Research on the Spatial Differentiation Pattern and Driving Factors of Chinese Ski Resorts from a Multi-scale Perspective

WANG Peipei, WANG Jiao, CAI Yongmei
School of Information Management, Xinjiang University of Finance and Economics, Urumqi 830000, Xinjiang, China

Abstract

This study examines the spatial distribution patterns of 899 ski resorts across China from a multi-scale perspective, integrating national natural geographical zones and provincial regions. Using spatial analysis techniques including the Voronoi coefficient of variation, kernel density estimation, and geographic detector models, we investigated both the spatial differentiation characteristics and driving factors of Chinese ski resort distribution. Our analysis revealed three key findings: (1) Regional distribution pattern: China's ski resorts exhibit a distinct "dense in the north, sparse in the south; more in the east, less in the west" spatial configuration. The primary concentrations appear in north China, northeast China, east China, and northwest China. (2) Spatial agglomeration structure: A "one core, three areas, multiple facets" pattern emerges at the national scale. High-density areas are predominantly concentrated in northeast China (Heilongjiang and Jilin Provinces), north China (Beijing City, Hebei Province), and northwest China (Xinjiang Uygur Autonomous Region, Shaanxi Province). By contrast, central, south China, and southwest China show a sparse distribution of ski resorts. (3) Hierarchical driving factors: The determinants of ski resort spatial differentiation rank as follows: Natural environment > transport capacity > socio-economic development > tourism development level. Significant interaction exists among these factors, primarily through dual-factor enhancement mechanisms, demonstrating that both environmental and socioeconomic variables jointly shape spatial distribution. Based on these findings, we recommend leveraging spatial agglomeration advantages, implementing regionally differentiated development strategies, and strengthening infrastructure to promote high-quality development of China's winter sports economy.

Keywords: multi-scale; ski field; spatial pattern; geographic detector; driving factors

Introduction

Under the boost of the "Winter Olympics effect," the concept that "ice and snow are also invaluable assets" has charted a clear course for transforming ice and snow resource advantages into economic competitive advantages. According to the China Ice and Snow Industry Development Research Report, the scale of China's ice and snow industry grew from 0.27×10¹² yuan to 0.89×10¹² yuan in 2023, with projections reaching 1.5×10¹² yuan. As crucial carriers of the ice and snow economy, ski resorts not only provide core venues for winter sports but also serve as preferred destinations for ice and snow tourism and cultural activities. The number of ski resorts in China increased by 6.74% year-over-year in 2023, establishing them as key engines for driving industrial development and local economic growth. Consequently, exploring the spatial differentiation patterns of ski resorts is essential for rationally allocating ice and snow resources according to local conditions and fostering an integrated, full-industry chain encompassing ice and snow sports, culture, equipment, and tourism.

Current research on ski resorts varies in focus between domestic and international scholarship. International studies tend to integrate ski resorts into broader ski industry or ski tourism resort concepts, with particular attention to climate change impacts (shortened snow seasons, reduced snowfall) on the ski industry and corresponding adaptation measures. Other research examines the advantages and disadvantages of ski tourism resorts, develops decision tree models for ski resort promotion strategies, and evaluates the impact of business intelligence applications on resort performance. Domestic scholarship has traditionally approached China's ice and snow industry development from sports, economic, and management perspectives, analyzing current conditions, influencing factors, and development pathways. Under the influence of the digital economy era, scholars have explored how digital technologies empower ice and snow industry development, while others have investigated ski resort accessibility, market potential, network attention, and spatial vitality evaluation from tourism perspectives. With the rise of ice and snow tourism in recent years, geographers have increasingly focused on ski resort spatial patterns and their driving factors.

However, existing research exhibits several limitations. First, regarding spatial scale, geospatial studies of ski resorts remain in their infancy, with most literature examining either national-scale patterns or individual provinces/autonomous regions in isolation. This neglects scale-dependent variations in ski resort distribution and, more importantly, overlooks how China's distinct natural geographic zones influence the spatial configuration of this resource-intensive industry. Second, while some studies have employed analytic hierarchy process, structural equation modeling, and geographic detectors to analyze driving factors, they typically select individual indicators rather than constructing comprehensive indicator systems. Moreover, few have incorporated tourism development level as a factor influencing emerging demand-driven ski resorts into their analytical frameworks.

