Area	1	1	5	10	119	35	5	1	10	1
World Devil City Scenic Area	5	10	5	5	194	56	1	1	5	1
359 Cultural and Tourism Area	5	10	5	1	132	25	5	5	5	1
Kashgar Old City Scenic Area	10	5	5	10	178	25	5	10	10	10
Pamir Tourism Area	1	5	5	1	137	26	1	1	1	1

Source: Xinjiang Statistical Yearbook; Weather Report website; the high-precision map data from the Department of Transportation.

.

4.4. Standardized Data Processing

Given the significant differences in magnitude, units, and distribution among the variables covered in the original dataset, directly utilizing these data for comprehensive evaluation and analysis can lead to unjust and inaccurate results. If unadjusted, these disparities might allow certain variables due to their larger numerical ranges to disproportionately influence the analysis outcomes, thereby obscuring the real effects of other variables. Therefore, to ensure the fairness and accuracy of the analysis, this study implements steps for data normalization. This process aims to eliminate the potential biases brought by these differences by adjusting all variables to a uniform scale [39]. The steps for data standardization are as follows:

Assuming there are m evaluation objects and n evaluation indicators, the original data matrix is $\mathrm{X}=\left[{\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right]\left(\mathrm{i}=\mathrm{1,2},\dots ,\mathrm{m};\mathrm{j}=\mathrm{1,2},\dots ,\mathrm{n}\right)$. Firstly, it is necessary to perform dimensionless processing on the raw data to make it comparable. The standardized data is recorded, and the commonly used standardization methods include range standardization and standard score standardization.

The formula for positive indicators (the higher the better) is as follows:

$${\mathrm{r}}_{\mathrm{i}\mathrm{j}}=\frac{{\mathrm{x}}_{\mathrm{i}\mathrm{j}}-\mathrm{m}\mathrm{i}\mathrm{n}\left({\mathrm{x}}_{\mathrm{j}}\right)}{\mathrm{m}\mathrm{a}\mathrm{x}\left({\mathrm{x}}_{\mathrm{j}}\right)-\mathrm{m}\mathrm{i}\mathrm{n}\left({\mathrm{x}}_{\mathrm{j}}\right)}$$

And for negative indicators (the lower the better) is as follows:

$${\mathrm{r}}_{\mathrm{i}\mathrm{j}}=\frac{\mathrm{m}\mathrm{a}\mathrm{x}\left({\mathrm{x}}_{\mathrm{j}}\right){-\mathrm{x}}_{\mathrm{i}\mathrm{j}}}{\mathrm{m}\mathrm{a}\mathrm{x}\left({\mathrm{x}}_{\mathrm{j}}\right)-\mathrm{m}\mathrm{i}\mathrm{n}\left({\mathrm{x}}_{\mathrm{j}}\right)}$$

The results of the data normalization are shown in

Table 6. Data standardization processing result (Part 1 and Part 2).

