Spatial distribution and driving factors of Chinese dragon and lion dance athletes based on GIS

2.1. Data sources

Dragon and Lion Dance Grade Certificates by the Chinese Dragon and Lion Dance Sports Association from 2018 to 2021 were taken as the research objects. The reason for selecting this period is that the implementation of Athletes' Technical Level Announcement began in 2018. To scientifically analyze the spatial distribution characteristics of the sport, this study chose the provincial administrative regions of China as the research scale. Because the dragon and lion dance is a team event, the sport's specific position on the provincial scale is not analyzed.

The athlete-related information data came from the official website of the Chinese Dragon and Lion Dance Sports Association (http://dragonlion.sport.org.cn), including the representative units, competition time, award levels, etc. The data on total GDP, the proportion of the tertiary industry, and the permanent resident population from 2018 to 2020 were derived from the statistical yearbooks of each city. Altitude data came from the Resources and Environment Science Data Center of the China Academy of Sciences (http://www.resdc.cn). The method of obtaining the frequency of the keywords "dragon and lion" was to enter the number of the keywords "dragon and lion" in the search pages of the municipal people's governments and sports bureaus. The nomination data of sports intangible cultural heritage came from the Central People's Government of the People's Republic of China (PRC) (http://www.gov.cn). The number of nominations for dragon and lion dance sport in the five batches of national intangible cultural heritage lists published by the State Council was calculated. According to the classified statistics of the prefecture-level cities where the declared areas were located, 67 intangible cultural heritage lists were obtained.

2.2. Research methods

• (1)

  Global trend analysis method

Global Trend Analysis Tool is to draw a three-dimensional trend chart based on the ArcGIS platform. This method was chosen because it can directly describe the changing trend of athletes in a specific direction. The Z-axis represents the number of athletes, the X-axis represents the east-west upward trend (the arrow indicates the east), and the Y-axis represents the south-north upward trend (the arrow indicates the north).

• (2)

  Standard deviational ellipse

Standard Deviational Ellipse (SDE) is a method that can represent the agglomeration characteristics and azimuth of geospatial elements in spatial statistical analysis [40]. The major axis and minor axis of the ellipse represent the dispersion degree of elements in the major trend and minor trend respectively. The azimuth of the ellipse indicates the main trend direction of the spatial distribution of elements. The average center of the ellipse indicates the relative position of the elements in the spatial distribution [41]. This method was chosen because it can not only calculate the major axis, minor axis, azimuth, and average center of an ellipse [42,43], the spatial concentration can also be calculated by comparing the ratio of the element ellipse to the reference ellipse. Based on the standard deviational ellipse method, Zhao Zuoquan's calculation method of ellipse spatial agglomeration degree judges whether there is a spatial agglomeration pattern in element distribution by comparing the ellipse of element distribution with the ellipse of reference distribution (Equation (1)) [44]. The reference distribution ellipse was assumed to be a random distribution of elements in the study area. Therefore, the area of each province in the study area was taken as the reference element. Then the reference distribution ellipse of any element in the study area was obtained.

The area is the elliptical area. The elliptical area of reference distribution was calculated by taking the area of each provincial administrative region in China as the factor benchmark. A is the degree of spatial concentration, which is between [0,1]. The closer the value A is to 1, the more obvious the spatial aggregation of dragon and lion dance athletes will be.

• (3)

  Exploratory spatial data analysis

Exploratory Spatial Data Analysis (ESDA) is an analytical method to explore the spatial agglomeration characteristics and spatial distribution patterns based on spatial distribution data [45,46]. Exploratory data analysis was chosen because it can be used not only to analyze the whole situation but also to test the spatial agglomeration characteristics of elements locally. Therefore, this paper used the global Moran's I to analyze the spatial characteristics of elements in the whole country (Equation (2)).