To address these gaps, this study utilizes 2023 data from 899 ski resorts across China, applying spatial statistics, kernel density analysis, and geographic detector models to compare spatial differentiation patterns across national, seven natural geographic zones, and provincial scales. We constructed a comprehensive evaluation indicator system and analyzed the significance and interactive effects of driving factors through geographic detectors. Our objective is to uncover the spatial differentiation patterns of ski resorts under multi-scale perspectives, providing a scientific foundation for coordinating regional ski resources, optimizing spatial allocation, and enhancing coordinated development of regional ice and snow tourism. This research offers scientific references for strategic planning and policy formulation to create new growth poles for China's ice and snow tourism.

1. Data and Methods

1.1 Data Sources

We obtained ski resort data through web scraping of the Gaode Map API, collecting names and addresses for ski resorts across China's 31 provinces, autonomous regions, and municipalities (excluding Hong Kong, Macao, and Taiwan). After cross-validation with the State General Administration of Sport's official ski resort list, we performed deduplication, cleaning, and comparison to construct a point vector database of 899 ski resorts, which we visualized using ArcGIS software to generate distribution maps. Data for influencing factors—including natural conditions, tourism development, socio-economic indicators, and transportation—were sourced from the Geospatial Data Cloud, Chinese Academy of Sciences Resource and Environmental Science Data Center, Ministry of Culture and Tourism website, 2023 provincial statistical yearbooks, national economic and social development statistical bulletins, and the State General Administration of Sport.

1.2 Research Methods

1.2.1 Voronoi Polygon Coefficient of Variation

The Voronoi polygon coefficient of variation measures spatial distribution uniformity and serves as an effective statistical indicator for testing nearest neighbor indices. Voronoi polygon areas vary according to the spatial distribution of random point sets, making the coefficient of variation suitable for quantifying area variation degree and characterizing sample spatial distribution patterns. During methodological development, scholars established classification criteria: when C_v < 0.33, point sets exhibit random distribution; when 0.33 ≤ C_v ≤ 0.64, they show clustered distribution; and when C_v > 0.64, they demonstrate uniform distribution. The calculation formula is:

$$C_v = \frac{\sigma}{\mu}$$

where σ represents standard deviation, μ denotes mean polygon area, and n is the number of polygons.

1.2.2 Kernel Density Analysis

Kernel density estimation reveals the overall distribution pattern of discrete samples across continuous regions. By calculating ski resort density in adjacent areas, this method provides intuitive visualization of spatial distribution patterns. The formula is expressed as:

$$\hat{f}(s) = \frac{1}{nh}\sum_{i=1}^{n}k\left(\frac{s-s_i}{h}\right)$$

where h is the bandwidth determining kernel function coverage, n is the total sample points, k is the spatial weight function, and s-s_i represents the distance between estimation point s and sample s_i. Kernel density results primarily depend on spatial weight functions, bandwidth, and kernel functions, with bandwidth being the most critical parameter.

1.2.3 Geographic Detector

Geographic detector comprises factor detection and interaction detection components. Factor detection identifies internal driving factors of spatial differentiation phenomena, while interaction detection analyzes combined effects when multiple influencing factors interact. This method analyzes internal driving factors without requiring excessive assumptions and has been widely applied in social, economic, and natural science research. We employed factor detection to examine influencing factors of ski resort spatial distribution among 899 resorts and analyzed interactive effects among factor categories.

2. Results and Analysis

2.1 Spatial Distribution Characteristics

2.1.1 Spatial Distribution Patterns

To comprehensively understand ski resort spatial differentiation characteristics across scales, we analyzed spatial agglomeration and dispersion patterns, generating a spatial distribution map of Chinese ski resorts (Figure 1). According to China's seven natural geographic divisions, we categorized ski resort distribution into east China, north China, central China, south China, southwest China, northwest China, and northeast China regions. Overall, 31.85% of ski resorts are located in north China, 21.77% in northeast China, 16.50% in east China, 15.27% in northwest China, 7.26% in central China, 5.62% in southwest China, and 1.73% in south China, demonstrating a "dense north, sparse south; more east, less west" pattern concentrated primarily north of the Qinling-Huaihe line. At the provincial scale, provinces with moderate-to-high ski resort counts include Hebei, Heilongjiang, Jilin, Shandong, Shanxi, Xinjiang Uygur Autonomous Region, and Inner Mongolia Autonomous Region.