Part 1
	Z1	Z2	Z3	Z4	Z5	Z6	Z7	Z8	Z9	Z10
Tianshan Tianchi Scenic Area	−0.58	0.06	0.11	−0.67	−0.63	2.43	1.21	1.77	−0.23	0.36
Grape Valley Scenic Area	−0.42	−0.53	−0.37	−0.61	−0.54	0.24	−0.03	1.57	−0.23	0.89
Kanas Lake Scenic Area	−0.54	−0.50	0.05	−0.29	−0.43	−0.64	−1.02	−0.63	−0.23	0.22
Nalati Tourist Scenic Area	−0.83	−0.61	−0.24	−0.92	−0.90	−0.43	1.21	−0.27	−0.23	0.65
Karakoram Scenic Area	−0.83	−0.61	−0.84	−0.92	−0.90	0.24	−1.02	−0.55	−0.23	0.65
Bayinbuluke Scenic Area	0.59	0.02	0.45	−0.11	0.15	−0.64	−0.03	−0.02	1.17	−0.50
Bosten Lake Scenic Area	−0.46	-0.05	−0.06	1.24	0.99	−0.67	1.21	1.40	−0.23	−0.85
Zepu Jinhu Yang Scenic Area	0.44	0.06	0.36	0.15	0.09	−0.54	−1.02	−0.76	−1.36	−0.32
Tianshan Grand Canyon Scenic Area	3.00	1.63	1.96	2.90	3.00	2.46	1.21	2.00	1.17	2.53
Baisha Lake Scenic Area	−0.87	0.78	0.97	−0.89	−0.93	−0.59	−1.02	0.02	1.17	−0.14
Keketuohai Scenic Area	−0.87	−0.62	−1.00	−0.25	−0.42	−0.29	−1.02	−0.33	1.17	−1.72
Sailimu Lake Scenic Area	−0.46	−0.05	−0.31	1.24	0.99	0.51	−0.03	−0.80	1.17	−0.85
Jiangbulake Scenic Area	1.25	0.06	−0.84	0.18	0.66	0.27	−1.02	−0.62	−1.36	0.31
World Devil City Scenic Area	0.25	2.44	2.10	−0.47	−0.40	−0.67	−0.03	−0.83	−0.23	0.25
359 Cultural and Tourism Area	−0.56	0.88	0.36	−0.87	−0.92	−0.54	1.21	−0.55	−1.36	−0.25
Kashgar Old City Scenic Area	0.44	−1.47	−1.35	0.15	0.09	−0.54	1.21	−0.63	1.17	0.40
Pamir Tourism Area	0.44	−1.47	−1.35	0.15	0.09	−0.62	−1.02	−0.76	−1.36	−1.62
Part 2
	Z11	Z12	Z13	Z14	Z15	Z16	Z17	Z18	Z19	Z20
Tianshan Tianchi Scenic Area	1.17	−0.16	0.54	−0.44	−1.28	−0.38	0.21	1.33	0.98	1.32
Grape Valley Scenic Area	1.17	−1.49	0.54	−0.44	1.30	−1.15	0.21	1.33	0.98	1.32
Kanas Lake Scenic Area	−0.23	−0.16	0.54	0.85	1.59	1.75	0.21	−0.24	−0.72	−0.08
Nalati Tourist Scenic Area	1.17	−0.16	−1.75	0.85	−0.10	1.75	2.01	−0.24	0.98	−0.08
Karakoram Scenic Area	1.17	−0.16	−1.75	−0.44	−0.10	1.75	−1.22	1.33	−0.72	−0.08
Bayinbuluke Scenic Area	−0.23	−0.16	0.54	0.85	1.37	−0.67	0.21	−0.24	0.98	−0.08
Bosten Lake Scenic Area	−0.23	−0.16	0.54	0.85	−0.32	−0.41	0.21	−0.24	−0.72	1.32
Zepu Jinhu Yang Scenic Area	−0.23	−0.16	0.54	−1.47	−0.28	−1.30	0.21	−0.24	−0.72	−0.08
Tianshan Grand Canyon Scenic Area	1.17	−1.49	0.54	0.85	1.94	−0.27	2.01	1.33	0.98	1.32
Baisha Lake Scenic Area	−1.36	1.51	−1.75	−1.47	−0.95	0.76	0.21	−0.24	−0.72	−0.08
Keketuohai Scenic Area	−1.36	−0.16	−1.75	0.85	−0.86	0.61	−1.22	−0.24	−0.72	−1.20
Sailimu Lake Scenic Area	−0.23	1.51	0.54	0.85	−1.13	−0.27	−1.22	−0.24	0.98	−0.08
Jiangbulake Scenic Area	−1.36	−1.49	0.54	0.85	−0.77	−0.38	0.21	−1.49	0.98	−1.20
World Devil City Scenic Area	−0.23	1.51	0.54	−0.44	0.45	0.39	−1.22	−1.49	−0.72	−1.20
359 Cultural and Tourism Area	−0.23	1.51	0.54	−1.47	−0.56	−0.75	0.21	−0.24	−0.72	−1.20
Kashgar Old City Scenic Area	1.17	−0.16	0.54	0.85	0.19	−0.75	0.21	1.33	0.98	1.32
Pamir Tourism Area	−1.36	−0.16	0.54	−1.47	−0.48	−0.71	−1.22	−1.49	−2.07	−1.20

.