$I$ is the global Moran's I value. $n$ is the total number of provinces where the elements are located. ${\mathrm{W}}_{\text{ij}}$ is the spatial weight Rook matrix. $X_i$ and $X_j$ are the number of provinces where the elements are located. $\bar{X}$ is the average value of quantity. $S^2=\frac{1}{n}\sum _{i=1}^n{\left(X_i-\bar{X}\right)}^2$. The value range of $I$ is between [-1,1]. When $I$ > 0 and the closer it is to 1, the higher the concentration degree of spatial distribution of factors among provinces will be. When $I$ = 0, it means that the number of elements is randomly distributed spatially and there is no spatial correlation. When $I$ < 0, the smaller the value is, the more obvious the difference in the spatial distribution of elements will be.

The local spatial autocorrelation index is an investigation of the spatial characteristics of elements in the local range (Equation (3)). Based on global spatial autocorrelation, that which local regions made great contributions were further explored.

$I_p$ is the local Moran index value. When $I_p$ > 0, it means that a local region with many elements is surrounded by another local region with many elements (H–H type), or a local region with few elements is surrounded by another local region with few elements (L-L type). When $I_p$ < 0, it means that a local area with many elements is surrounded by another local area with few elements (H-L type), or a local area with few elements is surrounded by another local area with many elements (L-H type).

• (4)

  Geographic detector

Although the manifestations of dragon and lion dance sport are different, there is a high degree of consistency in symbolism, national cultural value identity, and function. Therefore, the research data were merged when studying the driving factors of the spatial distribution of dragon and lion dance athletes. After analyzing the data, to explore the spatial distribution driving factors scientifically, this paper took prefecture-level administrative regions as the statistical units. Geographic detectors are statistics that reveal the driving factors behind and the spatial patterns (Equation (4)). The reason why geographical detectors were used to analyze the driving factors of spatial distribution differences is that geographical detectors have two special advantages. One advantage is that quantitative and qualitative data can be input, and another advantage is that geographical detectors can analyze the influence degree of interaction factors on dependent variables [47].

$q$ is the degree of a certain influence factor (X) to explaining the spatial distribution (Y) spatial differentiation pattern of elements. The value range is between [0,1]. The closer the value is to 1, the higher the degree of interpretation of Y will be. $L$ is the number of driving factors. $h$ is the stratified sample size. $N_h$ and $N$ represent the number of units in $h$ and in the whole region respectively. ${\sigma }_h^2$ and ${\sigma }^2$ represent the variance of $h$ and of the whole region respectively.

3.1. Spatial distribution characteristics of quantity

To visually show the number distribution of dragon and lion dance athletes nationwide, the natural fracture method was used in ArcGIS, the graded color was selected and used to represent the quantity. As shown in Table 1, from 2018 to 2021, a total of 630 dragon-dance athletes and 161 lion-dance athletes in China were awarded athlete certificates. As it can be seen from Fig. 1, Jiangsu, Hubei, and Zhejiang provinces had more dragon-dance athletes. These areas are economically developed centers, including the Yangtze River Delta urban agglomeration, Chengdu-Chongqing urban agglomeration, and Wuhan urban agglomeration in China. There were more lion-dance athletes in Hubei, Guangdong, and Beijing (Fig. 2). These areas are in China's Beijing-Tianjin-Hebei urban agglomeration, Pearl River Delta urban agglomeration, and the Wuhan city circle, which are not only political centers but also regional economic centers. It showed that the distribution of dragon and lion dance athletes was related to regional politics and economy.

China has a vast territory and complex terrain. According to the classification method of the National Bureau of Statistics, Chinese territory is divided into four regions: east, west, middle, and northeast. To further analyze the distribution characteristics of dragon and lion dance athletes in China, this paper described and analyzed the athletes’ number and proportion from four geographical divisions. As it can be seen from Table 1 and Fig. 3(A), the distribution of dragon dance athletes was significantly different, and the most distributed area of the athletes was the eastern region, accounting for 56.19 % of the total, in which athletes were mainly distributed in economically developed coastal areas, such as Jiangsu, Zhejiang, and Shanghai. Followed by the central region with a total of 156 dragon dance athletes, accounting for 24.76 % of the total (Fig. 3(A)). These athletes were mainly distributed in cities such as Wuhan and Changsha. The western region ranked third, with 109 athletes, accounting for 17.3 % of the total (Fig. 3(A)). Northeast China had the fewest athletes, with 11 being all distributed in Jilin, accounting for 1.75 % of the total (Fig. 1, Fig. 3).