2.1.2 Spatial Agglomeration Degree

We calculated the Voronoi polygon coefficient of variation for ski resorts at national and natural geographic zone scales (Table 1). Results show that the national Voronoi polygon coefficient of variation is 0.633, with all seven natural geographic zones exhibiting C_v values between 0.33 and 0.64. This indicates that ski resort distribution demonstrates clustered patterns at both national and regional scales. East China shows the highest agglomeration degree (C_v = 0.742), while central and south China exhibit relatively lower C_v values (0.521 and 0.542 respectively) and consequently lower agglomeration intensity.

2.1.3 Spatial Distribution Density

Using ArcGIS kernel density analysis, we generated a spatial distribution density map of Chinese ski resorts (Figure 2). From a national perspective, ski resorts display a "one core, three areas, multiple facets" agglomeration pattern. The "one core" represents a high-density center radiating from Beijing and Hebei Province, while the "three areas" constitute sub-high-density zones centered in Heilongjiang-Jilin, Shandong, and Xinjiang-Shaanxi, forming dense块状 clusters. At the natural geographic zone scale, northeast China's ski resorts concentrate along the Harbin-Dalian Railway, Suifenhe-Manzhouli Railway, and Changbai Mountain ranges, representing one of China's most densely distributed ski regions, primarily in Heilongjiang and Jilin provinces. North China ski resorts concentrate in the Beijing-Tianjin-Hebei region, where the Winter Olympics dramatically accelerated ski industry development, creating prominent agglomeration centers. Northwest China ski resorts concentrate in Xinjiang Uygur Autonomous Region and Shaanxi Province, with Xinjiang hosting the most resorts in Urumqi, Changji Hui Autonomous Prefecture, and Altay Region. East China ski resorts concentrate in Shandong and Jiangsu provinces, while central China resorts are mainly in northern Henan. South China resorts cluster in the Pearl River Delta region, and southwest China resorts concentrate in the Chengdu Plain and the Dalou Mountains-Wumeng Mountains stretching from Chongqing to Guizhou in a northeast-southwest orientation.

2.2 Driving Factor Analysis

2.2.1 Basis for Factor Selection

As vital carriers of ice and snow activities, ski resort formation and layout are influenced by comprehensive natural environment and economic development factors. First, natural factors—climate conditions and topography—are prerequisites for resource-based ski resort layout, determining resort grade, scale, and operational season. Second, socio-economic factors increasingly influence ski resort location and development as ice and snow sports popularize and ski resorts become preferred winter tourism destinations with substantial market potential. Regional tourism development level, socio-economic development, and transportation capacity all affect ski resort siting and operations. Based on previous research and comprehensive indicator system comparison, we selected 12 specific indicators across four dimensions—natural factors, tourism development level, socio-economic development, and transportation capacity—to analyze driving factors of ski resort spatial differentiation (Table 2). Natural factors include elevation, slope, temperature, and precipitation; tourism development level includes total tourism revenue, total tourist arrivals, and number of star-rated hotels; socio-economic development includes per capita disposable income and proportion of tertiary industry value-added; transportation capacity includes expressway comprehensive density, railway network comprehensive density, and passenger turnover volume.

2.2.2 Geographic Detector Model Results

(1) Single-Factor Detection Analysis

Factor detection results reveal the influence power (q-values) of each driving factor on ski resort spatial differentiation (Table 3). All 12 factors pass significance tests. Specifically, the q-value ranking for factors influencing ski resort distribution is: precipitation (X₄) > temperature (X₃) > passenger turnover volume (X₁₂) > expressway comprehensive density (X₁₀) > proportion of tertiary industry value-added (X₉) > per capita disposable income (X₈) > number of star-rated hotels (X₆) > railway network comprehensive density (X₁₁) > total tourist arrivals (X₅) > slope (X₂) > total tourism revenue (X₇) > elevation (X₁).

Regarding natural environment factors, elevation, slope, temperature, and precipitation all significantly influence ski resort distribution, reflecting strong spatial dependence on natural geography. Temperature and precipitation affect snow quality and operational season length, while slope and elevation influence skiing experience and safety. Ski resorts concentrate in areas with abundant precipitation, low temperatures, and moderate slopes. The "Altai Mountains-Tianshan Mountains" range boasts rich snow resources within the "golden latitude belt" for ski resorts, making Jilin Province and Altay Region in Xinjiang famous winter sports destinations with rapid ice and snow economic development.