4.5. Index Weight Calculation

4.5.1. Determining the Entropy Value of the Factor

Entropy is a measure of the degree of disorder in a system, which can be used to measure the amount of effective information contained in known data and determine the weight. If the entropy value is small, then the index provides a large amount of effective information, and its weight should be large; conversely, its weight should be small [40]. The formula is as follows:

Firstly, calculate the proportion ${\mathrm{p}}_{\mathrm{i}\mathrm{j}}$ of each evaluation object under each indicator:

$${\mathrm{p}}_{\mathrm{i}\mathrm{j}}=\frac{{\mathrm{r}}_{\mathrm{i}\mathrm{j}}}{\sum _{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{r}}_{\mathrm{i}\mathrm{j}}}$$

if ${\mathrm{r}}_{\mathrm{i}\mathrm{j}}=0$ corresponds to all $\mathrm{i}$, then ${\mathrm{p}}_{\mathrm{i}\mathrm{j}}=0$

Then, calculate the entropy value ${\mathrm{e}}_{\mathrm{j}}$ for each indicator:

$${\mathrm{e}}_{\mathrm{j}}=-\mathrm{k}{\sum }_{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{p}}_{\mathrm{i}\mathrm{j}}\ln \left({\mathrm{p}}_{\mathrm{i}\mathrm{j}}\right)$$

wherein $\mathrm{k}=\frac{1}{\ln \mathrm{m}}$ is a constant. When ${\mathrm{p}}_{\mathrm{i}\mathrm{j}}=0$, define $0\ln 0=0$

The entropy values of each factor are shown in

Table 7. Factor entropy value.

Number	Second-Level Index	e
Z1	GDP of the City	0.806903
Z2	Per Capita Disposable Income of the City	0.914236
Z3	Resident Consumption Capacity	0.897744
Z4	Total Tourism Revenue	0.818963
Z5	Total Number of Tourists	0.823267
Z6	Number of Surrounding A-grade Tourist Attractions	0.704075
Z7	Number of Main Traffic Arteries	0.789756
Z8	Tourist Passenger Volume	0.777638
Z9	Tourist Hotspot Routes	0.877751
Z10	Land Costs	0.965137
Z11	Degree of Local Policy Support	0.877751
Z12	Labor Costs	0.90381
Z13	Geological Disaster Situation	0.905425
Z14	Altitude	0.886497
Z15	Weather Conditions	0.89403
Z16	Climate Conditions	0.898789
Z17	Water Source Conditions	0.855301
Z18	Number of Gas Stations	0.902859
Z19	Number of Car Repair Shops	0.951849
Z20	Number of Tertiary Hospitals	0.848828

.

4.5.2. Calculation of Objective Weight

After calculating the entropy values, the formula for calculating the objective weights ${\mathrm{w}}_{\mathrm{j}}$ of the indicators is as follows:

$${\mathrm{w}}_{\mathrm{j}}=\frac{(1-{\mathrm{e}}_{\mathrm{j}})}{{\sum }_{\mathrm{j}=1}^{\mathrm{n}}(1-{\mathrm{e}}_{\mathrm{j}})}$$

4.5.3. Scenic Spot Score Evaluation

The site selection evaluation of self-driving tour campsites requires a comprehensive evaluation of multiple types, subjects, and influencing factors. The formula is as follows:

$$\mathrm{S}=\sum {\mathrm{X}}_{\mathrm{m}\mathrm{n}}{\mathrm{U}}_{\mathrm{n}}$$

where n is the index number of the influencing factor, m is the index number of the scenic spot, ${\mathrm{X}}_{\mathrm{m}\mathrm{n}}$ is the score of the factor n for the scenic spot m, and ${\mathrm{U}}_{\mathrm{n}}$ is the weight of the factor n (refer to

Table 8. Factor weight.

Number	Second-Level Index	w
Z1	GDP of the City	0.071534
Z2	Per Capita Disposable Income of the City	0.031772
Z3	Resident Consumption Capacity	0.037881
Z4	Total Tourism Revenue	0.067066
Z5	Total Number of Tourists	0.065472
Z6	Number of Surrounding A-grade Tourist Attractions	0.109627
Z7	Number of Main Traffic Arteries	0.077886
Z8	Tourist Passenger Volume	0.082375
Z9	Tourist Hotspot Routes	0.045288
Z10	Land Costs	0.012915
Z11	Degree of Local Policy Support	0.045288
Z12	Labor Costs	0.035634
Z13	Geological Disaster Situation	0.035036
Z14	Altitude	0.042048
Z15	Weather Conditions	0.039257
Z16	Climate Conditions	0.037494
Z17	Water Source Conditions	0.053604
Z18	Number of Gas Stations	0.035986
Z19	Number of Car Repair Shops	0.017838
Z20	Number of Tertiary Hospitals	0.056002

for specific factor weights).

