Fig 1.
The MASs spatial distribution type diagram.
(A) The evaluation of spatial distribution distance of martial arts schools was derived from the Moran’s I calculation tool of ArcGIS software. (B) The P-value was significant at the 10% level, indicating that Wushu schools presented agglomeration distribution characteristics in local space, and a small part presented random distribution characteristics.
Table 1.
Statistics on the number of MASs in China’s provinces.
Fig 2.
The distribution of martial arts schools in China (
http://bzdt.ch.mnr.gov.cn/browse.html?picId=%224o28b0625501ad13015501ad2bfc0272%22). (A) The map is based on the standard map service website of the National Bureau of Surveying, Mapping and Geographic Information Service No.GS (2019)1682 standard map drawing, the base map has not been modified. (B) Excluding data on martial arts schools in Hong Kong, Macau, and Taiwan.
Fig 3.
The Lorenz curve of distribution of Wushu schools of China’s Province.
(A) The data for the Lorenz curve comes from the cumulative sum of the proportion of martial arts schools in 31 provinces of China. (B) Excluding data on martial arts schools in Hong Kong, Macau, and Taiwan.
Fig 4.
The kernel density analysis of arts schools in China
(http://bzdt.ch.mnr.gov.cn/browse.html?picId=%224o28b0625501ad13015501ad2bfc0272%22). (A) The map is based on the standard map service website of the National Bureau of Surveying, Mapping and Geographic Information Service No.GS (2019)1682 standard map drawing, the base map has not been modified. (B) The map projection for the martial arts school was obtained from the Baidu Map API using latitude and longitude data in Python. (C) Using ArcGIS software and applying the kernel density calculation formula (4), the spatial density projection of martial arts schools is derived.
Table 2.
Moran’s I index and its test results of martial arts school.
Fig 5.
The hot and cold point distribution of martial arts schools in China
(http://bzdt.ch.mnr.gov.cn/browse.html?picId=%224o28b0625501ad13015501ad2bfc0272%22). (A) The map is based on the standard map service website of the National Bureau of Surveying, Mapping and Geographic Information Service No.GS (2019)1682 standard map drawing, the base map has not been modified. (B) The map projection for the martial arts school was obtained from the Baidu Map API using latitude and longitude data in Python. (C) Using ArcGIS software and applying the kernel density calculation formula (6), the spatial autocorrelation characteristics of martial arts schools are derived, and further classified into hot spot and cold spot areas.
Table 3.
The meaning and calculation method of variables in GWR model.
Table 4.
The results of OLS model.
Fig 6.
The distribution of martial arts schools and Wushu hometowns in China
(http://bzdt.ch.mnr.gov.cn/browse.html?picId=%224o28b0625501ad13015501ad2bfc0272%22). (A) The map is based on the standard map service website of the National Bureau of Surveying, Mapping and Geographic Information Service No.GS (2019)1682 standard map drawing, the base map has not been modified. (B) The map projection for the martial arts school was obtained from the Baidu Map API using latitude and longitude data in Python. (C) The map projection for the Wushu hometowns was obtained from the 88 Wushu hometowns data in China. (D) Excluding data on Wushu hometowns in Hong Kong, Macau, and Taiwan.
Table 5.
Global regression parameters of OLS model.
Table 6.
Regional regression parameters of GWR model.