For tourism development factors, star-rated hotels significantly affect ski resort distribution, reflecting regional reception capacity. As accommodation and dining providers, star-rated hotels constitute important carriers for ice and snow tourism, forming positive interactions with surrounding ski resorts.

Regarding socio-economic factors, per capita disposable income substantially influences ski resort distribution because it determines consumption capacity. Higher consumption capacity correlates with stronger participation willingness in skiing activities. Per capita GDP reflects regional economic levels that provide the financial foundation for ski resort construction and infrastructure development. The proportion of tertiary industry value-added affects regional service capacity and attractiveness, with well-developed service sectors offering superior skiing experiences. Consequently, east China, despite less favorable natural conditions than northeast, north, and northwest China, hosts numerous ski resorts due to robust economic development, high consumption levels, and strong demand for winter sports.

For transportation capacity factors, expressway comprehensive density, railway network comprehensive density, and passenger turnover volume all significantly influence ski resort distribution. Passenger turnover volume reflects regional transportation activity and capacity, with efficient transport systems enhancing ski resort attractiveness. Expressway and railway network densities indicate regional accessibility, affecting tourist travel choices and ski resort market reach.

(2) Interaction Detection Results

Building upon single-factor analysis, interaction detection identifies relationships between factor pairs (Table 4). Diagonal q-values represent individual factor effects, while off-diagonal values indicate interactive effects. Results demonstrate that all factor pairs exhibit enhanced effects greater than individual factors, with interaction types classified as either non-linear enhancement or dual-factor enhancement. Specifically, interactions between slope (X₂) and precipitation (X₄), temperature (X₃) and precipitation (X₄), temperature (X₃) and per capita disposable income (X₈), and precipitation (X₄) and per capita disposable income (X₈) show non-linear enhancement, while all other factor pairs demonstrate dual-factor enhancement.

Notably, interactions between elevation (X₁) and temperature (X₃), slope (X₂) and temperature (X₃), temperature (X₃) and total tourism revenue (X₇), temperature (X₃) and expressway comprehensive density (X₁₀), precipitation (X₄) and passenger turnover volume (X₁₂), total tourist arrivals (X₅) and per capita disposable income (X₈), and total tourist arrivals (X₅) and proportion of tertiary industry value-added (X₉) produce relatively large q-values. This indicates that ski resort spatial distribution is significantly driven by interactions among natural environment, tourism development, socio-economic development, and transportation capacity factors. Ski resorts are most abundant in regions with suitable climate and topography, mature tourism development, and strong socio-economic foundations. This complexity arises because ski resort distribution results from multiple interacting factors rather than single-factor causation. Suitable terrain and climate provide basic construction conditions, tourism development generates demand for ski facilities (particularly for artificial snow resorts), and developed service sectors enhance visitor comfort and attractiveness. High socio-economic levels boost consumption capacity, creating tourism groups with diverse demands that stimulate ski industry development.

3. Conclusions and Recommendations

3.1 Conclusions

This study examined spatial differentiation characteristics and driving factors of China's ski resorts from multi-scale perspectives (national, natural geographic zone, and provincial) using 2023 data from 899 ski resorts, Voronoi spatial statistics, kernel density analysis, and geographic detector models. Key findings include:

(1) Spatial Distribution Pattern: China's ski resorts exhibit a "dense north, sparse south; more east, less west" distribution, concentrating primarily north of the Qinling-Huaihe line. Nearly 70% of resorts are located in north China, northeast China, east China, and northwest China, with sparse distribution in regions south of the Yangtze River.

(2) Spatial Agglomeration Structure: A "one core, three areas, multiple facets" pattern emerges nationally. High-density areas concentrate in northeast China (Heilongjiang, Jilin), north China (Beijing, Hebei), and northwest China (Xinjiang, Shaanxi), while central, south, and southwest China show sparse distribution.

(3) Driving Factor Hierarchy: Factors rank as natural environment > transportation capacity > socio-economic development > tourism development level. Twelve factors show significant interactive enhancement effects, primarily through dual-factor enhancement mechanisms, with only four factor pairs (slope-precipitation, temperature-precipitation, temperature-per capita disposable income, precipitation-per capita disposable income) exhibiting non-linear enhancement. This confirms that ski resort spatial differentiation results from complex interactions among natural environment, socio-economic conditions, transportation, and tourism development rather than single-factor effects.