$\sum {\mathrm{U}}_{\mathrm{n}}=1$ is the score of each influencing factor, obtained by applying the standardization results of the basic data of each indicator in the previous text, and the comprehensive score is shown in

Table 9. Factor scores at all levels (Part 1 and Part 2).

Part 1
	Z1	Z2	Z3	Z4	Z5	Z6	Z7	Z8	Z9	Z10
Tianshan Tianchi Scenic Area	0.01	0.01	0.02	0.00	0.00	0.11	0.08	0.08	0.02	0.01
Grape Valley Scenic Area	0.01	0.01	0.01	0.01	0.01	0.03	0.03	0.07	0.02	0.00
Kanas Lake Scenic Area	0.01	0.01	0.02	0.01	0.01	0.00	0.00	0.01	0.02	0.01
Nalati Tourist Scenic Area	0.00	0.01	0.01	0.00	0.00	0.01	0.08	0.02	0.02	0.01
Karakoram Scenic Area	0.00	0.01	0.01	0.00	0.00	0.03	0.00	0.01	0.02	0.01
Bayinbuluke Scenic Area	0.03	0.01	0.02	0.01	0.02	0.00	0.03	0.02	0.05	0.01
Bosten Lake Scenic Area	0.01	0.01	0.01	0.04	0.03	0.00	0.08	0.07	0.02	0.01
Zepu Jinhu Yang Scenic Area	0.02	0.01	0.02	0.02	0.02	0.00	0.00	0.00	0.00	0.01
Tianshan Grand Canyon Scenic Area	0.07	0.03	0.04	0.07	0.07	0.11	0.08	0.08	0.05	0.00
Baisha Lake Scenic Area	0.00	0.02	0.03	0.00	0.00	0.00	0.00	0.02	0.05	0.01
Keketuohai Scenic Area	0.00	0.01	0.00	0.01	0.01	0.01	0.00	0.01	0.05	0.01
Sailimu Lake Scenic Area	0.01	0.01	0.01	0.04	0.03	0.04	0.03	0.00	0.05	0.01
Jiangbulake Scenic Area	0.04	0.01	0.01	0.02	0.03	0.03	0.00	0.01	0.00	0.01
World Devil City Scenic Area	0.02	0.03	0.04	0.01	0.01	0.00	0.03	0.00	0.02	0.01
359 Cultural and Tourism Area	0.01	0.02	0.02	0.00	0.00	0.00	0.08	0.01	0.00	0.01
Kashgar Old City Scenic Area	0.02	0.00	0.00	0.02	0.02	0.00	0.08	0.01	0.05	0.01
Pamir Tourism Area	0.02	0.00	0.00	0.02	0.02	0.00	0.00	0.00	0.00	0.01
Part 2
	Z11	Z12	Z13	Z14	Z15	Z16	Z17	Z18	Z19	Z20
Tianshan Tianchi Scenic Area	0.05	0.02	0.04	0.02	0.00	0.01	0.02	0.04	0.02	0.06
Grape Valley Scenic Area	0.05	0.00	0.04	0.02	0.03	0.00	0.02	0.04	0.02	0.06
Kanas Lake Scenic Area	0.02	0.02	0.04	0.04	0.04	0.04	0.02	0.02	0.01	0.02
Nalati Tourist Scenic Area	0.05	0.02	0.00	0.04	0.01	0.04	0.05	0.02	0.02	0.02
Karakoram Scenic Area	0.05	0.02	0.00	0.02	0.01	0.04	0.00	0.04	0.01	0.02
Bayinbuluke Scenic Area	0.02	0.02	0.04	0.04	0.03	0.01	0.02	0.02	0.02	0.02
Bosten Lake Scenic Area	0.02	0.02	0.04	0.04	0.01	0.01	0.02	0.02	0.01	0.06
Zepu Jinhu Yang Scenic Area	0.02	0.02	0.04	0.00	0.01	0.00	0.02	0.02	0.01	0.02
Tianshan Grand Canyon Scenic Area	0.05	0.00	0.04	0.04	0.04	0.01	0.05	0.04	0.02	0.06
Baisha Lake Scenic Area	0.00	0.04	0.00	0.00	0.00	0.03	0.02	0.02	0.01	0.02
Keketuohai Scenic Area	0.00	0.02	0.00	0.04	0.01	0.02	0.00	0.02	0.01	0.00
Sailimu Lake Scenic Area	0.02	0.04	0.04	0.04	0.00	0.01	0.00	0.02	0.02	0.02
Jiangbulake Scenic Area	0.00	0.00	0.04	0.04	0.01	0.01	0.02	0.00	0.02	0.00
World Devil City Scenic Area	0.02	0.04	0.04	0.02	0.02	0.02	0.00	0.00	0.01	0.00
359 Cultural and Tourism Area	0.02	0.04	0.04	0.00	0.01	0.01	0.02	0.02	0.01	0.00
Kashgar Old City Scenic Area	0.05	0.02	0.04	0.04	0.02	0.01	0.02	0.04	0.02	0.06
Pamir Tourism Area	0.00	0.02	0.04	0.00	0.01	0.01	0.00	0.00	0.00	0.00