By analyzing the statistical results of the distribution of lion-dance athletes in the four regions (Table 1), we can find that the distribution was highly concentrated and there were obvious differences between regions. 58.39 % of the athletes were distributed in the eastern region, mainly in Guangdong Province and Beijing (Fig. 2, Fig. 3). 30.43 % of the athletes were distributed in Hubei Province of the central region. There were 18 athletes in the western region, accounting for 11.18 % of the total number, who were mainly distributed in Sichuan Province and Shaanxi Province. From the results, we can see that different regions had different emphases, which may be related to regional cultures.

3.2. Trend characteristics of quantitative space

Trend surface analysis in ArcGIS10.8 was used. To draw the trend chart clearly, the X, Y, and Z coordinate lines were all given a value of 3. The curve was then set the colors shown in Fig. 4. As it can be seen from Fig. 4(A), the dragon dance athletes had an approximate linear growth trend in the west-east direction, which showed a trend of being low in the west and high in the east. It shows that the number of economically developed coastal areas in the east was bigger than that in the inland areas in the west. It was inverted U-shaped in the south-north direction, showing an obvious trend of being high in the middle and low in the north and south (Fig. 4(A)). It shows that the number of athletes decreases from the central region to the north and south. The number of lion dance athletes approximated the trend of log function in the west-east direction, showing the trend of being low in the west and high in the middle and east (Fig. 4(B)). It showed that there were fewer inland areas in west China and more athletes in middle and eastern China. In the south-north direction, there was no obvious inverted U-shaped trend, but it can be seen that in the middle, it was high and in the south it was low (Fig. 4(B)). This showed that the number in the central region was bigger than that in the north and south regions.

3.3. Spatial agglomeration of regional distribution

Through the above spatial visual analysis and trend analysis of athletes, it can be seen that the spatial distribution was extremely unbalanced. Therefore, to further explore the spatial distribution and agglomeration characteristics of dragon and lion dance athletes in China, this paper explored the spatial distribution and agglomeration characteristics based on standard deviational ellipse and exploratory data analysis respectively.

3.4. Driving factors of distribution difference between dragon and lion dance athletes

4.1. Conclusion

The inheritance and development of intangible cultural heritage is one of the top agendas in the world today. Dragon and lion dance is not only a Chinese sport but also the world's cultural heritage. Cultivating dragon and lion dance athletes is pioneering work that conforms to the development of the times. Due to the influence of the regional economy, culture, and policies, dragon dance athletes are mainly distributed in economically developed areas, such as the eastern coastal areas and Hubei province. Lion dance athletes are mainly distributed in political and economic centers, such as Hubei, Guangdong, and Beijing. For the results of clustering characteristics, the spatial distribution of dragon and lion dance athletes was highly clustered, and local Moran's I scatterplot was mostly distributed in the first and third quadrant. The leading factors of the distribution difference between dragon and lion dance athletes in the study area were the nomination of sports intangible cultural heritage, the total GDP, the permanent population, the level of urbanization development, the number of colleges and universities, and the proportion of tertiary industry. Degree of the results of interaction factors was greater than the driving degree of a single factor, which showed that the spatial differentiation of dragon and lion dance athletes was the result of interaction of multiple factors. Therefore, it is necessary to strengthen the protection of the dragon and lion dance cultural heritage, increase capital investment, enhance public participation, and raise government attention.

4.2. Limitations and future research

The analysis of limitations of prior research has expanded the possible areas for future research on the dragon and lion project. Because of the limitation of research data, the spatial evolution trend is not predicted. Therefore, this section provides a valuable direction for scholars' follow-up research. In addition, taking into account the enactment of targeted policies and initiatives by policymakers. Therefore, it remains to be tested to dig deep into their influence from the perspective of time.