3.2 Recommendations

Based on systematic analysis of ski resort spatial differentiation patterns and driving factors, we propose the following strategies for optimizing ski resort layout and promoting high-quality development:

(1) Leverage Spatial Agglomeration Advantages: Construct an ice and snow economy spatial layout of "one region, two belts, multiple nodes." Establish an internationally influential northern ice and snow economic leadership region centered on Inner Mongolia, Liaoning, Jilin, Heilongjiang, and Xinjiang. Develop ice and snow Silk Road belts in Beijing's Yanqing District, Hebei's Chongli District, Heilongjiang's Yabuli Town, Jilin's Changbai Mountain area, and Xinjiang's Altay Region to create ice and snow economic clusters that demonstrate exemplary leadership.

(2) Implement Regionally Differentiated Development Strategies: For the Beijing-Tianjin-Hebei region, capitalize on post-Winter Olympics growth by developing comprehensive ski resorts centered on international winter sports events. For Xinjiang, Heilongjiang, and Jilin with abundant snow resources and long snow seasons, upgrade infrastructure and service quality to enhance comprehensive operational capacity during snow seasons. For Shanghai, Jiangsu, and Zhejiang with economic advantages but limited natural conditions, accelerate indoor ski resort construction through technological innovation, implementing the "Northern Ice, Southern Expansion" strategy to promote balanced winter sports development in southern China.

(3) Strengthen Infrastructure Development: Ski resort distribution is jointly influenced by natural environment, socio-economic development, transportation, and tourism factors. Transportation capacity and tourism development level particularly warrant attention. Implement a comprehensive ice and snow "grand transportation" strategy by establishing integrated rail, road, and air multimodal transport networks to reduce space-time distances to ice and snow destinations, enhance visitor travel experiences, and develop public transport systems with ice and snow characteristics. Accelerate infrastructure improvements, expand hotel capacity in ice and snow scenic areas, and enhance overall operational and service capabilities.

Our study reveals the clustered spatial distribution of China's ski resorts and their misalignment with natural resource endowments. While Tibet Autonomous Region and Inner Mongolia Autonomous Region possess abundant ice and snow resources, ski resort development remains constrained by transportation and economic factors. Conversely, following the successful Winter Olympics, the Beijing-Tianjin-Hebei region has seized new opportunities for ice and snow economic development through favorable policies, industrial planning, integrated culture-sports-tourism development, and improved service infrastructure, making the ice and snow industry a vital force for local economic transformation and high-quality development. As ice and snow sports, equipment, and tourism industries rapidly emerge, developing the ice and snow economy has become crucial for cultivating new consumption growth points and stimulating investment. As key carriers of this economy, ski resorts will be significantly influenced by social drivers including capital, policy, and market factors. Future research should deeply investigate the influence mechanisms and magnitude of these social factors on ski resort spatial distribution. Additionally, since ski resort clustering closely relates to ice and snow equipment and tourism enterprises, further micro-level analysis from business operation perspectives is needed to explore the evolution mechanisms, pathways, and related issues of ski resorts transitioning from spatial clustering to industrial agglomeration.

References

[1] Ghaderi Z, Khoshkam M, Henderson J C. From snow skiing to grass skiing: Implications of climate change for the ski industry in Dizin, Iran[J]. Anatolia: An International Journal of Tourism Hospitality Research, 2014, 25(1): 96-107.

[2] Hopkins D. The sustainability of climate change adaptation strategies in New Zealand ski industry: A range of stakeholder perceptions[J]. Sustainable Tourism, 2014, 22(1): 107-126.

[3] Scott D, Mcboyle G. Climate change adaptation in the ski industry[J]. Mitigation Adaptation Strategies for Global Change, 2007, 12(8): 1411-1431.

[4] Pickering C M, Buckley R C. Climate response by the ski industry: The shortcomings of snow making for Australian resorts[J]. Ambio, 2010, 39(5): 430-437.

[5] Duchessi P. Decision tree models for profiling ski resorts promotional and advertising strategies impact on sales[J]. Expert Systems with Applications, 2013, 40(15): 5822-5829.

[6] Gowers J. Ski resort gets a lift from business intelligence[J]. Baseline, 2008(87): 38-40.

[7] Dou Wenkang, Wang Shijin, Han Tongtong, et al. Measurement of accessibility and market potential of ski resorts in China[J]. Scientia Geographica Sinica, 2021, 41(2): 319-327.