.

The integration of the criterion-level scores from Table 9 has resulted in the comprehensive evaluation table for the assessed locations, as presented in

Table 10. Evaluation scores of 5A-level scenic spots in Xinjiang.

Rank	Scenic Spot Name	Comprehensive Score
1	Tianshan Grand Canyon	0.918573
2	Tianshan Tianchi Scenic Area	0.591984
3	Bosten Lake Scenic Area	0.515994
4	Kashgar Old City Scenic Area	0.496752
5	Grape Valley Scenic Area	0.466354
6	Bayinbuluke Scenic Area	0.44031
7	Sailimu Lake Scenic Area	0.438685
8	Nalati Tourist Scenic Area	0.416386
9	Kanas Lake Scenic Area	0.340904
10	World Devil City Scenic Area	0.328226
11	359 Cultural and Tourism Area	0.297768
12	Jiangbulake Scenic Area	0.284637
13	Karakoram Scenic Area	0.280022
14	Baisha Lake Scenic Area	0.262589
15	Zepu Jinhu Yang Scenic Area	0.262333
16	Keketuohai Scenic Area	0.227377
17	Pamir Tourism Area	0.14438

. This comprehensive scoring table reflects the scores of each location across various criteria and serves as a crucial reference for the final decision-making.

5. Result

In the study of site selection and spatial layout strategies for self-driving tourism campsites, given that major 5A-level scenic spots typically exhibit a concentrated distribution, ArcGIS 10.2 was used to calculate and analyze the isochrones between each 5A-level scenic spot, as shown in Figure 2. These orange areas represent the regions vehicles can reach within three hours of driving from a certain 5A-level scenic spot. Through this isochrone analysis, the mutual accessibility of various scenic spots in the region can be determined, especially the efficiency of mutual visits between 5A-level scenic spots. The analysis results showed that several scenic spots can be reached within a short period of time. To optimize tourism development planning, priority was given to the top-ranked scenic spots as potential locations for future self-driving tourism campsites. As a result, eight scenic spots were selected for the site selection and layout of self-driving tourism campsites.

In summary, the selected sites for the self-driving tourism campsites in Xinjiang are the Kanas Scenic Area, Tianshan Grand Canyon Scenic Area, Sayram Lake Scenic Area, Bayinbuluke Scenic Area, Kashgar Old City Scenic Area, 359 Cultural Tourism Area, Bosten Lake Scenic Area, and World Devil City.