[8] Wang Bei. Research on the high quality development path of ice and snow sports industry in Beijing Tianjin Hebei region in the post Olympic period[J]. Bulletin of Sport Science, 2023, 31(12): 219-223.

[9] Kan Junchang, Wang Fei. Driving factors and innovative paths of skiing industry upgrading under the strategy of Winter Olympics in China[J]. China Sport Science, 2016, 36(6): 11-21.

[10] Wang Lulu, Chen Dan, Gao Xiaobo. The problems and countermeasures of ice and snow industry development in southern China[J]. Sports Culture Guide, 2019(4): 94-99.

[11] Wang Jinwei, Guo Jiaxin, Liu Yi, et al. Spatial distribution characteristics and influencing factors of ski resorts in China[J]. Geographical Research, 2022, 41(2): 390-405.

[12] Ba Duoxun, Wang Chu, Lu Changbao, et al. Spatio temporal evolution and influencing factors of network attention of representative ski resorts in China[J]. Journal of Natural Disasters, 2022, 37(9): 2367-2386.

[13] Guo Wenyao, Liu Weigang. Research on digital technology empowering high quality development of ice and snow industry chain[J]. Enterprise Economics, 2024, 43(6): 50-58.

[14] Wang Jinwei, Zou Mingle, Xu Xiaowen, et al. Spatiotemporal evolution of ski resorts in the three provinces of northeast China and its influencing factors[J]. Economic Geography, 2023, 43(8): 187-199.

[15] Li Ying, Zhu Tianlong, Yao Bing, et al. The development and spatial distribution characteristics of ski resorts in Beijing and Zhangjiakou in recent 20 years[J]. Economic Geography, 2022, 42(1): 191-198.

[16] Liang Shifei, Xie Xia, Tian Xiaoxia. Analysis on spatial distribution characteristics and influencing factors of Xinjiang S class ski resorts[J]. Journal of Xinjiang University (Natural Science Edition), 2020, 37(3): 370-378.

[17] Wang Hengli, Zhou Wenjing. Analysis of factors influencing the development of ice and snow sports tourism industry based on AHP[J]. Journal of Jianghan University (Natural Science Edition), 2018, 46(4): 374-378.

[18] Li Jia, He Zhixuan. Image perception of ice and snow tourism destination based on structural equation model: A case study of Changchun new ice snow world[J]. Resource Development and Market, 2024, 40(7): 1112-1120.

[19] Xie Jia, Wang Shijin, Dou Wenkang, et al. The spatio temporal characteristics and evolution law of Chinese ski resorts from 1989 to 2019[J]. Scientia Geographica Sinica, 2022, 42(6): 1064-1072.

[20] Zhu Lei, Li Yannan, Xu Jiahui, et al. Spatial distribution pattern and causes of ice and snow tourist attractions in the south of China[J]. Journal of Southwest University (Natural Science Edition), 2023, 45(11): 116-129.

[21] Wang Jinfeng, Liao Yilan, Liu Xin. Lectures on spatial data analysis[M]. Beijing: Science Press, 2010: 67-83.

[22] Fang Yan, Xu Haibin, Jiang Yiyi. Evaluation of spatial vitality of China's ski resorts based on multi source data[J]. Geographical Research, 2023, 42(2): 389-406.

[23] Guo Hui, Zhang Limei. Analysis of influencing factors and strategies for the development of China's ski tourism industry[J]. The Border Economy and Culture, 2022(2): 59-61.

[24] He Wenyi, Guo Bin, Zhang Rui. Research on the essence and development path of China's ice and snow industry in the New Era[J]. Journal of Beijing Sport University, 2020, 43(1): 29-38.

[25] Zhao Lin, Wang Fei. Value orientation and implementation path of digital transformation of ice and snow sports tourism during the 14th Five-Year Plan period[J]. Sports Culture Guide, 2021(9): 1-7.

[26] Wang Chao, Du Wei, Du Chunhua. Theoretical connotation, practical dilemma, and implementation paths of high quality development of the ice and snow industry empowered by digital technology[J]. Journal of Shenyang Sport University, 2022, 41(5): 21-27.

[27] Jia Guodong, Wang Liguo, Zhu Hai, et al. Study on the spatial characteristics of Jiangxi red tourism resources and their development suitability based on GIS spatial analysis[J]. Tourism Forum, 2022, 15(6): 49-61.