6. Discussion

Given that the selected sites in this study are within scenic areas, the primary task faced is to ensure sustainability [41]. To achieve this goal, a comprehensive set of measures must be taken to protect the surrounding natural environment while providing a first-class tourist experience. When establishing a campsite, eco-friendly building materials and design principles should be used, such as utilizing natural light, improving energy efficiency, using solar panels and other renewable energy sources, and constructing rainwater collection and recycling systems. Furthermore, the campsite layout should minimize disturbance to the native ecosystem. The management should establish strict waste disposal and recycling plans to ensure all waste is properly categorized, thereby minimizing environmental pollution [42]. Encouraging on-site shops and restaurants to offer locally produced food can reduce the carbon emissions from transportation and support the local economy [43]. In terms of activities, educational programs, such as eco-tours and wildlife observation, can be provided to cultivate the tourists’ awareness of and respect for environmental protection. Regular environmental impact assessments should be conducted to monitor the campsites’ effects on the scenic area environment, ensuring the maintenance of ecological balance and adjusting business strategies based on the assessment results. Establishing cooperative relationships with the local communities and participating in environmental protection projects are also crucial, as this will enhance the campsites’ connection with the community and drive community involvement in eco-tourism. Moreover, effective tourist management is essential for sustainable tourism [44]. Limiting the number of campsite occupants to avoid overcrowding and reduce pressure on the scenic area environment is important. Tourist education is also crucial, ensuring that tourists understand how to minimize their environmental footprint during their visit.

In conclusion, campsite operations guided by sustainable development principles will not only protect and enhance the natural values of the scenic area but also ensure that resources are used for future ecological tourism development [45]. Through these methods, campsites not only provide tourists with a way to reconnect with nature and relax but also contribute to the protection and display of natural landscapes, gradually achieving harmonious coexistence among the tourism industry, environment, and local communities.

7. Conclusions

This study developed an evaluation system for selecting road trip campsites in Xinjiang. It incorporated various methods such as literature review, questionnaires, surveys, ArcGIS spatial analysis, and network text analysis. This study also combined the Delphi method and the entropy weight method to determine the weight of the influencing factors. Through this comprehensive approach, this study identified the spatial imbalance in the road trip camp layout across Xinjiang, and eight scenic spots of road trip camps were proposed. In addition, this study innovatively combined the AHP with the word frequency analysis of the online travel notes and deeply explored the real experience and preferences of tourists, providing a new perspective and methodology for the site selection and evaluation of road trip camps. The study in this paper also has certain limitations; the data sources utilized in this study may introduce bias, potentially affecting the representativeness of the research findings. Online travel blogs are primarily authored by tourists who voluntarily share their experiences, thereby potentially skewing the results towards reflecting the perspectives and experiences of this specific demographic. Of particular note is that older individuals may be less familiar with using smartphones or social media platforms, resulting in their viewpoints and experiences being underrepresented in the data, thus constraining the comprehensiveness and representativeness of the research findings.

On the basis of the AHP and the word frequency analysis of the online travel notes, an evaluation model for the location of the road trip camps in Xinjiang was established, and the index weights were calculated. The results showed that convenient transportation, attractive tourism resources, economic vitality, local policy support, and natural and ecological environmental conditions are the key factors to consider in the site selection of road trip camps. These factors not only reflect the core elements of a successful campsite operation but also guide how to enhance campsite attraction, ensure the satisfaction of tourists, and promote the sustainable development of tourism in Xinjiang through scientific and reasonable site selection strategies. In practice, the influence of these factors should be balanced, and geographical location, market demand, environmental protection, and policy orientation must be comprehensively considered to optimize the layout of road trip camps, provide tourists with rich and varied travel experiences, and promote the harmonious development of local economy and society.

In view of the fact that the site selection of road trip camps in this study is mainly located in a scenic area, considerable importance should be attached to the principle of sustainable development in site selection and subsequent development planning. As a concentrated expression of natural and cultural resources, scenic spots must take a cautious and responsible attitude when developing and utilizing these resources because of their fragile ecological environment and nonrenewable cultural heritage. Therefore, in specific campsite selection and planning processes, the impact on the natural environment must be minimized. Furthermore, environmental monitoring indicators should be established, such as environmental, economic, and social sustainability indicators (for example, see the UNWTO indicators), to monitor impacts and establish an early warning system to prevent damage to the local ecosystems and cultural heritage. At the same time, we must also explore the use of green energy, recycling, and other environmental protection technologies and methods to achieve the green development of campsites. In addition, enhancing the tourists’ awareness of environmental protection and sense of participation not only can enhance their travel experience but also contribute to the long-term sustainable development of the camp and jointly promote the healthy development of tourism in Xinjiang and the prosperity of the regional economy.